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The increase in wildfires, particularly in the western U.S., represents one of the greatest threats to multiple native ecosystems. Despite this threat, there is currently no central repository to store both past and current wildfire perimeter data. Currently, wildfire boundaries can only be found in disparate local or national datasets. These datasets are generally restricted to specific locations, fire sizes, or time periods. Our objective was to create a comprehensive national wildfire perimeter dataset by combining all freely available wildfire datasets that we could download. We combined and dissolved individual wildfire polygons from multiple datasets if they were in the same year and overlapped each other or were within 1km of the fire boundary. This combined dataset includes spatial summary statistics such as number of times burned, earliest fire of record, and most recent fire of record.

This dataset represents percent area burned in each burn severity class for wildfires within individual local and accumulated upstream catchments for NHDPlusV2 Waterbodies for each year for 1984-2018.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. See: https://catalog.data.gov/dataset/monitoring-trends-in-burn-severity-burned-area-boundaries-feature-layer-27201 and https://www.mtbs.gov/product-descriptions

This dataset represents percent area burned in each burn severity class for wildfires within individual local and accumulated upstream catchments for NHDPlusV2 Waterbodies for each year for 1984-2018.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. See: https://catalog.data.gov/dataset/monitoring-trends-in-burn-severity-burned-area-boundaries-feature-layer-27201 and https://www.mtbs.gov/product-descriptions

This dataset represents percent area burned in each burn severity class for wildfires within individual local and accumulated upstream catchments for NHDPlusV2 Waterbodies for each year for 1984-2018.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. See: https://catalog.data.gov/dataset/monitoring-trends-in-burn-severity-burned-area-boundaries-feature-layer-27201 and https://www.mtbs.gov/product-descriptions

This dataset represents percent area burned in each burn severity class for wildfires within individual local and accumulated upstream catchments for NHDPlusV2 Waterbodies for each year for 1984-2018.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. See: https://catalog.data.gov/dataset/monitoring-trends-in-burn-severity-burned-area-boundaries-feature-layer-27201 and https://www.mtbs.gov/product-descriptions

LANDFIRE's 2023 Update (LF 2023) Forest Canopy Cover (CC) describes the percent cover of the tree canopy in a stand. CC is a vertical projection of the tree canopy cover onto an imaginary horizontal plane. In fire behavior models, CC supplies information to determine the probability of crown fire initiation, provides input in the spotting model, and aids in calculating wind reductions and fuel moisture conditioning. To create CC, LANDFIRE's Existing Vegetation Cover (EVC) product must be produced first. EVC is a continuous scaled product which assigns cover to all life forms in the LF data. CC is then derived from EVC by assigning bins of 10% for fuel production and use in fire behavior software. CC is used in the calculation of Forest Canopy Bulk Density (CBD) and Base Height (CBH). To designate disturbed areas where CC is modified, the aggregated Annual Disturbance products from 2014 to 2023 in the LF Fuel Disturbance (FDist) product are used. All existing disturbances between 2014-2023 are represented in LF 2023, and the products are intended to be used in 2024 (the year of release). When using any product from the LF 2023 fuel product suite, users should consider adjusting fuel layers for disturbances that occurred after the end of the 2023 fiscal year (after October 1st, 2023). Disturbances that occurred after the end of the 2023 fiscal year are not accounted for within LF 2023 fuel products. Learn more about LF 2023 at https://landfire.gov/data/lf2023.

LANDFIRE's (LF) 2022 update (LF 2022) Existing Vegetation Cover (EVC) represents the vertically projected percent cover of the live canopy for a 30-m cell. EVC is produced separately for tree, shrub, and herbaceous lifeforms. Training data depicting percentages of canopy cover are obtained from plot-level ground-based visual assessments and lidar observations. These are combined with Landsat imagery (from multiple seasons), to inform models built independently for each lifeform. Tree, shrub, and herbaceous lifeforms each have a potential range from 10% to 100% (cover values less than 10% are binned into the 10% value). The three independent lifeform datasets are merged into a single product based on the dominant lifeform of each pixel. The EVC product is then reconciled through QA/QC measures to ensure lifeform is synchronized with Existing Vegetation Height (EVH). Urban and developed areas are derived from the National Land Cover Database (NLCD), and the latest available Microsoft Building Footprint dataset. Agricultural lands originate from the 2022 Cropland Data Layer (CDL) and the 2019 California Statewide Crop Mapping layer. Disturbance events after 2016 are accounted for by incorporating transition rulesets using LF 2022 Fuel Disturbance (FDist). LF uses EVC as an input for LF 2022 Fuel Vegetation Cover (FVC).