The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The The April 1 Snow Water Equivalent Percent Change data were modeled using the spatial analog models of Luce et al. [https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013WR014844] (see also https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR020752). These models are built on precipitation and snow data from Snowpack Telemetry (SNOTEL) stations across the western United States and temperature data from 1975-2005. To simulate historical and future gridded snow, the models ingest gridded winter cumulative precipitation and winter average temperature (from the MACAv2-Metdata dataset). We used the MACAv2-metdata monthly precipitation and minimum and maximum temperature datasets for the period 2071-2090 (RCP8.5). Average temperature was calculated as the arithmetic mean of minimum and maximum temperature datasets. Average temperature was averaged over the winter months (November through March) and precipitation was summed over the winter months. More information on the project associated with this dataset is available from the U.S. Forest Service Rocky Mountain Research Station.
The April 1 Snow Water Equivalent Historical data were modeled using the spatial analog models of Luce et al. [https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013WR014844] (see also https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR020752). These models are built on precipitation and snow data from Snowpack Telemetry (SNOTEL) stations across the western United States and temperature data from 1975-2005. To simulate historical and future gridded snow, the models ingest gridded winter cumulative precipitation and winter average temperature (from the MACAv2-Metdata dataset). We used the MACAv2-metdata monthly precipitation and minimum and maximum temperature datasets for the period 2071-2090 (RCP8.5). Average temperature was calculated as the arithmetic mean of minimum and maximum temperature datasets. Average temperature was averaged over the winter months (November through March) and precipitation was summed over the winter months. More information on the project associated with this dataset is available from the U.S. Forest Service Rocky Mountain Research Station.
The April Snow Water Equivalent data were modeled using the spatial analog models of Luce et al. [https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013WR014844] (see also https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR020752). These models are built on precipitation and snow data from Snowpack Telemetry (SNOTEL) stations across the western United States and temperature data from 1975-2005. To simulate historical and future gridded snow, the models ingest gridded winter cumulative precipitation and winter average temperature (from the MACAv2-Metdata dataset). We used the MACAv2-metdata monthly precipitation and minimum and maximum temperature datasets for the period 2071-2090 (RCP8.5). Average temperature was calculated as the arithmetic mean of minimum and maximum temperature datasets. Average temperature was averaged over the winter months (November through March) and precipitation was summed over the winter months. More information on the project associated with this dataset is available from the U.S. Forest Service Rocky Mountain Research Station.
The April Snow Water Equivalent data were modeled using the spatial analog models of Luce et al. [https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013WR014844] (see also https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR020752). These models are built on precipitation and snow data from Snowpack Telemetry (SNOTEL) stations across the western United States and temperature data from 1975-2005. To simulate historical and future gridded snow, the models ingest gridded winter cumulative precipitation and winter average temperature (from the MACAv2-Metdata dataset). We used the MACAv2-metdata monthly precipitation and minimum and maximum temperature datasets for the period 2071-2090 (RCP8.5). Average temperature was calculated as the arithmetic mean of minimum and maximum temperature datasets. Average temperature was averaged over the winter months (November through March) and precipitation was summed over the winter months. More information on the project associated with this dataset is available from the U.S. Forest Service Rocky Mountain Research Station.
The SnowResidenceTime_PctChange contains modeled percent changes in snow residence time (SRT) calculated using a spatial analog model. The model ingests mean winter average temperature and precipitation. For more information about the model, please see Luce et al., [2014] and Lute and Luce [2017]. Absolute changes in snow residence time are in units of days. The temperature and precipitation data are ensemble mean values across 20 global climate models from the CMIP5 experiment [Taylor et al., 2012], downscaled to a 4km grid. For more information on the downscaling method and to access the raw data used to create this dataset, please see Abatzoglou and Brown, [2012] and the Northwest Climate Science Center. We used the MACAv2-metdata monthly precipitation and minimum and maximum temperature datasets for the period 1975-2005 (historical) and 2071-2090 (RCP8.5, future). Average temperature was calculated as the arithmetic mean of minimum and maximum temperature datasets. Average temperature was averaged over the winter months (November through March) and precipitation was summed over the winter months. More information on the project associated with this dataset is available from the U.S. Forest Service Rocky Mountain Research Station.
This raster contains historical snow residence time values calculated using a spatial analog model. The model ingests mean winter average temperature and precipitation. For more information about the model, please see Luce et al., [2014] and Lute and Luce [2017]. Absolute changes in snow residence time are in units of days. The temperature and precipitation data are ensemble mean values across 20 global climate models from the CMIP5 experiment [Taylor et al., 2012], downscaled to a 4km grid. For more information on the downscaling method and to access the raw data used to create this dataset, please see Abatzoglou and Brown, [2012] and the Northwest Climate Science Center. We used the MACAv2-metdata monthly precipitation and minimum and maximum temperature datasets for the period 1975-2005. Average temperature was calculated as the arithmetic mean of minimum and maximum temperature datasets. Average temperature was averaged over the winter months (November through March) and precipitation was summed over the winter months. More information on the project associated with this dataset is available from the U.S. Forest Service Rocky Mountain Research Station.
The Snow Residence Time; Absolute Changes contains modeled absolute changes in snow residence time (SRT) calculated using a spatial analog model. The model ingests mean winter average temperature and precipitation. For more information about the model, please see Luce et al., [2014] and Lute and Luce [2017]. Absolute changes in snow residence time are in units of days. The temperature and precipitation data are ensemble mean values across 20 global climate models from the CMIP5 experiment [Taylor et al., 2012], downscaled to a 4km grid. For more information on the downscaling method and to access the raw data used to create this dataset, please see Abatzoglou and Brown, [2012] and the Northwest Climate Science Center. We used the MACAv2-metdata monthly precipitation and minimum and maximum temperature datasets for the period 1975-2005 (historical) and 2071-2090 (RCP8.5, future). Average temperature was calculated as the arithmetic mean of minimum and maximum temperature datasets. Average temperature was averaged over the winter months (November through March) and precipitation was summed over the winter months. More information on the project associated with this dataset is available from the U.S. Forest Service Rocky Mountain Research Station.
The Snow Residence Time 2080 data contains modeled future snow residence time values calculated using a spatial analog model. The model ingests mean winter average temperature and precipitation. For more information about the model, please see Luce et al., [2014] and Lute and Luce [2017]. Absolute changes in snow residence time are in units of days. The temperature and precipitation data are ensemble mean values across 20 global climate models from the CMIP5 experiment [Taylor et al., 2012], downscaled to a 4km grid. For more information on the downscaling method and to access the raw data used to create this dataset, please see Abatzoglou and Brown, [2012] and the Northwest Climate Science Center. We used the MACAv2-metdata monthly precipitation and minimum and maximum temperature datasets for the period 2071-2090 (RCP8.5). Average temperature was calculated as the arithmetic mean of minimum and maximum temperature datasets. Average temperature was averaged over the winter months (November through March) and precipitation was summed over the winter months.
Heat Zones map the distribution of potential heat stress for plants and animals, including humans. Heat zones, defined as the number of days per year with maximum daily temperature >= 30 °C (86 °F). Daily maximum temperature values >= 30 °C during the period 2040 - 2069 were tallied annually and are reported as the average annual number of days during a 30-year period.
Heat Zones map the distribution of potential heat stress for plants and animals, including humans. Heat zones, defined as the number of days per year with maximum daily temperature >= 30 °C (86 °F). Daily maximum temperature values >= 30 °C during the period 2070 - 2099 were tallied annually and are reported as the average annual number of days during a 30-year period.
Heat Zones map the distribution of potential heat stress for plants and animals, including humans. Heat zones, defined as the number of days per year with maximum daily temperature >= 30 °C (86 °F). Daily maximum temperature values >= 30 °C during the period 1980 - 2009 were tallied annually and are reported as the average annual number of days during a 30-year period.
Heat Zones map the distribution of potential heat stress for plants and animals, including humans. Heat zones, defined as the number of days per year with maximum daily temperature >= 30 °C (86 °F). Daily maximum temperature values >= 30 °C during the period 1980 - 2009 were tallied annually and are reported as the average annual number of days during a 30-year period.
", "keyword": [ "Climate", "Temperature", "Heat", "Zones", "Historical", "CONUS", "US Forest Service", "USFS", "IIPP", "Interdepartmental Imagery Publication Platform", "Enterprise Data Warehouse", "EDW" ], "issued": "2024-05-06T22:55:54.000Z", "modified": "2025-10-22T20:26:54.000Z", "publisher": { "name": "U.S. Forest Service", "source": "U.S. Forest Service" }, "contactPoint": { "@type": "vcard:Contact", "fn": "USFSEnterpriseContent", "hasEmail": "mailto:SM.FS.data@usda.gov" }, "accessLevel": "public", "spatial": "-124.8125,25.0772,-67.0625,52.8125", "license": "https://creativecommons.org/licenses/by/4.0/", "programCode": [ "005:059" ], "bureauCode": [ "005:96" ], "theme": [ "geospatial" ], "progressCode": "onGoing", "distribution": [ { "@type": "dcat:Distribution", "title": "ISO-19139 metadata", "conformsTo": "https://www.isotc211.org/2005/gmi", "mediaType": "text/xml", "accessURL": "https://www.arcgis.com/sharing/rest/content/items/e8ed903192ae4459bf8d0be6de874cb1/info/metadata/metadata.xml?format=iso19139" }, { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://data-usfs.hub.arcgis.com/datasets/usfs::climate-change-pressures-heat-zones-mean-days-over-30-degrees-celsius-rcp45-2010-2039" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://imagery.geoplatform.gov/iipp/rest/services/Weather_Climate/USFS_EDW_ClimateChange_HeatZones_RCP45_2010_2039/ImageServer" } ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=cc5069c0d2e54abab76732d20d2a10b7", "landingPage": "https://data-usfs.hub.arcgis.com/datasets/usfs::drought-and-moisture-surplus-for-the-conterminous-united-states-annual-data-5-year-windows-image-service", "title": "Drought and Moisture Surplus for the Conterminous United States, Annual Data 5-Year Windows (Image Service)", "description": "The Moisture Deficit and Surplus map uses moisture difference z-score (MDZ) datasets developed by scientists Frank Koch, John Coulston, and William Smith of the Forest Service Southern Research Station to represent drought and moisture surplus across the contiguous United States. A z-score is a statistical method for assessing how different a value is from the mean. Mean moisture values over 1-year windows were derived from monthly historical precipitation and temperature data from PRISM, between 1900 and 2023, and compared against a 1900-2017 baseline. The greater the z-value, the larger the departure from average conditions, indicating larger moisture deficits (droughts) or surpluses. Thus, the dark orange areas on the map indicate a 1-year window with extreme drought, relative to the average conditions over the past century. For further reading on the methodology used to build these maps, see the publication here: https://www.fs.usda.gov/treesearch/pubs/43361
Detailed technical methods for this analysis are available here: https://www.fs.usda.gov/treesearch/pubs/43361. This is derived from monthly PRISM temperature and precipitation data, located here: ftp://prism.nacse.org/monthly/. Monthly temperature data are used to calculate potential evapotranspiration (PET) using the Thornthwaite PET equation. Monthly precipitation and PET data are then used to calculate a moisture index (MI) for each month within a 1-year time window. The mean moisture index (MMI) across the months of the target window is compared to an appropriate long-term normal, in this case the average of the MMI for all windows between 1900 and 2017. Then, a moisture difference z-score (MDZ) is calculated from the MMI for the window of interest. This is done by subtracting the 1900-2017 normal MMI from the MMI for a given year, and then dividing by the standard deviation over the baseline period. Equations for calculating modified moisture index are adopted from Willmott, C.J. and Feddema, J.J. 1992. A more rational climatic moisture index. Professional Geographer 44(1): 84-87. The z-score values were then reclassified using the classification scheme below: z-score less than -2 -- extremely dry compared to normal conditions z-score -2 to -1.5 -- severely dry compared to normal conditions z-score -1.5 to -1 -- moderately dry compared to normal conditions z-score -1 to -0.5 - mildly dry compared to normal conditions z-score -0.5 to 0.5 -- near normal conditions z-score 0.5 to 1 -- mildly wet compared to normal conditions z-score 1 to 1.5 -- moderately wet compared to normal conditions z-score 1.5 to 2 -- severely wet compared to normal conditions z-score more than 2 -- extremely wet compared to normal conditions.
The Moisture Deficit and Surplus map uses moisture difference z-score (MDZ) datasets developed by scientists Frank Koch, John Coulston, and William Smith of the Forest Service Southern Research Station to represent drought and moisture surplus across the contiguous United States. A z-score is a statistical method for assessing how different a value is from the mean. Mean moisture values over 1-year windows were derived from monthly historical precipitation and temperature data from PRISM, between 1900 and 2023, and compared against a 1900-2017 baseline. The greater the z-value, the larger the departure from average conditions, indicating larger moisture deficits (droughts) or surpluses. Thus, the dark orange areas on the map indicate a 1-year window with extreme drought, relative to the average conditions over the past century. For further reading on the methodology used to build these maps, see the publication here: https://www.fs.usda.gov/treesearch/pubs/43361
Detailed technical methods for this analysis are available here: https://www.fs.usda.gov/treesearch/pubs/43361. This is derived from monthly PRISM temperature and precipitation data, located here: ftp://prism.nacse.org/monthly/. Monthly temperature data are used to calculate potential evapotranspiration (PET) using the Thornthwaite PET equation. Monthly precipitation and PET data are then used to calculate a moisture index (MI) for each month within a 1-year time window. The mean moisture index (MMI) across the months of the target window is compared to an appropriate long-term normal, in this case the average of the MMI for all windows between 1900 and 2017. Then, a moisture difference z-score (MDZ) is calculated from the MMI for the window of interest. This is done by subtracting the 1900-2017 normal MMI from the MMI for a given year, and then dividing by the standard deviation over the baseline period. Equations for calculating modified moisture index are adopted from Willmott, C.J. and Feddema, J.J. 1992. A more rational climatic moisture index. Professional Geographer 44(1): 84-87. The z-score values were then reclassified using the classification scheme below: z-score less than -2 -- extremely dry compared to normal conditions z-score -2 to -1.5 -- severely dry compared to normal conditions z-score -1.5 to -1 -- moderately dry compared to normal conditions z-score -1 to -0.5 - mildly dry compared to normal conditions z-score -0.5 to 0.5 -- near normal conditions z-score 0.5 to 1 -- mildly wet compared to normal conditions z-score 1 to 1.5 -- moderately wet compared to normal conditions z-score 1.5 to 2 -- severely wet compared to normal conditions z-score more than 2 -- extremely wet compared to normal conditions.
The Moisture Deficit and Surplus map uses moisture difference z-score (MDZ) datasets developed by scientists Frank Koch, John Coulston, and William Smith of the Forest Service Southern Research Station to represent drought and moisture surplus across the contiguous United States. A z-score is a statistical method for assessing how different a value is from the mean. Mean moisture values over 1-year windows were derived from monthly historical precipitation and temperature data from PRISM, between 1900 and 2023, and compared against a 1900-2017 baseline. The greater the z-value, the larger the departure from average conditions, indicating larger moisture deficits (droughts) or surpluses. Thus, the dark orange areas on the map indicate a 1-year window with extreme drought, relative to the average conditions over the past century. For further reading on the methodology used to build these maps, see the publication here: https://www.fs.usda.gov/treesearch/pubs/43361
Detailed technical methods for this analysis are available here: https://www.fs.usda.gov/treesearch/pubs/43361. This is derived from monthly PRISM temperature and precipitation data, located here: ftp://prism.nacse.org/monthly/. Monthly temperature data are used to calculate potential evapotranspiration (PET) using the Thornthwaite PET equation. Monthly precipitation and PET data are then used to calculate a moisture index (MI) for each month within a 1-year time window. The mean moisture index (MMI) across the months of the target window is compared to an appropriate long-term normal, in this case the average of the MMI for all windows between 1900 and 2017. Then, a moisture difference z-score (MDZ) is calculated from the MMI for the window of interest. This is done by subtracting the 1900-2017 normal MMI from the MMI for a given year, and then dividing by the standard deviation over the baseline period. Equations for calculating modified moisture index are adopted from Willmott, C.J. and Feddema, J.J. 1992. A more rational climatic moisture index. Professional Geographer 44(1): 84-87. The z-score values were then reclassified using the classification scheme below: z-score less than -2 -- extremely dry compared to normal conditions z-score -2 to -1.5 -- severely dry compared to normal conditions z-score -1.5 to -1 -- moderately dry compared to normal conditions z-score -1 to -0.5 - mildly dry compared to normal conditions z-score -0.5 to 0.5 -- near normal conditions z-score 0.5 to 1 -- mildly wet compared to normal conditions z-score 1 to 1.5 -- moderately wet compared to normal conditions z-score 1.5 to 2 -- severely wet compared to normal conditions z-score more than 2 -- extremely wet compared to normal conditions.
National data on burn probability (BP) were generated for the conterminous United States (CONUS), Alaska, and Hawaii using a geospatial Fire Simulation (FSim) system developed by the USDA Forest Service Missoula Fire Sciences Laboratory. The FSim system includes modules for weather generation, wildfire occurrence, fire growth, and fire suppression. FSim is designed to simulate the occurrence and growth of wildfires under tens of thousands of hypothetical contemporary fire seasons in order to estimate the probability of a given area (i.e., pixel) burning under current (end of 2020) landscape conditions and fire management practices. The data presented here represent modeled BP and FLPs for the United States (US) at a 270-meter grid spatial resolution. Flame-length probability is estimated for six standard Fire Intensity Levels. The six FILs correspond to flame-length classes as follows: FLP1 = < 2 feet (ft); FLP2 = 2 < 4 ft.; FLP3 = 4 < 6 ft.; FLP4 = 6 < 8 ft.; FLP5 = 8 < 12 ft.; FLP6 = 12+ ft. Because they indicate conditional probabilities (i.e., representing the likelihood of burning at a certain intensity level, given that a fire occurs), the FLP data must be used in conjunction with the BP data for risk assessment.These data are a newer edition of the Short et al. (2016, 2020) data publications. This third edition is based on circa 2020 landscape data, which were the most current LANDFIRE products available at the time of production. The methods used to generate these data generally followed the same process used in previous editions, with improvements made at specific steps. The process steps outlined in the Data Quality, Lineage section of this metadata document are expanded from previous editions to more fully explain each step and provide additional details on methods for this edition. Beyond the newer input landscape data from LANDFIRE, we also used updated datasets for other inputs such as fire occurrence, observed gridded daily weather, and wind data from weather stations. To better capture recent climate conditions, we also shortened the time period of historical weather records used to inform the generation of simulated weather streams for simulation runs, using the most recent 15 years this time (2006-2020) rather than full record from 1972-2012 in the second edition. See the process steps described in the Data Quality, Lineage section for more details.
National data on burn probability (BP) and conditional flame-length probability (FLP) were generated for the conterminous United States (CONUS), Alaska, and Hawaii using a geospatial Fire Simulation (FSim) system developed by the USDA Forest Service Missoula Fire Sciences Laboratory. The FSim system includes modules for weather generation, wildfire occurrence, fire growth, and fire suppression. FSim is designed to simulate the occurrence and growth of wildfires under tens of thousands of hypothetical contemporary fire seasons in order to estimate the probability of a given area (i.e., pixel) burning under current (end of 2020) landscape conditions and fire management practices. The data presented here represent modeled BP and FLPs for the United States (US) at a 270-meter grid spatial resolution. Flame-length probability is estimated for six standard Fire Intensity Levels. The six FILs correspond to flame-length classes as follows: FLP1 = < 2 feet (ft); FLP2 = 2 < 4 ft.; FLP3 = 4 < 6 ft.; FLP4 = 6 < 8 ft.; FLP5 = 8 < 12 ft.; FLP6 = 12+ ft. Because they indicate conditional probabilities (i.e., representing the likelihood of burning at a certain intensity level, given that a fire occurs), the FLP data must be used in conjunction with the BP data for risk assessment.
These data are a newer edition of the Short et al. (2016, 2020) data publications. This third edition is based on circa 2020 landscape data, which were the most current LANDFIRE products available at the time of production. The methods used to generate these data generally followed the same process used in previous editions, with improvements made at specific steps. The process steps outlined in the Data Quality, Lineage section of this metadata document are expanded from previous editions to more fully explain each step and provide additional details on methods for this edition. Beyond the newer input landscape data from LANDFIRE, we also used updated datasets for other inputs such as fire occurrence, observed gridded daily weather, and wind data from weather stations. To better capture recent climate conditions, we also shortened the time period of historical weather records used to inform the generation of simulated weather streams for simulation runs, using the most recent 15 years this time (2006-2020) rather than full record from 1972-2012 in the second edition. See the process steps described in the Data Quality, Lineage section for more details.
This feature class includes States, Counties or Boroughs, Congressional Districts, Alaska Recording Districts, County Subdivisions, and Places boundaries that are derived from the latest official Census Bureau and Alaska Department of Natural Resources datasets. Features within Forest Service Administrative Forest boundaries may have been modified by the Forest Service for improved accuracy and spatial coincidence(vertical integration).
This dataset shows information about the USDA Forest Service recreation sites used to populate the public facing web pages. This information is the descriptive and qualitative information used to set appropriate expectations for visitor use.
National data on burn probability (BP) and conditional flame-length probability (FLP) were generated for the conterminous United States (CONUS), Alaska, and Hawaii using a geospatial Fire Simulation (FSim) system developed by the USDA Forest Service Missoula Fire Sciences Laboratory. The FSim system includes modules for weather generation, wildfire occurrence, fire growth, and fire suppression. FSim is designed to simulate the occurrence and growth of wildfires under tens of thousands of hypothetical contemporary fire seasons in order to estimate the probability of a given area (i.e., pixel) burning under current (end of 2020) landscape conditions and fire management practices. The data presented here represent modeled BP and FLPs for the United States (US) at a 270-meter grid spatial resolution. Flame-length probability is estimated for six standard Fire Intensity Levels. The six FILs correspond to flame-length classes as follows: FLP1 = < 2 feet (ft); FLP2 = 2 < 4 ft.; FLP3 = 4 < 6 ft.; FLP4 = 6 < 8 ft.; FLP5 = 8 < 12 ft.; FLP6 = 12+ ft. Because they indicate conditional probabilities (i.e., representing the likelihood of burning at a certain intensity level, given that a fire occurs), the FLP data must be used in conjunction with the BP data for risk assessment.
These data are a newer edition of the Short et al. (2016, 2020) data publications. This third edition is based on circa 2020 landscape data, which were the most current LANDFIRE products available at the time of production. The methods used to generate these data generally followed the same process used in previous editions, with improvements made at specific steps. The process steps outlined in the Data Quality, Lineage section of this metadata document are expanded from previous editions to more fully explain each step and provide additional details on methods for this edition. Beyond the newer input landscape data from LANDFIRE, we also used updated datasets for other inputs such as fire occurrence, observed gridded daily weather, and wind data from weather stations. To better capture recent climate conditions, we also shortened the time period of historical weather records used to inform the generation of simulated weather streams for simulation runs, using the most recent 15 years this time (2006-2020) rather than full record from 1972-2012 in the second edition. See the process steps described in the Data Quality, Lineage section for more details.
National data on burn probability (BP) and conditional flame-length probability (FLP) were generated for the conterminous United States (CONUS), Alaska, and Hawaii using a geospatial Fire Simulation (FSim) system developed by the USDA Forest Service Missoula Fire Sciences Laboratory. The FSim system includes modules for weather generation, wildfire occurrence, fire growth, and fire suppression. FSim is designed to simulate the occurrence and growth of wildfires under tens of thousands of hypothetical contemporary fire seasons in order to estimate the probability of a given area (i.e., pixel) burning under current (end of 2020) landscape conditions and fire management practices. The data presented here represent modeled BP and FLPs for the United States (US) at a 270-meter grid spatial resolution. Flame-length probability is estimated for six standard Fire Intensity Levels. The six FILs correspond to flame-length classes as follows: FLP1 = < 2 feet (ft); FLP2 = 2 < 4 ft.; FLP3 = 4 < 6 ft.; FLP4 = 6 < 8 ft.; FLP5 = 8 < 12 ft.; FLP6 = 12+ ft. Because they indicate conditional probabilities (i.e., representing the likelihood of burning at a certain intensity level, given that a fire occurs), the FLP data must be used in conjunction with the BP data for risk assessment.
These data are a newer edition of the Short et al. (2016, 2020) data publications. This third edition is based on circa 2020 landscape data, which were the most current LANDFIRE products available at the time of production. The methods used to generate these data generally followed the same process used in previous editions, with improvements made at specific steps. The process steps outlined in the Data Quality, Lineage section of this metadata document are expanded from previous editions to more fully explain each step and provide additional details on methods for this edition. Beyond the newer input landscape data from LANDFIRE, we also used updated datasets for other inputs such as fire occurrence, observed gridded daily weather, and wind data from weather stations. To better capture recent climate conditions, we also shortened the time period of historical weather records used to inform the generation of simulated weather streams for simulation runs, using the most recent 15 years this time (2006-2020) rather than full record from 1972-2012 in the second edition. See the process steps described in the Data Quality, Lineage section for more details.
National data on burn probability (BP) and conditional flame-length probability (FLP) were generated for the conterminous United States (CONUS), Alaska, and Hawaii using a geospatial Fire Simulation (FSim) system developed by the USDA Forest Service Missoula Fire Sciences Laboratory. The FSim system includes modules for weather generation, wildfire occurrence, fire growth, and fire suppression. FSim is designed to simulate the occurrence and growth of wildfires under tens of thousands of hypothetical contemporary fire seasons in order to estimate the probability of a given area (i.e., pixel) burning under current (end of 2020) landscape conditions and fire management practices. The data presented here represent modeled BP and FLPs for the United States (US) at a 270-meter grid spatial resolution. Flame-length probability is estimated for six standard Fire Intensity Levels. The six FILs correspond to flame-length classes as follows: FLP1 = < 2 feet (ft); FLP2 = 2 < 4 ft.; FLP3 = 4 < 6 ft.; FLP4 = 6 < 8 ft.; FLP5 = 8 < 12 ft.; FLP6 = 12+ ft. Because they indicate conditional probabilities (i.e., representing the likelihood of burning at a certain intensity level, given that a fire occurs), the FLP data must be used in conjunction with the BP data for risk assessment.
These data are a newer edition of the Short et al. (2016, 2020) data publications. This third edition is based on circa 2020 landscape data, which were the most current LANDFIRE products available at the time of production. The methods used to generate these data generally followed the same process used in previous editions, with improvements made at specific steps. The process steps outlined in the Data Quality, Lineage section of this metadata document are expanded from previous editions to more fully explain each step and provide additional details on methods for this edition. Beyond the newer input landscape data from LANDFIRE, we also used updated datasets for other inputs such as fire occurrence, observed gridded daily weather, and wind data from weather stations. To better capture recent climate conditions, we also shortened the time period of historical weather records used to inform the generation of simulated weather streams for simulation runs, using the most recent 15 years this time (2006-2020) rather than full record from 1972-2012 in the second edition. See the process steps described in the Data Quality, Lineage section for more details.
National data on burn probability (BP) and conditional flame-length probability (FLP) were generated for the conterminous United States (CONUS), Alaska, and Hawaii using a geospatial Fire Simulation (FSim) system developed by the USDA Forest Service Missoula Fire Sciences Laboratory. The FSim system includes modules for weather generation, wildfire occurrence, fire growth, and fire suppression. FSim is designed to simulate the occurrence and growth of wildfires under tens of thousands of hypothetical contemporary fire seasons in order to estimate the probability of a given area (i.e., pixel) burning under current (end of 2020) landscape conditions and fire management practices. The data presented here represent modeled BP and FLPs for the United States (US) at a 270-meter grid spatial resolution. Flame-length probability is estimated for six standard Fire Intensity Levels. The six FILs correspond to flame-length classes as follows: FLP1 = < 2 feet (ft); FLP2 = 2 < 4 ft.; FLP3 = 4 < 6 ft.; FLP4 = 6 < 8 ft.; FLP5 = 8 < 12 ft.; FLP6 = 12+ ft. Because they indicate conditional probabilities (i.e., representing the likelihood of burning at a certain intensity level, given that a fire occurs), the FLP data must be used in conjunction with the BP data for risk assessment.
These data are a newer edition of the Short et al. (2016, 2020) data publications. This third edition is based on circa 2020 landscape data, which were the most current LANDFIRE products available at the time of production. The methods used to generate these data generally followed the same process used in previous editions, with improvements made at specific steps. The process steps outlined in the Data Quality, Lineage section of this metadata document are expanded from previous editions to more fully explain each step and provide additional details on methods for this edition. Beyond the newer input landscape data from LANDFIRE, we also used updated datasets for other inputs such as fire occurrence, observed gridded daily weather, and wind data from weather stations. To better capture recent climate conditions, we also shortened the time period of historical weather records used to inform the generation of simulated weather streams for simulation runs, using the most recent 15 years this time (2006-2020) rather than full record from 1972-2012 in the second edition. See the process steps described in the Data Quality, Lineage section for more details.
National data on burn probability (BP) and conditional flame-length probability (FLP) were generated for the conterminous United States (CONUS), Alaska, and Hawaii using a geospatial Fire Simulation (FSim) system developed by the USDA Forest Service Missoula Fire Sciences Laboratory. The FSim system includes modules for weather generation, wildfire occurrence, fire growth, and fire suppression. FSim is designed to simulate the occurrence and growth of wildfires under tens of thousands of hypothetical contemporary fire seasons in order to estimate the probability of a given area (i.e., pixel) burning under current (end of 2020) landscape conditions and fire management practices. The data presented here represent modeled BP and FLPs for the United States (US) at a 270-meter grid spatial resolution. Flame-length probability is estimated for six standard Fire Intensity Levels. The six FILs correspond to flame-length classes as follows: FLP1 = < 2 feet (ft); FLP2 = 2 < 4 ft.; FLP3 = 4 < 6 ft.; FLP4 = 6 < 8 ft.; FLP5 = 8 < 12 ft.; FLP6 = 12+ ft. Because they indicate conditional probabilities (i.e., representing the likelihood of burning at a certain intensity level, given that a fire occurs), the FLP data must be used in conjunction with the BP data for risk assessment.
These data are a newer edition of the Short et al. (2016, 2020) data publications. This third edition is based on circa 2020 landscape data, which were the most current LANDFIRE products available at the time of production. The methods used to generate these data generally followed the same process used in previous editions, with improvements made at specific steps. The process steps outlined in the Data Quality, Lineage section of this metadata document are expanded from previous editions to more fully explain each step and provide additional details on methods for this edition. Beyond the newer input landscape data from LANDFIRE, we also used updated datasets for other inputs such as fire occurrence, observed gridded daily weather, and wind data from weather stations. To better capture recent climate conditions, we also shortened the time period of historical weather records used to inform the generation of simulated weather streams for simulation runs, using the most recent 15 years this time (2006-2020) rather than full record from 1972-2012 in the second edition. See the process steps described in the Data Quality, Lineage section for more details.
The USDA Forest Service Rapid Assessment of Vegetation Condition after Wildfire (RAVG) program produces geospatial data and maps of post-fire vegetation condition using standardized change detection methods based on Landsat or similar multispectral satellite imagery. RAVG data products characterize vegetation condition within a fire perimeter, and include estimates of percent change in basal area (BA), percent change in canopy cover (CC), and a standardized composite burn index (CBI). Standard thematic products include 7-class percent change in basal area (BA-7), 5-class percent change in canopy cover (CC-5), and 4-class CBI (CBI-4). Contingent upon the availability of suitable imagery, RAVG products are prepared for all wildland fires reported within the conterminous United States (CONUS) that include at least 1000 acres of forested National Forest System (NFS) land (500 acres for Regions 8 and 9 as of 2016). Data for individual fires are typically made available within 45 days after fire containment (\"initial assessments\"). Late-season fires, however, may be deferred until the following spring or summer (\"extended assessments\"). National mosaics of each thematic product are prepared annually. The associated burned area perimeters are available via the Enterprise Data Warehouse (EDW, see https://data.fs.usda.gov/geodata/edw/datasets.php). Assessment type (initial or extended assessment) for each fire is included as an attribute in the perimeter dataset.
The USDA Forest Service Rapid Assessment of Vegetation Condition after Wildfire (RAVG) program produces geospatial data and maps of post-fire vegetation condition using standardized change detection methods based on Landsat or similar multispectral satellite imagery. RAVG data products characterize vegetation condition within a fire perimeter, and include estimates of percent change in basal area (BA), percent change in canopy cover (CC), and a standardized composite burn index (CBI). Standard thematic products include 7-class percent change in basal area (BA-7), 5-class percent change in canopy cover (CC-5), and 4-class CBI (CBI-4). Contingent upon the availability of suitable imagery, RAVG products are prepared for all wildland fires reported within the conterminous United States (CONUS) that include at least 1000 acres of forested National Forest System (NFS) land (500 acres for Regions 8 and 9 as of 2016). Data for individual fires are typically made available within 45 days after fire containment (\"initial assessments\"). Late-season fires, however, may be deferred until the following spring or summer (\"extended assessments\"). National mosaics of each thematic product are prepared annually. The associated burned area perimeters are available via the Enterprise Data Warehouse (EDW, see https://data.fs.usda.gov/geodata/edw/datasets.php). Assessment type (initial or extended assessment) for each fire is included as an attribute in the perimeter dataset.
The USDA Forest Service Rapid Assessment of Vegetation Condition after Wildfire (RAVG) program produces geospatial data and maps of post-fire vegetation condition using standardized change detection methods based on Landsat or similar multispectral satellite imagery. RAVG data products characterize vegetation condition within a fire perimeter, and include estimates of percent change in basal area (BA), percent change in canopy cover (CC), and a standardized composite burn index (CBI). Standard thematic products include 7-class percent change in basal area (BA-7), 5-class percent change in canopy cover (CC-5), and 4-class CBI (CBI-4). Contingent upon the availability of suitable imagery, RAVG products are prepared for all wildland fires reported within the conterminous United States (CONUS) that include at least 1000 acres of forested National Forest System (NFS) land (500 acres for Regions 8 and 9 as of 2016). Data for individual fires are typically made available within 45 days after fire containment (\"initial assessments\"). Late-season fires, however, may be deferred until the following spring or summer (\"extended assessments\"). National mosaics of each thematic product are prepared annually. The associated burned area perimeters are available via the Enterprise Data Warehouse (EDW, see https://data.fs.usda.gov/geodata/edw/datasets.php). Assessment type (initial or extended assessment) for each fire is included as an attribute in the perimeter dataset.
This QA bit product is part of the Landscape Change Monitoring System (LCMS) data suite. It provides information about each pixel of the annual composites that are used as inputs to LandTrendr data used in the model. This information includes whether the data value is an observation or is interpolated, the Landsat sensor that observed that data value, and the Julian day of that observation. See additional information about QA in the Entity_and_Attribute_Information or Fields section below.
LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a \"best available\" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS Change, Land Cover, and Land Use maps offer a holistic depiction of landscape change across the United States over the past four decades.
Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a Random Forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).
Outputs fall into three categories: Change, Land Cover, and Land Use. Change relates specifically to vegetation cover and includes slow loss (not included for PRUSVI), Fast Loss (which also includes hydrologic changes such as Inundation or Desiccation), and Gain. These values are predicted for each year of the time series and serve as the foundational products for LCMS.
This QA bit product is part of the Landscape Change Monitoring System (LCMS) data suite. It provides information about each pixel of the annual composites that are used as inputs to LandTrendr data used in the model. This information includes whether the data value is an observation or is interpolated, the Landsat sensor that observed that data value, and the Julian day of that observation. See additional information about QA in the Entity_and_Attribute_Information or Fields section below.
LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a \"best available\" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS Change, Land Cover, and Land Use maps offer a holistic depiction of landscape change across the United States over the past four decades.
Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a Random Forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).
Outputs fall into three categories: Change, Land Cover, and Land Use. Change relates specifically to vegetation cover and includes slow loss (not included for PRUSVI), Fast Loss (which also includes hydrologic changes such as Inundation or Desiccation), and Gain. These values are predicted for each year of the time series and serve as the foundational products for LCMS.
This QA bit product is part of the Landscape Change Monitoring System (LCMS) data suite. It provides information about each pixel of the annual composites that are used as inputs to LandTrendr data used in the model. This information includes whether the data value is an observation or is interpolated, the Landsat sensor that observed that data value, and the Julian day of that observation. See additional information about QA in the Entity_and_Attribute_Information or Fields section below.
LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a \"best available\" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS Change, Land Cover, and Land Use maps offer a holistic depiction of landscape change across the United States over the past four decades.
Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a Random Forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).
Outputs fall into three categories: Change, Land Cover, and Land Use. Change relates specifically to vegetation cover and includes slow loss (not included for PRUSVI), Fast Loss (which also includes hydrologic changes such as Inundation or Desiccation), and Gain. These values are predicted for each year of the time series and serve as the foundational products for LCMS.
This product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS modeled Land Use classes for each year. See additional information about Land Use in the Entity_and_Attribute_Information or Fields section below.
LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a \"best available\" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS Change, Land Cover, and Land Use maps offer a holistic depiction of landscape change across the United States over the past four decades.
Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a Random Forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).
Outputs fall into three categories: Change, Land Cover, and Land Use. At its foundation, Change maps areas of Disturbance, Vegetation Successional Growth, and Stable landscape. More detailed levels of Change products are available and are intended to address needs centered around monitoring causes and types of variations in vegetation cover, water extent, or snow/ice extent that may or may not result in a transition of land cover and/or land use. Change, Land Cover, and Land Use are predicted for each year of the time series and serve as the foundational products for LCMS.
This product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS modeled Land Cover classes for each year. See additional information about Land Cover in the Entity_and_Attribute_Information or Fields section below.
LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a \"best available\" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS Change, Land Cover, and Land Use maps offer a holistic depiction of landscape change across the United States over the past four decades.
Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a Random Forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).
Outputs fall into three categories: Change, Land Cover, and Land Use. At its foundation, Change maps areas of Disturbance, Vegetation Successional Growth, and Stable landscape. More detailed levels of Change products are available and are intended to address needs centered around monitoring causes and types of variations in vegetation cover, water extent, or snow/ice extent that may or may not result in a transition of land cover and/or land use. Change, Land Cover, and Land Use are predicted for each year of the time series and serve as the foundational products for LCMS.
This product is part of the Landscape Change Monitoring System (LCMS) data suite. It supplies LCMS Change classes for each year that are a refinement of the modeled LCMS Change classes (Slow Loss, Fast Loss, and Gain) and provide information on the cause of landscape change. See additional information about Change in the Entity_and_Attribute_Information or Fields section below.
LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a \"best available\" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS Change, Land Cover, and Land Use maps offer a holistic depiction of landscape change across the United States over the past four decades.
Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a Random Forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).
Outputs fall into three categories: Change, Land Cover, and Land Use. At its foundation, Change maps areas of Disturbance, Vegetation Successional Growth, and Stable landscape. More detailed levels of Change products are available and are intended to address needs centered around monitoring causes and types of variations in vegetation cover, water extent, or snow/ice extent that may or may not result in a transition of land cover and/or land use. Change, Land Cover, and Land Use are predicted for each year of the time series and serve as the foundational products for LCMS.
This QA bit product is part of the Landscape Change Monitoring System (LCMS) data suite. It provides information about each pixel of the annual composites that are used as inputs to LandTrendr data used in the model. This information includes whether the data value is an observation or is interpolated, the Landsat sensor that observed that data value, and the Julian day of that observation. See additional information about QA in the Entity_and_Attribute_Information or Fields section below.
LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a \"best available\" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS Change, Land Cover, and Land Use maps offer a holistic depiction of landscape change across the United States over the past four decades.
Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a Random Forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).
Outputs fall into three categories: Change, Land Cover, and Land Use. Change relates specifically to vegetation cover and includes slow loss (not included for PRUSVI), Fast Loss (which also includes hydrologic changes such as Inundation or Desiccation), and Gain. These values are predicted for each year of the time series and serve as the foundational products for LCMS.
This product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS modeled Land Use classes for each year. See additional information about Land Use in the Entity_and_Attribute_Information or Fields section below.
LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a \"best available\" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS Change, Land Cover, and Land Use maps offer a holistic depiction of landscape change across the United States over the past four decades.
Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a Random Forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).
Outputs fall into three categories: Change, Land Cover, and Land Use. At its foundation, Change maps areas of Disturbance, Vegetation Successional Growth, and Stable landscape. More detailed levels of Change products are available and are intended to address needs centered around monitoring causes and types of variations in vegetation cover, water extent, or snow/ice extent that may or may not result in a transition of land cover and/or land use. Change, Land Cover, and Land Use are predicted for each year of the time series and serve as the foundational products for LCMS.
This product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS modeled Land Cover classes for each year. See additional information about Land Cover in the Entity_and_Attribute_Information or Fields section below.
LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a \"best available\" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS Change, Land Cover, and Land Use maps offer a holistic depiction of landscape change across the United States over the past four decades.
Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a Random Forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).
Outputs fall into three categories: Change, Land Cover, and Land Use. At its foundation, Change maps areas of Disturbance, Vegetation Successional Growth, and Stable landscape. More detailed levels of Change products are available and are intended to address needs centered around monitoring causes and types of variations in vegetation cover, water extent, or snow/ice extent that may or may not result in a transition of land cover and/or land use. Change, Land Cover, and Land Use are predicted for each year of the time series and serve as the foundational products for LCMS.
This feature class includes States, Counties or Boroughs, Congressional Districts, Alaska Recording Districts, County Subdivisions, and Places boundaries that are derived from the latest official Census Bureau and Alaska Department of Natural Resources datasets. Features within Forest Service Administrative Forest boundaries may have been modified by the Forest Service for improved accuracy and spatial coincidence(vertical integration).
This product is part of the Landscape Change Monitoring System (LCMS) data suite. It supplies LCMS Change classes for each year that are a refinement of the modeled LCMS Change classes (Slow Loss, Fast Loss, and Gain) and provide information on the cause of landscape change. See additional information about Change in the Entity_and_Attribute_Information or Fields section below.
LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a \"best available\" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS Change, Land Cover, and Land Use maps offer a holistic depiction of landscape change across the United States over the past four decades.
Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a Random Forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).
Outputs fall into three categories: Change, Land Cover, and Land Use. At its foundation, Change maps areas of Disturbance, Vegetation Successional Growth, and Stable landscape. More detailed levels of Change products are available and are intended to address needs centered around monitoring causes and types of variations in vegetation cover, water extent, or snow/ice extent that may or may not result in a transition of land cover and/or land use. Change, Land Cover, and Land Use are predicted for each year of the time series and serve as the foundational products for LCMS.
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Objective: Identify ecosystems that have a significant proportion of their area burning less frequently than what occurred under historic fire regimes. Often, missed fire cycles were due to human factors, principally fire suppression since early in the 20th Century.
Data: The area of a landscape with a deficient fire frequency was estimated by comparing observed fire frequencies to historical mean fire return intervals. Observed fire frequencies were calculated by determining the frequency of fire for each pixel based on raster data of observed fires from the Monitoring Trends in Burn Severity (MTBS) project. Areas in the unburned to low severity class were removed from this analysis. The ratio of observed to historical fire frequencies (as determined by Landfire MFRI) was used to determine pixels that are burning less frequently than expected. These pixels were then used to determine the percent area of the landscape deficient in fire. For areas with fire return intervals longer than the remote sensing record, the history of effective fire suppression was used to assume not enough fire occurred back to the implementation of widespread suppression starting in the 1920s through 1930s with policies such as the 10 AM rule. Prior to the 1920s, it is assumed fire return intervals were closer to the historic range of variation, although likely reduced due to the constrained role of Indigenous stewardship. Areas highly modified by anthropogenic activities (i.e., those with developed landcovers or agricultural landcovers of pasture and crop cultivation) and areas with a landcover that does not typically burn (i.e., barren or ice) were not included in the calculation. There is a 2 year lag between the most recent MTBS data and the TCA Assessment year (i.e. in the 2024 TCA Assessment, available MTBS data were from 1984-2022). To reduce this lag to 1 year, we compute the Composite Burn Index (CBI) for the year prior to the current assessment year using fire perimeters from National Interagency Fire Center (NIFC) with methods adapted from Parks et al. 2019. The CBI methods were added for the 2022 assessment. Landfire versions used for MFRI and the associated TCA Assessment are: Landfire 2014 (TCA Assessment 2020), Landfire 2016 (TCA Assessments 2021 and 2022) and Landfire 2020 (TCA Assessments 2023 and 2024).
Format: Raster data are ordinal: (1) Deficient (2) Sufficient (3) Excessive.
Resolution: 90m (2023 and 2024 assessments), 30m (2020-2022 assessments).
Source: MTBS fire perimeters, expected fire return interval , CBI methods, National Interagency Fire Center Perimeters (for CBI)
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
*if you have trouble viewing the Dashboard, please submit a Tableau Viewer Access Request
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Objective: Identify locations where vegetation composition and successional stage are departed from the natural range of variation.
Data: Vegetation departure within a TCA landscape ecosystem describes the degree to which the observed proportion (percent, 0-100%) of successional classes (i.e., A through E) derived from Landfire Succession Classes (SClass) dataset, is departed from the proportions one would expect based on the reference condition contained in the Landfire Biophysical Settings (BpS) dataset at each cell location. Departure is calculated as:
Departure = 100 – Similarity
where Similarity = ∑ (min(Ai,Bi)) over all classes i,…n
Departure is determined for each BPS within an LTA, and then weighted average for departure is calculated for the LTA based on the areal extent of the BPS. After being added to the assessment in 2022, the update timeline of the input source data from LANDFIRE did not align with the timing of TCA assessment in 2023, so data from 2022 was reused. The 2024 TCA Assessment used updated source data to create new vegetation departure indicator data.
Units: Percent
Format: Raster data are continuous
Resolution: 30m
Source: Landfire Succession Class and Biophysical Setting, methods from Swaty et al. 2022
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
*if you have trouble viewing the Dashboard, please submit a Tableau Viewer Access Request
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Objective: Identify areas with unhealthy forest conditions as evidenced by a combination of stand and environmental characteristics making them uncharacteristically vulnerable to insect and pathogen outbreaks.
Data: The 2018 National Insect and Disease Risk Map (NIDRM) developed by USFS-FHAAST indicates locations where forests are stressed and susceptible to outbreaks of native and non-native insects and diseases. These include forests with overly high stand densities and where soil/site conditions contribute to drought. The basis for assigning risk is the expectation that 25% or more of live basal area (three times the natural background rate of mortality) will die over the next 15 years due to insects and diseases. Because the NIDRM 2018 map is the most recent update, it has been reused each year for TCA Assessments from 2020-present.
Format: Raster data are binary: (1) at risk or (0) not at risk
Resolution: 240m
Source: NIDRM 2018
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
*if you have trouble viewing the Dashboard, please submit a Tableau Viewer Access Request
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Objective: Assess impacts to vegetation from acute exposure to ozone (O3) during the growing season (April to September).
Data: Two exposure indices were calculated from hourly O3 data collected by the EPA to track effects from chronic O3 exposure (W126 index) and acute O3 exposure (N100). The W126 index is a weighted measure of chronic (lower level) ozone exposure with an explanation of its daily, monthly, annual, and multi-year average values is available here. For both indices, hourly data was used to calculate monthly values. Monitoring sites were only included if at least 75% of the hourly observations were available, and monthly values were corrected to account for any missing data following the procedures of Lefohn, Knudsen, and Shadwick (2011). Corrected monthly values were totaled across the growing season to generate an annual value. Annual values were averaged over the most recently available 3 year period to generate a value for each site. Site values were then interpolated using inverse distance weighting to attribute values to landscapes within 40 km of a monitoring site. There is typically a 1 year lag between the TCA assessment year and the most recently available for instance, for the 2024 TCA Assessment, ozone data from 2021-2023 were used to compute the 3-year average. For the 2023 TCA Assessment, 2022 ozone source data was not available so the same data from the prior assessment was used. Source data ranges and assessment year in parentheses are: 2017-2019 (TCA Assessment 2020), 2018-2020 (TCA Assessment 2021), 2019-2021 (TCA Assessment 2022), 2019-2021 (TCA Assessment 2023), and 2021-2023 (TCA Assessment 2024).
Data Format: Point (site data) interpolated through inverse distance weighting and filtered to areas only within 40 km of an O3 monitor.
Units: W126 is expressed in parts per million of O3-hours or ppm-hours
Spatial Resolution: 1000m (1km)
Source data: EPA Hourly Ozone data
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
*if you have trouble viewing the Dashboard, please submit a Tableau Viewer Access Request
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Objective: Assess impacts to vegetation from acute exposure to ozone (O3) during the growing season (April to September).
Data: Two exposure indices were calculated from hourly O3 data collected by the EPA to track effects from chronic O3 exposure (W126 index) and acute O3 exposure (N100). The N100 index is the sum of hours over 100 ppb in a given month and captures peak events of high or acute exposure. For both indices, hourly data was used to calculate monthly values. Monitoring sites were only included if at least 75% of the hourly observations were available, and monthly values were corrected to account for any missing data following the procedures of Lefohn, Knudsen, and Shadwick (2011). Corrected monthly values were totaled across the growing season to generate an annual value. Annual values were averaged over the most recently available 3 year period to generate a value for each site. Site values were then interpolated using inverse distance weighting to attribute values to landscapes within 40 km of a monitoring site. There is typically a 1 year lag between the TCA assessment year and the most recently available for instance, for the 2024 TCA Assessment, ozone data from 2021-2023 were used to compute the 3-year average. For the 2023 TCA Assessment, 2022 ozone source data was not available so the same data from the prior assessment was used. Source data ranges and assessment year in parentheses are: 2017-2019 (TCA Assessment 2020), 2018-2020 (TCA Assessment 2021), 2019-2021 (TCA Assessment 2022), 2019-2021 (TCA Assessment 2023), and 2021-2023 (TCA Assessment 2024).
Data Format: Point (site data) interpolated through inverse distance weighting and filtered to areas only within 40 km of an O3 monitor.
Units: N100 is expressed in hours
Spatial Resolution: 1000m (1km)
Source data: EPA Hourly Ozone data
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
*if you have trouble viewing the Dashboard, please submit a Tableau Viewer Access Request
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Objective: Evaluate the impact of atmospheric nitrogen deposition on various ecosystem components including lichens, tree species (both growth and mortality), mycorrhizal communities, herbaceous plant species (changes in species richness), and leaching.
Data: Deposition data is the 3-year average output from the Total Deposition (TDep) modeling system (https://nadp.slh.wisc.edu/committees/tdep/). TDep uses measurement-model fusion to bias correct modelled estimates (Tdep Program, https://nadp.slh.wisc.edu/committees/tdep/). TDep uses observations of air concentrations from the Clean Air Status and Trends Network (CASTNET) for measurements of dry deposition and observations of precipitation and precipitation chemistry from various National Atmospheric Deposition Program (NADP) networks. These observations are interpolated and used to create seasonal bias surfaces for results generated from CMAQ. Bias-adjusted CMAQ results are then merged with observations to create a set of deposition estimates that better align with measurements than model results alone. The full description of this methodology is available in: Schwede et al., 2014. There is a two year lag between the source data and the TCA Assessment year, so source data 3-year ranges with assessment year in parentheses are: 2016-2018 (2020 assessment), 2017-2019 (2021 assessment), 2018-2020 (2022 assessment), 2019-2021 (2023 assessment), 2020-2022* (2024 assessment). *The data used in the 2024 assessment had a small gap in western North and South Dakota, which was filled with data from the 2019-2021 product due to the small amount of interannual variability in the region.
Data Format: Raster data are continuous
Units: kg/ha/yr
Spatial Resolution: 400m (resampled from 4km)
Source data: Total Deposition Science Committee (TDep)
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
*if you have trouble viewing the Dashboard, please submit a Tableau Viewer Access Request
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Objective: Identify locations where roads are present at a density that impacts the quality of wildlife habitat and fragments the landscape.
Data: A national roads vector dataset has been acquired from the USGS National Transportation Dataset. The data were reattributed to represent three classes: (1) Unimproved, (2) Paved, (3) Light Duty roads. The new classes correspond to the following classes from the National Transportation Dataset:
Data Format: Polylines were converted to a raster for each TCA assessment year. The value at a pixel corresponds to the class of road at that location. Values differ slightly between the years:
2024: 1- Light Duty, 2- Paved, 3- Unimproved, 4- Other
2023: 1- Light Duty, 2- Paved, 3- Unimproved, 4- Other
2022: 1- Light Duty, 2- Paved, 3- Unimproved
2021: 1- Light Duty, 2- Paved, 3- Unimproved
2020: 1- Light Duty, 2- Paved, 3- Unimproved, 4- Other, 5- Decommissioned
Spatial Resolution: 10m
Source data: USGS National Transportation Dataset
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
*if you have trouble viewing the Dashboard, please submit a Tableau Viewer Access Request
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Objective: Identify areas of negative relative change in annual grassland vegetation productivity.
Data: The indicator of biomass production departure is an estimate based on the percent area of a landscape with a negative relative change between a recent time period (most recent 5 years) and the historical record (beginning in 1984). There is a 1 year lag between the source data and the TCA Assessment year. For instance in 2024, mean annual productivity data used were 1984-2018 (historical record) and 2019-2023 (recent conditions). Note that in 2022, data only covers the western US, in all other years it covers CONUS.
Format: Raster data are binary: (1) negative or (0) not negative.
Resolution: 250m
Source: Annual RPMS Products from FuelCast. More info on the Rangeland Production Monitoring Service on the product page
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
*if you have trouble viewing the Dashboard, please submit a Tableau Viewer Access Request
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Objective: An indicator that identifies where tree density has exceeded the levels presumed to be present in pre-Euro-American settlement non-forest vegetation. By our definition, non-forest areas cannot exceed 25% tree canopy cover. Fire exclusion, climate, and human activities affect the progression of trees into non-forested ecosystems. Encroached areas may require restoration to meet ecological objectives. Restoration practices will be ecosystem specific.
Data: The indicator uses areas identified as grasslands using LANDFIRE’s Biophysical Settings (BpS) raster dataset (US_140BPS_20180618) along with a cross-walk based on Reeves MC, Mitchell JE (2011) (https://research.fs.usda.gov/treesearch/41872) to identify BpS units that were likely to be grasslands during pre-European times. Presence of conifer tree species was developed using USGS National Landcover Database (NLCD) legend class “42 – Evergreen Forest”. The Evergreen Forest class represents “areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. More than 75% of the tree species maintain their leaves all year. Canopy is never without green foliage.” Grassland conifer encroachment is identified where raster pixels are identified as both “grasslands” and NLCD “Evergreen Forest”. In years when NLCD is not updated, data is reused from the previous year, which is why there is only data for 3 of the 5 assessment years from 2020-2024. NLCD versions used: NLCD 2011 (TCA Assessment 2020), NLCD 2019 (TCA Assessment 2022), NLCD 2021 (TCA Assessment 2023). Encroachment for the 2023 TCA Assessment is 1-bit and therefore only contains values of 1 indicating where encroachment was observed, rather than the binary data from other years.
Format: Raster data are binary: (1) likely encroached or (0) not encroached
Resolution: 250m
Source: Custom grassland-BpS crosswalk, NLCD conifer presence
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
*if you have trouble viewing the Dashboard, please submit a Tableau Viewer Access Request
Direct Download (Raster Data Gateway)
Objective: Identify areas where significant fuel buildup has occurred due to fire suppression, extensive mortality due to natural or anthropogenic causes, or for other reasons.
Data: The Wildland Hazard Potential (WHP) represents areas of significant fuel buildup. It is an index that quantifies the relative potential for wildfire that may be difficult to manage and is used as a measure to help prioritize where fuel treatments may be needed. The WHP is a raster geospatial product produced by the USFS Fire Modeling Institute in the Fire, Fuel, and Smoke Program. Higher values have a higher probability of high-intensity fire, with torching, crowning, and other forms of extreme fire behavior. Data used for this metric represent areas classified as “moderate”, “high”, or “very high” wildland hazard potential within fire regime groups I and II*. A mask for non-burnable areas is applied to focus on ecosystems with vegetation that can be modified through management to reduce hazards. The fire regime attribute within the LANDFIRE Biophysical Settings (LF2020_BPS_220) was used to identify moderate fire regimes (Regime groups I and II). Fire regime groups represent expected historical fire regimes based on interactions between vegetation dynamics, fire spread, fire effects, and spatial context. The definitions of the regimes are outlined in the Interagency Fire Regime Condition Class Guidebook.
*The 2023 assessment only used moderate hazard potential pixels from the WHP product instead of all three hazard classes.
Data Format: Raster data are binary: (1) “moderate”, “high”, or “very high” risk or (0) other in Fire Regimes I and II
Spatial Resolution: 30m (*data for 2023 assessment is 270m)
Source data: The WHP data are part of the Wildfire Risk to Communities suite of data along with several other data products. More information on the data used for the 2024 assessment can be found here. Links for data used in previous year TCA assessments: 2020-2022 , 2023. Three different versions of LANDFIRE data were used over the years: LF 2014 NGDA US_140_FRG (2020 and 2021), LF 2016 LC16_BPS_200 (2022 and 2023), and LF 2020 LC20_BPS_220 (2024)
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
Direct Download (Raster Data Gateway)
Objective: Identify areas where significant fuel buildup has occurred due to fire suppression, extensive mortality due to natural or anthropogenic causes, or for other reasons.
Data: The Wildland Hazard Potential (WHP) represents areas of significant fuel buildup. It is an index that quantifies the relative potential for wildfire that may be difficult to manage, and is used as a measure to help prioritize where fuel treatments may be needed. The WHP is a raster geospatial product produced by the USFS Fire Modeling Institute in the Fire, Fuel, and Smoke Program. Higher values have a higher probability of high-intensity fire, with torching, crowning, and other forms of extreme fire behavior. Data used to represent this metric are areas classified as “high” or “very high” risk. A mask for non-burnable areas is applied to focus on ecosystems with vegetation that can be modified through management to reduce hazard.
Data Format: Raster data are binary: (1) “high”, or “very high” risk or (0) other
Spatial Resolution: 30m (*data for 2023 assessment is 270m)
Source data: The WHP data are part of the Wildfire Risk to Communities suite of data along with several other data products. More information on the data used for the 2024 assessment can be found here. Links for data used in previous year TCA assessments: 2020-2022 , 2023. Three different versions of LANDFIRE data were used over the years: LF 2014 NGDA US_140_FRG (2020 and 2021), LF 2016 LC16_BPS_200 (2022 and 2023), and LF 2020 LC20_BPS_220 (2024)
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
Direct Download (Raster Data Gateway)
Objective: The TCA Uncharacteristic Disturbance Indicator identifies locations that have had recent catastrophic disturbance including uncharacteristically severe wildfires resulting in needs for reforestation, revegetation or other restoration work. Potential exists to include other major disturbances (e.g., landslides).
Data: The LANDFIRE Biophysical Settings (BpS) raster datasets (LF16_BPS_200) were combined with the Monitoring Trends in Burn Severity (MTBS) data (1984-most recent) and Composite Burn Index (Parks et al. 2018) data for the year prior to the assessment year derived from NIFC Fire Perimeters (1000 ac West, 500 ac East) to identify areas that have experienced unnaturally severe wildfires in the recent past, since 1984. The CBI data serves as a bridge until MTBS data are available. The “Moderate severity” metric (layer) represents areas mapped with a dominant condition of mixed severity fire (based on BpS) but had a high severity MTBS fire in that location.
Data Format: Raster data are binary: (1) “unnaturally severe fire” or (0) other
Spatial Resolution: 30m
Source data: MTBS fire severity, expected fire severity , CBI methods, National Interagency Fire Center Perimeters (for CBI)
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
Direct Download (Raster Data Gateway)
Objective: Identify locations where roads are present at a density that impacts the quality of wildlife habitat and fragments the landscape.
Data: A national roads vector dataset has been acquired from the USGS National Transportation Dataset. The data were reattributed to represent three classes: (1) Unimproved, (2) Paved, (3) Light Duty roads. The new classes correspond to the following classes from the National Transportation Dataset:
Unimproved: Four-wheel drive roads.
Paved: Controlled access highway, Secondary Highway or Major Connecting Road, Ramps
Light Duty: Local Connecting Roads, Local Roads
Data Format: Polylines were converted to a raster for each TCA assessment year. The value at a pixel corresponds to the class of road at that location. Values differ slightly between the years due to data improvements and updated information from state and local sources:
2024: 1- Light Duty, 2- Paved, 3- Unimproved, 4- Other
2023: 1- Light Duty, 2- Paved, 3- Unimproved, 4- Other
2022: 1- Light Duty, 2- Paved, 3- Unimproved
2021: 1- Light Duty, 2- Paved, 3- Unimproved, 4- Other
2020: 1- Light Duty, 2- Paved, 3- Unimproved, 4- Other
Spatial Resolution: 10m
Source data: USGS National Transportation Dataset
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
*if you have trouble viewing the Dashboard, please submit a Tableau Viewer Access Request
Direct Download (Raster Data Gateway)
Objective: Identify areas where significant fuel buildup has occurred due to fire suppression, extensive mortality due to natural or anthropogenic causes, or for other reasons.
Data: The Wildland Hazard Potential (WHP) represents areas of significant fuel buildup. It is an index that quantifies the relative potential for wildfire that may be difficult to manage and is used as a measure to help prioritize where fuel treatments may be needed. The WHP is a raster geospatial product produced by the USFS Fire Modeling Institute in the Fire, Fuel, and Smoke Program. Higher values have a higher probability of high-intensity fire, with torching, crowning, and other forms of extreme fire behavior. Data used to represent this metric are areas classified as “high” or “very high” risk. A mask for non-burnable areas is applied to focus on ecosystems with vegetation that can be modified through management to reduce hazard.
Data Format: Raster data are binary: (1) “high”, or “very high” risk or (0) other
Spatial Resolution: 30m (*data for 2023 assessment is 270m)
Source data: The WHP data are part of the Wildfire Risk to Communities suite of data along with several other data products. More information on the data used for the 2024 assessment can be found here. Links for data used in previous year TCA assessments: 2020-2022 , 2023.
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
*if you have trouble viewing the Dashboard, please submit a Tableau Viewer Access Request
Direct Download (Raster Data Gateway)
Objective: The TCA Uncharacteristic Disturbance Indicator identifies locations that have had recent catastrophic disturbance including uncharacteristically severe wildfires resulting in needs for reforestation, revegetation or other restoration work. Potential exists to include other major disturbances (e.g., landslides).
Data: The LANDFIRE Biophysical Settings (BpS) raster datasets (LF16_BPS_200) were combined with the Monitoring Trends in Burn Severity (MTBS) data (1984-most recent) and Composite Burn Index (Parks et. al 2018) data for the year prior to the assessment year derived from NIFC Fire Perimeters (1000 ac West, 500 ac East) to identify areas that have experienced unnaturally severe wildfires in the recent past, since 1984. The CBI data serves as a bridge until MTBS data are available. The “Low severity” metric (layer) represents areas mapped with a dominant condition of low severity fire (based on BpS) but had a moderate or high severity MTBS fire in that location.
Data Format: Raster data are binary: (1) “unnaturally severe fire” or (0) other
Spatial Resolution: 30m
Source data: MTBS fire severity, expected fire severity , CBI methods, National Interagency Fire Center Perimeters (for CBI)
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
Direct Download (Raster Data Gateway)
Objective: Characterize recent impacts to terrestrial ecosystems due to outbreaks of major invasive, non-native, and native forest insects and pathogens.
Data: Data are derived from aerial detection surveys for tree defoliation and mortality from the USFS Forest Health Assessment and Applied Sciences Team (FHAAST) National Forest Pest Conditions Database. Polygons are created by aerial sketch mapping, and coded for defoliation and mortality, in addition to other damage codes. Defoliation and mortality layers were created from the polygon data and the attribute codes. The layers were merged to compensate for difficulties in identifying defoliation separately from mortality in hardwoods vs. conifer forests. Areas that were defoliated during three of the five years in each time period have significant impacts and likely are experiencing mortality, so these polygons were added to the mortality layer. The layer includes areas with mortality classed as “very light”. There is a one-year lag between the most recent available source data and the TCA Assessment year. For instance, the 2024 TCA Assessment used the data for the years 2014-2028 to calculate the 6-10 year mortality metric, with all other assessment years following this same pattern.
Data Format: Raster data are binary: (1) presence or (0) absence.
Spatial Resolution: 240m.
Source data: USFS FHAAST custom product
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
Direct Download (Raster Data Gateway)
Objective: Characterize recent impacts to terrestrial ecosystems due to outbreaks of major invasive, non-native, and native forest insects and pathogens.
Data: Data are derived from aerial detection surveys for tree defoliation and mortality from the USFS Forest Health Assessment and Applied Sciences Team (FHAAST) National Forest Pest Conditions Database. Polygons are created by aerial sketch mapping, and coded for defoliation and mortality, in addition to other damage codes. Defoliation and mortality layers were created from the polygon data and the attribute codes. The layers were merged to compensate for difficulties in identifying defoliation separately from mortality in hardwoods vs. conifer forests. Areas that were defoliated during three of the five years in each time period have significant impacts and likely are experiencing mortality, so these polygons were added to the mortality layer. The layer includes areas with mortality classed as “very light”. There is a one-year lag between the source data and the TCA Assessment year. For instance, the 2024 TCA Assessment used data from 2019-2023 aerial detection surveys to calculate the 0-5 year mortality metric, with all other assessment years following this same pattern.
Data Format: Raster data are binary: (1) presence or (0) absence.
Spatial Resolution: 240m.
Source data: USFS FHAAST custom product
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
This feature class includes States, Counties or Boroughs, Congressional Districts, Alaska Recording Districts, County Subdivisions, and Places boundaries that are derived from the latest official Census Bureau and Alaska Department of Natural Resources datasets. Features within Forest Service Administrative Forest boundaries may have been modified by the Forest Service for improved accuracy and spatial coincidence(vertical integration).
Direct Download (Raster Data Gateway)
Objective: Identify ecosystems that have a significant proportion of their area burning less frequently than what occurred under historic fire regimes. Often, missed fire cycles were due to human factors, principally fire suppression since early in the 20th Century.
Data: The area of a landscape with a deficient fire frequency was estimated by comparing observed fire frequencies to historical mean fire return intervals. Observed fire frequencies were calculated by determining the frequency of fire for each pixel based on raster data of observed fires from the Monitoring Trends in Burn Severity (MTBS) project. Areas in the unburned to low severity class were removed from this analysis. The ratio of observed to historical fire frequencies (as determined by Landfire MFRI) was used to determine pixels that are burning less frequently than expected. These pixels were then used to determine the percent area of the landscape deficient in fire. For areas with fire return intervals longer than the remote sensing record, the history of effective fire suppression was used to assume not enough fire occurred back to the implementation of widespread suppression starting in the 1920s through 1930s with policies such as the 10 AM rule. Prior to the 1920s, it is assumed fire return intervals were closer to the historic range of variation, although likely reduced due to the constrained role of Indigenous stewardship. Beginning with the 2022 TCA Assessment, areas highly modified by anthropogenic activities (i.e., those with developed landcovers or agricultural landcovers of pasture and crop cultivation) and areas with a landcover that does not typically burn (i.e., barren or ice) were not included in the calculation. There is a 2 year lag between the most recent MTBS data and the TCA Assessment year (i.e. in the 2024 TCA Assessment, available MTBS data were from 1984-2022). To reduce this lag to 1 year, we compute the Composite Burn Index (CBI) for the year prior to the current assessment year using fire perimeters from National Interagency Fire Center (NIFC) with methods adapted from Parks et al. 2019. The CBI methods were added for the 2021 assessment. Landfire versions used for MFRI and the associated Alaska TCA Assessment are: Landfire 2010 (TCA Assessment 2020-2022) and Landfire 2014 (TCA Assessments 2023 and 2024).
Format: Raster data are ordinal: (1) Deficient (2) Sufficient (3) Excessive.
Resolution: 90m (2023 and 2024 assessments), 30m (2020-2022 assessments).
Source: MTBS fire perimeters, expected fire return interval , CBI methods, National Interagency Fire Center Perimeters (for CBI)
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
*if you have trouble viewing the Dashboard, please submit a Tableau Viewer Access Request
Direct Download (Raster Data Gateway)
Objective: Characterize recent drought severity in Alaska.
Data: The indicator represents drought severity as a weighted sum of the number of droughts in each category over the most recent 3-year period using data from the U.S. Drought Monitor. A 30km grid was overlaid on Alaska and the number of droughts in each category we calculated over the time period. The weighted sum of droughts (DwSum) was calculated using the equation:
(D0 [abnormally dry] * 1) + (D1 [moderate drought] *2) + (D2 [severe drought] * 3) + (D3 [extreme drought] * 4) + (D4 [exceptional drought] * 5)
There is a lag between the assessment year and the most recent drought data, for example, the 2024 TCA Assessment used drought data from 2021-2023. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding landscape (LTA).
Data Format: Raster data are continuous
Units: Number of droughts (weighted sum)
Spatial Resolution: 30000m (30km)
Source data: US Drought Monitor
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
*if you have trouble viewing the Dashboard, please submit a Tableau Viewer Access Request
Direct Download (Raster Data Gateway)
Objective: Identify areas with unhealthy forest conditions as evidenced by a combination of stand and environmental characteristics making them uncharacteristically vulnerable to insect and pathogen outbreaks.
Data: The 2018 National Insect and Disease Risk Map (NIDRM) developed by USFS-FHAAST indicates locations where forests are stressed and susceptible to outbreaks of native and non-native insects and diseases. These include forests with overly high stand densities and where soil/site conditions contribute to drought. The basis for assigning risk is the expectation that 25% or more of live basal area (three times the natural background rate of mortality) will die over the next 15 years due to insects and diseases. Because the NIDRM 2018 map is the most recent update, it has been reused each year for TCA Assessments from 2020-present.
Format: Raster data are binary: (1) at risk or (0) not at risk
Resolution: 240m
Source: NIDRM 2018
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
*if you have trouble viewing the Dashboard, please submit a Tableau Viewer Access Request
Direct Download (Raster Data Gateway)
Objective: Characterize recent impacts to terrestrial ecosystems due to outbreaks of major invasive, non-native, and native forest insects and pathogens.
Data: Data are derived from aerial detection surveys for tree defoliation and mortality from the USFS Forest Health Assessment and Applied Sciences Team (FHAAST) National Forest Pest Conditions Database. Polygons are created by aerial sketch mapping, and coded for defoliation and mortality, in addition to other damage codes. Defoliation and mortality layers were created from the polygon data and the attribute codes. The layers were merged to compensate for difficulties in identifying defoliation separately from mortality in hardwoods vs. conifer forests. Areas that were defoliated during two of the five years in each time period have significant impacts and likely are experiencing mortality, so these polygons were added to the mortality layer. The layer includes areas with mortality classed as “very light”. There is a one-year lag between the source data and the TCA Assessment year. For instance, the 2024 TCA Assessment used data from 2019-2023 aerial detection surveys to calculate the 0-5 year mortality metric, with all other assessment years following this same pattern.
Data Format: Raster data are binary: (1) presence or (0) absence.
Spatial Resolution: 240m.
Source data: USFS FHAAST custom product
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
*if you have trouble viewing the Dashboard, please submit a Tableau Viewer Access Request
Direct Download (Raster Data Gateway)
Objective: Characterize recent impacts to terrestrial ecosystems due to outbreaks of major invasive, non-native, and native forest insects and pathogens.
Data: Data are derived from aerial detection surveys for tree defoliation and mortality from the USFS Forest Health Assessment and Applied Sciences Team (FHAAST) National Forest Pest Conditions Database. Polygons are created by aerial sketch mapping, and coded for defoliation and mortality, in addition to other damage codes. Defoliation and mortality layers were created from the polygon data and the attribute codes. The layers were merged to compensate for difficulties in identifying defoliation separately from mortality in hardwoods vs. conifer forests. Areas that were defoliated during two of the five years in each time period have significant impacts and likely are experiencing mortality, so these polygons were added to the mortality layer. The layer includes areas with mortality classed as “very light”. There is a one-year lag between the most recent available source data and the TCA Assessment year. For instance, the 2024 TCA Assessment used the data for the years 2014-2028 to calculate the 6-10 year mortality metric, with all other assessment years following this same pattern.
Data Format: Raster data are binary: (1) presence or (0) absence.
Spatial Resolution: 240m.
Source data: USFS FHAAST custom product
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
*if you have trouble viewing the Dashboard, please submit a Tableau Viewer Access Request
Direct Download (Raster Data Gateway)
Objective: The TCA Uncharacteristic Disturbance Indicator identifies locations that have had recent catastrophic disturbance including uncharacteristically severe wildfires resulting in needs for reforestation, revegetation or other restoration work.
Data: The LANDFIRE Historical Percent of Mixed Severity raster dataset for Alaska (AK_120_PMS) was combined with the Monitoring Trends in Burn Severity (MTBS) data (1984-most recent) and Composite Burn Index (Parks et al. 2018) data for the year prior to the assessment year derived from NIFC Fire Perimeters (>1000 ac) to identify areas that have experienced unnaturally severe wildfires in the recent past, since 1984. There is a 2-year lag between the most recent MTBS data and the TCA Assessment year (i.e. in the 2024 TCA Assessment, available MTBS data were from 1984-2022). To reduce this lag to 1 year, we compute the Composite Burn Index (CBI) for the year prior to the current assessment year using fire perimeters from National Interagency Fire Center (NIFC) with methods adapted from Parks et al. 2019. The CBI methods were added for the 2021 assessment. The “Moderate severity” metric (layer) represents areas mapped with a dominant condition of mixed severity fire (based on PMS) but had a high severity MTBS fire in that location.
Data Format: Raster data are binary: (1) “unnaturally severe fire” or (0) other
Spatial Resolution: 30m
Source data: MTBS fire severity, expected fire severity , CBI methods, National Interagency Fire Center Perimeters (for CBI)
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
*if you have trouble viewing the Dashboard, please submit a Tableau Viewer Access Request
Direct Download (Raster Data Gateway)
Objective: Characterize climate departures for the most recent 5 years as compared with the historical record, identifying locations where recent conditions indicate a significant change from the historical baseline.
Data: Gridded coverages based on PRISM climate data were used to calculate departure of precipitation in winter (Dec. – Feb.), spring (Mar. – May), summer (Jun. – Aug.), and fall (Sep. – Nov.) for the recent 5-year period as compared with previous years in the historical record. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding landscape (LTA). There is a one-year lag between the most recent available PRISM data and the TCA Assessment year, for example, the 2024 TCA Assessment used 2019-2023 data for the most recent time period, and 1900-2018 data for the historical baseline.
Data Format: Raster data are continuous;
Units: Inches
Spatial Resolution: 4000m (4km)
Source data: PRISM
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
*if you have trouble viewing the Dashboard, please submit a Tableau Viewer Access Request
Direct Download (Raster Data Gateway)
Objective: Characterize climate departures for the most recent 5 years as compared with the historical record, identifying locations where recent conditions indicate a significant change from the historical baseline.
Data: Gridded coverages based on PRISM climate data were used to calculate departure of precipitation in winter (Dec. – Feb.), spring (Mar. – May), summer (Jun. – Aug.), and fall (Sep. – Nov.) for the recent 5-year period as compared with previous years in the historical record. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding landscape (LTA). There is a one-year lag between the most recent available PRISM data and the TCA Assessment year, for example, the 2024 TCA Assessment used 2019-2023 data for the most recent time period, and 1900-2018 data for the historical baseline.
Data Format: Raster data are continuous;
Units: Percent change
Spatial Resolution: 4000m (4km)
Source data: PRISM
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
*if you have trouble viewing the Dashboard, please submit a Tableau Viewer Access Request
Direct Download (Raster Data Gateway)
Objective: Characterize current moisture deficit and surplus relative to the long-term average.
Data: The indicator represents areas of moisture deficit as a z-score. A z-score is a statistical method for assessing how different a value (i.e., most recent 3 years) is from the mean (historical average). The source climate data is from PRISM. Mean moisture values were derived from historical data on precipitation and Thornthwaite potential evapotranspiration, from 1900 to t-minus 4 (t = current TCA Assessment year). The greater the negative z-value, the larger the departure from average conditions, indicating larger moisture deficits.
These data highlight regions of moisture deficit as a z-score calculated from the reference time period and current conditions. There is a lag between the assessment year and the most recent drought data, for example, the 2024 TCA Assessment used 2021-2023 to represent current conditions and 1900-2020 for the reference time period. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding landscape (LTA).
Data Format: Raster data are continuous
Units: Mean difference in z-score
Spatial Resolution: 4000m (4km)
Source data: PRISM; z-score calculations provided by the USFS Office of Sustainability and Climate based on the methods found here: https://www.fs.usda.gov/research/treesearch/43361
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
*if you have trouble viewing the Dashboard, please submit a Tableau Viewer Access Request
Direct Download (Raster Data Gateway)
Objective: Characterize climate departures for the most recent 5 years as compared with the historical record, identifying locations where recent conditions indicate a significant change from the historical baseline.
Data: Gridded coverages based on PRISM climate data were used to calculate departure of mean temperature in winter (Dec. – Feb.), spring (Mar. – May), summer (Jun. – Aug.), and fall (Sep. – Nov.) for the recent 5-year period as compared with previous years in the historical record. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding landscape (LTA). There is a one-year lag between the most recent available PRISM data and the TCA Assessment year, for example, the 2024 TCA Assessment used 2019-2023 data for the most recent time period, and 1900-2018 data for the historical baseline.
Data Format: Raster data are continuous;
Units: Fahrenheit
Spatial Resolution: 4000m (4km)
Source data: PRISM
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: Dashboard link when available
Direct Download (Raster Data Gateway)
Objective: Characterize climate departures for the most recent 5 years as compared with the historical record, identifying locations where recent conditions indicate a significant change from the historical baseline.
Data: Gridded coverages based on DAYMET climate data were used to calculate departure of mean temperature in winter (Dec. – Feb.), spring (Mar. – May), summer (Jun. – Aug.), and fall (Sep. – Nov.) for the recent 5-year period as compared with previous years in the historical record. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding landscape (LTA). There is a one-year lag between the most recent available DAYMET data and the TCA Assessment year, for example, the 2024 TCA Assessment used 2019-2023 data for the most recent time period, and 1980-2018 data for the historical baseline.
Data Format: Raster data are continuous;
Units: Fahrenheit
Spatial Resolution: 1000m (1km)
Source data: DAYMET
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
*if you have trouble viewing the Dashboard, please submit a Tableau Viewer Access Request
This product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS modeled Land Use classes for each year. See additional information about Land Use in the Entity_and_Attribute_Information or Fields section below.
LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a \"best available\" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS Change, Land Cover, and Land Use maps offer a holistic depiction of landscape change across the United States over the past four decades.
Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a Random Forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).
Outputs fall into three categories: Change, Land Cover, and Land Use. At its foundation, Change maps areas of Disturbance, Vegetation Successional Growth, and Stable landscape. More detailed levels of Change products are available and are intended to address needs centered around monitoring causes and types of variations in vegetation cover, water extent, or snow/ice extent that may or may not result in a transition of land cover and/or land use. Change, Land Cover, and Land Use are predicted for each year of the time series and serve as the foundational products for LCMS.
This feature class includes States, Counties or Boroughs, Congressional Districts, Alaska Recording Districts, County Subdivisions, and Places boundaries that are derived from the latest official Census Bureau and Alaska Department of Natural Resources datasets. Features within Forest Service Administrative Forest boundaries may have been modified by the Forest Service for improved accuracy and spatial coincidence(vertical integration).
This feature class includes States, Counties or Boroughs, Congressional Districts, Alaska Recording Districts, County Subdivisions, and Places boundaries that are derived from the latest official Census Bureau and Alaska Department of Natural Resources datasets. Features within Forest Service Administrative Forest boundaries may have been modified by the Forest Service for improved accuracy and spatial coincidence(vertical integration).
This feature class includes States, Counties or Boroughs, Congressional Districts, Alaska Recording Districts, County Subdivisions, and Places boundaries that are derived from the latest official Census Bureau and Alaska Department of Natural Resources datasets. Features within Forest Service Administrative Forest boundaries may have been modified by the Forest Service for improved accuracy and spatial coincidence(vertical integration).
This feature class includes States, Counties or Boroughs, Congressional Districts, Alaska Recording Districts, County Subdivisions, and Places boundaries that are derived from the latest official Census Bureau and Alaska Department of Natural Resources datasets. Features within Forest Service Administrative Forest boundaries may have been modified by the Forest Service for improved accuracy and spatial coincidence(vertical integration).
This product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS modeled Land Use classes for each year. See additional information about Land Use in the Entity_and_Attribute_Information or Fields section below.
LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a \"best available\" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS Change, Land Cover, and Land Use maps offer a holistic depiction of landscape change across the United States over the past four decades.
Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a Random Forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).
Outputs fall into three categories: Change, Land Cover, and Land Use. At its foundation, Change maps areas of Disturbance, Vegetation Successional Growth, and Stable landscape. More detailed levels of Change products are available and are intended to address needs centered around monitoring causes and types of variations in vegetation cover, water extent, or snow/ice extent that may or may not result in a transition of land cover and/or land use. Change, Land Cover, and Land Use are predicted for each year of the time series and serve as the foundational products for LCMS.
In 2021, the Federal Accounting Standards Advisory Board (FASAB) initiated a tracking mechanism that requires all federal agencies that own or manage land to categorize the acres of those lands into three broad categories of predominant land use and produce an annual report. In this context the USDA Forest Service has considered the definitions of each category and classified all acres of National Forest System (NFS) lands into 1. Conservation/Preservation, 2. Commercial, 3. Operational designations. Those designations are additionally separated by whether the lands have been identified as Stewardship lands, or if the lands were acquired by the federal government for General Plant, Property &amp; Equipment purposes. In response, the Washington Office Lands and Realty Management staff determined the appropriate data sources for this annual report and developed classification rules and a geoprocessing methodology to overlay and extract a seamless and complete NFS acreage total by the required categorizations. The acres of land in each category are dynamic, as the status of surface ownership may change from year to year based on administrative and congressional designations, purchases, dispositions, or exchanges. The data used to generate the report are updated weekly, and this translates into continuously refreshed reporting and mapping products. Presented here is the spatial representation of the rule-based land category designations of NFS lands.The FASAB land categories are defined as follows:Conservation/ Preservation: land or land rights that are predominantly used for conservation or preservation purposes.Conservation: protection and proper use of natural resources.Preservation: the protection of buildings, objects, and landscapes.Commercial: land and permanent land rights that are predominately used to generate inflows of resources derived from the land itself or activities that nonfederal third parties perform on the land, usually through special use permits, right-of-way grants, and leases.Operational: land that is used for general or administrative purposes.The Land_FASAB dataset covers National Forest System Lands including federally owned units of forest, range, and related land consisting of national forests, purchase units, national grasslands, land utilization project areas, experimental forest areas, experimental range areas, designated experimental areas, other land areas, water areas, and interests in lands that are administered by the U.S. Department of Agriculture (USDA) Forest Service or designated for administration through the Forest Service. Additional information on this FASAB and the geoprocessing used to produce this dataset can be found here: https://www.fs.usda.gov/sites/default/files/Land-FASAB-dataset-FAQ.pdf
This layer includes both Proclaimed Forest and National Grassland boundary areas. A Proclaimed Forest boundary is the boundary encompassing National Forest System land within a national forest that is set aside and reserved from the public domain by executive order or proclamation; congressional action is required to terminate a proclaimed boundary; if, at some point in time, no National Forest System land remains within the proclaimed boundary, then there is no legal significance to the proclaimed boundary, however, its legal status remains. National Grasslands are lands designated \"National Grasslands\" by the Secretary of Agriculture and permanently held by the Department of Agriculture for administration under Title III of the Bankhead-Jones Farm Tenant Act.
Download via FSGeodata Clearinghouse
This dataset shows information about the USDA Forest Service constructed recreation sites used to populate the public facing webpages. This information is the descriptive and qualitative information used to set appropriate expectations for visitor use and may not contain all the exact engineering, constructed features. View Metadata.
This product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS modeled Land Cover classes for each year. See additional information about Land Cover in the Entity_and_Attribute_Information or Fields section below.
LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a \"best available\" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS Change, Land Cover, and Land Use maps offer a holistic depiction of landscape change across the United States over the past four decades.
Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a Random Forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).
Outputs fall into three categories: Change, Land Cover, and Land Use. At its foundation, Change maps areas of Disturbance, Vegetation Successional Growth, and Stable landscape. More detailed levels of Change products are available and are intended to address needs centered around monitoring causes and types of variations in vegetation cover, water extent, or snow/ice extent that may or may not result in a transition of land cover and/or land use. Change, Land Cover, and Land Use are predicted for each year of the time series and serve as the foundational products for LCMS.
This data publication contains a spatial database of wildfires that occurred in the United States (US) from 1992 to 2024. It is the sixth update of a publication originally generated to support the national Fire Program Analysis (FPA) system. The wildfire records were acquired from the reporting systems of federal, state, and local fire organizations. The following core data elements were required for records to be included in this data publication: discovery date, final fire size, and a point location at least as precise as a Public Land Survey System (PLSS) section (1-square mile grid). The data were transformed to conform, when possible, to the data standards of the National Wildfire Coordinating Group (NWCG), including an updated wildfire-cause standard (approved August 2020). Basic error-checking was performed, and redundant records were identified and removed to the degree possible. The resulting product, referred to as the Fire Program Analysis fire-occurrence database (FPA FOD), includes 2.66 million geo-referenced wildfire records, representing a total of 209 million acres burned during the 33-year period. Identifiers necessary to link the point-based, final-fire-reporting information to published large-fire-perimeter and operational-situation-reporting datasets are included.
This product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS modeled Land Cover classes for each year. See additional information about Land Cover in the Entity_and_Attribute_Information or Fields section below.
LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a \"best available\" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS Change, Land Cover, and Land Use maps offer a holistic depiction of landscape change across the United States over the past four decades.
Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a Random Forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).
Outputs fall into three categories: Change, Land Cover, and Land Use. At its foundation, Change maps areas of Disturbance, Vegetation Successional Growth, and Stable landscape. More detailed levels of Change products are available and are intended to address needs centered around monitoring causes and types of variations in vegetation cover, water extent, or snow/ice extent that may or may not result in a transition of land cover and/or land use. Change, Land Cover, and Land Use are predicted for each year of the time series and serve as the foundational products for LCMS.
NIDRM data can be used to:
For a quick overview of the 2013 - 2027 assessment and to learn more information on the differences between the 2006 and 2012 NIDRMs download the executive summary (2 MB PDF).
Explore forests vulnerable to attack from major insects and diseases by viewing the Interactive Story Map of the National Insect and Disease Risk Map
The dataset for Range Permits is needed to implement the solution for the NRM Range Permit Map modernization. The current legacy custom solution is being modernized with an out of the box solution leveraging ArcGIS Online and ArcPro. This internal read-only dataset for Range Permits and associated Allotments will be the input to this modernized solution for the NRM Range Permit Map. This will deliver value to the Range Management program and make generating official range permit maps easier and more efficient. This provides benefit to the government overall by leveraging Commercial Off The Shelf (COTS) products over custom development to migrate the current legacy application from ArcMap and legacy Citrix to ArcGIS Pro in VDI. This modernized solution will be accessible via web browser in any environment to Range Permit Administrators on the FS network. Sensitive data regarging permit holder information has been removed from this dataset.
This product is part of the Landscape Change Monitoring System (LCMS) data suite. It supplies LCMS Change classes for each year that are a refinement of the modeled LCMS Change classes (Slow Loss, Fast Loss, and Gain) and provide information on the cause of landscape change. See additional information about Change in the Entity_and_Attribute_Information or Fields section below.
LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a \"best available\" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS Change, Land Cover, and Land Use maps offer a holistic depiction of landscape change across the United States over the past four decades.
Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a Random Forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).
Outputs fall into three categories: Change, Land Cover, and Land Use. At its foundation, Change maps areas of Disturbance, Vegetation Successional Growth, and Stable landscape. More detailed levels of Change products are available and are intended to address needs centered around monitoring causes and types of variations in vegetation cover, water extent, or snow/ice extent that may or may not result in a transition of land cover and/or land use. Change, Land Cover, and Land Use are predicted for each year of the time series and serve as the foundational products for LCMS.
This feature class depicts area boundaries of Wild Horse and Burro Territories to correspond to tabular data in the RIMS (Rangeland Information Management System).
Depicts structural range improvements that are lines. This will include fences, Stock Driftway/Feedway (handling facility) and distribution pipelines (Range Water System). These improvements are assets tracking expenditures on the ground across the landscape.
Depicts structural range improvements that are points. This will include handling facilities and Range Water System components like source, storage and pumps. These improvements are assets tracking expenditures on the ground across the landscape.
With the Food, Conservation, and Energy Act of 2008 (the 2008 Farm Bill), Congress tasked states and territories to craft assessments of the forests within their boundaries and develop strategies to address threats and forest management opportunities. Now known as Forest Action Plans, these assessments and strategies provide an analysis of forest conditions and trends in the state and delineate priority forest landscape areas. They offer long-term plans for investing state, federal, and other resources where they can be most effective in achieving national conservation goals by addressing the State and Private Forestry (SPF) national priorities and objectives: 1) Conserve working forest lands, 2) Protect forests from harm, and 3) Enhance public benefits from trees and forests. Administered by the US Forest Service and implemented by State forestry agencies, the SPF Forest Stewardship Program encourages private forest landowners to manage their lands using professionally prepared Forest Stewardship plans. Participation in the Forest Stewardship Program requires that states and territories submit a raster dataset of priority areas specific to the Program - aligned with priority landscapes identified in Forest Action Plans - called Forest Stewardship Priority Areas, where they will focus their Program delivery efforts. Program performance measures include acres covered by active Forest Stewardship plans that are within Forest Stewardship Priority Areas
State and territory Forest Stewardship Priority Areas data set submissions are valid for up to 10 (ten) years, but must be reviewed every 5 years and may be updated as frequently as annually.
The Forest Stewardship Program Investment Areas are divided into a Federal Investment Area and a Non-Federal Investment Area. The Federal Investment Areas represent where Federal funding can be used for plan development, implementation, and monitoring. For plans outside of the Federal Investment Area, other funding will need to be used for those activities. Federal funding can be used for landowner education and assistance throughout the Forest Stewardship Program Area.
<!· Forest Stewardship Federal Investment Areas: Represents no more than 50 percent of State or Territory’s nationally defined maximum acres for prioritization. Federally funded Forest Stewardship Program activities related to plan development, implementation, and monitoring with nonindustrial private forest landowners are used to accomplish:
o Reducing wildfire risk to communities (such as fuels reduction, prescribed fire)
o Enhancing wildlife habitat (such as game, and non-game)
o Protecting water resources (such as water quality, watershed management)
o Supporting jobs (such as rural prosperity)
<!· Forest Stewardship Non-Federal Investment Areas: This is the remainder of the Forest Stewardship Program area where State and other leveraged funds can be used for Forest Stewardship Program plan activities. They should be used in accordance with state or territorial objectives identified in their forest action plan. This area is not subjected to any acreage caps.
This data publication contains 250 meter raster data depicting the spatial distribution of forest ownership types in the conterminous United States. The data are a modeled representation of forest land by ownership type, and include three types of public ownership: federal, state, and local; three types of private: family (includes individuals and families), corporate, and other private (includes conservation and natural resource organizations, and unincorporated partnerships and associations); as well as Native American tribal lands. The most up-to-date data available were used in creating this data publication. A plurality of the ownership data were from 2014, but some data were as old as 2004.
", "keyword": [ "Forest Land", "Forest Ownership", "Non-forest", "Owner Types", "Public", "Private", "Corporate", "Tribal", "Federal(Public)", "State(Public)", "Local(Public)", "Family(Private)", "Corporate(Private)", "Other Private" ], "issued": "2026-02-10T17:43:11.000Z", "modified": "2026-08-20T17:36:39.000Z", "publisher": { "name": "U.S. Forest Service", "source": "U.S. Forest Service" }, "contactPoint": { "@type": "vcard:Contact", "fn": "USFSEnterpriseContent", "hasEmail": "mailto:SM.FS.data@usda.gov" }, "accessLevel": "public", "spatial": "-128.4902,22.6919,-65.0669,51.9304", "license": "https://creativecommons.org/licenses/by/4.0/", "programCode": [ "005:059" ], "bureauCode": [ "005:96" ], "theme": [ "geospatial" ], "progressCode": "onGoing", "distribution": [ { "@type": "dcat:Distribution", "title": "ISO-19139 metadata", "conformsTo": "https://www.isotc211.org/2005/gmi", "mediaType": "text/xml", "accessURL": "https://www.arcgis.com/sharing/rest/content/items/e245632d51704d2b9d2a59844896f063/info/metadata/metadata.xml?format=iso19139" }, { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://data-usfs.hub.arcgis.com/datasets/usfs::us-forest-atlas-fia-forest-ownership-circa-2014-for-national-regional-scale-analysis-image-service" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://imagery.geoplatform.gov/iipp/rest/services/Vegetation/USFS_EDW_FIA_ForestAtlas_ForestOwnership_501/ImageServer" } ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=ea9add6e651b45b38fe54c1a4c34a357", "landingPage": "https://data-usfs.hub.arcgis.com/datasets/usfs::landscape-change-monitoring-system-lcms-alaska-annual-change", "title": "Landscape Change Monitoring System (LCMS) Alaska Annual Change", "description": "This product is part of the Landscape Change Monitoring System (LCMS) data suite. It supplies LCMS Change classes for each year that are a refinement of the modeled LCMS Change classes (Slow Loss, Fast Loss, and Gain) and provide information on the cause of landscape change. See additional information about Change in the Entity_and_Attribute_Information or Fields section below.
LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a \"best available\" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS Change, Land Cover, and Land Use maps offer a holistic depiction of landscape change across the United States over the past four decades.
Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a Random Forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).
Outputs fall into three categories: Change, Land Cover, and Land Use. At its foundation, Change maps areas of Disturbance, Vegetation Successional Growth, and Stable landscape. More detailed levels of Change products are available and are intended to address needs centered around monitoring causes and types of variations in vegetation cover, water extent, or snow/ice extent that may or may not result in a transition of land cover and/or land use. Change, Land Cover, and Land Use are predicted for each year of the time series and serve as the foundational products for LCMS.
This data product contains raster data depicting the spatial distribution of forest ownership types in the conterminous United States circa 2020. The data are a modeled representation of forest land by ownership type, and include three types of public ownership: federal, state, and local, as well as three types of private: family (includes individuals and families), corporate, and other private (includes conservation and natural resource organizations, unincorporated partnerships and associations, and Native American tribal lands).
This data product contains raster data depicting the spatial distribution of forest ownership types in the conterminous United States circa 2009. The data are a modeled representation of forest land by ownership type, and include three types of public ownership: federal, state, and local, as well as three types of private ownership: family (includes individuals and families), corporate, and other private (includes conservation and natural resource organizations, unincorporated partnerships and associations, and Native American tribal lands).
The data are designed for strategic analyses at a national or regional scale which require spatially explicit information regarding the extent, distribution, and prevalence of the ownership types represented. The data are not recommended for tactical analyses on a sub-regional scale, or for informing local management decisions. Furthermore, map accuracies vary considerably and thus the utility of these data can vary geographically under different ownership patterns.
", "keyword": [ "USFS", "EDW", "Public", "Private", "Forest", "Ownership", "CONUS", "Federal", "State", "Local", "Family", "Corporate" ], "issued": "2025-10-28T20:41:24.000Z", "modified": "2026-08-20T17:31:33.000Z", "publisher": { "name": "U.S. Forest Service", "source": "U.S. Forest Service" }, "contactPoint": { "@type": "vcard:Contact", "fn": "USFSEnterpriseContent", "hasEmail": "mailto:SM.FS.data@usda.gov" }, "accessLevel": "public", "spatial": "-128.4896,22.4279,-64.7384,51.9286", "license": "https://creativecommons.org/licenses/by/4.0/", "programCode": [ "005:059" ], "bureauCode": [ "005:96" ], "theme": [ "geospatial" ], "progressCode": "onGoing", "distribution": [ { "@type": "dcat:Distribution", "title": "ISO-19139 metadata", "conformsTo": "https://www.isotc211.org/2005/gmi", "mediaType": "text/xml", "accessURL": "https://www.arcgis.com/sharing/rest/content/items/b1757340c79545c99742da8014e7d851/info/metadata/metadata.xml?format=iso19139" }, { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://data-usfs.hub.arcgis.com/datasets/usfs::public-and-private-forest-ownership-conterminous-united-states-2009-for-national-regional-scale-analysis-image-service" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://imagery.geoplatform.gov/iipp/rest/services/Lands_Realty/USFS_EDW_PublicPrivateForestOwnership_CONUS/ImageServer" } ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=b766ca0db5a24cccab584caf15f089aa", "landingPage": "https://data-usfs.hub.arcgis.com/documents/usfs::mtbs-wildfire-occurrence", "title": "MTBS Wildfire Occurrence", "description": "The Monitoring Trends in Burn Severity MTBS project assesses the frequency, extent, and magnitude (size and severity) of all large wildland fires (includes wildfire, wildland fire use, and prescribed fire) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico for the period of 1984 through 2018. All fires reported as greater than 1,000 acres in the western U.S. and greater than 500 acres in the eastern U.S. are mapped across all ownerships. MTBS produces a series of geospatial and tabular data for analysis at a range of spatial, temporal, and thematic scales and are intended to meet a variety of information needs that require consistent data about fire effects through space and time. This map layer is a vector point of the location of all currently inventoried and mappable fires occurring between calendar year 1984 and 2018 for the continental United States, Alaska, Hawaii and Puerto Rico. The point location represents the geographic centroid for the _BURN_AREA_BOUNDARY polygon(s) associated with each fire. Map Service Feature Layer", "keyword": [ "NGDA", "National Geospatial Data Asset", "Land Use Land Cover Theme", "MTBS", "Monitoring Trends in Burn Severity", "Fire and Aviation", "Open Data", "Fire Occurrence", "fire location", "fire occurrence", "landsat", "wildland fire", "geospatial", "NGDA110", "Land Use - Land Cover Theme", "NGDA116" ], "issued": "2020-06-10T21:40:37.000Z", "modified": "2026-08-11T20:29:33.000Z", "publisher": { "name": "U.S. Forest Service", "source": "U.S. Forest Service" }, "contactPoint": { "@type": "vcard:Contact", "fn": "USFSEnterpriseContent", "hasEmail": "mailto:SM.FS.data@usda.gov" }, "accessLevel": "public", "spatial": "-131.3620,6.8980,-65.6380,72.6220", "license": "https://creativecommons.org/licenses/by/4.0/", "programCode": [ "005:059" ], "bureauCode": [ "005:96" ], "theme": [ "geospatial" ], "progressCode": "onGoing", "distribution": [ { "@type": "dcat:Distribution", "title": "ISO-19139 metadata", "conformsTo": "https://www.isotc211.org/2005/gmi", "mediaType": "text/xml", "accessURL": "https://www.arcgis.com/sharing/rest/content/items/b766ca0db5a24cccab584caf15f089aa/info/metadata/metadata.xml?format=iso19139" }, { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://data-usfs.hub.arcgis.com/documents/usfs::mtbs-wildfire-occurrence" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://www.mtbs.gov/" } ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=81284bfaf86a4fa2a7d49c74424ffe1e&sublayer=62", "landingPage": "https://data-usfs.hub.arcgis.com/datasets/usfs::monitoring-trends-in-burn-severity-fire-occurrence-locations-feature-layer", "title": "Monitoring Trends in Burn Severity Fire Occurrence Locations (Feature Layer)", "description": "The Monitoring Trends in Burn Severity MTBS project assesses the frequency, extent, and magnitude (size and severity) of all large wildland fires (includes wildfire, wildland fire use, and prescribed fire) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico from the beginning of the Landsat Thematic Mapper archive to the present. All fires reported as greater than 1,000 acres in the western U.S. and greater than 500 acres in the eastern U.S. are mapped across all ownerships. MTBS produces a series of geospatial and tabular data for analysis at a range of spatial, temporal, and thematic scales and are intended to meet a variety of information needs that require consistent data about fire effects through space and time. This map layer is a vector point of the location of all currently inventoried and mappable fires occurring between calendar year 1984 and the current MTBS release for CONUS, Alaska, Hawaii and Puerto Rico. Please visit https://mtbs.gov/announcements to determine the current release. Fires omitted from this mapped inventory are those where suitable satellite imagery was not available or fires were not discernable from available imagery. The point location represents the geographic centroid for the _BURN_AREA_BOUNDARY polygon(s) associated with each fire. Metadata", "keyword": [ "NGDA", "National Geospatial Data Asset", "Land Use Land Cover Theme", "MTBS", "Monitoring Trends in Burn Severity", "Fire and Aviation", "Open Data", "Fire Occurrence", "fire location", "fire occurrence", "landsat", "wildland fire", "geospatial", "NGDA116", "Land Use - Land Cover Theme" ], "issued": "2017-05-04T13:45:03.000Z", "modified": "2026-08-11T20:28:34.000Z", "publisher": { "name": "U.S. Forest Service", "source": "U.S. Forest Service" }, "contactPoint": { "@type": "vcard:Contact", "fn": "USFSEnterpriseContent", "hasEmail": "mailto:SM.FS.data@usda.gov" }, "accessLevel": "public", "spatial": "-166.0911,17.9566,-65.3506,70.1393", "license": "https://creativecommons.org/licenses/by/4.0/", "programCode": [ "005:059" ], "bureauCode": [ "005:96" ], "theme": [ "geospatial" ], "progressCode": "onGoing", "distribution": [ { "@type": "dcat:Distribution", "title": "ISO-19139 metadata", "conformsTo": "https://www.isotc211.org/2005/gmi", "mediaType": "text/xml", "accessURL": "https://www.arcgis.com/sharing/rest/content/items/81284bfaf86a4fa2a7d49c74424ffe1e/info/metadata/metadata.xml?format=iso19139" }, { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://data-usfs.hub.arcgis.com/datasets/usfs::monitoring-trends-in-burn-severity-fire-occurrence-locations-feature-layer" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://apps.fs.usda.gov/arcx/rest/services/EDW/EDW_MTBS_01/MapServer/62" }, { "@type": "dcat:Distribution", "title": "OGC WMS", "format": "OGC WMS", "mediaType": "application/vnd.ogc.wms_xml", "accessURL": "https://apps.fs.usda.gov/arcx/services/EDW/EDW_MTBS_01/MapServer/WMSServer?request=GetCapabilities&service=WMS" }, { "@type": "dcat:Distribution", "title": "CSV", "format": "CSV", "mediaType": "text/csv", "accessURL": "https://data-usfs.hub.arcgis.com/api/download/v1/items/81284bfaf86a4fa2a7d49c74424ffe1e/csv?layers=62" }, { "@type": "dcat:Distribution", "title": "Shapefile", "format": "ZIP", "mediaType": "application/zip", "accessURL": "https://data-usfs.hub.arcgis.com/api/download/v1/items/81284bfaf86a4fa2a7d49c74424ffe1e/shapefile?layers=62" }, { "@type": "dcat:Distribution", "title": "GeoJSON", "format": "GeoJSON", "mediaType": "application/vnd.geo+json", "accessURL": "https://data-usfs.hub.arcgis.com/api/download/v1/items/81284bfaf86a4fa2a7d49c74424ffe1e/geojson?layers=62" }, { "@type": "dcat:Distribution", "title": "KML", "format": "KML", "mediaType": "application/vnd.google-earth.kml+xml", "accessURL": "https://data-usfs.hub.arcgis.com/api/download/v1/items/81284bfaf86a4fa2a7d49c74424ffe1e/kml?layers=62" } ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=5946b5a117a5434fb9659c167fb29528", "landingPage": "https://data-usfs.hub.arcgis.com/datasets/usfs::wildfire-suppression-difficulty-index-80th-percentile-2026-image-service", "title": "Wildfire Suppression Difficulty Index 80th Percentile 2026 (Image Service)", "description": "Wildfire Suppression Difficulty Index (SDI) 97th Percentile is a rating of relative difficulty in performing fire control work under regionally appropriate fuel moisture and 20 mph uphill winds (@ 20 ft); Wildfire Suppression Difficulty Index (SDI) 90th Percentile is a rating of relative difficulty in performing fire control work under regionally appropriate fuel moisture and 15 mph uphill winds (@ 20 ft). Wildfire Suppression Difficulty Index (SDI) 80th Percentile is a rating of relative difficulty in performing fire control work under regionally appropriate fuel moisture and 10 mph uphill winds (@ 20 ft).
SDI factors in topography, fuels, expected fire behavior under prevailing conditions, fireline production rates in various fuel types with and without heavy equipment, and access via roads, trails, or cross-country travel. SDI does not account for standing snags or other overhead hazards to firefighters, so it is not a firefighter hazard map. It is only showing in relative terms where it is harder or easier to perform suppression work.
Wildfire Suppression Difficulty Index (SDI) 97th Percentile is a rating of relative difficulty in performing fire control work under regionally appropriate fuel moisture and 20 mph uphill winds (@ 20 ft); Wildfire Suppression Difficulty Index (SDI) 90th Percentile is a rating of relative difficulty in performing fire control work under regionally appropriate fuel moisture and 15 mph uphill winds (@ 20 ft). Wildfire Suppression Difficulty Index (SDI) 80th Percentile is a rating of relative difficulty in performing fire control work under regionally appropriate fuel moisture and 10 mph uphill winds (@ 20 ft).
SDI factors in topography, fuels, expected fire behavior under prevailing conditions, fireline production rates in various fuel types with and without heavy equipment, and access via roads, trails, or cross-country travel. SDI does not account for standing snags or other overhead hazards to firefighters, so it is not a firefighter hazard map. It is only showing in relative terms where it is harder or easier to perform suppression work.
Wildfire Suppression Difficulty Index (SDI) 97th Percentile is a rating of relative difficulty in performing fire control work under regionally appropriate fuel moisture and 20 mph uphill winds (@ 20 ft); Wildfire Suppression Difficulty Index (SDI) 90th Percentile is a rating of relative difficulty in performing fire control work under regionally appropriate fuel moisture and 15 mph uphill winds (@ 20 ft). Wildfire Suppression Difficulty Index (SDI) 80th Percentile is a rating of relative difficulty in performing fire control work under regionally appropriate fuel moisture and 10 mph uphill winds (@ 20 ft).
SDI factors in topography, fuels, expected fire behavior under prevailing conditions, fireline production rates in various fuel types with and without heavy equipment, and access via roads, trails, or cross-country travel. SDI does not account for standing snags or other overhead hazards to firefighters, so it is not a firefighter hazard map. It is only showing in relative terms where it is harder or easier to perform suppression work.
This dataset is the 2023 version of wildfire hazard potential (WHP) for the United States. The files included in this data publication represent an update to any previous versions of WHP or wildland fire potential (WFP) published by the USDA Forest Service. WHP is an index that quantifies the relative potential for high-intensity wildfire that may be difficult to manage, used as a measure to help prioritize where fuel treatments may be needed. This 2023 version of WHP was created from updated national wildfire hazard datasets of annual burn probability and fire intensity generated by the USDA Forest Service, Rocky Mountain Research Station with the large fire simulation system (FSim). Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were the primary inputs to the updated FSim modeling work and therefore form the foundation for this version of the WHP. As such, the data presented here reflect landscape conditions as of the end of 2020. LANDFIRE 2020 vegetation and fuels data were also used directly in the WHP mapping process, along with updated point locations of fire occurrence ca. 1992-2020. With these datasets as inputs, we produced an index of WHP for all of the conterminous United States at 270-meter resolution. We present the final WHP map in two forms: 1) continuous integer values, and 2) five WHP classes of very low, low, moderate, high, and very high. On its own, WHP is not an explicit map of wildfire threat or risk, but when paired with spatial data depicting highly valued resources and assets such as structures or powerlines, it can approximate relative wildfire risk to those specific resources and assets. WHP is also not a forecast or wildfire outlook for any particular season, as it does not include any information on current or forecasted weather or fuel moisture conditions. It is instead intended for long-term strategic fuels management.
These new data represent an update to all previous versions of WHP or WFP published by the USDA Forest Service. On 07/17/2024 this data package was updated to correct a data processing error that caused a very small number of pixels to be Nodata in the initial classified version that should have been Very High WHP. This update also included the addition of summaries tables by management jurisdictions. To check for the latest version of the WHP geospatial data and map graphics, as well as documentation on the mapping process, see: https://www.firelab.org/project/wildfire-hazard- potential. Details about the Wildfire Hazard Potential mapping process can be found in Dillon et al. (2015). Steps described in this paper about weighting for crown fire potential were dropped in the 2018 and subsequent versions due to changes to the FSim modeling products used as the primary inputs to WHP mapping.
The intended use of this image service is to show, over large landscapes, where wildfires are likely to occur and how intense they may be.
Limitations of use: WHP is also not a forecast or wildfire outlook for any particular season, as it does not include any information on current or forecasted weather or fuel moisture conditions. It is instead intended for long-term strategic fuels management.
Update frequency: Data is static. This is the 2023 version of WHP for the United States. Future versions of this dataset will be published as separate services.
For more information, refer to: https://research.fs.usda.gov/firelab/products/dataandtools/wildfire-hazard-potential
The data included in this publication depict components of wildfire risk specifically for populated areas in the United States. These datasets represent where people live in the United States and the in situ risk from wildfire, i.e., the risk at the location where the adverse effects take place.
National wildfire hazard datasets of annual burn probability and fire intensity, generated by the USDA Forest Service, Rocky Mountain Research Station and Pyrologix LLC, form the foundation of the Wildfire Risk to Communities data. Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were used as input to two different but related geospatial fire simulation systems. Annual burn probability was produced with the USFS geospatial fire simulator (FSim) at a relatively coarse cell size of 270 meters (m). To bring the burn probability raster data down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30 m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability into developed areas represented in LANDFIRE fuels data as non-burnable. Burn probability rasters represent landscape conditions as of the end of 2020. Fire intensity characteristics were modeled at 30 m resolution using a process that performs a comprehensive set of FlamMap runs spanning the full range of weather-related characteristics that occur during a fire season and then integrates those runs into a variety of results based on the likelihood of those weather types occurring. Before the fire intensity modeling, the LANDFIRE 2020 data were updated to reflect fuels disturbances occurring in 2021 and 2022. As such, the fire intensity datasets represent landscape conditions as of the end of 2022. The data products in this publication that represent where people live, reflect 2021 estimates of housing unit and population counts from the U.S. Census Bureau, combined with building footprint data from Onegeo and USA Structures, both reflecting 2022 conditions.
The specific raster datasets included in this publication include:
Building Count: Building Count is a 30-m raster representing the count of buildings in the building footprint dataset located within each 30-m pixel.
Building Density: Building Density is a 30-m raster representing the density of buildings in the building footprint dataset (buildings per square kilometer [km²]).
Building Coverage: Building Coverage is a 30-m raster depicting the percentage of habitable land area covered by building footprints.
Population Count (PopCount): PopCount is a 30-m raster with pixel values representing residential population count (persons) in each pixel.
Population Density (PopDen): PopDen is a 30-m raster of residential population density (people/km²).
Housing Unit Count (HUCount): HUCount is a 30-m raster representing the number of housing units in each pixel.
Housing Unit Density (HUDen): HUDen is a 30-m raster of housing-unit density (housing units/km²).
Housing Unit Exposure (HUExposure): HUExposure is a 30-m raster that represents the expected number of housing units within a pixel potentially exposed to wildfire in a year. This is a long-term annual average and not intended to represent the actual number of housing units exposed in any specific year.
Housing Unit Impact (HUImpact): HUImpact is a 30-m raster that represents the relative potential impact of fire to housing units at any pixel, if a fire were to occur. It is an index that incorporates the general consequences of fire on a home as a function of fire intensity and uses flame length probabilities from wildfire modeling to capture likely intensity of fire.
Housing Unit Risk (HURisk): HURisk is a 30-m raster that integrates all four primary elements of wildfire risk - likelihood, intensity, susceptibility, and exposure - on pixels where housing unit density is greater than zero.
Additional methodology documentation is provided with the data publication download. Metadata and Downloads: (https://www.fs.usda.gov/rds/archive/catalog/RDS-2020-0060-2).
Note: Pixel values in this image service have been altered from the original raster dataset due to data requirements in web services. The service is intended primarily for data visualization. Relative values and spatial patterns have been largely preserved in the service, but users are encouraged to download the source data for quantitative analysis.
This dataset is the 2023 version of wildfire hazard potential (WHP) for the United States. The files included in this data publication represent an update to any previous versions of WHP or wildland fire potential (WFP) published by the USDA Forest Service. WHP is an index that quantifies the relative potential for high-intensity wildfire that may be difficult to manage, used as a measure to help prioritize where fuel treatments may be needed. This 2023 version of WHP was created from updated national wildfire hazard datasets of annual burn probability and fire intensity generated by the USDA Forest Service, Rocky Mountain Research Station with the large fire simulation system (FSim). Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were the primary inputs to the updated FSim modeling work and therefore form the foundation for this version of the WHP. As such, the data presented here reflect landscape conditions as of the end of 2020. LANDFIRE 2020 vegetation and fuels data were also used directly in the WHP mapping process, along with updated point locations of fire occurrence ca. 1992-2020. With these datasets as inputs, we produced an index of WHP for all of the conterminous United States at 270-meter resolution. We present the final WHP map in two forms: 1) continuous integer values, and 2) five WHP classes of very low, low, moderate, high, and very high. On its own, WHP is not an explicit map of wildfire threat or risk, but when paired with spatial data depicting highly valued resources and assets such as structures or powerlines, it can approximate relative wildfire risk to those specific resources and assets. WHP is also not a forecast or wildfire outlook for any particular season, as it does not include any information on current or forecasted weather or fuel moisture conditions. It is instead intended for long-term strategic fuels management.
These new data represent an update to all previous versions of WHP or WFP published by the USDA Forest Service. On 07/17/2024 this data package was updated to correct a data processing error that caused a very small number of pixels to be Nodata in the initial classified version that should have been Very High WHP. This update also included the addition of summaries tables by management jurisdictions. To check for the latest version of the WHP geospatial data and map graphics, as well as documentation on the mapping process, see: https://www.firelab.org/project/wildfire-hazard- potential. Details about the Wildfire Hazard Potential mapping process can be found in Dillon et al. (2015). Steps described in this paper about weighting for crown fire potential were dropped in the 2018 and subsequent versions due to changes to the FSim modeling products used as the primary inputs to WHP mapping.
The intended use of this image service is to show, over large landscapes, where wildfires are likely to occur and how intense they may be.
Limitations of use: WHP is also not a forecast or wildfire outlook for any particular season, as it does not include any information on current or forecasted weather or fuel moisture conditions. It is instead intended for long-term strategic fuels management.
Update frequency: Data is static. This is the 2023 version of WHP for the United States. Future versions of this dataset will be published as separate services.
For more information, refer to: https://research.fs.usda.gov/firelab/products/dataandtools/wildfire-hazard-potential
", "keyword": [ "Environment", "geoscientific Information", "society", "structure", "Ecology", "Ecosystems", "& Environment", "Fire", "Fire detection", "Fire ecology", "Fire effects on environment", "Fire suppression", "pre-suppression", "Prescribed fire", "Environment and People", "Forest management", "Landscape management", "burn probability", "hazard", "fuels management", "fire suppression", "fire likelihood", "fire planning", "risk assessment", "wildfire hazard potential", "United States", "conterminous United States", "CONUS", "Alaska", "Hawaii" ], "issued": "2025-10-06T20:29:05.000Z", "modified": "2026-05-20T20:00:05.000Z", "publisher": { "name": "U.S. Forest Service", "source": "USFS" }, "contactPoint": { "@type": "vcard:Contact", "fn": "USFSEnterpriseContent", "hasEmail": "mailto:SM.FS.data@usda.gov" }, "accessLevel": "public", "spatial": "-180.0000,18.8388,-64.0513,71.5630", "license": "https://creativecommons.org/licenses/by/4.0/", "programCode": [ "005:059" ], "bureauCode": [ "005:96" ], "theme": [ "geospatial" ], "progressCode": "onGoing", "distribution": [ { "@type": "dcat:Distribution", "title": "ISO-19139 metadata", "conformsTo": "https://www.isotc211.org/2005/gmi", "mediaType": "text/xml", "accessURL": "https://www.arcgis.com/sharing/rest/content/items/c984aeeecfcc4bef887a3f72a5b4e65a/info/metadata/metadata.xml?format=iso19139" }, { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://data-usfs.hub.arcgis.com/datasets/usfs::wildfire-hazard-potential-continuous-2023-united-states-and-alaska-image-service" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://imagery.geoplatform.gov/iipp/rest/services/Fire_Aviation/USFS_EDW_RMRS_WildfireHazardPotentialContinuous/ImageServer" } ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=31f5eddfb87f42bfa49a932e041d5e03&sublayer=0", "landingPage": "https://data-usfs.hub.arcgis.com/datasets/usfs::edw-maplandeasement-01-forsync-hosted-feature-service", "title": "EDW MAPLandEasement 01 ForSync (Hosted Feature Service)", "description": "
The Act requires agencies to define a GIS transfer spatial data layer depicting access easements to federal land. The MAPLand Act Easement Areas layer depicts areas that represents federal interests in non-federal land, also known as easements, that may be used to provide public recreational access that are granted to, or reserved and managed by the following agencies: BLM, NPS, FWS, USBR, USACE and FS. The intent of a public access easement is to allow for ingress and egress for the general public to utilize or travel through a piece of property to access federal interests on non-federal land or to allow for the public to travel through non-federal land to reach adjacent public land. The data provided in this layer represents some level of public access, however, users should refer to the restriction information field and additional information from the agency as appropriate. For additional information about a specific feature, please use the origin agency field and contact the specific agency.
This Softwood type dataset portrays 16 forest type groups across the contiguous United States. These data were derived from MODIS composite images from the 2002 and 2003 growing seasons in combination with nearly 100 other geospatial data layers, including elevation, slope, aspect, ecoregions, and PRISM climate data. The dataset was developed as a collaborative effort between the USFS Forest Inventory and Analysis and Forest Health Monitoring programs and the USFS Remote Sensing.
", "keyword": [ "Moderate Resolution Imaging Spectroradiometer", "Forest Inventory and Analysis", "FIA", "CONUS", "Contiguous United States", "2002", "2003", "109", "Softwood", "White/Red/Jack Pine Group", "Spruce/Fir Group", "Longleaf/Slash Pine Group", "Loblolly/Shortleaf Pine Group", "Pinyon/Juniper Group", "Douglas-fir Group", "Ponderosa Pine Group", "Western White Pine Group", "Fir/Spruce/Mountain Hemlock Group", "Lodgepole Pine Group", "Hemlock/Sitka Spruce Group", "Western Larch Group", "Redwood Group", "Other Western Softwood Group", "California Mixed Conifer Group", "Exotic Softwoods Group", "Fir" ], "issued": "2026-02-19T18:29:56.000Z", "modified": "2026-02-26T20:44:42.000Z", "publisher": { "name": "U.S. Forest Service", "source": "U.S. Forest Service" }, "contactPoint": { "@type": "vcard:Contact", "fn": "USFSEnterpriseContent", "hasEmail": "mailto:SM.FS.data@usda.gov" }, "accessLevel": "public", "spatial": "-127.9584,22.8075,-65.2605,51.6509", "license": "https://creativecommons.org/licenses/by/4.0/", "programCode": [ "005:059" ], "bureauCode": [ "005:96" ], "theme": [ "geospatial" ], "progressCode": "onGoing", "distribution": [ { "@type": "dcat:Distribution", "title": "ISO-19139 metadata", "conformsTo": "https://www.isotc211.org/2005/gmi", "mediaType": "text/xml", "accessURL": "https://www.arcgis.com/sharing/rest/content/items/f999a7b188fe472f913fcf05fda98d6b/info/metadata/metadata.xml?format=iso19139" }, { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://data-usfs.hub.arcgis.com/datasets/usfs::us-forest-atlas-fia-forest-type-groups-softwoods-image-service" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://imagery.geoplatform.gov/iipp/rest/services/Vegetation/USFS_EDW_FIA_ForestAtlas_Softwoods_109/ImageServer" } ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=50f38db2453c4aff9438aa2a204d9dc7&sublayer=2", "landingPage": "https://data-usfs.hub.arcgis.com/datasets/usfs::ecosys-ecomapprovinces-2025", "title": "Ecosys EcoMapProvinces 2025", "description": "The Ecomap 2025 contains a feature class for five different levels of the National Hierarchical Framework of Ecological Units (the Hierarchy. FSM 2060): Ecological Domains, Ecological Divisions, Ecological Provinces, Ecological Sections, and Ecological Subsections. The Ecomap 2025 data set describes the ecological hierarchy within the conterminous United States. Each feature class contains polygons attributed with the code and name of the ecological unit along with the codes and names of broader scale units into which a given ecological unit is nested. It contains regional geographic delineations for analysis of ecological relationships across ecological units. ECOMAP is the term used for a USDA Forest Service initiative to map ecological units and encourage their use in ecosystem-based approaches to forest and range land conservation and management. This is a collaborative effort with many partners. It is coordinated at the national and regional levels by USDA Forest Service staff and implemented in cooperation with State forestry agencies and others. ECOMAP mapping criteria are outlined in the National Hierarchical Framework of Ecological Units (Cleland et al. 1997, FSM 2060). The framework systematically divides the country into progressively smaller areas that have similar geophysical and biological characteristics and ecological processes.
The Ecomap 2025 contains a feature class for five different levels of the National Hierarchical Framework of Ecological Units (the Hierarchy. FSM 2060): Ecological Domains, Ecological Divisions, Ecological Provinces, Ecological Sections, and Ecological Subsections. The Ecomap 2025 data set describes the ecological hierarchy within the conterminous United States. Each feature class contains polygons attributed with the code and name of the ecological unit along with the codes and names of broader scale units into which a given ecological unit is nested. It contains regional geographic delineations for analysis of ecological relationships across ecological units. ECOMAP is the term used for a USDA Forest Service initiative to map ecological units and encourage their use in ecosystem-based approaches to forest and range land conservation and management. This is a collaborative effort with many partners. It is coordinated at the national and regional levels by USDA Forest Service staff and implemented in cooperation with State forestry agencies and others. ECOMAP mapping criteria are outlined in the National Hierarchical Framework of Ecological Units (Cleland et al. 1997, FSM 2060). The framework systematically divides the country into progressively smaller areas that have similar geophysical and biological characteristics and ecological processes.
The Ecomap 2025 contains a feature class for five different levels of the National Hierarchical Framework of Ecological Units (the Hierarchy. FSM 2060): Ecological Domains, Ecological Divisions, Ecological Provinces, Ecological Sections, and Ecological Subsections. The Ecomap 2025 data set describes the ecological hierarchy within the conterminous United States. Each feature class contains polygons attributed with the code and name of the ecological unit along with the codes and names of broader scale units into which a given ecological unit is nested. It contains regional geographic delineations for analysis of ecological relationships across ecological units. ECOMAP is the term used for a USDA Forest Service initiative to map ecological units and encourage their use in ecosystem-based approaches to forest and range land conservation and management. This is a collaborative effort with many partners. It is coordinated at the national and regional levels by USDA Forest Service staff and implemented in cooperation with State forestry agencies and others. ECOMAP mapping criteria are outlined in the National Hierarchical Framework of Ecological Units (Cleland et al. 1997, FSM 2060). The framework systematically divides the country into progressively smaller areas that have similar geophysical and biological characteristics and ecological processes.
The Ecomap 2025 contains a feature class for five different levels of the National Hierarchical Framework of Ecological Units (the Hierarchy. FSM 2060): Ecological Domains, Ecological Divisions, Ecological Provinces, Ecological Sections, and Ecological Subsections. The Ecomap 2025 data set describes the ecological hierarchy within the conterminous United States. Each feature class contains polygons attributed with the code and name of the ecological unit along with the codes and names of broader scale units into which a given ecological unit is nested. It contains regional geographic delineations for analysis of ecological relationships across ecological units. ECOMAP is the term used for a USDA Forest Service initiative to map ecological units and encourage their use in ecosystem-based approaches to forest and range land conservation and management. This is a collaborative effort with many partners. It is coordinated at the national and regional levels by USDA Forest Service staff and implemented in cooperation with State forestry agencies and others. ECOMAP mapping criteria are outlined in the National Hierarchical Framework of Ecological Units (Cleland et al. 1997, FSM 2060). The framework systematically divides the country into progressively smaller areas that have similar geophysical and biological characteristics and ecological processes.
The Ecomap 2025 contains a feature class for five different levels of the National Hierarchical Framework of Ecological Units (the Hierarchy. FSM 2060): Ecological Domains, Ecological Divisions, Ecological Provinces, Ecological Sections, and Ecological Subsections. The Ecomap 2025 data set describes the ecological hierarchy within the conterminous United States. Each feature class contains polygons attributed with the code and name of the ecological unit along with the codes and names of broader scale units into which a given ecological unit is nested. It contains regional geographic delineations for analysis of ecological relationships across ecological units. ECOMAP is the term used for a USDA Forest Service initiative to map ecological units and encourage their use in ecosystem-based approaches to forest and range land conservation and management. This is a collaborative effort with many partners. It is coordinated at the national and regional levels by USDA Forest Service staff and implemented in cooperation with State forestry agencies and others. ECOMAP mapping criteria are outlined in the National Hierarchical Framework of Ecological Units (Cleland et al. 1997, FSM 2060). The framework systematically divides the country into progressively smaller areas that have similar geophysical and biological characteristics and ecological processes.
This Hardwoods type dataset portrays 12 forest type groups across the contiguous United States. These data were derived from MODIS composite images from the 2002 and 2003 growing seasons in combination with nearly 100 other geospatial data layers, including elevation, slope, aspect, ecoregions, and PRISM climate data. The dataset was developed as a collaborative effort between the USFS Forest Inventory and Analysis and Forest Health Monitoring programs and the USFS Remote Sensing.
", "keyword": [ "Moderate Resolution Imaging Spectroradiometer", "Forest Inventory and Analysis", "FIA", "CONUS", "Contiguous United States", "2002", "2003", "109", "Hardwoods", "Oak/Pine Group", "Oak/Hickory Group", "Oak/Gum/Cypress Group", "Elm/Ash/Cottonwood Group", "Maple/Beech/Birch Group", "Aspen/Birch Group", "Alder/Maple Group", "Western Oak Group", "Tanoak/Laurel Group", "Other Western Hardwoods Group", "Tropical Hardwoods Group", "Exotic Hardwoods Group", "Ash" ], "issued": "2026-02-19T18:18:33.000Z", "modified": "2026-02-19T18:25:06.000Z", "publisher": { "name": "U.S. Forest Service", "source": "U.S. Forest Service" }, "contactPoint": { "@type": "vcard:Contact", "fn": "USFSEnterpriseContent", "hasEmail": "mailto:SM.FS.data@usda.gov" }, "accessLevel": "public", "spatial": "-127.9584,22.8075,-65.2605,51.6509", "license": "https://creativecommons.org/licenses/by/4.0/", "programCode": [ "005:059" ], "bureauCode": [ "005:96" ], "theme": [ "geospatial" ], "progressCode": "onGoing", "distribution": [ { "@type": "dcat:Distribution", "title": "ISO-19139 metadata", "conformsTo": "https://www.isotc211.org/2005/gmi", "mediaType": "text/xml", "accessURL": "https://www.arcgis.com/sharing/rest/content/items/c6044e6e38264904aa3dba386aa16846/info/metadata/metadata.xml?format=iso19139" }, { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://data-usfs.hub.arcgis.com/datasets/usfs::us-forest-atlas-fia-forest-type-groups-hardwoods-image-service" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://imagery.geoplatform.gov/iipp/rest/services/Vegetation/USFS_EDW_FIA_ForestAtlas_Hardwoods_109/ImageServer" } ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=30ec7897e25b427f9785659abacbd4a2", "landingPage": "https://data-usfs.hub.arcgis.com/datasets/usfs::us-forest-atlas-fia-forest-type-groups-all-conus-image-service", "title": "US Forest Atlas FIA Forest Type Groups All CONUS (Image Service)", "description": "This dataset portrays FIA National Forest Type Groups. Each group is a subset of the National Forest Type dataset which portrays 28 Forest Type Groups across the contiguous United States. Forest Type Groups are aggregations of forest types into logical ecological groupings. These data were derived from MODIS composite images from the 2002 and 2003 growing seasons in combination with nearly 100 other geospatial data layers, including elevation, slope, aspect, ecoregions, and PRISM climate data.
", "keyword": [ "Forest Type Group" ], "issued": "2026-02-10T18:42:10.000Z", "modified": "2026-02-19T18:17:22.000Z", "publisher": { "name": "U.S. Forest Service", "source": "U.S. Forest Service" }, "contactPoint": { "@type": "vcard:Contact", "fn": "USFSEnterpriseContent", "hasEmail": "mailto:SM.FS.data@usda.gov" }, "accessLevel": "public", "spatial": "-127.9584,22.8075,-65.2605,51.6509", "license": "https://creativecommons.org/licenses/by/4.0/", "programCode": [ "005:059" ], "bureauCode": [ "005:96" ], "theme": [ "geospatial" ], "progressCode": "onGoing", "distribution": [ { "@type": "dcat:Distribution", "title": "ISO-19139 metadata", "conformsTo": "https://www.isotc211.org/2005/gmi", "mediaType": "text/xml", "accessURL": "https://www.arcgis.com/sharing/rest/content/items/30ec7897e25b427f9785659abacbd4a2/info/metadata/metadata.xml?format=iso19139" }, { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://data-usfs.hub.arcgis.com/datasets/usfs::us-forest-atlas-fia-forest-type-groups-all-conus-image-service" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://imagery.geoplatform.gov/iipp/rest/services/Vegetation/USFS_EDW_FIA_ForestAtlas_ForestTypeGroups_109_CONUS/ImageServer" } ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=42318360cea941b3b26a353ae77f6581", "landingPage": "https://data-usfs.hub.arcgis.com/datasets/usfs::us-forest-atlas-fia-forest-type-groups-all-alaska-image-service", "title": "US Forest Atlas FIA Forest Type Groups All Alaska (Image Service)", "description": "This dataset portrays the forest type group for Alaska. Each group is a subset of the National Forest Type dataset which portrays 28 forest type groups across the contiguous United States. These data were derived from MODIS composite images from the 2002 and 2003 growing seasons in combination with nearly 100 other geospatial data layers, including elevation, slope, aspect, ecoregions, and PRISM climate data.
", "keyword": [ "forest type group", "Alaska", "AK", "109" ], "issued": "2026-02-10T18:50:52.000Z", "modified": "2026-02-10T18:58:44.000Z", "publisher": { "name": "U.S. Forest Service", "source": "U.S. Forest Service" }, "contactPoint": { "@type": "vcard:Contact", "fn": "USFSEnterpriseContent", "hasEmail": "mailto:SM.FS.data@usda.gov" }, "accessLevel": "public", "spatial": "-172.7753,51.3038,-121.5848,70.4573", "license": "https://creativecommons.org/licenses/by/4.0/", "programCode": [ "005:059" ], "bureauCode": [ "005:96" ], "theme": [ "geospatial" ], "progressCode": "onGoing", "distribution": [ { "@type": "dcat:Distribution", "title": "ISO-19139 metadata", "conformsTo": "https://www.isotc211.org/2005/gmi", "mediaType": "text/xml", "accessURL": "https://www.arcgis.com/sharing/rest/content/items/42318360cea941b3b26a353ae77f6581/info/metadata/metadata.xml?format=iso19139" }, { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://data-usfs.hub.arcgis.com/datasets/usfs::us-forest-atlas-fia-forest-type-groups-all-alaska-image-service" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://imagery.geoplatform.gov/iipp/rest/services/Vegetation/USFS_EDW_FIA_ForestAtlas_ForestTypeGroups_109_AK/ImageServer" } ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=5bc8d7902d1841849ba130ec4041192f", "landingPage": "https://data-usfs.hub.arcgis.com/datasets/usfs::us-forest-atlas-fia-forest-type-groups-image-service", "title": "US Forest Atlas FIA Forest Type Groups (Image Service)", "description": "FIA Modeled Abundance:
This data depicts current species abundance and distribution across the contiguous United States, modeled by using FIA field plot data. Although the absolute values associated with the maps differ from species to species, the highest values within each map are always associated with darker colors. This is a digital representation of maps by Elbert L. Little, Jr., published between 1971 and 1977. These maps were based on botanical lists, forest surveys, field notes and herbarium specimens.
The data included in this publication depict the 2024 version of components of wildfire risk for all lands in the United States that: 1) are landscape-wide (i.e., measurable at every pixel across the landscape); and 2) represent in situ risk - risk at the location where the adverse effects take place on the landscape.
National wildfire hazard datasets of annual burn probability and fire intensity, generated by the USDA Forest Service, Rocky Mountain Research Station and Pyrologix LLC, form the foundation of the Wildfire Risk to Communities data. Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were used as input to two different but related geospatial fire simulation systems. Annual burn probability was produced with the USFS geospatial fire simulator (FSim) at a relatively coarse cell size of 270 meters (m). To bring the burn probability raster data down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30 m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability into developed areas represented in LANDFIRE fuels data as non-burnable. Burn probability rasters represent landscape conditions as of the end of 2020. Fire intensity characteristics were modeled at 30 m resolution using a process that performs a comprehensive set of FlamMap runs spanning the full range of weather-related characteristics that occur during a fire season and then integrates those runs into a variety of results based on the likelihood of those weather types occurring. Before the fire intensity modeling, the LANDFIRE 2020 data were updated to reflect fuels disturbances occurring in 2021 and 2022. As such, the fire intensity datasets represent landscape conditions as of the end of 2022. Additional methodology documentation is provided in a methods document (\\Supplements\\WRC_V2_Methods_Landscape-wideRisk.pdf) packaged in the data download.
The specific raster datasets in this publication include:
Risk to Potential Structures (RPS): A measure that integrates wildfire likelihood and intensity with generalized consequences to a home on every pixel. For every place on the landscape, it poses the hypothetical question, \"What would be the relative risk to a house if one existed here?\" This allows comparison of wildfire risk in places where homes already exist to places where new construction may be proposed. This dataset is referred to as Risk to Homes in the Wildfire Risk to Communities web application.
Conditional Risk to Potential Structures (cRPS): The potential consequences of fire to a home at a given location, if a fire occurs there and if a home were located there. Referred to as Wildfire Consequence in the Wildfire Risk to Communities web application.
Exposure Type: Exposure is the spatial coincidence of wildfire likelihood and intensity with communities. This layer delineates where homes are directly exposed to wildfire from adjacent wildland vegetation, indirectly exposed to wildfire from indirect sources such as embers and home-to-home ignition, or not exposed to wildfire due to distance from direct and indirect ignition sources.
Burn Probability (BP): The annual probability of wildfire burning in a specific location. Referred to as Wildfire Likelihood in the Wildfire Risk to Communities web application.
Conditional Flame Length (CFL): The mean flame length for a fire burning in the direction of maximum spread (headfire) at a given location if a fire were to occur; an average measure of wildfire intensity. Flame Length Exceedance Probability - 4 ft (FLEP4): The conditional probability that flame length at a pixel will exceed 4 feet if a fire occurs; indicates the potential for moderate to high wildfire intensity.
Flame Length Exceedance Probability - 8 ft (FLEP8): the conditional probability that flame length at a pixel will exceed 8 feet if a fire occurs; indicates the potential for high wildfire intensity. Wildfire Hazard Potential (WHP): An index that quantifies the relative potential for wildfire that may be difficult to manage, used as a measure to help prioritize where fuel treatments may be needed.
Additional methodology documentation is provided with the data publication download. https://www.fs.usda.gov/rds/archive/Catalog/RDS-2020-0016-2
Note: Pixel values in this image service have been altered from the original raster dataset due to data requirements in web services. The service is intended primarily for data visualization. Relative values and spatial patterns have been largely preserved in the service, but users are encouraged to download the source data for quantitative analysis.
The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2023-5 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The NLCD product suite includes data for years 1985 through 2023. The NCLD data are processed to mask TCC from non-treed features such as water and non-tree crops, and to reduce interannual noise and smooth the NLCD time series. TCC pixel values range from 0 to 100 percent. The non-processing area is represented by value 254, and the background is represented by the value 255. The Science and NLCD tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms. For information on the Science data and processing steps see the Science metadata. Information on the NLCD data and processing steps are included here. Data Download and Methods Documents: - https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/
The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2023-5 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The NLCD product suite includes data for years 1985 through 2023. The NCLD data are processed to mask TCC from non-treed features such as water and non-tree crops, and to reduce interannual noise and smooth the NLCD time series. TCC pixel values range from 0 to 100 percent. The non-processing area is represented by value 254, and the background is represented by the value 255. The Science and NLCD tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms. For information on the Science data and processing steps see the Science metadata. Information on the NLCD data and processing steps are included here. Data Download and Methods Documents: - https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/
The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2023-5 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The NLCD product suite includes data for years 1985 through 2023. The NCLD data are processed to mask TCC from non-treed features such as water and non-tree crops, and to reduce interannual noise and smooth the NLCD time series. TCC pixel values range from 0 to 100 percent. The non-processing area is represented by value 254, and the background is represented by the value 255. The Science and NLCD tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms. For information on the Science data and processing steps see the Science metadata. Information on the NLCD data and processing steps are included here. Data Download and Methods Documents: - https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/
The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2023-5 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The NLCD product suite includes data for years 1985 through 2023. The NCLD data are processed to mask TCC from non-treed features such as water and non-tree crops, and to reduce interannual noise and smooth the NLCD time series. TCC pixel values range from 0 to 100 percent. The non-processing area is represented by value 254, and the background is represented by the value 255. The Science and NLCD tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms. For information on the Science data and processing steps see the Science metadata. Information on the NLCD data and processing steps are included here. Data Download and Methods Documents: - https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/
The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2023-5 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The NLCD product suite includes data for years 1985 through 2023. The NCLD data are processed to mask TCC from non-treed features such as water and non-tree crops, and to reduce interannual noise and smooth the NLCD time series. TCC pixel values range from 0 to 100 percent. The non-processing area is represented by value 254, and the background is represented by the value 255. The Science and NLCD tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms. For information on the Science data and processing steps see the Science metadata. Information on the NLCD data and processing steps are included here. Data Download and Methods Documents: - https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/
The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2023-5 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The NLCD product suite includes data for years 1985 through 2023. The NCLD data are processed to mask TCC from non-treed features such as water and non-tree crops, and to reduce interannual noise and smooth the NLCD time series. TCC pixel values range from 0 to 100 percent. The non-processing area is represented by value 254, and the background is represented by the value 255. The Science and NLCD tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms. For information on the Science data and processing steps see the Science metadata. Information on the NLCD data and processing steps are included here.
Data Download and Methods Documents: - https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/
The 2010 North American Land Cover data set was produced as part of the North American Land Change Monitoring System (NALCMS), a trilateral effort between the Canada Centre for Remote Sensing, the United States Geological Survey, and three Mexican organizations including the National Institute of Statistics and Geography (Instituto Nacional de Estadistica y Geografia), National Commission for the Knowledge and Use of the Biodiversity (Comisión Nacional Para el Conocimiento y Uso de la Biodiversidad), and the National Forestry Commission of Mexico (Comisión Nacional Forestal). The collaboration is facilitated by the Commission for Environmental Cooperation, an international organization created by the Canada, Mexico, and United States governments under the North American Agreement on Environmental Cooperation to promote environmental collaboration between the three countries. The general objective of NALCMS is to devise, through collective effort, a harmonized multi-scale land cover monitoring approach which ensures high accuracy and consistency in monitoring land cover changes at the North American scale and which meets each countryÂs specific requirements. The initial data set of North American Land Cover at 250 meters reflected land cover information for 2005. This 2010 data set was produced by updating the 2005 data to show land cover changes as determined from more recent data. No changes were mapped in Hawaii because newer data were not available. Land cover classification changed between 2005 and 2010 for approximately 1 percent of the continental area. For the continental data sets (including surrounding water fringe) 4150241 pixels (1.03% of the area) changed in the update. The following national counts exclude the water fringe: Canada, 3264779 pixels changed (2.05%); Mexico, 47070 pixels changed (0.15%), and U.S., 836706 pixels changed (0.55%). The initial data set used to generate land cover information over North America was produced by the Canada Centre for Remote Sensing from observations acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS/Terra). All seven land spectral bands were processed from Level 1 granules into top-of-atmosphere reflectance covering North America at a 250-meter spatial and 10-day temporal resolution. In order to generate a seamless and consistent land cover map of North America, national maps were generated for Canada by the CCRS; for Mexico by INEGI, CONABIO, and CONAFOR; and for the United States by the USGS. Each country used specific training data and land cover mapping methodologies to create national data sets. This North America data set was produced by combining the national land cover data sets. The countries worked together to produce a definitive list of land cover classifications for the 2005 data; the same classifications were used for the 2010 data. This document is available for download from the same site as the data and is entitled: North American Land Cover Classifications (2005).
The 2010 North American Land Cover 30-meter dataset was produced as part of the North American Land Change Monitoring
System (NALCMS), a trilateral effort between Natural Resources Canada, the United States Geological Survey, and three Mexican
organizations including the National Institute of Statistics and Geography (Instituto Nacional de Estadística y Geografía),
National Commission for the Knowledge and Use of the Biodiversity (Comisión Nacional Para el Conocimiento y Uso de la
Biodiversidad), and the National Forestry Commission of Mexico (Comisión Nacional Forestal). The collaboration is facilitated
by the Commission for Environmental Cooperation, an international organization created by the Canada, Mexico, and United States
governments under the North American Agreement on Environmental Cooperation to promote environmental collaboration between the
three countries.
The general objective of NALCMS is to devise, through collective effort, a harmonized multi-scale land cover monitoring
approach which ensures high accuracy and consistency in monitoring land cover changes at the North American scale and
which meets each country’s specific requirements.
This 30-meter dataset of North American Land Cover reflects land cover information for 2010 from Mexico and Canada and 2011
for the United States. Each country developed its own classification method to identify Land Cover classes and then provided
an input layer to produce a continental Land Cover map across North America. Canada, Mexico, and the United States developed
their own 30-meter land cover products; see specific sections on data generation below.
The main inputs for image classification were 30-meter Landsat data. Image selection processes and reduction to specific
spectral bands varied among the countries due to study-site-specific requirements. While Canada selected most images from
the year 2010 with a few from 2009 and 2011, the United States employed mainly images from 2011. Mexico used all available
images from 2010.
In order to generate a seamless and consistent land cover map of North America, national maps were generated for Canada by
the CCRS; for Mexico by CONABIO, INEGI, and CONAFOR; and for the United States by the USGS. Each country chose their own
approaches, ancillary data, and land cover mapping methodologies to create national datasets. This North America dataset
was produced by combining the national land cover datasets.
This data publication contains 2015 high-resolution land cover data for each of the 105 counties within Kansas. These data are a digital representation of land cover derived from 1-meter aerial imagery from the National Agriculture Imagery Program (NAIP). There is a separate file for each county. Data are intended for use in rural areas and therefore do not include land cover in cities and towns. Land cover classes (tree cover, other land cover, water, or city/town) were mapped using an object-based image analysis approach and supervised classification
This product is part of the Landscape Change Monitoring System (LCMS) data suite. It is a summary of all annual Gain into a single layer showing the most recent year LCMS detected Gain. See additional information about Gain in the Entity_and_Attribute_Information or Fields section below.
LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a \"best available\" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS Change, Land Cover, and Land Use maps offer a holistic depiction of landscape change across the United States over the past four decades.
Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a random forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).
Outputs fall into three categories: Change, Land Cover, and Land Use. At its foundation, Change maps areas of Disturbance, Vegetation Successional Growth, and Stable landscape. More detailed levels of Change products are available and are intended to address needs centered around monitoring causes and types of variations in vegetation cover, water extent, or snow/ice extent that may or may not result in a transition of land cover and/or land use. Change, Land Cover, and Land Use are predicted for each year of the time series and serve as the foundational products for LCMS.
This product is part of the Landscape Change Monitoring System (LCMS) data suite. It is a summary of all annual Gain into a single layer showing the most recent year LCMS detected Gain. See additional information about Gain in the Entity_and_Attribute_Information or Fields section below.
LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a \"best available\" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS Change, Land Cover, and Land Use maps offer a holistic depiction of landscape change across the United States over the past four decades.
Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a random forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).
Outputs fall into three categories: Change, Land Cover, and Land Use. At its foundation, Change maps areas of Disturbance, Vegetation Successional Growth, and Stable landscape. More detailed levels of Change products are available and are intended to address needs centered around monitoring causes and types of variations in vegetation cover, water extent, or snow/ice extent that may or may not result in a transition of land cover and/or land use. Change, Land Cover, and Land Use are predicted for each year of the time series and serve as the foundational products for LCMS.
This product is part of the Landscape Change Monitoring System (LCMS) data suite. It is a summary of all annual Slow Loss into a single layer showing the most recent year LCMS detected Slow Loss. See additional information about Slow Loss in the Entity_and_Attribute_Information or Fields section below.
LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a \"best available\" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS Change, Land Cover, and Land Use maps offer a holistic depiction of landscape change across the United States over the past four decades.
Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a random forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).
Outputs fall into three categories: Change, Land Cover, and Land Use. At its foundation, Change maps areas of Disturbance, Vegetation Successional Growth, and Stable landscape. More detailed levels of Change products are available and are intended to address needs centered around monitoring causes and types of variations in vegetation cover, water extent, or snow/ice extent that may or may not result in a transition of land cover and/or land use. Change, Land Cover, and Land Use are predicted for each year of the time series and serve as the foundational products for LCMS.
This product is part of the Landscape Change Monitoring System (LCMS) data suite. It is a summary of all annual Fast Loss into a single layer showing the most recent year LCMS detected Fast Loss. See additional information about Fast Loss in the Entity_and_Attribute_Information or Fields section below.
LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a \"best available\" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS Change, Land Cover, and Land Use maps offer a holistic depiction of landscape change across the United States over the past four decades.
Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a random forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).
Outputs fall into three categories: Change, Land Cover, and Land Use. At its foundation, Change maps areas of Disturbance, Vegetation Successional Growth, and Stable landscape. More detailed levels of Change products are available and are intended to address needs centered around monitoring causes and types of variations in vegetation cover, water extent, or snow/ice extent that may or may not result in a transition of land cover and/or land use. Change, Land Cover, and Land Use are predicted for each year of the time series and serve as the foundational products for LCMS.
This product is part of the Landscape Change Monitoring System (LCMS) data suite. It is a summary of all annual Gain into a single layer showing the most recent year LCMS detected Gain. See additional information about Gain in the Entity_and_Attribute_Information or Fields section below.
LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a \"best available\" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS Change, Land Cover, and Land Use maps offer a holistic depiction of landscape change across the United States over the past four decades.
Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a random forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).
Outputs fall into three categories: Change, Land Cover, and Land Use. At its foundation, Change maps areas of Disturbance, Vegetation Successional Growth, and Stable landscape. More detailed levels of Change products are available and are intended to address needs centered around monitoring causes and types of variations in vegetation cover, water extent, or snow/ice extent that may or may not result in a transition of land cover and/or land use. Change, Land Cover, and Land Use are predicted for each year of the time series and serve as the foundational products for LCMS.
This product is part of the Landscape Change Monitoring System (LCMS) data suite. It is a summary of all annual Slow Loss into a single layer showing the most recent year LCMS detected Slow Loss. See additional information about Slow Loss in the Entity_and_Attribute_Information or Fields section below.
LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a \"best available\" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS Change, Land Cover, and Land Use maps offer a holistic depiction of landscape change across the United States over the past four decades.
Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a random forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).
Outputs fall into three categories: Change, Land Cover, and Land Use. At its foundation, Change maps areas of Disturbance, Vegetation Successional Growth, and Stable landscape. More detailed levels of Change products are available and are intended to address needs centered around monitoring causes and types of variations in vegetation cover, water extent, or snow/ice extent that may or may not result in a transition of land cover and/or land use. Change, Land Cover, and Land Use are predicted for each year of the time series and serve as the foundational products for LCMS.
The U.S. has been providing national-scale estimates of forest carbon stocks and stock change to meet United Nations Framework Convention on Climate Change reporting requirements for years. Through application of a nearest-neighbor imputation approach, mapped estimates of forest biomass density were developed for the contiguous United States using the annual forest inventory conducted by the USDA Forest Service Forest Inventory and Analysis (FIA) program, MODIS satellite imagery, and ancillary geospatial datasets. This data product would contain the following 7 raster maps: Aboveground Forest Biomass, Belowground Forest Biomass, Forest Tree Bole Biomass, Forest Sapling Biomass, Forest Stump Biomass, Forest Top Biomass, Woodland Specias Biomass. All layers have a 250 meter pixel resolution and values represent biomass pounds per acre. The paper on which these maps are based may be found here: https://dx.doi.org/10.2737/RDS-2013-0004 Access to full metadata and other information can be accessed here: https://dx.doi.org/10.2737/RDS-2013-0004
This product is part of the Landscape Change Monitoring System (LCMS) data suite. It is a summary of all annual Fast Loss into a single layer showing the most recent year LCMS detected Fast Loss. See additional information about Fast Loss in the Entity_and_Attribute_Information or Fields section below.
LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a \"best available\" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS Change, Land Cover, and Land Use maps offer a holistic depiction of landscape change across the United States over the past four decades.
Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a random forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).
Outputs fall into three categories: Change, Land Cover, and Land Use. At its foundation, Change maps areas of Disturbance, Vegetation Successional Growth, and Stable landscape. More detailed levels of Change products are available and are intended to address needs centered around monitoring causes and types of variations in vegetation cover, water extent, or snow/ice extent that may or may not result in a transition of land cover and/or land use. Change, Land Cover, and Land Use are predicted for each year of the time series and serve as the foundational products for LCMS.
This product is part of the Landscape Change Monitoring System (LCMS) data suite. It is a summary of all annual Fast Loss into a single layer showing the most recent year LCMS detected Fast Loss. See additional information about Fast Loss in the Entity_and_Attribute_Information or Fields section below.
LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a \"best available\" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS Change, Land Cover, and Land Use maps offer a holistic depiction of landscape change across the United States over the past four decades.
Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a random forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).
Outputs fall into three categories: Change, Land Cover, and Land Use. At its foundation, Change maps areas of Disturbance, Vegetation Successional Growth, and Stable landscape. More detailed levels of Change products are available and are intended to address needs centered around monitoring causes and types of variations in vegetation cover, water extent, or snow/ice extent that may or may not result in a transition of land cover and/or land use. Change, Land Cover, and Land Use are predicted for each year of the time series and serve as the foundational products for LCMS.
This product is part of the Landscape Change Monitoring System (LCMS) data suite. It is a summary of all annual Slow Loss into a single layer showing the most recent year LCMS detected Slow Loss. See additional information about Slow Loss in the Entity_and_Attribute_Information or Fields section below.
LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a \"best available\" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS Change, Land Cover, and Land Use maps offer a holistic depiction of landscape change across the United States over the past four decades.
Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a random forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).
Outputs fall into three categories: Change, Land Cover, and Land Use. At its foundation, Change maps areas of Disturbance, Vegetation Successional Growth, and Stable landscape. More detailed levels of Change products are available and are intended to address needs centered around monitoring causes and types of variations in vegetation cover, water extent, or snow/ice extent that may or may not result in a transition of land cover and/or land use. Change, Land Cover, and Land Use are predicted for each year of the time series and serve as the foundational products for LCMS.
This product is part of the Landscape Change Monitoring System (LCMS) data suite. It is a summary of all annual Fast Loss into a single layer showing the most recent year LCMS detected Fast Loss. See additional information about Fast Loss in the Entity_and_Attribute_Information or Fields section below.
LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a \"best available\" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS Change, Land Cover, and Land Use maps offer a holistic depiction of landscape change across the United States over the past four decades.
Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a random forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).
Outputs fall into three categories: Change, Land Cover, and Land Use. At its foundation, Change maps areas of Disturbance, Vegetation Successional Growth, and Stable landscape. More detailed levels of Change products are available and are intended to address needs centered around monitoring causes and types of variations in vegetation cover, water extent, or snow/ice extent that may or may not result in a transition of land cover and/or land use. Change, Land Cover, and Land Use are predicted for each year of the time series and serve as the foundational products for LCMS.
This product is part of the Landscape Change Monitoring System (LCMS) data suite. It is a summary of all annual Gain into a single layer showing the most recent year LCMS detected Gain. See additional information about Gain in the Entity_and_Attribute_Information or Fields section below.
LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a \"best available\" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS Change, Land Cover, and Land Use maps offer a holistic depiction of landscape change across the United States over the past four decades.
Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a random forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).
Outputs fall into three categories: Change, Land Cover, and Land Use. At its foundation, Change maps areas of Disturbance, Vegetation Successional Growth, and Stable landscape. More detailed levels of Change products are available and are intended to address needs centered around monitoring causes and types of variations in vegetation cover, water extent, or snow/ice extent that may or may not result in a transition of land cover and/or land use. Change, Land Cover, and Land Use are predicted for each year of the time series and serve as the foundational products for LCMS.
This product is part of the Landscape Change Monitoring System (LCMS) data suite. It is a summary of all annual Slow Loss into a single layer showing the year LCMS detected Slow Loss with the highest model confidence. See additional information about Slow Loss in the Entity_and_Attribute_Information or Fields section below.
LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a \"best available\" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS Change, Land Cover, and Land Use maps offer a holistic depiction of landscape change across the United States over the past four decades.
Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a random forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).
Outputs fall into three categories: Change, Land Cover, and Land Use. At its foundation, Change maps areas of Disturbance, Vegetation Successional Growth, and Stable landscape. More detailed levels of Change products are available and are intended to address needs centered around monitoring causes and types of variations in vegetation cover, water extent, or snow/ice extent that may or may not result in a transition of land cover and/or land use. Change, Land Cover, and Land Use are predicted for each year of the time series and serve as the foundational products for LCMS.
This product is part of the Landscape Change Monitoring System (LCMS) data suite. It is a summary of all annual Slow Loss into a single layer showing the year LCMS detected Slow Loss with the highest model confidence. See additional information about Slow Loss in the Entity_and_Attribute_Information or Fields section below.
LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a \"best available\" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS Change, Land Cover, and Land Use maps offer a holistic depiction of landscape change across the United States over the past four decades.
Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a random forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).
Outputs fall into three categories: Change, Land Cover, and Land Use. At its foundation, Change maps areas of Disturbance, Vegetation Successional Growth, and Stable landscape. More detailed levels of Change products are available and are intended to address needs centered around monitoring causes and types of variations in vegetation cover, water extent, or snow/ice extent that may or may not result in a transition of land cover and/or land use. Change, Land Cover, and Land Use are predicted for each year of the time series and serve as the foundational products for LCMS.
This product is part of the Landscape Change Monitoring System (LCMS) data suite. It is a summary of all annual Slow Loss into a single layer showing the year LCMS detected Slow Loss with the highest model confidence. See additional information about Slow Loss in the Entity_and_Attribute_Information or Fields section below.
LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a \"best available\" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS Change, Land Cover, and Land Use maps offer a holistic depiction of landscape change across the United States over the past four decades.
Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a random forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).
Outputs fall into three categories: Change, Land Cover, and Land Use. At its foundation, Change maps areas of Disturbance, Vegetation Successional Growth, and Stable landscape. More detailed levels of Change products are available and are intended to address needs centered around monitoring causes and types of variations in vegetation cover, water extent, or snow/ice extent that may or may not result in a transition of land cover and/or land use. Change, Land Cover, and Land Use are predicted for each year of the time series and serve as the foundational products for LCMS.
This product is part of the Landscape Change Monitoring System (LCMS) data suite. It is a summary of all annual Gain into a single layer showing the year LCMS detected Gain with the highest model confidence. See additional information about Gain in the Entity_and_Attribute_Information or Fields section below.
LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a \"best available\" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS Change, Land Cover, and Land Use maps offer a holistic depiction of landscape change across the United States over the past four decades.
Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a random forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).
Outputs fall into three categories: Change, Land Cover, and Land Use. At its foundation, Change maps areas of Disturbance, Vegetation Successional Growth, and Stable landscape. More detailed levels of Change products are available and are intended to address needs centered around monitoring causes and types of variations in vegetation cover, water extent, or snow/ice extent that may or may not result in a transition of land cover and/or land use. Change, Land Cover, and Land Use are predicted for each year of the time series and serve as the foundational products for LCMS.
This product is part of the Landscape Change Monitoring System (LCMS) data suite. It is a summary of all annual Gain into a single layer showing the year LCMS detected Gain with the highest model confidence. See additional information about Gain in the Entity_and_Attribute_Information or Fields section below.
LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a \"best available\" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS Change, Land Cover, and Land Use maps offer a holistic depiction of landscape change across the United States over the past four decades.
Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a random forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).
Outputs fall into three categories: Change, Land Cover, and Land Use. At its foundation, Change maps areas of Disturbance, Vegetation Successional Growth, and Stable landscape. More detailed levels of Change products are available and are intended to address needs centered around monitoring causes and types of variations in vegetation cover, water extent, or snow/ice extent that may or may not result in a transition of land cover and/or land use. Change, Land Cover, and Land Use are predicted for each year of the time series and serve as the foundational products for LCMS.
This product is part of the Landscape Change Monitoring System (LCMS) data suite. It is a summary of all annual Gain into a single layer showing the year LCMS detected Gain with the highest model confidence. See additional information about Gain in the Entity_and_Attribute_Information or Fields section below.
LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a \"best available\" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS Change, Land Cover, and Land Use maps offer a holistic depiction of landscape change across the United States over the past four decades.
Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a random forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).
Outputs fall into three categories: Change, Land Cover, and Land Use. At its foundation, Change maps areas of Disturbance, Vegetation Successional Growth, and Stable landscape. More detailed levels of Change products are available and are intended to address needs centered around monitoring causes and types of variations in vegetation cover, water extent, or snow/ice extent that may or may not result in a transition of land cover and/or land use. Change, Land Cover, and Land Use are predicted for each year of the time series and serve as the foundational products for LCMS.
This product is part of the Landscape Change Monitoring System (LCMS) data suite. It is a summary of all annual Fast Loss into a single layer showing the year LCMS detected Fast Loss with the highest model confidence. See additional information about Fast Loss in the Entity_and_Attribute_Information or Fields section below.
LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a \"best available\" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS Change, Land Cover, and Land Use maps offer a holistic depiction of landscape change across the United States over the past four decades.
Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a random forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).
Outputs fall into three categories: Change, Land Cover, and Land Use. At its foundation, Change maps areas of Disturbance, Vegetation Successional Growth, and Stable landscape. More detailed levels of Change products are available and are intended to address needs centered around monitoring causes and types of variations in vegetation cover, water extent, or snow/ice extent that may or may not result in a transition of land cover and/or land use. Change, Land Cover, and Land Use are predicted for each year of the time series and serve as the foundational products for LCMS.
This product is part of the Landscape Change Monitoring System (LCMS) data suite. It is a summary of all annual Fast Loss into a single layer showing the year LCMS detected Fast Loss with the highest model confidence. See additional information about Fast Loss in the Entity_and_Attribute_Information or Fields section below.
LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a \"best available\" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS Change, Land Cover, and Land Use maps offer a holistic depiction of landscape change across the United States over the past four decades.
Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a random forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).
Outputs fall into three categories: Change, Land Cover, and Land Use. At its foundation, Change maps areas of Disturbance, Vegetation Successional Growth, and Stable landscape. More detailed levels of Change products are available and are intended to address needs centered around monitoring causes and types of variations in vegetation cover, water extent, or snow/ice extent that may or may not result in a transition of land cover and/or land use. Change, Land Cover, and Land Use are predicted for each year of the time series and serve as the foundational products for LCMS.
This product is part of the Landscape Change Monitoring System (LCMS) data suite. It is a summary of all annual Fast Loss into a single layer showing the year LCMS detected Fast Loss with the highest model confidence. See additional information about Fast Loss in the Entity_and_Attribute_Information or Fields section below.
LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a \"best available\" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS Change, Land Cover, and Land Use maps offer a holistic depiction of landscape change across the United States over the past four decades.
Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a random forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).
Outputs fall into three categories: Change, Land Cover, and Land Use. At its foundation, Change maps areas of Disturbance, Vegetation Successional Growth, and Stable landscape. More detailed levels of Change products are available and are intended to address needs centered around monitoring causes and types of variations in vegetation cover, water extent, or snow/ice extent that may or may not result in a transition of land cover and/or land use. Change, Land Cover, and Land Use are predicted for each year of the time series and serve as the foundational products for LCMS.
This product is part of the Landscape Change Monitoring System (LCMS) data suite. It is a summary of all annual Fast Loss into a single layer showing the year LCMS detected Fast Loss with the highest model confidence. See additional information about Fast Loss in the Entity_and_Attribute_Information or Fields section below.
LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a \"best available\" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS Change, Land Cover, and Land Use maps offer a holistic depiction of landscape change across the United States over the past four decades.
Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a random forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).
Outputs fall into three categories: Change, Land Cover, and Land Use. At its foundation, Change maps areas of Disturbance, Vegetation Successional Growth, and Stable landscape. More detailed levels of Change products are available and are intended to address needs centered around monitoring causes and types of variations in vegetation cover, water extent, or snow/ice extent that may or may not result in a transition of land cover and/or land use. Change, Land Cover, and Land Use are predicted for each year of the time series and serve as the foundational products for LCMS.
Rasters of air temperature, including both Alaskan and lower 48 datasets, with historical and projected data, and projected change.
The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including April 1 snow water equivalent (SWE), and snow residence time), and stream flow.
Rasters of precipitation including both Alaskan and lower 48 datasets, with historical and projected data, and projected change.
Heat Stress Index, compared between historical and future time periods, including historical and percent change
The maps and tables presented here represent potential variability of projected climate change across the conterminous United States during three 30-year periods in this century and emphasizes the importance of evaluating multiple signals of change across large spatial domains. Maps of growing degree days, plant hardiness zones, heat zones, and cumulative drought severity depict the potential for markedly shifting conditions and highlight regions where changes may be multifaceted across these metrics. In addition to the maps, the potential change in these climate variables are summarized in tables according to the seven regions of the fourth National Climate Assessment to provide additional regional context. Viewing these data collectively further emphasizes the potential for novel climatic space under future projections of climate change and signals the wide disparity in these conditions based on relatively near-term human decisions of curtailing (or not) greenhouse gas emissions. More information available at https://www.fs.usda.gov/nrs/pubs/rmap/rmap_nrs9.pdf.
Date of freeze for historical (1985-2005) and future (2071-2090, RCP 8.5) time periods, and absolute change between them, based on analysis of MACAv2METDATA.
Greatest number of consecutive summer (May-Sept) dry days (<1/10 inch of rain) was calculated for each year over the historical (1985-2004) and future (RCP 8.5 2071-2090) time periods; absolute and percent change between these was then calculated. This includes three versions: one based on the 20-year average of summer maxima, one based on the overall maximum, and one based on the 90th percentile value of 20-year maxima.
Date of thaw for historical (1985-2005) and future (2071-2090, RCP 8.5) time periods, and absolute change between them, based on analysis of MACAv2METDATA.
Date of freeze for historical (1985-2005) and future (2071-2090, RCP 8.5) time periods, and absolute change between them, based on analysis of MACAv2METDATA.
The maps and tables presented here represent potential variability of projected climate change across the conterminous United States during three 30-year periods in this century and emphasizes the importance of evaluating multiple signals of change across large spatial domains. Maps of growing degree days, plant hardiness zones, heat zones, and cumulative drought severity depict the potential for markedly shifting conditions and highlight regions where changes may be multifaceted across these metrics. In addition to the maps, the potential change in these climate variables are summarized in tables according to the seven regions of the fourth National Climate Assessment to provide additional regional context. Viewing these data collectively further emphasizes the potential for novel climatic space under future projections of climate change and signals the wide disparity in these conditions based on relatively near-term human decisions of curtailing (or not) greenhouse gas emissions. More information available at https://www.fs.usda.gov/nrs/pubs/rmap/rmap_nrs9.pdf. This map includes plant hardiness zones for 4 time periods (1980-2009, 2010-2039, 2040-2069, and 2070-2099) and 2 RCPs (4.5 and 8.5), representing medium and high emissions scenarios.
The maps and tables presented here represent potential variability of projected climate change across the conterminous United States during three 30-year periods in this century and emphasizes the importance of evaluating multiple signals of change across large spatial domains. Maps of growing degree days, plant hardiness zones, heat zones, and cumulative drought severity depict the potential for markedly shifting conditions and highlight regions where changes may be multifaceted across these metrics. In addition to the maps, the potential change in these climate variables are summarized in tables according to the seven regions of the fourth National Climate Assessment to provide additional regional context. Viewing these data collectively further emphasizes the potential for novel climatic space under future projections of climate change and signals the wide disparity in these conditions based on relatively near-term human decisions of curtailing (or not) greenhouse gas emissions. More information available at https://www.fs.usda.gov/nrs/pubs/rmap/rmap_nrs9.pdf. This dataset represents heat zones, or the mean number of days over 30 C, in 4 time periods (1980-2009, 2010-2039, 2040-2069, and 2070-2099), using two emissions scenarios (RCP 4.5 and 8.5, the medium and high scenarios, respectively).
Projected change in average number of days of precipitation (>0.1 inch) between 1985-2005 and 2071-2090 (RCP 8.5) time periods
Average historical temperature change, between 1948-1968 and 1996-2016 averages, in Celsius. Calculated using averages of minimum and maximum monthly values during these time periods. Values are based on TopoWx data, downloaded from here: http://www.scrimhub.org/resources/topowx/
The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
This Biomass and Carbon per Acre dataset was compiled for the 48-state CONUS Area in order to identify local, state and regional trends in forest cover and landscape composition. It can be used as a graphic, as inputs to GIS overlay procedures, or to summarize average or total amounts of forest attributes within analysis areas of interest. Through application of a nearest-neighbor imputation approach, mapped estimates of forest biomass density were developed for the contiguous United States using the annual forest inventory conducted by the USDA Forest Service Forest Inventory and Analysis (FIA) program, MODIS satellite imagery, and ancillary geospatial datasets. This data product contains the following 7 raster maps: Aboveground Forest Biomass, Belowground Forest Biomass, Forest Tree Bole Biomass, Forest Sapling Biomass, Forest Stump Biomass, Forest Top Biomass, Woodland Species Biomass. All layers have a 250 meter pixel resolution and values represent biomass pounds per acre. The paper on which these maps are based may be found here: https://dx.doi.org/10.2737/RDS-2013-0004 Access to full metadata and other information can be accessed here: https://dx.doi.org/10.2737/RDS-2013-0004
Through application of a nearest-neighbor imputation approach, mapped estimates of forest carbon density were developed for the contiguous United States using the annual forest inventory conducted by the USDA Forest Service Forest Inventory and Analysis (FIA) program, MODIS satellite imagery, and ancillary geospatial datasets. This data product contains the following 8 raster maps: total forest carbon in all stocks, live tree aboveground forest carbon, live tree belowground forest carbon, forest down dead carbon, forest litter carbon, forest standing dead carbon, forest soil organic carbon, and forest understory carbon.
The paper on which these maps are based may be found here: https://dx.doi.org/10.2737/RDS-2013-0004.
Access to full metadata and other information can be accessed here: https://dx.doi.org/10.2737/RDS-2013-0004.
The U.S. has been providing national-scale estimates of forest carbon stocks and stock change to meet United Nations Framework Convention on Climate Change reporting requirements for years. Through application of a nearest-neighbor imputation approach, mapped estimates of forest biomass density were developed for the contiguous United States using the annual forest inventory conducted by the USDA Forest Service Forest Inventory and Analysis (FIA) program, MODIS satellite imagery, and ancillary geospatial datasets. This data product would contain the following 7 raster maps: Aboveground Forest Biomass, Belowground Forest Biomass, Forest Tree Bole Biomass, Forest Sapling Biomass, Forest Stump Biomass, Forest Top Biomass, Woodland Specias Biomass. All layers have a 250 meter pixel resolution and values represent biomass pounds per acre. The paper on which these maps are based may be found here: https://dx.doi.org/10.2737/RDS-2013-0004 Access to full metadata and other information can be accessed here: https://dx.doi.org/10.2737/RDS-2013-0004
This raster dataset depicts rangelands in the coterminous U.S., including transitional rangelands and small patch-size rangelands. Each 30 meter pixel is assigned a land cover category, including Rangeland, Afforested Rangeland (experiencing encroachment by trees [> 25% tree cover]) and Transitional Rangeland (currently dominated by herbs or shrubs that will likely become forested without management intervention).
This raw lbs/acre data that the Z-scores were derived from as well as the Z-scores dataset can be downloaded from: https://data.fs.usda.gov/geodata/rastergateway/rangelands/index.php.
Production data were generated using the Normalized Difference Vegetation Index (NDVI) from the Thematic Mapper Suite from 1984 to 2023 at 250 m resolution. The NDVI is converted to production estimates using two regression formulas depending on the level of the NDVI; there is one equation for lower values (and thus lower production values) and one for higher values. This raster dataset yields estimates of annual production of rangeland vegetation and should be useful for understanding trends and variability in forage resources. These results were then converted to Z-scores for easier comparison of annual relative productivity in coterminous U.S. rangelands, and for rapid display in online time-enabled applications.
Production data were generated using the Normalized Difference Vegetation Index (NDVI) from the Thematic Mapper Suite from 1984 to 2023 at 250 m resolution. The NDVI is converted to production estimates using two regression formulas depending on the level of the NDVI; there is one equation for lower values (and thus lower production values) and one for higher values. This raster dataset yields estimates of annual production of rangeland vegetation and should be useful for understanding trends and variability in forage resources.
This raw lbs/acre data that the Z-scores were derived from as well as the Z-scores dataset can be downloaded from: https://data.fs.usda.gov/geodata/rastergateway/rangelands/index.php.
This raster dataset depicts rangelands in the coterminous U.S., including transitional rangelands and small patch-size rangelands. Each 30 meter pixel is assigned a land cover category, including Rangeland, Afforested Rangeland (experiencing encroachment by trees [> 25% tree cover]) and Transitional Rangeland (currently dominated by herbs or shrubs that will likely become forested without management intervention). The dataset can be downloaded from the following website: https://data.fs.usda.gov/geodata/rastergateway/rangelands/index.php
Note: To download this raster dataset, go to ArcGIS Open Data Set and click the download button, and under additional resources select raster download option. An image service that depicts rangelands in the coterminous U.S., including transitional rangelands and small patch-size rangelands.
The USDA Forest Service Burned Area Emergency Response (BAER) Imagery Support program produces geospatial data of post-fire burn severities using standardized change detection methods based on Landsat, Sentinel-2, or similar multispectral satellite imagery. The BAER Imagery Support program provides an initial Burned Area Reflectance Classification (BARC) product to BAER teams. The BARC is a preliminary vegetation burn severity product which assesses severity based on the magnitude of detectable change to above ground biomass. The BAER teams collect field data on post-fire soil conditions that is used with the BARC to create the final soil burn severity (SBS) classification product for each BAER assessment. The SBS data is preferred for sharing publicly since it has been validated by the BAER team in the field. BAER teams use the soil burn severity product to assess the need for and application of emergency soil stabilization treatments following a wildfire. National mosaics compiling SBS classification products from individual BAER assessments are prepared on an annual basis. This SBS dataset is a compilation of final burn severity data products created by USDA Forest Service BAER teams from 2017 through the most recent completed fire year. It is a thematic raster dataset with four burn severity classes: unburned to very low, low, moderate, and high. The spatial resolution is variable based on the imagery used to create the initial BARC product and subsequent processing by the BAER teams. Datasets are typically 30m for Landsat assessments and 20m for Sentinel-2 assessments.
Each archived data set (i.e., "data publication") contains at least one data set, complete metadata for the data set(s), and any other documentation the researcher deemed important to understanding the data set(s). The data catalog entries present the metadata and a link to the data. In some cases the data link is to a different archive.
", "keyword": [ "USFS", "Forest Service", "Research Data Archive", "RDA", "Index", "USDA Forest Service", "Open Data" ], "issued": "2017-11-01T21:05:10.000Z", "modified": "2025-09-04T16:52:34.000Z", "publisher": { "name": "U.S. Forest Service", "source": "U.S. Forest Service" }, "contactPoint": { "@type": "vcard:Contact", "fn": "USFSEnterpriseContent", "hasEmail": "mailto:SM.FS.data@usda.gov" }, "accessLevel": "public", "spatial": "-131.3620,6.8980,-65.6380,72.6220", "license": "https://creativecommons.org/licenses/by/4.0/", "programCode": [ "005:059" ], "bureauCode": [ "005:96" ], "theme": [ "geospatial" ], "progressCode": "onGoing", "distribution": [ { "@type": "dcat:Distribution", "title": "ISO-19139 metadata", "conformsTo": "https://www.isotc211.org/2005/gmi", "mediaType": "text/xml", "accessURL": "https://www.arcgis.com/sharing/rest/content/items/eaa8f2f9700a4c99986dbee09162f56f/info/metadata/metadata.xml?format=iso19139" }, { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://data-usfs.hub.arcgis.com/documents/usfs::forest-service-research-data-archive-index" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://usfs.maps.arcgis.com/home/item.html?id=047396bd4d7c4dbf98daf439b27f7aa8" } ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=e639a401ad8647e5a798f585f851b600", "landingPage": "https://data-usfs.hub.arcgis.com/datasets/usfs::forest-health-protection-tree-species-metrics-stand-density-index", "title": "Forest Health Protection Tree Species Metrics Stand Density Index", "description": "These data are a product of a multi-year effort by the FHTET (Forest Health Technology Enterprise Team) Remote Sensing Program to develop raster datasets of forest parameters for each of the tree species measured in the Forest Service’s Forest Inventory and Analysis (FIA) program. This dataset was created to support the 2013–2027 National Insect and Disease Risk Map (NIDRM) assessment. The statistical modeling approach used data-mining software and an archive of geospatial information to find the complex relationships between GIS layers and the presence/abundance of tree species as measured in over 300,000 FIA plot locations. Unique statistical models were developed from predictor layers consisting of climate, terrain, soils, and satellite imagery. Modeled basal area (BA) and stand density index (SDI) datasets for individual tree species were further post-processed to 1) match BA and SDI histograms of FIA data, 2) ensure that the sum of individual species BA and SDI on a pixel did not exceed separately modeled total for all species BA and SDI raster datasets, 3) derive additional tree parameters like quadratic mean diameter and trees per acre. With Landsat image collection dates ranging from 1985 to 2005, and a mean collection date for treed areas of 2002, and FIA plot data generally ranging from 1999 to 2005, the vintage of the base parameter datasets varies based on location, but can be roughly considered as 2002
Basal Area (BA). 30 meter pixel resolution. Data represents forest conditions circa 2002.
These data are a product of a multi-year effort by the FHTET (Forest Health Technology Enterprise Team) Remote Sensing Program to develop raster datasets of forest parameters for each of the tree species measured in the Forest Service’s Forest Inventory and Analysis (FIA) program. This dataset was created to support the 2013–2027 National Insect and Disease Risk Map (NIDRM) assessment. The statistical modeling approach used data-mining software and an archive of geospatial information to find the complex relationships between GIS layers and the presence/abundance of tree species as measured in over 300,000 FIA plot locations. Unique statistical models were developed from predictor layers consisting of climate, terrain, soils, and satellite imagery. Modeled basal area (BA) and stand density index (SDI) datasets for individual tree species were further post-processed to 1) match BA and SDI histograms of FIA data, 2) ensure that the sum of individual species BA and SDI on a pixel did not exceed separately modeled total for all species BA and SDI raster datasets, 3) derive additional tree parameters like quadratic mean diameter and trees per acre. With Landsat image collection dates ranging from 1985 to 2005, and a mean collection date for treed areas of 2002, and FIA plot data generally ranging from 1999 to 2005, the vintage of the base parameter datasets varies based on location, but can be roughly considered as 2002
This imagery layer shows national riparian areas for the conterminous United States. Riparian areas are an important natural resource with high biological diversity. These ecosystems contain specific vegetation and soil characteristics which support irreplaceable values and multiple ecosystem functions and are very responsive to changes in land management activities. Delineating and quantifying riparian areas is an essential step in riparian monitoring, planning, management, and policy decisions. USDA Forest Service supports the development and implementation of a national context framework with a multi-scale approach to define riparian areas utilizing free available national geospatial datasets.
Why was this layer created?
To estimate 50-year flood height riparian areas to support statistical analysis, map display, and model parameterization.
Provide a framework and an end product to stakeholders and apply the information into management actions and strategies.
Multi-scale approach to provide a national and regional report map. Create a product for managers to easily understand where to apply the information at various scales.
Develop a national context inventory of riparian areas and their condition within national forests and rangelands.
How was this layer created?
Using freely available data.
Develop cost effective modeling approach & technique.
Multi-scale (national, regional, & local).
Promote technology transfer to train/reach out to our partners.
Fifty-year flood heights were estimated using U.S. Geological Survey (USGS) stream gage information. NHDPlus version 2.1 was used as the hydrologic framework to delineate riparian areas. The U.S. Fish and Wildlife Service's National Wetland Inventory and USGS 10-meter digital elevation models were also used in processing these data.
The data are '1' if in the riparian zone and 'NoData' if outside the riparian zone. When displayed on a map, riparian zone cells are color-coded 'blue' with 25% transparency.
For additional information regarding methodologies for modeling and processing these data, see Abood et al. (2012) and the National Riparian Areas Base Map StoryMap
Data Download: https://www.fs.usda.gov/rds/archive/catalog/RDS-2019-0030
The estimated ground evacuation time layer is intended to provide a broad view of medical care accessibility to inform incident- and regional-level strategic risk assessment and mitigation planning. The estimated ground evacuation times account for hospital locations, driving speeds on the current road network, and off-road walking speeds influenced by topography, vegetation, slash/blowdown, and waterbodies. Evacuation times are binned into broad classes (0-1 hours, 1-2 hours, 2-4 hours, 4-6 hours, and & >6 hours) commensurate with the precision of the analysis. There are several limitations of this estimated ground evacuation time dataset that users should understand. The analysis assumes that the closest general acute care or critical access hospital will be the first stop to stabilize the patient. Incident level safety and medical planning may identify different preferred medical facilities based on their actual travel times and/or capabilities. These estimated evacuation times only account for the travel portion of an evacuation and should therefore be viewed as best-case estimates. Complicated injuries may require additional time for patient evaluation, care, packaging for transport, and/or transfer to a vehicle. Travel times are calculated under the assumptions that the litter crew can travel at the median pedestrian rate (influenced by landscape factors) and that the ambulance or other transport vehicle can travel at the designated road speed limits. Users of this dataset should be thoughtful of factors not accounted for in this analysis that could slow evacuations such as additional landscape characteristics, crew condition, or dynamic factors like weather, traffic, or road closures.
The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2023-5 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The Science data are the initial annual model outputs that consist of two images: percent tree canopy cover (TCC) and standard error. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset, and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the years 1985 through 2023 are available. The Science data were produced using a random forest regression algorithm. For standard error data, the initial standard error estimates that ranged from 0 to approximately 45 were multiplied by 100 to maintain data precision (e.g., 45 = 4500). Therefore, standard error estimates pixel values range from 0 to approximately 4500. The value 65534 represents the non-processing area mask where no cloud or cloud shadow-free data are available to produce an output, and 65535 represents the background value. The Science data are accessible for multiple user communities, through multiple channels and platforms. For information on the NLCD TCC data and processing steps see the NLCD metadata. Information on the Science data and processing steps are included here. Data Download and Methods Documents: - https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/
This image service contains high-resolution land cover data for the states of Nebraska, South Dakota, and North Dakota. These data are a digital representation of land cover derived from 1-meter aerial imagery from the USDA National Agriculture Imagery Program (NAIP.) The year of NAIP used for each state was 2014.
Data are intended for use in rural areas and therefore do not include land cover in cities and towns. Land cover classes (tree cover, other land cover, or water) were mapped using an object-based image analysis approach and supervised classification.
These data are designed for conducting geospatial analyses and for producing cartographic products. In particular, these data are intended to depict the location of tree cover in the county. The mapping procedures were developed specifically for agricultural landscapes that are dominated by annual crops, rangeland, and pasture and where tree cover is often found in narrow configurations, such as windbreaks and riparian corridors. Because much of the tree cover in agricultural areas of the United States occurs in windbreaks and narrow riparian corridors, many geospatial datasets derived from coarser-resolution satellite data (such as Landsat), do not capture these landscape features. This dataset is intended to address this particular data gap.
These data can be downloaded by county at the Forest Service Research Data Archive.
Nebraska: https://www.fs.usda.gov/rds/archive/catalog/RDS-2019-0038
South Dakota: https://www.fs.usda.gov/rds/archive/catalog/RDS-2022-0068
North Dakota: https://www.fs.usda.gov/rds/archive/catalog/RDS-2022-0067
A Kansas dataset was also developed using the same methods and is located at:
Kansas data download: https://www.fs.usda.gov/rds/archive/catalog/RDS-2019-0052
Kansas map service: https://data-usfs.hub.arcgis.com/documents/high-resolution-tree-cover-of-kansas-2015-map-service/explore
The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2021-4 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The Science data are the initial annual model outputs that consist of two images: percent tree canopy cover (TCC) and standard error. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset, and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the years 2008 through 2021 are available. The Science data were produced using a random forests regression algorithm. For standard error data, the initial standard error estimates that ranged from 0 to approximately 45 were multiplied by 100 to maintain data precision (e.g., 45 = 4500). Therefore, standard error estimates pixel values range from 0 to approximately 4500. The value 65534 represents the non-processing area mask where no cloud or cloud shadow-free data are available to produce an output, and 65535 represents the background value. The Science data are accessible for multiple user communities, through multiple channels and platforms. For information on the NLCD TCC data and processing steps see the NLCD metadata. Information on the Science data and processing steps are included here. Data Download and Methods Documents: - https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/
The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2021-4 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The Science data are the initial annual model outputs that consist of two images: percent tree canopy cover (TCC) and standard error. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset, and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the years 2008 through 2021 are available. The Science data were produced using a random forests regression algorithm. For standard error data, the initial standard error estimates that ranged from 0 to approximately 45 were multiplied by 100 to maintain data precision (e.g., 45 = 4500). Therefore, standard error estimates pixel values range from 0 to approximately 4500. The value 65534 represents the non-processing area mask where no cloud or cloud shadow-free data are available to produce an output, and 65535 represents the background value. The Science data are accessible for multiple user communities, through multiple channels and platforms. For information on the NLCD TCC data and processing steps see the NLCD metadata. Information on the Science data and processing steps are included here. Data Download and Methods Documents: - https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/
The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2021-4 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The Science data are the initial annual model outputs that consist of two images: percent tree canopy cover (TCC) and standard error. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset, and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the years 2008 through 2021 are available. The Science data were produced using a random forests regression algorithm. For standard error data, the initial standard error estimates that ranged from 0 to approximately 45 were multiplied by 100 to maintain data precision (e.g., 45 = 4500). Therefore, standard error estimates pixel values range from 0 to approximately 4500. The value 65534 represents the non-processing area mask where no cloud or cloud shadow-free data are available to produce an output, and 65535 represents the background value. The Science data are accessible for multiple user communities, through multiple channels and platforms. For information on the NLCD TCC data and processing steps see the NLCD metadata. Information on the Science data and processing steps are included here. Data Download and Methods Documents: - https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/
The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2023-5 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The Science data - the focus of this metadata - are the initial annual model outputs that consist of two images: percent tree canopy cover (TCC) and standard error. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset, and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the years 1985 through 2023 are available. The Science data were produced using a random forest regression algorithm. TCC pixel values range from 0 to 100 percent. The value 254 represents the non-processing area mask where no cloud or cloud shadow-free data are available to produce an output, and 255 represents the background value. The Science data are accessible for multiple user communities, through multiple channels and platforms. For information on the NLCD TCC data and processing steps see the NLCD metadata. Information on the Science data and processing steps are included here. Data Download and Methods Documents: - https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/
The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2023-5 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The NLCD product suite includes data for years 1985 through 2023. The NCLD data are processed to mask TCC from non-treed features such as water and non-tree crops, and to reduce interannual noise and smooth the NLCD time series. TCC pixel values range from 0 to 100 percent. The non-processing area is represented by value 254, and the background is represented by the value 255. The Science and NLCD tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms. For information on the Science data and processing steps see the Science metadata. Information on the NLCD data and processing steps are included here. Data Download and Methods Documents: - https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/
The geospatial products described and distributed here depict the probability of high-severity fire, if a fire were to occur, for several ecoregions in the contiguous western US.
The ecological effects of wildland fire – also termed the fire severity – are often highly heterogeneous in space and time. This heterogeneity is a result of spatial variability in factors such as fuel, topography, and climate (e.g. mean annual temperature). However, temporally variable factors such as daily weather and climatic extremes (e.g. an unusually warm year) also may play a key role.
Scientists from the US Forest Service Rocky Mountain Research Station and the University of Montana conducted a study in which observed data were used to produce statistical models describing the probability of high severity fire as a function of fuel, topography, climate, and fire weather. Observed data from over 2000 fires (from 2002-2015) were used to build individual models for each of 19 ecoregions in the contiguous US (see Parks et al. 2018, Figure 1). High severity fire was measured using a fire severity metric termed the relativized burn ratio, which uses pre- and post-fire Landsat imagery to measure fire-induced ecological change. Fuel included pre-fire metrics of live fuel amount such as NDVI. Topography included factors such as slope and potential solar radiation. Climate summarized 30-year averages of factors such as mean summer temperature that spatially vary across the study area. Lastly, fire weather incorporated temporally variable factors such as daily and annual temperature.
In turn, these statistical models were used to generate \"wall-to-wall\" maps depicting the probability of high severity fire, if a fire were to occur, for 13 of the 19 ecoregions. Maps were not produced for ecoregions in which model quality was deemed inadequate. All maps use fuel data representing the year 2016 and therefore provide a fairly up-to-date assessment of the potential for high severity fire. For those ecoregions in which the relative influence of fire weather was fairly strong (n=6), two additional maps were produced, one depicting the probability of high severity fire under moderate weather and the other under extreme weather. An important consideration is that only pixels defined as forest were used to build the models; consequently maps exclude pixels considered non-forest.
The Monitoring Trends in Burn Severity (MTBS) Program assesses the frequency, extent, and magnitude (size and severity) of all large wildland fires (wildfires and prescribed fires) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico for the period 1984 and beyond. All fires reported as greater than 1,000 acres in the western U.S. and greater than 500 acres in the eastern U.S. are mapped across all ownerships. MTBS produces a series of geospatial and tabular data for analysis at a range of spatial, temporal, and thematic scales and are intended to meet a variety of information needs that require consistent data about fire effects through space and time. This map layer is a thematic raster image of MTBS burn severity classes for all inventoried fires occurring in Puerto_Rico. Fires omitted from this mapped inventory are those where suitable satellite imagery was not available, or fires were not discernable from available imagery.
The Monitoring Trends in Burn Severity (MTBS) Program assesses the frequency, extent, and magnitude (size and severity) of all large wildland fires (wildfires and prescribed fires) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico for the period 1984 and beyond. All fires reported as greater than 1,000 acres in the western U.S. and greater than 500 acres in the eastern U.S. are mapped across all ownerships. MTBS produces a series of geospatial and tabular data for analysis at a range of spatial, temporal, and thematic scales and are intended to meet a variety of information needs that require consistent data about fire effects through space and time. This map layer is a thematic raster image of MTBS burn severity classes for all inventoried fires occurring in Hawaii during calendar year 2022. Fires omitted from this mapped inventory are those where suitable satellite imagery was not available, or fires were not discernable from available imagery.
The Monitoring Trends in Burn Severity (MTBS) Program assesses the frequency, extent, and magnitude (size and severity) of all large wildland fires (wildfires and prescribed fires) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico for the period 1984 and beyond. All fires reported as greater than 1,000 acres in the western U.S. and greater than 500 acres in the eastern U.S. are mapped across all ownerships. MTBS produces a series of geospatial and tabular data for analysis at a range of spatial, temporal, and thematic scales and are intended to meet a variety of information needs that require consistent data about fire effects through space and time. This map layer is a thematic raster image of MTBS burn severity classes for all inventoried fires occurring in CONUS. Fires omitted from this mapped inventory are those where suitable satellite imagery was not available, or fires were not discernable from available imagery.
The Monitoring Trends in Burn Severity (MTBS) Program assesses the frequency, extent, and magnitude (size and severity) of all large wildland fires (wildfires and prescribed fires) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico for the period 1984 and beyond. All fires reported as greater than 1,000 acres in the western U.S. and greater than 500 acres in the eastern U.S. are mapped across all ownerships. MTBS produces a series of geospatial and tabular data for analysis at a range of spatial, temporal, and thematic scales and are intended to meet a variety of information needs that require consistent data about fire effects through space and time. This map layer is a thematic raster image of MTBS burn severity classes for all inventoried fires occurring in Alaska. Fires omitted from this mapped inventory are those where suitable satellite imagery was not available, or fires were not discernable from available imagery.
The data included in this publication depict the 2024 version of components of wildfire risk for all lands in the United States that: 1) are landscape-wide (i.e., measurable at every pixel across the landscape); and 2) represent in situ risk - risk at the location where the adverse effects take place on the landscape.
National wildfire hazard datasets of annual burn probability and fire intensity, generated by the USDA Forest Service, Rocky Mountain Research Station and Pyrologix LLC, form the foundation of the Wildfire Risk to Communities data. Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were used as input to two different but related geospatial fire simulation systems. Annual burn probability was produced with the USFS geospatial fire simulator (FSim) at a relatively coarse cell size of 270 meters (m). To bring the burn probability raster data down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30 m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability into developed areas represented in LANDFIRE fuels data as non-burnable. Burn probability rasters represent landscape conditions as of the end of 2020. Fire intensity characteristics were modeled at 30 m resolution using a process that performs a comprehensive set of FlamMap runs spanning the full range of weather-related characteristics that occur during a fire season and then integrates those runs into a variety of results based on the likelihood of those weather types occurring. Before the fire intensity modeling, the LANDFIRE 2020 data were updated to reflect fuels disturbances occurring in 2021 and 2022. As such, the fire intensity datasets represent landscape conditions as of the end of 2022. Additional methodology documentation is provided in a methods document (\\Supplements\\WRC_V2_Methods_Landscape-wideRisk.pdf) packaged in the data download.
The specific raster datasets in this publication include:
Risk to Potential Structures (RPS): A measure that integrates wildfire likelihood and intensity with generalized consequences to a home on every pixel. For every place on the landscape, it poses the hypothetical question, \"What would be the relative risk to a house if one existed here?\" This allows comparison of wildfire risk in places where homes already exist to places where new construction may be proposed. This dataset is referred to as Risk to Homes in the Wildfire Risk to Communities web application.
Conditional Risk to Potential Structures (cRPS): The potential consequences of fire to a home at a given location, if a fire occurs there and if a home were located there. Referred to as Wildfire Consequence in the Wildfire Risk to Communities web application.
Exposure Type: Exposure is the spatial coincidence of wildfire likelihood and intensity with communities. This layer delineates where homes are directly exposed to wildfire from adjacent wildland vegetation, indirectly exposed to wildfire from indirect sources such as embers and home-to-home ignition, or not exposed to wildfire due to distance from direct and indirect ignition sources.
Burn Probability (BP): The annual probability of wildfire burning in a specific location. Referred to as Wildfire Likelihood in the Wildfire Risk to Communities web application.
Conditional Flame Length (CFL): The mean flame length for a fire burning in the direction of maximum spread (headfire) at a given location if a fire were to occur; an average measure of wildfire intensity.
Flame Length Exceedance Probability - 4 ft (FLEP4): The conditional probability that flame length at a pixel will exceed 4 feet if a fire occurs; indicates the potential for moderate to high wildfire intensity.
Flame Length Exceedance Probability - 8 ft (FLEP8): the conditional probability that flame length at a pixel will exceed 8 feet if a fire occurs; indicates the potential for high wildfire intensity.
Wildfire Hazard Potential (WHP): An index that quantifies the relative potential for wildfire that may be difficult to manage, used as a measure to help prioritize where fuel treatments may be needed.
Additional methodology documentation is provided with the data publication download. https://www.fs.usda.gov/rds/archive/Catalog/RDS-2020-0016-2
Note: Pixel values in this image service have been altered from the original raster dataset due to data requirements in web services. The service is intended primarily for data visualization. Relative values and spatial patterns have been largely preserved in the service, but users are encouraged to download the source data for quantitative analysis.
The data included in this publication depict the 2024 version of components of wildfire risk for all lands in the United States that: 1) are landscape-wide (i.e., measurable at every pixel across the landscape); and 2) represent in situ risk - risk at the location where the adverse effects take place on the landscape.
National wildfire hazard datasets of annual burn probability and fire intensity, generated by the USDA Forest Service, Rocky Mountain Research Station and Pyrologix LLC, form the foundation of the Wildfire Risk to Communities data. Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were used as input to two different but related geospatial fire simulation systems. Annual burn probability was produced with the USFS geospatial fire simulator (FSim) at a relatively coarse cell size of 270 meters (m). To bring the burn probability raster data down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30 m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability into developed areas represented in LANDFIRE fuels data as non-burnable. Burn probability rasters represent landscape conditions as of the end of 2020. Fire intensity characteristics were modeled at 30 m resolution using a process that performs a comprehensive set of FlamMap runs spanning the full range of weather-related characteristics that occur during a fire season and then integrates those runs into a variety of results based on the likelihood of those weather types occurring. Before the fire intensity modeling, the LANDFIRE 2020 data were updated to reflect fuels disturbances occurring in 2021 and 2022. As such, the fire intensity datasets represent landscape conditions as of the end of 2022. Additional methodology documentation is provided in a methods document (\\Supplements\\WRC_V2_Methods_Landscape-wideRisk.pdf) packaged in the data download.
The specific raster datasets in this publication include:
Risk to Potential Structures (RPS): A measure that integrates wildfire likelihood and intensity with generalized consequences to a home on every pixel. For every place on the landscape, it poses the hypothetical question, \"What would be the relative risk to a house if one existed here?\" This allows comparison of wildfire risk in places where homes already exist to places where new construction may be proposed. This dataset is referred to as Risk to Homes in the Wildfire Risk to Communities web application.
Conditional Risk to Potential Structures (cRPS): The potential consequences of fire to a home at a given location, if a fire occurs there and if a home were located there. Referred to as Wildfire Consequence in the Wildfire Risk to Communities web application.
Exposure Type: Exposure is the spatial coincidence of wildfire likelihood and intensity with communities. This layer delineates where homes are directly exposed to wildfire from adjacent wildland vegetation, indirectly exposed to wildfire from indirect sources such as embers and home-to-home ignition, or not exposed to wildfire due to distance from direct and indirect ignition sources.
Burn Probability (BP): The annual probability of wildfire burning in a specific location. Referred to as Wildfire Likelihood in the Wildfire Risk to Communities web application.
Conditional Flame Length (CFL): The mean flame length for a fire burning in the direction of maximum spread (headfire) at a given location if a fire were to occur; an average measure of wildfire intensity.
Flame Length Exceedance Probability - 4 ft (FLEP4): The conditional probability that flame length at a pixel will exceed 4 feet if a fire occurs; indicates the potential for moderate to high wildfire intensity.
Flame Length Exceedance Probability - 8 ft (FLEP8): the conditional probability that flame length at a pixel will exceed 8 feet if a fire occurs; indicates the potential for high wildfire intensity.
Wildfire Hazard Potential (WHP): An index that quantifies the relative potential for wildfire that may be difficult to manage, used as a measure to help prioritize where fuel treatments may be needed.
Additional methodology documentation is provided with the data publication download. (https://www.fs.usda.gov/rds/archive/Catalog/RDS-2020-0016-2)
Note: Pixel values in this image service have been altered from the original raster dataset due to data requirements in web services. The service is intended primarily for data visualization. Relative values and spatial patterns have been largely preserved in the service, but users are encouraged to download the source data for quantitative analysis.
The data included in this publication depict components of wildfire risk specifically for populated areas in the United States. These datasets represent where people live in the United States and the in situ risk from wildfire, i.e., the risk at the location where the adverse effects take place.
National wildfire hazard datasets of annual burn probability and fire intensity, generated by the USDA Forest Service, Rocky Mountain Research Station and Pyrologix LLC, form the foundation of the Wildfire Risk to Communities data. Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were used as input to two different but related geospatial fire simulation systems. Annual burn probability was produced with the USFS geospatial fire simulator (FSim) at a relatively coarse cell size of 270 meters (m). To bring the burn probability raster data down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30 m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability into developed areas represented in LANDFIRE fuels data as non-burnable. Burn probability rasters represent landscape conditions as of the end of 2020. Fire intensity characteristics were modeled at 30 m resolution using a process that performs a comprehensive set of FlamMap runs spanning the full range of weather-related characteristics that occur during a fire season and then integrates those runs into a variety of results based on the likelihood of those weather types occurring. Before the fire intensity modeling, the LANDFIRE 2020 data were updated to reflect fuels disturbances occurring in 2021 and 2022. As such, the fire intensity datasets represent landscape conditions as of the end of 2022. The data products in this publication that represent where people live, reflect 2021 estimates of housing unit and population counts from the U.S. Census Bureau, combined with building footprint data from Onegeo and USA Structures, both reflecting 2022 conditions.
The specific raster datasets included in this publication include:
Building Count: Building Count is a 30-m raster representing the count of buildings in the building footprint dataset located within each 30-m pixel.
Building Density: Building Density is a 30-m raster representing the density of buildings in the building footprint dataset (buildings per square kilometer [km²]).
Building Coverage: Building Coverage is a 30-m raster depicting the percentage of habitable land area covered by building footprints.
Population Count (PopCount): PopCount is a 30-m raster with pixel values representing residential population count (persons) in each pixel.
Population Density (PopDen): PopDen is a 30-m raster of residential population density (people/km²).
Housing Unit Count (HUCount): HUCount is a 30-m raster representing the number of housing units in each pixel.
Housing Unit Density (HUDen): HUDen is a 30-m raster of housing-unit density (housing units/km²).
Housing Unit Exposure (HUExposure): HUExposure is a 30-m raster that represents the expected number of housing units within a pixel potentially exposed to wildfire in a year. This is a long-term annual average and not intended to represent the actual number of housing units exposed in any specific year.
Housing Unit Impact (HUImpact): HUImpact is a 30-m raster that represents the relative potential impact of fire to housing units at any pixel, if a fire were to occur. It is an index that incorporates the general consequences of fire on a home as a function of fire intensity and uses flame length probabilities from wildfire modeling to capture likely intensity of fire.
Housing Unit Risk (HURisk): HURisk is a 30-m raster that integrates all four primary elements of wildfire risk - likelihood, intensity, susceptibility, and exposure - on pixels where housing unit density is greater than zero.
Additional methodology documentation is provided with the data publication download. Metadata and Downloads: (https://www.fs.usda.gov/rds/archive/catalog/RDS-2020-0060-2).
Note: Pixel values in this image service have been altered from the original raster dataset due to data requirements in web services. The service is intended primarily for data visualization. Relative values and spatial patterns have been largely preserved in the service, but users are encouraged to download the source data for quantitative analysis.
The data included in this publication depict components of wildfire risk specifically for populated areas in the United States. These datasets represent where people live in the United States and the in situ risk from wildfire, i.e., the risk at the location where the adverse effects take place.
National wildfire hazard datasets of annual burn probability and fire intensity, generated by the USDA Forest Service, Rocky Mountain Research Station and Pyrologix LLC, form the foundation of the Wildfire Risk to Communities data. Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were used as input to two different but related geospatial fire simulation systems. Annual burn probability was produced with the USFS geospatial fire simulator (FSim) at a relatively coarse cell size of 270 meters (m). To bring the burn probability raster data down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30 m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability into developed areas represented in LANDFIRE fuels data as non-burnable. Burn probability rasters represent landscape conditions as of the end of 2020. Fire intensity characteristics were modeled at 30 m resolution using a process that performs a comprehensive set of FlamMap runs spanning the full range of weather-related characteristics that occur during a fire season and then integrates those runs into a variety of results based on the likelihood of those weather types occurring. Before the fire intensity modeling, the LANDFIRE 2020 data were updated to reflect fuels disturbances occurring in 2021 and 2022. As such, the fire intensity datasets represent landscape conditions as of the end of 2022. The data products in this publication that represent where people live, reflect 2021 estimates of housing unit and population counts from the U.S. Census Bureau, combined with building footprint data from Onegeo and USA Structures, both reflecting 2022 conditions.
The specific raster datasets included in this publication include:
Building Count: Building Count is a 30-m raster representing the count of buildings in the building footprint dataset located within each 30-m pixel.
Building Density: Building Density is a 30-m raster representing the density of buildings in the building footprint dataset (buildings per square kilometer [km²]).
Building Coverage: Building Coverage is a 30-m raster depicting the percentage of habitable land area covered by building footprints.
Population Count (PopCount): PopCount is a 30-m raster with pixel values representing residential population count (persons) in each pixel.
Population Density (PopDen): PopDen is a 30-m raster of residential population density (people/km²).
Housing Unit Count (HUCount): HUCount is a 30-m raster representing the number of housing units in each pixel.
Housing Unit Density (HUDen): HUDen is a 30-m raster of housing-unit density (housing units/km²).
Housing Unit Exposure (HUExposure): HUExposure is a 30-m raster that represents the expected number of housing units within a pixel potentially exposed to wildfire in a year. This is a long-term annual average and not intended to represent the actual number of housing units exposed in any specific year.
Housing Unit Impact (HUImpact): HUImpact is a 30-m raster that represents the relative potential impact of fire to housing units at any pixel, if a fire were to occur. It is an index that incorporates the general consequences of fire on a home as a function of fire intensity and uses flame length probabilities from wildfire modeling to capture likely intensity of fire.
Housing Unit Risk (HURisk): HURisk is a 30-m raster that integrates all four primary elements of wildfire risk - likelihood, intensity, susceptibility, and exposure - on pixels where housing unit density is greater than zero.
Additional methodology documentation is provided with the data publication download. Metadata and Downloads: (https://www.fs.usda.gov/rds/archive/catalog/RDS-2020-0060-2).
Note: Pixel values in this image service have been altered from the original raster dataset due to data requirements in web services. The service is intended primarily for data visualization. Relative values and spatial patterns have been largely preserved in the service, but users are encouraged to download the source data for quantitative analysis.
The data included in this publication depict components of wildfire risk specifically for populated areas in the United States. These datasets represent where people live in the United States and the in situ risk from wildfire, i.e., the risk at the location where the adverse effects take place.
National wildfire hazard datasets of annual burn probability and fire intensity, generated by the USDA Forest Service, Rocky Mountain Research Station and Pyrologix LLC, form the foundation of the Wildfire Risk to Communities data. Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were used as input to two different but related geospatial fire simulation systems. Annual burn probability was produced with the USFS geospatial fire simulator (FSim) at a relatively coarse cell size of 270 meters (m). To bring the burn probability raster data down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30 m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability into developed areas represented in LANDFIRE fuels data as non-burnable. Burn probability rasters represent landscape conditions as of the end of 2020. Fire intensity characteristics were modeled at 30 m resolution using a process that performs a comprehensive set of FlamMap runs spanning the full range of weather-related characteristics that occur during a fire season and then integrates those runs into a variety of results based on the likelihood of those weather types occurring. Before the fire intensity modeling, the LANDFIRE 2020 data were updated to reflect fuels disturbances occurring in 2021 and 2022. As such, the fire intensity datasets represent landscape conditions as of the end of 2022. The data products in this publication that represent where people live, reflect 2021 estimates of housing unit and population counts from the U.S. Census Bureau, combined with building footprint data from Onegeo and USA Structures, both reflecting 2022 conditions.
The specific raster datasets included in this publication include:
Building Count: Building Count is a 30-m raster representing the count of buildings in the building footprint dataset located within each 30-m pixel.
Building Density: Building Density is a 30-m raster representing the density of buildings in the building footprint dataset (buildings per square kilometer [km²]).
Building Coverage: Building Coverage is a 30-m raster depicting the percentage of habitable land area covered by building footprints.
Population Count (PopCount): PopCount is a 30-m raster with pixel values representing residential population count (persons) in each pixel.
Population Density (PopDen): PopDen is a 30-m raster of residential population density (people/km²).
Housing Unit Count (HUCount): HUCount is a 30-m raster representing the number of housing units in each pixel.
Housing Unit Density (HUDen): HUDen is a 30-m raster of housing-unit density (housing units/km²).
Housing Unit Exposure (HUExposure): HUExposure is a 30-m raster that represents the expected number of housing units within a pixel potentially exposed to wildfire in a year. This is a long-term annual average and not intended to represent the actual number of housing units exposed in any specific year.
Housing Unit Impact (HUImpact): HUImpact is a 30-m raster that represents the relative potential impact of fire to housing units at any pixel, if a fire were to occur. It is an index that incorporates the general consequences of fire on a home as a function of fire intensity and uses flame length probabilities from wildfire modeling to capture likely intensity of fire.
Housing Unit Risk (HURisk): HURisk is a 30-m raster that integrates all four primary elements of wildfire risk - likelihood, intensity, susceptibility, and exposure - on pixels where housing unit density is greater than zero.
Additional methodology documentation is provided with the data publication download. Metadata and Downloads: (https://www.fs.usda.gov/rds/archive/catalog/RDS-2020-0060-2).
Note: Pixel values in this image service have been altered from the original raster dataset due to data requirements in web services. The service is intended primarily for data visualization. Relative values and spatial patterns have been largely preserved in the service, but users are encouraged to download the source data for quantitative analysis.
The data included in this publication depict components of wildfire risk specifically for populated areas in the United States. These datasets represent where people live in the United States and the in situ risk from wildfire, i.e., the risk at the location where the adverse effects take place.
National wildfire hazard datasets of annual burn probability and fire intensity, generated by the USDA Forest Service, Rocky Mountain Research Station and Pyrologix LLC, form the foundation of the Wildfire Risk to Communities data. Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were used as input to two different but related geospatial fire simulation systems. Annual burn probability was produced with the USFS geospatial fire simulator (FSim) at a relatively coarse cell size of 270 meters (m). To bring the burn probability raster data down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30 m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability into developed areas represented in LANDFIRE fuels data as non-burnable. Burn probability rasters represent landscape conditions as of the end of 2020. Fire intensity characteristics were modeled at 30 m resolution using a process that performs a comprehensive set of FlamMap runs spanning the full range of weather-related characteristics that occur during a fire season and then integrates those runs into a variety of results based on the likelihood of those weather types occurring. Before the fire intensity modeling, the LANDFIRE 2020 data were updated to reflect fuels disturbances occurring in 2021 and 2022. As such, the fire intensity datasets represent landscape conditions as of the end of 2022. The data products in this publication that represent where people live, reflect 2021 estimates of housing unit and population counts from the U.S. Census Bureau, combined with building footprint data from Onegeo and USA Structures, both reflecting 2022 conditions.
The specific raster datasets included in this publication include:
Building Count: Building Count is a 30-m raster representing the count of buildings in the building footprint dataset located within each 30-m pixel.
Building Density: Building Density is a 30-m raster representing the density of buildings in the building footprint dataset (buildings per square kilometer [km²]).
Building Coverage: Building Coverage is a 30-m raster depicting the percentage of habitable land area covered by building footprints.
Population Count (PopCount): PopCount is a 30-m raster with pixel values representing residential population count (persons) in each pixel.
Population Density (PopDen): PopDen is a 30-m raster of residential population density (people/km²).
Housing Unit Count (HUCount): HUCount is a 30-m raster representing the number of housing units in each pixel.
Housing Unit Density (HUDen): HUDen is a 30-m raster of housing-unit density (housing units/km²).
Housing Unit Exposure (HUExposure): HUExposure is a 30-m raster that represents the expected number of housing units within a pixel potentially exposed to wildfire in a year. This is a long-term annual average and not intended to represent the actual number of housing units exposed in any specific year.
Housing Unit Impact (HUImpact): HUImpact is a 30-m raster that represents the relative potential impact of fire to housing units at any pixel, if a fire were to occur. It is an index that incorporates the general consequences of fire on a home as a function of fire intensity and uses flame length probabilities from wildfire modeling to capture likely intensity of fire.
Housing Unit Risk (HURisk): HURisk is a 30-m raster that integrates all four primary elements of wildfire risk - likelihood, intensity, susceptibility, and exposure - on pixels where housing unit density is greater than zero.
Additional methodology documentation is provided with the data publication download. Metadata and Downloads: (https://www.fs.usda.gov/rds/archive/catalog/RDS-2020-0060-2).
Note: Pixel values in this image service have been altered from the original raster dataset due to data requirements in web services. The service is intended primarily for data visualization. Relative values and spatial patterns have been largely preserved in the service, but users are encouraged to download the source data for quantitative analysis.
The data included in this publication depict components of wildfire risk specifically for populated areas in the United States. These datasets represent where people live in the United States and the in situ risk from wildfire, i.e., the risk at the location where the adverse effects take place.
National wildfire hazard datasets of annual burn probability and fire intensity, generated by the USDA Forest Service, Rocky Mountain Research Station and Pyrologix LLC, form the foundation of the Wildfire Risk to Communities data. Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were used as input to two different but related geospatial fire simulation systems. Annual burn probability was produced with the USFS geospatial fire simulator (FSim) at a relatively coarse cell size of 270 meters (m). To bring the burn probability raster data down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30 m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability into developed areas represented in LANDFIRE fuels data as non-burnable. Burn probability rasters represent landscape conditions as of the end of 2020. Fire intensity characteristics were modeled at 30 m resolution using a process that performs a comprehensive set of FlamMap runs spanning the full range of weather-related characteristics that occur during a fire season and then integrates those runs into a variety of results based on the likelihood of those weather types occurring. Before the fire intensity modeling, the LANDFIRE 2020 data were updated to reflect fuels disturbances occurring in 2021 and 2022. As such, the fire intensity datasets represent landscape conditions as of the end of 2022. The data products in this publication that represent where people live, reflect 2021 estimates of housing unit and population counts from the U.S. Census Bureau, combined with building footprint data from Onegeo and USA Structures, both reflecting 2022 conditions.
The specific raster datasets included in this publication include:
Building Count: Building Count is a 30-m raster representing the count of buildings in the building footprint dataset located within each 30-m pixel.
Building Density: Building Density is a 30-m raster representing the density of buildings in the building footprint dataset (buildings per square kilometer [km²]). Building Coverage: Building Coverage is a 30-m raster depicting the percentage of habitable land area covered by building footprints.
Population Count (PopCount): PopCount is a 30-m raster with pixel values representing residential population count (persons) in each pixel.
Population Density (PopDen): PopDen is a 30-m raster of residential population density (people/km²).
Housing Unit Count (HUCount): HUCount is a 30-m raster representing the number of housing units in each pixel.
Housing Unit Density (HUDen): HUDen is a 30-m raster of housing-unit density (housing units/km²).
Housing Unit Exposure (HUExposure): HUExposure is a 30-m raster that represents the expected number of housing units within a pixel potentially exposed to wildfire in a year. This is a long-term annual average and not intended to represent the actual number of housing units exposed in any specific year.
Housing Unit Impact (HUImpact): HUImpact is a 30-m raster that represents the relative potential impact of fire to housing units at any pixel, if a fire were to occur. It is an index that incorporates the general consequences of fire on a home as a function of fire intensity and uses flame length probabilities from wildfire modeling to capture likely intensity of fire.
Housing Unit Risk (HURisk): HURisk is a 30-m raster that integrates all four primary elements of wildfire risk - likelihood, intensity, susceptibility, and exposure - on pixels where housing unit density is greater than zero.
Additional methodology documentation is provided with the data publication download. Metadata and Downloads: (https://www.fs.usda.gov/rds/archive/catalog/RDS-2020-0060-2).
Note: Pixel values in this image service have been altered from the original raster dataset due to data requirements in web services. The service is intended primarily for data visualization. Relative values and spatial patterns have been largely preserved in the service, but users are encouraged to download the source data for quantitative analysis.
The data included in this publication depict components of wildfire risk specifically for populated areas in the United States. These datasets represent where people live in the United States and the in situ risk from wildfire, i.e., the risk at the location where the adverse effects take place.
National wildfire hazard datasets of annual burn probability and fire intensity, generated by the USDA Forest Service, Rocky Mountain Research Station and Pyrologix LLC, form the foundation of the Wildfire Risk to Communities data. Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were used as input to two different but related geospatial fire simulation systems. Annual burn probability was produced with the USFS geospatial fire simulator (FSim) at a relatively coarse cell size of 270 meters (m). To bring the burn probability raster data down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30 m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability into developed areas represented in LANDFIRE fuels data as non-burnable. Burn probability rasters represent landscape conditions as of the end of 2020. Fire intensity characteristics were modeled at 30 m resolution using a process that performs a comprehensive set of FlamMap runs spanning the full range of weather-related characteristics that occur during a fire season and then integrates those runs into a variety of results based on the likelihood of those weather types occurring. Before the fire intensity modeling, the LANDFIRE 2020 data were updated to reflect fuels disturbances occurring in 2021 and 2022. As such, the fire intensity datasets represent landscape conditions as of the end of 2022. The data products in this publication that represent where people live, reflect 2021 estimates of housing unit and population counts from the U.S. Census Bureau, combined with building footprint data from Onegeo and USA Structures, both reflecting 2022 conditions.
The specific raster datasets included in this publication include:
Building Count: Building Count is a 30-m raster representing the count of buildings in the building footprint dataset located within each 30-m pixel.
Building Density: Building Density is a 30-m raster representing the density of buildings in the building footprint dataset (buildings per square kilometer [km²]).
Building Coverage: Building Coverage is a 30-m raster depicting the percentage of habitable land area covered by building footprints.
Population Count (PopCount): PopCount is a 30-m raster with pixel values representing residential population count (persons) in each pixel.
Population Density (PopDen): PopDen is a 30-m raster of residential population density (people/km²).
Housing Unit Count (HUCount): HUCount is a 30-m raster representing the number of housing units in each pixel.
Housing Unit Density (HUDen): HUDen is a 30-m raster of housing-unit density (housing units/km²).
Housing Unit Exposure (HUExposure): HUExposure is a 30-m raster that represents the expected number of housing units within a pixel potentially exposed to wildfire in a year. This is a long-term annual average and not intended to represent the actual number of housing units exposed in any specific year.
Housing Unit Impact (HUImpact): HUImpact is a 30-m raster that represents the relative potential impact of fire to housing units at any pixel, if a fire were to occur. It is an index that incorporates the general consequences of fire on a home as a function of fire intensity and uses flame length probabilities from wildfire modeling to capture likely intensity of fire.
Housing Unit Risk (HURisk): HURisk is a 30-m raster that integrates all four primary elements of wildfire risk - likelihood, intensity, susceptibility, and exposure - on pixels where housing unit density is greater than zero.
Additional methodology documentation is provided with the data publication download. Metadata and Downloads: (https://www.fs.usda.gov/rds/archive/catalog/RDS-2020-0060-2).
Note: Pixel values in this image service have been altered from the original raster dataset due to data requirements in web services. The service is intended primarily for data visualization. Relative values and spatial patterns have been largely preserved in the service, but users are encouraged to download the source data for quantitative analysis.
The data included in this publication depict the 2024 version of components of wildfire risk for all lands in the United States that: 1) are landscape-wide (i.e., measurable at every pixel across the landscape); and 2) represent in situ risk - risk at the location where the adverse effects take place on the landscape.
National wildfire hazard datasets of annual burn probability and fire intensity, generated by the USDA Forest Service, Rocky Mountain Research Station and Pyrologix LLC, form the foundation of the Wildfire Risk to Communities data. Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were used as input to two different but related geospatial fire simulation systems. Annual burn probability was produced with the USFS geospatial fire simulator (FSim) at a relatively coarse cell size of 270 meters (m). To bring the burn probability raster data down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30 m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability into developed areas represented in LANDFIRE fuels data as non-burnable. Burn probability rasters represent landscape conditions as of the end of 2020. Fire intensity characteristics were modeled at 30 m resolution using a process that performs a comprehensive set of FlamMap runs spanning the full range of weather-related characteristics that occur during a fire season and then integrates those runs into a variety of results based on the likelihood of those weather types occurring. Before the fire intensity modeling, the LANDFIRE 2020 data were updated to reflect fuels disturbances occurring in 2021 and 2022. As such, the fire intensity datasets represent landscape conditions as of the end of 2022. Additional methodology documentation is provided in a methods document (\\Supplements\\WRC_V2_Methods_Landscape-wideRisk.pdf) packaged in the data download.
The specific raster datasets in this publication include:
Risk to Potential Structures (RPS): A measure that integrates wildfire likelihood and intensity with generalized consequences to a home on every pixel. For every place on the landscape, it poses the hypothetical question, \"What would be the relative risk to a house if one existed here?\" This allows comparison of wildfire risk in places where homes already exist to places where new construction may be proposed. This dataset is referred to as Risk to Homes in the Wildfire Risk to Communities web application.
Conditional Risk to Potential Structures (cRPS): The potential consequences of fire to a home at a given location, if a fire occurs there and if a home were located there. Referred to as Wildfire Consequence in the Wildfire Risk to Communities web application.
Exposure Type: Exposure is the spatial coincidence of wildfire likelihood and intensity with communities. This layer delineates where homes are directly exposed to wildfire from adjacent wildland vegetation, indirectly exposed to wildfire from indirect sources such as embers and home-to-home ignition, or not exposed to wildfire due to distance from direct and indirect ignition sources.
Burn Probability (BP): The annual probability of wildfire burning in a specific location. Referred to as Wildfire Likelihood in the Wildfire Risk to Communities web application.
Conditional Flame Length (CFL): The mean flame length for a fire burning in the direction of maximum spread (headfire) at a given location if a fire were to occur; an average measure of wildfire intensity.
Flame Length Exceedance Probability - 4 ft (FLEP4): The conditional probability that flame length at a pixel will exceed 4 feet if a fire occurs; indicates the potential for moderate to high wildfire intensity.
Flame Length Exceedance Probability - 8 ft (FLEP8): the conditional probability that flame length at a pixel will exceed 8 feet if a fire occurs; indicates the potential for high wildfire intensity.
Wildfire Hazard Potential (WHP): An index that quantifies the relative potential for wildfire that may be difficult to manage, used as a measure to help prioritize where fuel treatments may be needed.
Additional methodology documentation is provided with the data publication download. (https://www.fs.usda.gov/rds/archive/Catalog/RDS-2020-0016-2)
Note: Pixel values in this image service have been altered from the original raster dataset due to data requirements in web services. The service is intended primarily for data visualization. Relative values and spatial patterns have been largely preserved in the service, but users are encouraged to download the source data for quantitative analysis.
The data included in this publication depict the 2024 version of components of wildfire risk for all lands in the United States that: 1) are landscape-wide (i.e., measurable at every pixel across the landscape); and 2) represent in situ risk - risk at the location where the adverse effects take place on the landscape.
National wildfire hazard datasets of annual burn probability and fire intensity, generated by the USDA Forest Service, Rocky Mountain Research Station and Pyrologix LLC, form the foundation of the Wildfire Risk to Communities data. Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were used as input to two different but related geospatial fire simulation systems. Annual burn probability was produced with the USFS geospatial fire simulator (FSim) at a relatively coarse cell size of 270 meters (m). To bring the burn probability raster data down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30 m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability into developed areas represented in LANDFIRE fuels data as non-burnable. Burn probability rasters represent landscape conditions as of the end of 2020. Fire intensity characteristics were modeled at 30 m resolution using a process that performs a comprehensive set of FlamMap runs spanning the full range of weather-related characteristics that occur during a fire season and then integrates those runs into a variety of results based on the likelihood of those weather types occurring. Before the fire intensity modeling, the LANDFIRE 2020 data were updated to reflect fuels disturbances occurring in 2021 and 2022. As such, the fire intensity datasets represent landscape conditions as of the end of 2022. Additional methodology documentation is provided in a methods document (\\Supplements\\WRC_V2_Methods_Landscape-wideRisk.pdf) packaged in the data download.
The specific raster datasets in this publication include: Risk to Potential Structures (RPS): A measure that integrates wildfire likelihood and intensity with generalized consequences to a home on every pixel. For every place on the landscape, it poses the hypothetical question, \"What would be the relative risk to a house if one existed here?\" This allows comparison of wildfire risk in places where homes already exist to places where new construction may be proposed. This dataset is referred to as Risk to Homes in the Wildfire Risk to Communities web application.
Conditional Risk to Potential Structures (cRPS): The potential consequences of fire to a home at a given location, if a fire occurs there and if a home were located there. Referred to as Wildfire Consequence in the Wildfire Risk to Communities web application.
Exposure Type: Exposure is the spatial coincidence of wildfire likelihood and intensity with communities. This layer delineates where homes are directly exposed to wildfire from adjacent wildland vegetation, indirectly exposed to wildfire from indirect sources such as embers and home-to-home ignition, or not exposed to wildfire due to distance from direct and indirect ignition sources.
Burn Probability (BP): The annual probability of wildfire burning in a specific location. Referred to as Wildfire Likelihood in the Wildfire Risk to Communities web application.
Conditional Flame Length (CFL): The mean flame length for a fire burning in the direction of maximum spread (headfire) at a given location if a fire were to occur; an average measure of wildfire intensity.
Flame Length Exceedance Probability - 4 ft (FLEP4): The conditional probability that flame length at a pixel will exceed 4 feet if a fire occurs; indicates the potential for moderate to high wildfire intensity.
Flame Length Exceedance Probability - 8 ft (FLEP8): the conditional probability that flame length at a pixel will exceed 8 feet if a fire occurs; indicates the potential for high wildfire intensity.
Wildfire Hazard Potential (WHP): An index that quantifies the relative potential for wildfire that may be difficult to manage, used as a measure to help prioritize where fuel treatments may be needed.
Additional methodology documentation is provided with the data publication download. https://www.fs.usda.gov/rds/archive/Catalog/RDS-2020-0016-2
Note: Pixel values in this image service have been altered from the original raster dataset due to data requirements in web services. The service is intended primarily for data visualization. Relative values and spatial patterns have been largely preserved in the service, but users are encouraged to download the source data for quantitative analysis.
The data included in this publication depict the 2024 version of components of wildfire risk for all lands in the United States that: 1) are landscape-wide (i.e., measurable at every pixel across the landscape); and 2) represent in situ risk - risk at the location where the adverse effects take place on the landscape.
National wildfire hazard datasets of annual burn probability and fire intensity, generated by the USDA Forest Service, Rocky Mountain Research Station and Pyrologix LLC, form the foundation of the Wildfire Risk to Communities data. Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were used as input to two different but related geospatial fire simulation systems. Annual burn probability was produced with the USFS geospatial fire simulator (FSim) at a relatively coarse cell size of 270 meters (m). To bring the burn probability raster data down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30 m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability into developed areas represented in LANDFIRE fuels data as non-burnable. Burn probability rasters represent landscape conditions as of the end of 2020. Fire intensity characteristics were modeled at 30 m resolution using a process that performs a comprehensive set of FlamMap runs spanning the full range of weather-related characteristics that occur during a fire season and then integrates those runs into a variety of results based on the likelihood of those weather types occurring. Before the fire intensity modeling, the LANDFIRE 2020 data were updated to reflect fuels disturbances occurring in 2021 and 2022. As such, the fire intensity datasets represent landscape conditions as of the end of 2022. Additional methodology documentation is provided in a methods document (\\Supplements\\WRC_V2_Methods_Landscape-wideRisk.pdf) packaged in the data download.
The specific raster datasets in this publication include:
Risk to Potential Structures (RPS): A measure that integrates wildfire likelihood and intensity with generalized consequences to a home on every pixel. For every place on the landscape, it poses the hypothetical question, \"What would be the relative risk to a house if one existed here?\" This allows comparison of wildfire risk in places where homes already exist to places where new construction may be proposed. This dataset is referred to as Risk to Homes in the Wildfire Risk to Communities web application.
Conditional Risk to Potential Structures (cRPS): The potential consequences of fire to a home at a given location, if a fire occurs there and if a home were located there. Referred to as Wildfire Consequence in the Wildfire Risk to Communities web application.
Exposure Type: Exposure is the spatial coincidence of wildfire likelihood and intensity with communities. This layer delineates where homes are directly exposed to wildfire from adjacent wildland vegetation, indirectly exposed to wildfire from indirect sources such as embers and home-to-home ignition, or not exposed to wildfire due to distance from direct and indirect ignition sources.
Burn Probability (BP): The annual probability of wildfire burning in a specific location. Referred to as Wildfire Likelihood in the Wildfire Risk to Communities web application.
Conditional Flame Length (CFL): The mean flame length for a fire burning in the direction of maximum spread (headfire) at a given location if a fire were to occur; an average measure of wildfire intensity.
Flame Length Exceedance Probability - 4 ft (FLEP4): The conditional probability that flame length at a pixel will exceed 4 feet if a fire occurs; indicates the potential for moderate to high wildfire intensity.
Flame Length Exceedance Probability - 8 ft (FLEP8): the conditional probability that flame length at a pixel will exceed 8 feet if a fire occurs; indicates the potential for high wildfire intensity.
Wildfire Hazard Potential (WHP): An index that quantifies the relative potential for wildfire that may be difficult to manage, used as a measure to help prioritize where fuel treatments may be needed.
Additional methodology documentation is provided with the data publication download. (https://www.fs.usda.gov/rds/archive/Catalog/RDS-2020-0016-2)
Note: Pixel values in this image service have been altered from the original raster dataset due to data requirements in web services. The service is intended primarily for data visualization. Relative values and spatial patterns have been largely preserved in the service, but users are encouraged to download the source data for quantitative analysis.
The data included in this publication depict the 2024 version of components of wildfire risk for all lands in the United States that: 1) are landscape-wide (i.e., measurable at every pixel across the landscape); and 2) represent in situ risk - risk at the location where the adverse effects take place on the landscape.
National wildfire hazard datasets of annual burn probability and fire intensity, generated by the USDA Forest Service, Rocky Mountain Research Station and Pyrologix LLC, form the foundation of the Wildfire Risk to Communities data. Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were used as input to two different but related geospatial fire simulation systems. Annual burn probability was produced with the USFS geospatial fire simulator (FSim) at a relatively coarse cell size of 270 meters (m). To bring the burn probability raster data down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30 m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability into developed areas represented in LANDFIRE fuels data as non-burnable. Burn probability rasters represent landscape conditions as of the end of 2020. Fire intensity characteristics were modeled at 30 m resolution using a process that performs a comprehensive set of FlamMap runs spanning the full range of weather-related characteristics that occur during a fire season and then integrates those runs into a variety of results based on the likelihood of those weather types occurring. Before the fire intensity modeling, the LANDFIRE 2020 data were updated to reflect fuels disturbances occurring in 2021 and 2022. As such, the fire intensity datasets represent landscape conditions as of the end of 2022. Additional methodology documentation is provided in a methods document (\\Supplements\\WRC_V2_Methods_Landscape-wideRisk.pdf) packaged in the data download.
The specific raster datasets in this publication include:
Risk to Potential Structures (RPS): A measure that integrates wildfire likelihood and intensity with generalized consequences to a home on every pixel. For every place on the landscape, it poses the hypothetical question, \"What would be the relative risk to a house if one existed here?\" This allows comparison of wildfire risk in places where homes already exist to places where new construction may be proposed. This dataset is referred to as Risk to Homes in the Wildfire Risk to Communities web application.
Conditional Risk to Potential Structures (cRPS): The potential consequences of fire to a home at a given location, if a fire occurs there and if a home were located there. Referred to as Wildfire Consequence in the Wildfire Risk to Communities web application.
Exposure Type: Exposure is the spatial coincidence of wildfire likelihood and intensity with communities. This layer delineates where homes are directly exposed to wildfire from adjacent wildland vegetation, indirectly exposed to wildfire from indirect sources such as embers and home-to-home ignition, or not exposed to wildfire due to distance from direct and indirect ignition sources. Burn Probability (BP): The annual probability of wildfire burning in a specific location. Referred to as Wildfire Likelihood in the Wildfire Risk to Communities web application.
Conditional Flame Length (CFL): The mean flame length for a fire burning in the direction of maximum spread (headfire) at a given location if a fire were to occur; an average measure of wildfire intensity.
Flame Length Exceedance Probability - 4 ft (FLEP4): The conditional probability that flame length at a pixel will exceed 4 feet if a fire occurs; indicates the potential for moderate to high wildfire intensity.
Flame Length Exceedance Probability - 8 ft (FLEP8): the conditional probability that flame length at a pixel will exceed 8 feet if a fire occurs; indicates the potential for high wildfire intensity. Wildfire
Hazard Potential (WHP): An index that quantifies the relative potential for wildfire that may be difficult to manage, used as a measure to help prioritize where fuel treatments may be needed.
Additional methodology documentation is provided with the data publication download. (https://www.fs.usda.gov/rds/archive/Catalog/RDS-2020-0016-2)
Note: Pixel values in this image service have been altered from the original raster dataset due to data requirements in web services. The service is intended primarily for data visualization. Relative values and spatial patterns have been largely preserved in the service, but users are encouraged to download the source data for quantitative analysis.
This data publication includes Community Wildfire Risk Reduction Zone rasters, products delivered as part of the Wildfire Risk to Communities project. There are two types of data included: 1) raster spatial data that delineate Community Wildfire Risk Reduction Zones for all populated areas in the continental United States (CONUS), Alaska, and Hawaii; and 2) tabular summaries by communities, counties, tribal areas, and states of wildfire hazard and risk produced as part of the Wildfire Risk to Communities (WRC) project.
The Community Wildfire Risk Reduction Zones (CWiRRZ) product is a 30-m raster delineating areas where mitigation activities will be most effective at protecting homes from most types of wildfire. The zones are determined by the spatial coincidence of wildfire likelihood (Burn Probability), and populated areas. There are four Risk Reduction Zones: Minimal Exposure Zone, Indirect Exposure Zone, Direct Exposure Zone, and Wildfire Transmission Zone. However, the CWiRRZ raster can be further deconstructed into ten zones, wherein the Wildfire Transmission Zone is separated into the following surface fuel types: Tree, Shrub, Grass, Agriculture, Non-Vegetated, Water, and Outlying Wildlands (area beyond 2400-m from buildings).
Additional methodology documentation is provided with the data publication download. (https://www.fs.usda.gov/rds/archive/Catalog/RDS-2024-0030)
Note: Pixel values in this image service have been altered from the original raster dataset due to data requirements in web services. The service is intended primarily for data visualization. Relative values and spatial patterns have been largely preserved in the service, but users are encouraged to download the source data for quantitative analysis.
This data publication includes Community Wildfire Risk Reduction Zone rasters, products delivered as part of the Wildfire Risk to Communities project. There are two types of data included: 1) raster spatial data that delineate Community Wildfire Risk Reduction Zones for all populated areas in the continental United States (CONUS), Alaska, and Hawaii; and 2) tabular summaries by communities, counties, tribal areas, and states of wildfire hazard and risk produced as part of the Wildfire Risk to Communities (WRC) project.
The Community Wildfire Risk Reduction Zones (CWiRRZ) product is a 30-m raster delineating areas where mitigation activities will be most effective at protecting homes from most types of wildfire. The zones are determined by the spatial coincidence of wildfire likelihood (Burn Probability), and populated areas. There are four Risk Reduction Zones: Minimal Exposure Zone, Indirect Exposure Zone, Direct Exposure Zone, and Wildfire Transmission Zone. However, the CWiRRZ raster can be further deconstructed into ten zones, wherein the Wildfire Transmission Zone is separated into the following surface fuel types: Tree, Shrub, Grass, Agriculture, Non-Vegetated, Water, and Outlying Wildlands (area beyond 2400-m from buildings).
Additional methodology documentation is provided with the data publication download. (https://www.fs.usda.gov/rds/archive/Catalog/RDS-2024-0030)
Note: Pixel values in this image service have been altered from the original raster dataset due to data requirements in web services. The service is intended primarily for data visualization. Relative values and spatial patterns have been largely preserved in the service, but users are encouraged to download the source data for quantitative analysis.
The data included in this publication depict components of wildfire risk specifically for populated areas in the United States. These datasets represent where people live in the United States and the in situ risk from wildfire, i.e., the risk at the location where the adverse effects take place.
National wildfire hazard datasets of annual burn probability and fire intensity, generated by the USDA Forest Service, Rocky Mountain Research Station and Pyrologix LLC, form the foundation of the Wildfire Risk to Communities data. Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were used as input to two different but related geospatial fire simulation systems. Annual burn probability was produced with the USFS geospatial fire simulator (FSim) at a relatively coarse cell size of 270 meters (m). To bring the burn probability raster data down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30 m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability into developed areas represented in LANDFIRE fuels data as non-burnable. Burn probability rasters represent landscape conditions as of the end of 2020. Fire intensity characteristics were modeled at 30 m resolution using a process that performs a comprehensive set of FlamMap runs spanning the full range of weather-related characteristics that occur during a fire season and then integrates those runs into a variety of results based on the likelihood of those weather types occurring. Before the fire intensity modeling, the LANDFIRE 2020 data were updated to reflect fuels disturbances occurring in 2021 and 2022. As such, the fire intensity datasets represent landscape conditions as of the end of 2022. The data products in this publication that represent where people live, reflect 2021 estimates of housing unit and population counts from the U.S. Census Bureau, combined with building footprint data from Onegeo and USA Structures, both reflecting 2022 conditions.
The specific raster datasets included in this publication include:
Building Count: Building Count is a 30-m raster representing the count of buildings in the building footprint dataset located within each 30-m pixel.
Building Density: Building Density is a 30-m raster representing the density of buildings in the building footprint dataset (buildings per square kilometer [km²]).
Building Coverage: Building Coverage is a 30-m raster depicting the percentage of habitable land area covered by building footprints.
Population Count (PopCount): PopCount is a 30-m raster with pixel values representing residential population count (persons) in each pixel.
Population Density (PopDen): PopDen is a 30-m raster of residential population density (people/km²).
Housing Unit Count (HUCount): HUCount is a 30-m raster representing the number of housing units in each pixel.
Housing Unit Density (HUDen): HUDen is a 30-m raster of housing-unit density (housing units/km²).
Housing Unit Exposure (HUExposure): HUExposure is a 30-m raster that represents the expected number of housing units within a pixel potentially exposed to wildfire in a year. This is a long-term annual average and not intended to represent the actual number of housing units exposed in any specific year.
Housing Unit Impact (HUImpact): HUImpact is a 30-m raster that represents the relative potential impact of fire to housing units at any pixel, if a fire were to occur. It is an index that incorporates the general consequences of fire on a home as a function of fire intensity and uses flame length probabilities from wildfire modeling to capture likely intensity of fire.
Housing Unit Risk (HURisk): HURisk is a 30-m raster that integrates all four primary elements of wildfire risk - likelihood, intensity, susceptibility, and exposure - on pixels where housing unit density is greater than zero.
Additional methodology documentation is provided with the data publication download. Metadata and Downloads: (https://www.fs.usda.gov/rds/archive/catalog/RDS-2020-0060-2).
Note: Pixel values in this image service have been altered from the original raster dataset due to data requirements in web services. The service is intended primarily for data visualization. Relative values and spatial patterns have been largely preserved in the service, but users are encouraged to download the source data for quantitative analysis.
The data included in this publication depict components of wildfire risk specifically for populated areas in the United States. These datasets represent where people live in the United States and the in situ risk from wildfire, i.e., the risk at the location where the adverse effects take place.
National wildfire hazard datasets of annual burn probability and fire intensity, generated by the USDA Forest Service, Rocky Mountain Research Station and Pyrologix LLC, form the foundation of the Wildfire Risk to Communities data. Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were used as input to two different but related geospatial fire simulation systems. Annual burn probability was produced with the USFS geospatial fire simulator (FSim) at a relatively coarse cell size of 270 meters (m). To bring the burn probability raster data down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30 m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability into developed areas represented in LANDFIRE fuels data as non-burnable. Burn probability rasters represent landscape conditions as of the end of 2020. Fire intensity characteristics were modeled at 30 m resolution using a process that performs a comprehensive set of FlamMap runs spanning the full range of weather-related characteristics that occur during a fire season and then integrates those runs into a variety of results based on the likelihood of those weather types occurring. Before the fire intensity modeling, the LANDFIRE 2020 data were updated to reflect fuels disturbances occurring in 2021 and 2022. As such, the fire intensity datasets represent landscape conditions as of the end of 2022. The data products in this publication that represent where people live, reflect 2021 estimates of housing unit and population counts from the U.S. Census Bureau, combined with building footprint data from Onegeo and USA Structures, both reflecting 2022 conditions.
The specific raster datasets included in this publication include:
Building Count: Building Count is a 30-m raster representing the count of buildings in the building footprint dataset located within each 30-m pixel.
Building Density: Building Density is a 30-m raster representing the density of buildings in the building footprint dataset (buildings per square kilometer [km²]).
Building Coverage: Building Coverage is a 30-m raster depicting the percentage of habitable land area covered by building footprints.
Population Count (PopCount): PopCount is a 30-m raster with pixel values representing residential population count (persons) in each pixel.
Population Density (PopDen): PopDen is a 30-m raster of residential population density (people/km²).
Housing Unit Count (HUCount): HUCount is a 30-m raster representing the number of housing units in each pixel.
Housing Unit Density (HUDen): HUDen is a 30-m raster of housing-unit density (housing units/km²).
Housing Unit Exposure (HUExposure): HUExposure is a 30-m raster that represents the expected number of housing units within a pixel potentially exposed to wildfire in a year. This is a long-term annual average and not intended to represent the actual number of housing units exposed in any specific year.
Housing Unit Impact (HUImpact): HUImpact is a 30-m raster that represents the relative potential impact of fire to housing units at any pixel, if a fire were to occur. It is an index that incorporates the general consequences of fire on a home as a function of fire intensity and uses flame length probabilities from wildfire modeling to capture likely intensity of fire.
Housing Unit Risk (HURisk): HURisk is a 30-m raster that integrates all four primary elements of wildfire risk - likelihood, intensity, susceptibility, and exposure - on pixels where housing unit density is greater than zero.
Additional methodology documentation is provided with the data publication download. Metadata and Downloads: (https://www.fs.usda.gov/rds/archive/catalog/RDS-2020-0060-2).
Note: Pixel values in this image service have been altered from the original raster dataset due to data requirements in web services. The service is intended primarily for data visualization. Relative values and spatial patterns have been largely preserved in the service, but users are encouraged to download the source data for quantitative analysis.
The data included in this publication depict components of wildfire risk specifically for populated areas in the United States. These datasets represent where people live in the United States and the in situ risk from wildfire, i.e., the risk at the location where the adverse effects take place.
National wildfire hazard datasets of annual burn probability and fire intensity, generated by the USDA Forest Service, Rocky Mountain Research Station and Pyrologix LLC, form the foundation of the Wildfire Risk to Communities data. Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were used as input to two different but related geospatial fire simulation systems. Annual burn probability was produced with the USFS geospatial fire simulator (FSim) at a relatively coarse cell size of 270 meters (m). To bring the burn probability raster data down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30 m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability into developed areas represented in LANDFIRE fuels data as non-burnable. Burn probability rasters represent landscape conditions as of the end of 2020. Fire intensity characteristics were modeled at 30 m resolution using a process that performs a comprehensive set of FlamMap runs spanning the full range of weather-related characteristics that occur during a fire season and then integrates those runs into a variety of results based on the likelihood of those weather types occurring. Before the fire intensity modeling, the LANDFIRE 2020 data were updated to reflect fuels disturbances occurring in 2021 and 2022. As such, the fire intensity datasets represent landscape conditions as of the end of 2022. The data products in this publication that represent where people live, reflect 2021 estimates of housing unit and population counts from the U.S. Census Bureau, combined with building footprint data from Onegeo and USA Structures, both reflecting 2022 conditions.
The specific raster datasets included in this publication include:
Building Count: Building Count is a 30-m raster representing the count of buildings in the building footprint dataset located within each 30-m pixel.
Building Density: Building Density is a 30-m raster representing the density of buildings in the building footprint dataset (buildings per square kilometer [km²]).
Building Coverage: Building Coverage is a 30-m raster depicting the percentage of habitable land area covered by building footprints.
Population Count (PopCount): PopCount is a 30-m raster with pixel values representing residential population count (persons) in each pixel.
Population Density (PopDen): PopDen is a 30-m raster of residential population density (people/km²).
Housing Unit Count (HUCount): HUCount is a 30-m raster representing the number of housing units in each pixel.
Housing Unit Density (HUDen): HUDen is a 30-m raster of housing-unit density (housing units/km²).
Housing Unit Exposure (HUExposure): HUExposure is a 30-m raster that represents the expected number of housing units within a pixel potentially exposed to wildfire in a year. This is a long-term annual average and not intended to represent the actual number of housing units exposed in any specific year.
Housing Unit Impact (HUImpact): HUImpact is a 30-m raster that represents the relative potential impact of fire to housing units at any pixel, if a fire were to occur. It is an index that incorporates the general consequences of fire on a home as a function of fire intensity and uses flame length probabilities from wildfire modeling to capture likely intensity of fire.
Housing Unit Risk (HURisk): HURisk is a 30-m raster that integrates all four primary elements of wildfire risk - likelihood, intensity, susceptibility, and exposure - on pixels where housing unit density is greater than zero.
Additional methodology documentation is provided with the data publication download. (https://www.fs.usda.gov/rds/archive/catalog/RDS-2020-0060-2).
Note: Pixel values in this image service have been altered from the original raster dataset due to data requirements in web services. The service is intended primarily for data visualization. Relative values and spatial patterns have been largely preserved in the service, but users are encouraged to download the source data for quantitative analysis.
The data included in this publication depict components of wildfire risk specifically for populated areas in the United States. These datasets represent where people live in the United States and the in situ risk from wildfire, i.e., the risk at the location where the adverse effects take place.
National wildfire hazard datasets of annual burn probability and fire intensity, generated by the USDA Forest Service, Rocky Mountain Research Station and Pyrologix LLC, form the foundation of the Wildfire Risk to Communities data. Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were used as input to two different but related geospatial fire simulation systems. Annual burn probability was produced with the USFS geospatial fire simulator (FSim) at a relatively coarse cell size of 270 meters (m). To bring the burn probability raster data down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30 m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability into developed areas represented in LANDFIRE fuels data as non-burnable. Burn probability rasters represent landscape conditions as of the end of 2020. Fire intensity characteristics were modeled at 30 m resolution using a process that performs a comprehensive set of FlamMap runs spanning the full range of weather-related characteristics that occur during a fire season and then integrates those runs into a variety of results based on the likelihood of those weather types occurring. Before the fire intensity modeling, the LANDFIRE 2020 data were updated to reflect fuels disturbances occurring in 2021 and 2022. As such, the fire intensity datasets represent landscape conditions as of the end of 2022. The data products in this publication that represent where people live, reflect 2021 estimates of housing unit and population counts from the U.S. Census Bureau, combined with building footprint data from Onegeo and USA Structures, both reflecting 2022 conditions.
The specific raster datasets included in this publication include:
Building Count: Building Count is a 30-m raster representing the count of buildings in the building footprint dataset located within each 30-m pixel.
Building Density: Building Density is a 30-m raster representing the density of buildings in the building footprint dataset (buildings per square kilometer [km²]).
Building Coverage: Building Coverage is a 30-m raster depicting the percentage of habitable land area covered by building footprints.
Population Count (PopCount): PopCount is a 30-m raster with pixel values representing residential population count (persons) in each pixel.
Population Density (PopDen): PopDen is a 30-m raster of residential population density (people/km²).
Housing Unit Count (HUCount): HUCount is a 30-m raster representing the number of housing units in each pixel.
Housing Unit Density (HUDen): HUDen is a 30-m raster of housing-unit density (housing units/km²).
Housing Unit Exposure (HUExposure): HUExposure is a 30-m raster that represents the expected number of housing units within a pixel potentially exposed to wildfire in a year. This is a long-term annual average and not intended to represent the actual number of housing units exposed in any specific year.
Housing Unit Impact (HUImpact): HUImpact is a 30-m raster that represents the relative potential impact of fire to housing units at any pixel, if a fire were to occur. It is an index that incorporates the general consequences of fire on a home as a function of fire intensity and uses flame length probabilities from wildfire modeling to capture likely intensity of fire.
Housing Unit Risk (HURisk): HURisk is a 30-m raster that integrates all four primary elements of wildfire risk - likelihood, intensity, susceptibility, and exposure - on pixels where housing unit density is greater than zero.
Additional methodology documentation is provided with the data publication download. (https://www.fs.usda.gov/rds/archive/catalog/RDS-2020-0060-2).
Note: Pixel values in this image service have been altered from the original raster dataset due to data requirements in web services. The service is intended primarily for data visualization. Relative values and spatial patterns have been largely preserved in the service, but users are encouraged to download the source data for quantitative analysis.
The data included in this publication depict the 2024 version of components of wildfire risk for all lands in the United States that: 1) are landscape-wide (i.e., measurable at every pixel across the landscape); and 2) represent in situ risk - risk at the location where the adverse effects take place on the landscape.
National wildfire hazard datasets of annual burn probability and fire intensity, generated by the USDA Forest Service, Rocky Mountain Research Station and Pyrologix LLC, form the foundation of the Wildfire Risk to Communities data. Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were used as input to two different but related geospatial fire simulation systems. Annual burn probability was produced with the USFS geospatial fire simulator (FSim) at a relatively coarse cell size of 270 meters (m). To bring the burn probability raster data down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30 m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability into developed areas represented in LANDFIRE fuels data as non-burnable. Burn probability rasters represent landscape conditions as of the end of 2020. Fire intensity characteristics were modeled at 30 m resolution using a process that performs a comprehensive set of FlamMap runs spanning the full range of weather-related characteristics that occur during a fire season and then integrates those runs into a variety of results based on the likelihood of those weather types occurring. Before the fire intensity modeling, the LANDFIRE 2020 data were updated to reflect fuels disturbances occurring in 2021 and 2022. As such, the fire intensity datasets represent landscape conditions as of the end of 2022. Additional methodology documentation is provided in a methods document (\\Supplements\\WRC_V2_Methods_Landscape-wideRisk.pdf) packaged in the data download.
The specific raster datasets in this publication include:
Risk to Potential Structures (RPS): A measure that integrates wildfire likelihood and intensity with generalized consequences to a home on every pixel. For every place on the landscape, it poses the hypothetical question, \"What would be the relative risk to a house if one existed here?\" This allows comparison of wildfire risk in places where homes already exist to places where new construction may be proposed. This dataset is referred to as Risk to Homes in the Wildfire Risk to Communities web application.
Conditional Risk to Potential Structures (cRPS): The potential consequences of fire to a home at a given location, if a fire occurs there and if a home were located there. Referred to as Wildfire Consequence in the Wildfire Risk to Communities web application.
Exposure Type: Exposure is the spatial coincidence of wildfire likelihood and intensity with communities. This layer delineates where homes are directly exposed to wildfire from adjacent wildland vegetation, indirectly exposed to wildfire from indirect sources such as embers and home-to-home ignition, or not exposed to wildfire due to distance from direct and indirect ignition sources.
Burn Probability (BP): The annual probability of wildfire burning in a specific location. Referred to as Wildfire Likelihood in the Wildfire Risk to Communities web application.
Conditional Flame Length (CFL): The mean flame length for a fire burning in the direction of maximum spread (headfire) at a given location if a fire were to occur; an average measure of wildfire intensity.
Flame Length Exceedance Probability - 4 ft (FLEP4): The conditional probability that flame length at a pixel will exceed 4 feet if a fire occurs; indicates the potential for moderate to high wildfire intensity.
Flame Length Exceedance Probability - 8 ft (FLEP8): the conditional probability that flame length at a pixel will exceed 8 feet if a fire occurs; indicates the potential for high wildfire intensity.
Wildfire Hazard Potential (WHP): An index that quantifies the relative potential for wildfire that may be difficult to manage, used as a measure to help prioritize where fuel treatments may be needed.
Additional methodology documentation is provided with the data publication download (https://www.fs.usda.gov/rds/archive/Catalog/RDS-2020-0016-2)
Note: Pixel values in this image service have been altered from the original raster dataset due to data requirements in web services. The service is intended primarily for data visualization. Relative values and spatial patterns have been largely preserved in the service, but users are encouraged to download the source data for quantitative analysis.
Emergency Situation Determination (ESD) lands, per the Secretary's Memo 1078-006: Increasing Timber Production and Designating an Emergency Situation on National Forest System Lands, have been designated by either being at risk from insect and diseases under the Healthy Forest Restoration Act, or have a high or very high wildfire hazard potential. Only Forest Service owned lands are included in this feature. https://data.fs.usda.gov/geodata/edw/edw_resources/meta/BdyDesg_HFRA_EmergencySituationDetermination.xml
This is a collection of Digital Surface Models and Highest Hit rasters covering selected U.S. Forest Service and adjoining lands in the Southwest Region, encompassing Arizona and New Mexico. The data are presented in a time-enabled format, allowing the end-user to view available data year-by-year, or all available years at once, within a GIS system. The data encompass varying years, varying resolutions, and varying geographic extents, dependent upon available data as provided by the region. DSM and Highest Hit rasters represent elevation of Earth's surface, including its natural and human-made features, such as vegetation and buildings.
The data contains an attribute table. Notable attributes that may be of interest to an end-user are:
lowps: the pixel size of the source raster, given in meters.
highps: the pixel size of the top-most pyramid for the raster, given in meters.
beginyear: the first year of data acquisition for an individual dataset.
endyear: the final year of data acquisition for an individual dataset.
dataset_name: the name of the individual dataset within the collection.
metadata: A URL link to a file on IIPP's Portal containing metadata pertaining to an individual dataset within the image service.
resolution: The pixel size of the source raster, given in meters.
Terrain-related imagery are primarily derived from Lidar, stereoscopic aerial imagery, or Interferometric Synthetic Aperture Radar datasets. Consequently, these derivatives inherit the limitations and uncertainties of the parent sensor and platform and the processing techniques used to produce the imagery. The terrain images are orthographic; they have been georeferenced and displacement due to sensor orientation and topography have been removed, producing data that combines the characteristics of an image with the geometric qualities of a map. The orthographic images show ground features in their proper positions, without the distortion characteristic of unrectified aerial or satellite imagery. Digital orthoimages produced and used within the Forest Service are developed from imagery acquired through various national and regional image acquisition programs. The resulting orthoimages can be directly applied in remote sensing, GIS and mapping applications. They serve a variety of purposes, from interim maps to references for Earth science investigations and analysis. Because of the orthographic property, an orthoimage can be used like a map for measurement of distances, angles, and areas with scale being constant everywhere. Also, they can be used as map layers in GIS or other computer-based manipulation, overlaying, and analysis. An orthoimage differs from a map in a manner of depiction of detail; on a map only selected detail is shown by conventional symbols whereas on an orthoimage all details appear just as in original aerial or satellite imagery.
Tribal lands have been masked from this public service in accordance with Tribal agreements.
", "keyword": [ "Digital Surface Model", "DSM", "Highest Hit", "USFS", "Forest Service", "Geospatial Technology & Applications Center", "GTAC", "IIPP", "Interdepartmental Imagery Publication Platform", "Southwest Region", "Region 3", "R3", "NM", "New Mexico", "Carson", "Hermits Peak", "Calf Canyon", "Enchanted Circle", "2016", "2017", "2023", "2024" ], "issued": "2025-04-17T16:17:56.000Z", "modified": "2025-04-17T16:31:49.000Z", "publisher": { "name": "U.S. Forest Service", "source": "U.S. Forest Service" }, "contactPoint": { "@type": "vcard:Contact", "fn": "GTAC_Image_Services", "hasEmail": "mailto:SM.FS.data@usda.gov" }, "accessLevel": "public", "spatial": "0.0000,0.0000,0.0000,0.0000", "license": "https://creativecommons.org/licenses/by/4.0/", "programCode": [ "005:059" ], "bureauCode": [ "005:96" ], "theme": [ "geospatial" ], "progressCode": "onGoing", "distribution": [ { "@type": "dcat:Distribution", "title": "ISO-19139 metadata", "conformsTo": "https://www.isotc211.org/2005/gmi", "mediaType": "text/xml", "accessURL": "https://www.arcgis.com/sharing/rest/content/items/5543b4066fdb49dba232d0dce4ad43ab/info/metadata/metadata.xml?format=iso19139" }, { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://data-usfs.hub.arcgis.com/maps/usfs::dsm-multiyear-usfs-r3-southwest-multires-public-1" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://imagery.geoplatform.gov/iipp/services/Vegetation_Structure/DSM_MultiYear_USFS_R3_Southwest_multiRes_Public/ImageServer/WMSServer" } ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=632c16b772154032b16f149dc5e7940a", "landingPage": "https://data-usfs.hub.arcgis.com/datasets/usfs::dsm-multiyear-usfs-r3-southwest-multires-public", "title": "DSM MultiYear USFS R3 Southwest multiRes Public", "description": "This is a collection of Digital Surface Models and Highest Hit rasters covering selected U.S. Forest Service and adjoining lands in the Southwest Region, encompassing Arizona and New Mexico. The data are presented in a time-enabled format, allowing the end-user to view available data year-by-year, or all available years at once, within a GIS system. The data encompass varying years, varying resolutions, and varying geographic extents, dependent upon available data as provided by the region. DSM and Highest Hit rasters represent elevation of Earth's surface, including its natural and human-made features, such as vegetation and buildings.
The data contains an attribute table. Notable attributes that may be of interest to an end-user are:
lowps: the pixel size of the source raster, given in meters.
highps: the pixel size of the top-most pyramid for the raster, given in meters.
beginyear: the first year of data acquisition for an individual dataset.
endyear: the final year of data acquisition for an individual dataset.
dataset_name: the name of the individual dataset within the collection.
metadata: A URL link to a file on IIPP's Portal containing metadata pertaining to an individual dataset within the image service.
resolution: The pixel size of the source raster, given in meters.
Terrain-related imagery are primarily derived from Lidar, stereoscopic aerial imagery, or Interferometric Synthetic Aperture Radar datasets. Consequently, these derivatives inherit the limitations and uncertainties of the parent sensor and platform and the processing techniques used to produce the imagery. The terrain images are orthographic; they have been georeferenced and displacement due to sensor orientation and topography have been removed, producing data that combines the characteristics of an image with the geometric qualities of a map. The orthographic images show ground features in their proper positions, without the distortion characteristic of unrectified aerial or satellite imagery. Digital orthoimages produced and used within the Forest Service are developed from imagery acquired through various national and regional image acquisition programs. The resulting orthoimages can be directly applied in remote sensing, GIS and mapping applications. They serve a variety of purposes, from interim maps to references for Earth science investigations and analysis. Because of the orthographic property, an orthoimage can be used like a map for measurement of distances, angles, and areas with scale being constant everywhere. Also, they can be used as map layers in GIS or other computer-based manipulation, overlaying, and analysis. An orthoimage differs from a map in a manner of depiction of detail; on a map only selected detail is shown by conventional symbols whereas on an orthoimage all details appear just as in original aerial or satellite imagery.
Tribal lands have been masked from this public service in accordance with Tribal agreements.
This is a collection of Canopy Height rasters covering selected U.S. Forest Service and adjoining lands in the Southwest Region, encompassing Arizona and New Mexico. The data are presented in a time-enabled format, allowing the end-user to view available data year-by-year, or all available years at once, within a GIS system. The data encompass varying years, varying resolutions, and varying geographic extents, dependent upon available data as provided by the region. The data represents the height of vegetation above ground, measured in meters.
The data contains an attribute table. Notable attributes that may be of interest to an end-user are:
lowps: the pixel size of the source raster, given in meters.
highps: the pixel size of the top-most pyramid for the raster, given in meters.
beginyear: the first year of data acquisition for an individual dataset.
endyear: the final year of data acquisition for an individual dataset.
dataset_name: the name of the individual dataset within the collection.
metadata: A URL link to a file on IIPP's Portal containing metadata pertaining to an individual dataset within the image service.
resolution: The pixel size of the source raster, given in meters.
Canopy Height data are primarily derived from Lidar datasets. Consequently, these derivatives inherit the limitations and uncertainties of the parent sensor and platform and the processing techniques used to produce the imagery. The images are orthographic; they have been georeferenced and displacement due to sensor orientation and topography have been removed, producing data that combines the characteristics of an image with the geometric qualities of a map. The orthographic images show ground features in their proper positions, without the distortion characteristic of unrectified aerial or satellite imagery. Digital orthoimages produced and used within the Forest Service are developed from imagery acquired through various national and regional image acquisition programs. The resulting orthoimages can be directly applied in remote sensing, GIS and mapping applications. They serve a variety of purposes, from interim maps to references for Earth science investigations and analysis. Because of the orthographic property, an orthoimage can be used like a map for measurement of distances, angles, and areas with scale being constant everywhere. Also, they can be used as map layers in GIS or other computer-based manipulation, overlaying, and analysis. An orthoimage differs from a map in a manner of depiction of detail; on a map only selected detail is shown by conventional symbols whereas on an orthoimage all details appear just as in original aerial or satellite imagery.
Tribal lands have been masked from this public service in accordance with Tribal agreements.
", "keyword": [ "Canopy Height", "CHM", "USFS", "Forest Service", "Geospatial Technology & Applications Center", "GTAC", "IIPP", "Interdepartmental Imagery Publication Platform", "Southwest Region", "Region 3", "R3", "NM", "New Mexico", "Carson", "Santa Fe", "Hermits Peak", "2014", "2017", "2022", "2023" ], "issued": "2025-04-17T16:03:59.000Z", "modified": "2025-04-17T16:08:33.000Z", "publisher": { "name": "U.S. Forest Service", "source": "U.S. Forest Service" }, "contactPoint": { "@type": "vcard:Contact", "fn": "GTAC_Image_Services", "hasEmail": "mailto:SM.FS.data@usda.gov" }, "accessLevel": "public", "spatial": "0.0000,0.0000,0.0000,0.0000", "license": "https://creativecommons.org/licenses/by/4.0/", "programCode": [ "005:059" ], "bureauCode": [ "005:96" ], "theme": [ "geospatial" ], "progressCode": "onGoing", "distribution": [ { "@type": "dcat:Distribution", "title": "ISO-19139 metadata", "conformsTo": "https://www.isotc211.org/2005/gmi", "mediaType": "text/xml", "accessURL": "https://www.arcgis.com/sharing/rest/content/items/3ae3e741bb3e49e1909b1d20c8afa773/info/metadata/metadata.xml?format=iso19139" }, { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://data-usfs.hub.arcgis.com/maps/usfs::canopyheight-multiyear-usfs-r3-southwest-multires-public-1" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://imagery.geoplatform.gov/iipp/services/Vegetation_Structure/CanopyHeight_multiYear_USFS_R3_Southwest_multiRes_Public/ImageServer/WMSServer" } ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=412723ef52824989815ad5270657ac3f", "landingPage": "https://data-usfs.hub.arcgis.com/datasets/usfs::canopyheight-multiyear-usfs-r3-southwest-multires-public", "title": "CanopyHeight multiYear USFS R3 Southwest multiRes Public", "description": "This is a collection of Canopy Height rasters covering selected U.S. Forest Service and adjoining lands in the Southwest Region, encompassing Arizona and New Mexico. The data are presented in a time-enabled format, allowing the end-user to view available data year-by-year, or all available years at once, within a GIS system. The data encompass varying years, varying resolutions, and varying geographic extents, dependent upon available data as provided by the region. The data represents the height of vegetation above ground, measured in meters.
The data contains an attribute table. Notable attributes that may be of interest to an end-user are:
lowps: the pixel size of the source raster, given in meters.
highps: the pixel size of the top-most pyramid for the raster, given in meters.
beginyear: the first year of data acquisition for an individual dataset.
endyear: the final year of data acquisition for an individual dataset.
dataset_name: the name of the individual dataset within the collection.
metadata: A URL link to a file on IIPP's Portal containing metadata pertaining to an individual dataset within the image service.
resolution: The pixel size of the source raster, given in meters.
Canopy Height data are primarily derived from Lidar datasets. Consequently, these derivatives inherit the limitations and uncertainties of the parent sensor and platform and the processing techniques used to produce the imagery. The images are orthographic; they have been georeferenced and displacement due to sensor orientation and topography have been removed, producing data that combines the characteristics of an image with the geometric qualities of a map. The orthographic images show ground features in their proper positions, without the distortion characteristic of unrectified aerial or satellite imagery. Digital orthoimages produced and used within the Forest Service are developed from imagery acquired through various national and regional image acquisition programs. The resulting orthoimages can be directly applied in remote sensing, GIS and mapping applications. They serve a variety of purposes, from interim maps to references for Earth science investigations and analysis. Because of the orthographic property, an orthoimage can be used like a map for measurement of distances, angles, and areas with scale being constant everywhere. Also, they can be used as map layers in GIS or other computer-based manipulation, overlaying, and analysis. An orthoimage differs from a map in a manner of depiction of detail; on a map only selected detail is shown by conventional symbols whereas on an orthoimage all details appear just as in original aerial or satellite imagery.
Tribal lands have been masked from this public service in accordance with Tribal agreements.
This is a collection of bare-Earth digital elevation models covering selected U.S. Forest Service and adjoining lands in the Southwest Region, encompassing Arizona and New Mexico. The data are presented in a time-enabled format, allowing the end-user to view available data year-by-year, or all available years at once, within a GIS system. The data encompass varying years, varying resolutions, and varying geographic extents, dependent upon available data as provided by the region. Bare-Earth DEMs, also commonly called Digital Terrain Models (DTM), represent the ground topography after removal of persistent objects such as vegetation and buildings, and therefore show the natural terrain.
The data contains an attribute table. Notable attributes that may be of interest to an end-user are:
lowps: the pixel size of the source raster, given in meters.
highps: the pixel size of the top-most pyramid for the raster, given in meters.
beginyear: the first year of data acquisition for an individual dataset.
endyear: the final year of data acquisition for an individual dataset.
dataset_name: the name of the individual dataset within the collection.
metadata: A URL link to a file on IIPP's Portal containing metadata pertaining to an individual dataset within the image service.
resolution: The pixel size of the source raster, given in meters.
Terrain-related imagery are primarily derived from Lidar, stereoscopic aerial imagery, or Interferometric Synthetic Aperture Radar datasets. Consequently, these derivatives inherit the limitations and uncertainties of the parent sensor and platform and the processing techniques used to produce the imagery. The terrain images are orthographic; they have been georeferenced and displacement due to sensor orientation and topography have been removed, producing data that combines the characteristics of an image with the geometric qualities of a map. The orthographic images show ground features in their proper positions, without the distortion characteristic of unrectified aerial or satellite imagery. Digital orthoimages produced and used within the Forest Service are developed from imagery acquired through various national and regional image acquisition programs. The resulting orthoimages can be directly applied in remote sensing, GIS and mapping applications. They serve a variety of purposes, from interim maps to references for Earth science investigations and analysis. Because of the orthographic property, an orthoimage can be used like a map for measurement of distances, angles, and areas with scale being constant everywhere. Also, they can be used as map layers in GIS or other computer-based manipulation, overlaying, and analysis. An orthoimage differs from a map in a manner of depiction of detail; on a map only selected detail is shown by conventional symbols whereas on an orthoimage all details appear just as in original aerial or satellite imagery.
Tribal lands have been masked from this public service in accordance with Tribal agreements.
", "keyword": [ "Bare Earth", "DEM", "Digital Elevation Model", "DTM", "Digital Terrain Model", "USFS", "Forest Service", "Geospatial Technology & Applications Center", "GTAC", "IIPP", "Interdepartmental Imagery Publication Platform", "Southwest Region", "R3", "Region 3", "New Mexico", "Carson", "Santa Fe", "Hermits Peak", "Enchanted Circle", "2014", "2017", "2022", "2023", "2024" ], "issued": "2025-04-11T18:36:04.000Z", "modified": "2025-04-11T18:38:11.000Z", "publisher": { "name": "U.S. Forest Service", "source": "U.S. Forest Service" }, "contactPoint": { "@type": "vcard:Contact", "fn": "GTAC_Image_Services", "hasEmail": "mailto:SM.FS.data@usda.gov" }, "accessLevel": "public", "spatial": "0.0000,0.0000,0.0000,0.0000", "license": "https://creativecommons.org/licenses/by/4.0/", "programCode": [ "005:059" ], "bureauCode": [ "005:96" ], "theme": [ "geospatial" ], "progressCode": "onGoing", "distribution": [ { "@type": "dcat:Distribution", "title": "ISO-19139 metadata", "conformsTo": "https://www.isotc211.org/2005/gmi", "mediaType": "text/xml", "accessURL": "https://www.arcgis.com/sharing/rest/content/items/e885b1ed333c405a853472cf676bcb1a/info/metadata/metadata.xml?format=iso19139" }, { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://data-usfs.hub.arcgis.com/maps/usfs::bareearthdem-multiyear-usfs-r3-southwest-multires-public-1" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://imagery.geoplatform.gov/iipp/services/Terrain/BareEarthDEM_multiYear_USFS_R3_Southwest_multiRes_Public/ImageServer/WMSServer" } ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=3f7ed8a11d8247f9bc9a0a5dad1794d6", "landingPage": "https://data-usfs.hub.arcgis.com/datasets/usfs::bareearthdem-multiyear-usfs-r3-southwest-multires-public", "title": "BareEarthDEM multiYear USFS R3 Southwest multiRes Public", "description": "This is a collection of bare-Earth digital elevation models covering selected U.S. Forest Service and adjoining lands in the Southwest Region, encompassing Arizona and New Mexico. The data are presented in a time-enabled format, allowing the end-user to view available data year-by-year, or all available years at once, within a GIS system. The data encompass varying years, varying resolutions, and varying geographic extents, dependent upon available data as provided by the region. Bare-Earth DEMs, also commonly called Digital Terrain Models (DTM), represent the ground topography after removal of persistent objects such as vegetation and buildings, and therefore show the natural terrain.
The data contains an attribute table. Notable attributes that may be of interest to an end-user are:
lowps: the pixel size of the source raster, given in meters.
highps: the pixel size of the top-most pyramid for the raster, given in meters.
beginyear: the first year of data acquisition for an individual dataset.
endyear: the final year of data acquisition for an individual dataset.
dataset_name: the name of the individual dataset within the collection.
metadata: A URL link to a file on IIPP's Portal containing metadata pertaining to an individual dataset within the image service.
resolution: The pixel size of the source raster, given in meters.
Terrain-related imagery are primarily derived from Lidar, stereoscopic aerial imagery, or Interferometric Synthetic Aperture Radar datasets. Consequently, these derivatives inherit the limitations and uncertainties of the parent sensor and platform and the processing techniques used to produce the imagery. The terrain images are orthographic; they have been georeferenced and displacement due to sensor orientation and topography have been removed, producing data that combines the characteristics of an image with the geometric qualities of a map. The orthographic images show ground features in their proper positions, without the distortion characteristic of unrectified aerial or satellite imagery. Digital orthoimages produced and used within the Forest Service are developed from imagery acquired through various national and regional image acquisition programs. The resulting orthoimages can be directly applied in remote sensing, GIS and mapping applications. They serve a variety of purposes, from interim maps to references for Earth science investigations and analysis. Because of the orthographic property, an orthoimage can be used like a map for measurement of distances, angles, and areas with scale being constant everywhere. Also, they can be used as map layers in GIS or other computer-based manipulation, overlaying, and analysis. An orthoimage differs from a map in a manner of depiction of detail; on a map only selected detail is shown by conventional symbols whereas on an orthoimage all details appear just as in original aerial or satellite imagery.
Tribal lands have been masked from this public service in accordance with Tribal agreements.
This is a collection of aerial digital orthophotos covering selected U.S. Forest Service and adjoining lands in the Southwest Region, encompassing Arizona and New Mexico. The data are presented in a time-enabled format, allowing the end-user to view available data year-by-year, or all available years at once, within a GIS system. The data encompass varying years from 2000 to the present, varying resolutions, and varying geographic extents, dependent upon available imagery as provided by the region. The data contains four bands, representing red, green, blue, and near-infrared wavelengths, making the data suitable for analysis using either a true-color band combination (red, green, blue) or using a false-color band combination (eg. near-infrared, red, green).
The data contains an attribute table. Notable attributes that may be of interest to an end-user are:
lowps: the pixel size of the source raster, given in meters.
highps: the pixel size of the top-most pyramid for the raster, given in meters.
beginyear: the first year of data acquisition for an individual dataset.
endyear: the final year of data acquisition for an individual dataset.
dataset_name: the name of the individual dataset within the collection.
metadata: A URL link to a file on IIPP's Portal containing metadata pertaining to an individual dataset within the image service.
resolution: The pixel size of the source raster, given in meters.
A digital orthophoto is a georeferenced image prepared from aerial imagery, or other remotely-sensed data in which the displacement within the image due to sensor orientation and terrain relief has been removed. Orthophotos combine the characteristics of an image with the geometric qualities of a map. Orthoimages show ground features such as roads, buildings, and streams in their proper positions, without the distortion characteristic of unrectified aerial imagery. Digital orthoimages produced and used within the Forest Service are developed from imagery acquired through various national and regional image acquisition programs. The resulting orthoimages, also known as orthomaps, can be directly applied in remote sensing, GIS and mapping applications. They serve a variety of purposes, from interim maps to references for earth science investigations and analysis. Because of the orthographic property, an orthoimage can be used like a map for measurement of distances, angles, and areas with scale being constant everywhere. Also, they can be used as map layers in GIS or other computer-based manipulation, overlaying, and analysis. An orthoimage differs from a map in a manner of depiction of detail; on a map only selected detail is shown by conventional symbols, whereas on an orthoimage all details appear just as in original aerial or satellite imagery.
This data depicts terrestrial aerial fire retardant avoidance areas delivered as part of the 2011 Nationwide Aerial Application of Fire Retardant on National Forest System Land Environmental Impact Statement, the 2023 Supplemental Environmental Impact Statement, and the 2024 Record of Decision. This data shows areas, provided by each National Forest, where the aerial application of fire retardant should be avoided in order to prevent the potential of impacts to Federally listed threatened or endangered species as identified through consultation, or Forest Service sensitive species.This data is to be used in planning and implementation phases of U.S. Forest Service fire activities to help prevent intrusions of aerial fire retardant in known areas of TEPCS (Threatened, Endangered, Proposed, Candidate, Sensitive) species throughout National Forest lands. Provided here is a National merged dataset derived from each National Forest contribution. This data has been merged, dissolved, and erased of attributes contained in each original component dataset. For this purpose, specific attributes are not necessary, as any spatial areas depicted simply show areas where aerial fire retardant use is to be avoided as stated in U.S. Forest Service guidelines.
The Infrastructure Investment and Jobs Act and the Inflation Reduction Act include significant funding to execute fuels mitigation projects. Regions submitted proposed project boundaries designed to address community exposure to wildfire. The Executive Leadership Team of the Forest Service selected "Landscapes" for initial investment in fiscal years 2022 and 2023. Additional landscapes may be selected for future action. This dataset documents the official boundary of the landscapes selected for fuels treatment activities in the Wildfire Crisis Strategy. The public-facing version of these boundaries is called Wildfire Crisis Strategy Landscapes. This Dataset is current from June 9, 2023 to Present.
", "keyword": [ "IIJA", "boundaries", "Fuels Treatment", "BIL", "Wildfire Exposure", "Wildfire", "Open Data", "IRA" ], "issued": "2022-09-27T22:13:28.000Z", "modified": "2023-06-09T00:00:00.000Z", "publisher": { "name": "U.S. Forest Service", "source": "U.S. Forest Service" }, "contactPoint": { "@type": "vcard:Contact", "fn": "USFSEnterpriseContent", "hasEmail": "mailto:SM.FS.data@usda.gov" }, "accessLevel": "public", "spatial": "-124.0981,32.4414,-104.6818,49.0004", "license": "https://creativecommons.org/licenses/by/4.0/", "programCode": [ "005:059" ], "bureauCode": [ "005:96" ], "theme": [ "geospatial" ], "progressCode": "onGoing", "distribution": [ { "@type": "dcat:Distribution", "title": "ISO-19139 metadata", "conformsTo": "https://www.isotc211.org/2005/gmi", "mediaType": "text/xml", "accessURL": "https://www.arcgis.com/sharing/rest/content/items/caa2789ec1a74f3ea29034417316703a/info/metadata/metadata.xml?format=iso19139" }, { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://data-usfs.hub.arcgis.com/datasets/usfs::wildfire-crisis-strategy-landscapes-feature-layer" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://apps.fs.usda.gov/arcx/rest/services/EDW/EDW_BILLandscapeInvestments_01/MapServer/0" }, { "@type": "dcat:Distribution", "title": "CSV", "format": "CSV", "mediaType": "text/csv", "accessURL": "https://data-usfs.hub.arcgis.com/api/download/v1/items/caa2789ec1a74f3ea29034417316703a/csv?layers=0" }, { "@type": "dcat:Distribution", "title": "Shapefile", "format": "ZIP", "mediaType": "application/zip", "accessURL": "https://data-usfs.hub.arcgis.com/api/download/v1/items/caa2789ec1a74f3ea29034417316703a/shapefile?layers=0" }, { "@type": "dcat:Distribution", "title": "GeoJSON", "format": "GeoJSON", "mediaType": "application/vnd.geo+json", "accessURL": "https://data-usfs.hub.arcgis.com/api/download/v1/items/caa2789ec1a74f3ea29034417316703a/geojson?layers=0" }, { "@type": "dcat:Distribution", "title": "KML", "format": "KML", "mediaType": "application/vnd.google-earth.kml+xml", "accessURL": "https://data-usfs.hub.arcgis.com/api/download/v1/items/caa2789ec1a74f3ea29034417316703a/kml?layers=0" } ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=3e38347fd53444c994fe4407e9206cd9&sublayer=0", "landingPage": "https://data-usfs.hub.arcgis.com/datasets/usfs::fireshed-registry-fireshed-feature-layer", "title": "Fireshed Registry: Fireshed (Feature Layer)", "description": "The Fireshed Registry is a geospatial dashboard and decision tool built to organize information about wildfire transmission to buildings and monitor progress towards risk reduction for communities from management investments. The concept behind the Fireshed Registry is to identify and map the source of risk rather than what is at risk across all lands in the United States. While the Fireshed Registry was organized around mapping the source of fire risk to communities, the framework does not preclude the assessment of other resource management priorities and trends such as water, fish and aquatic or wildlife habitat, or recreation. The Fireshed Registry is also a multi-scale decision tool for quantifying, prioritizing, and geospatially displaying wildfire transmission to buildings in adjacent or nearby communities. Fireshed areas in the Fireshed Registry are approximately 250,000 acre accounting units that are delineated based on a smoothed building exposure map of the United States. These boundaries were created by dividing up the landscape into regular-sized units that represent similar source levels of community exposure to wildfire risk. Subfiresheds are approximately 25,000 acre accounting units nested within firesheds. Firesheds for the Conterminous U.S., Alaska, and Hawaii were generated in separate research efforts and are published in incremental versions in the Research Data Archive. They are combined here for ease of use.
The Monitoring Trends in Burn Severity (MTBS) Program assesses the frequency, extent, and magnitude (size and severity) of all large wildland fires (including wildfires and prescribed fires) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico from the beginning of the Landsat Thematic Mapper archive to the present. All fires reported as greater than 1,000 acres in the western U.S. and greater than 500 acres in the eastern U.S. are mapped across all ownerships. MTBS produces a series of geospatial and tabular data for analysis at a range of spatial, temporal, and thematic scales and are intended to meet a variety of information needs that require consistent data about fire effects through space and time. This map layer is a vector polygon shapefile of the location of all currently inventoried fires occurring between calendar year 1984 and the current MTBS release for CONUS, Alaska, Hawaii and Puerto Rico. Please visit https://mtbs.gov/announcements to determine the current release. Fires omitted from this mapped inventory are those where suitable satellite imagery was not available or fires were not discernable from available imagery.
This feature class includes States, Counties or Boroughs, Congressional Districts, Alaska Recording Districts, County Subdivisions, and Places boundaries that are derived from the latest official Census Bureau and Alaska Department of Natural Resources datasets. Features within Forest Service Administrative Forest boundaries may have been modified by the Forest Service for improved accuracy and spatial coincidence(vertical integration).
This feature class includes States, Counties or Boroughs, Congressional Districts, Alaska Recording Districts, County Subdivisions, and Places boundaries that are derived from the latest official Census Bureau and Alaska Department of Natural Resources datasets. Features within Forest Service Administrative Forest boundaries may have been modified by the Forest Service for improved accuracy and spatial coincidence(vertical integration).
The USDA Forest Service Rapid Assessment of Vegetation Condition after Wildfire (RAVG) program produces geospatial and related data representing post-fire vegetation condition by means of standardized change detection methods based on Landsat or similar multispectral satellite imagery. RAVG data products characterize the impact of disturbance (fire) on vegetation within a fire perimeter, and include estimates of percent change in live basal area (BA), percent change in canopy cover (CC), and the standardized composite burn index (CBI). Standard thematic products include 7-class percent change in basal area (BA-7), 5-class percent change in canopy cover (CC-5), and 4-class CBI (CBI-4). Contingent upon the availability of suitable imagery, RAVG products are prepared for all wildland fires reported within the conterminous United States (CONUS) that include at least 1000 acres of forested National Forest System (NFS) land (500 acres for Regions 8 and 9 as of 2016). Data for individual fires are typically made available within 45 days after fire containment (\"initial assessments\"). Late-season fires, however, may be deferred until the following spring or summer (\"extended assessments\"). Annual national mosaics of each thematic product are prepared at the end of the fire season and updated, as needed, when additional fires from the given year are processed. The annual mosaics are available via the Raster Data Warehouse (RDW, see https://apps.fs.usda.gov/arcx/rest/services/RDW_Wildfire). A combined perimeter dataset, including the burn boundaries for all published Forest Service RAVG fires from 2012 to the present, is likewise updated as needed (at least annually). This current dataset is derived from the combined perimeter dataset and adds spatial information about land ownership (National Forest) and wilderness status, as well as the areal extent of forested land (pre-fire) that experience a modeled BA loss above 50 and 75 percent.
", "keyword": [ "Basal Area", "Burn Severity", "Canopy Cover", "CBI", "Composite Burn Index", "CONUS", "Perimeter", "RAVG", "United States", "Vegetation Condition", "Wildfire", "Wildland Fire", "Biota", "Burn Area Boundary" ], "issued": "2017-03-27T22:01:27.000Z", "modified": "2024-05-31T15:29:09.000Z", "publisher": { "name": "U.S. Forest Service", "source": "USFS Chief Information Office, Enterprise Data Warehouse" }, "contactPoint": { "@type": "vcard:Contact", "fn": "USFSEnterpriseContent", "hasEmail": "mailto:SM.FS.data@usda.gov" }, "accessLevel": "public", "spatial": "-180.0000,-14.6018,180.0000,71.9996", "license": "https://creativecommons.org/licenses/by/4.0/", "programCode": [ "005:059" ], "bureauCode": [ "005:96" ], "theme": [ "geospatial" ], "progressCode": "onGoing", "distribution": [ { "@type": "dcat:Distribution", "title": "ISO-19139 metadata", "conformsTo": "https://www.isotc211.org/2005/gmi", "mediaType": "text/xml", "accessURL": "https://www.arcgis.com/sharing/rest/content/items/726e9f93a04249c7b8b52526d564f300/info/metadata/metadata.xml?format=iso19139" }, { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://data-usfs.hub.arcgis.com/datasets/usfs::geopolitical-units-adjusted-within-administrative-forest-boundaries-congressional-districts-fs-revised-2020-census-feature-layer" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://apps.fs.usda.gov/arcx/rest/services/EDW/EDW_GeopoliticalUnit_01/MapServer/4" }, { "@type": "dcat:Distribution", "title": "CSV", "format": "CSV", "mediaType": "text/csv", "accessURL": "https://data-usfs.hub.arcgis.com/api/download/v1/items/726e9f93a04249c7b8b52526d564f300/csv?layers=4" }, { "@type": "dcat:Distribution", "title": "Shapefile", "format": "ZIP", "mediaType": "application/zip", "accessURL": "https://data-usfs.hub.arcgis.com/api/download/v1/items/726e9f93a04249c7b8b52526d564f300/shapefile?layers=4" }, { "@type": "dcat:Distribution", "title": "GeoJSON", "format": "GeoJSON", "mediaType": "application/vnd.geo+json", "accessURL": "https://data-usfs.hub.arcgis.com/api/download/v1/items/726e9f93a04249c7b8b52526d564f300/geojson?layers=4" }, { "@type": "dcat:Distribution", "title": "KML", "format": "KML", "mediaType": "application/vnd.google-earth.kml+xml", "accessURL": "https://data-usfs.hub.arcgis.com/api/download/v1/items/726e9f93a04249c7b8b52526d564f300/kml?layers=4" } ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=2570889aeb3645b88443da33cb5350e6", "landingPage": "https://data-usfs.hub.arcgis.com/maps/usfs::geopolitical-units-adjusted-within-administrative-forest-boundaries", "title": "Geopolitical Units adjusted within Administrative Forest Boundaries", "description": "This feature class includes States, Counties or Boroughs, Congressional Districts, Alaska Recording Districts, County Subdivisions, and Places boundaries that are derived from the latest official Census Bureau and Alaska Department of Natural Resources datasets. Features within Forest Service Administrative Forest boundaries may have been modified by the Forest Service for improved accuracy and spatial coincidence(vertical integration).
This feature class includes States, Counties or Boroughs, Congressional Districts, Alaska Recording Districts, County Subdivisions, and Places boundaries that are derived from the latest official Census Bureau and Alaska Department of Natural Resources datasets. Features within Forest Service Administrative Forest boundaries may have been modified by the Forest Service for improved accuracy and spatial coincidence(vertical integration).