The RAVG (Rapid Assessment of Vegetation Condition after Wildfire) program provides assessments of vegetation conditions following large fires on forested lands. Fire effects are represented by three metrics: percent change in live basal area (BA), percent change in canopy cover (CC), and the standardized Composite Burn Index (CBI). These data are derived from moderate resolution multi-spectral imagery (e.g., Landsat 8 Operational Land Imager or Sentinel-2 Multispectral Instrument). The Relative Differenced Normalized Burn Ratio (RdNBR), which is correlated to the variation of burn severity within a fire, is calculated from a pair of images (pre- and postfire), judiciously selected to capture fire effects. The three-severity metrics are in turn calculated from RdNBR using regression equations developed from and calibrated with historical field data. This map layer is a thematic raster image of MTBS burn severity classes for all inventoried fires occurring in CONUS during calendar year 2021. Fires omitted from this mapped inventory are those where suitable satellite imagery was not available, or fires were not discernable from available imagery.

LANDFIRE's (LF) Annual Disturbance products provide temporal and spatial information related to landscape change. Annual Disturbance depicts areas of 4.5 hectares (11 acres) or larger that have experienced a natural or anthropogenic landscape change (or treatment) within a given year. For the creation of the Annual Disturbance product, information sources include national fire mapping programs such as Monitoring Trends in Burn Severity (MTBS), Burned Area Reflectance Classification (BARC) and Rapid Assessment of Vegetation Condition after Wildfire (RAVG), 18 types of agency-contributed "event" perimeters (see LF Public Events Geodatabase), and remotely sensed Landsat imagery. To create the LF Annual Disturbance products, individual Landsat scenes are stacked and made into composites representing the 50th percentile of all stacked pixels (band-by-band) to reduce data gaps caused by clouds or other anomalies. Composite imagery from the specified mapping year, the two prior years, and the following year serve as the base data from which change products such as the Normalized Differenced Vegetation Index (dNDVI), the Normalized Burn Ratio (dNBR), and the Multi-Index Integrated Change Algorithm (MIICA) (Jin et al. 2013) are derived. Image analysts collectively use these datasets (separately or in combination) to isolate the true change from false change (commission errors). False changes can be attributed to many anomalies but are mostly caused by differences in annual or seasonal phenology, and/or artifacts in the image composites. Fire-caused disturbances sourced from MTBS may contain data gaps where clouds obscure the full burn scar from being mapped. Models trained from pre-fire and post-fire Landsat data are used to fill these gaps. The result is gap-free continuous severity and extent information for all MTBS fire disturbances. MTBS pixels derived from gap filling techniques, such as modeling, are noted as such in the Annual Disturbance attribute table. Smaller fires that do not meet the size criteria set forth by MTBS may be attributed using Burned Area (BA), informed from Landsat Level-3 science products and only available in the lower 48 states. Causality and severity information assigned to a disturbance are prioritized by source, with the highest priorities reserved for fire mapping programs (MTBS, BARC, and RAVG) followed by user-contributed events contained in the LF Events Geodatabase, and lastly, Landsat image-based change.

These data support poscrptR (wright et al. 2021). poscrptR is a shiny app that predicts the probability of post-fire conifer regeneration for fire data supplied by the user. The predictive model was fit using presence/absence data collected in 4.4m radius plots (60 square meters). Please refer to Stewart et al. (2020) for more details concerning field data collection, the model fitting process, and limitations. Learn more about shiny apps at https://shiny.rstudio.com. The app is designed to simplify the process of predicting post-fire conifer regeneration under different precipitation and seed production scenarios. The app requires the user to upload two input data sets: 1. a raster of Relativized differenced Normalized Burn Ratio (RdNBR), and 2. a .zip folder containing a fire perimeter shapefile. The app was designed to use Rapid Assessment of Vegetative Condition (RAVG) data inputs. The RAVG website (https://fsapps.nwcg.gov/ravg) has both RdNBR and fire perimeter data sets available for all fires with at least 1,000 acres of National Forest land from 2007 to the present. The fire perimeter must be a zipped shapefile (.zip file, include all shapefile components: .cpg, .dbf, .prj, .sbn, .sbx, .shp, and .shx). RdNBR must be 30m resolution, and both the RdNBR and fire perimeter must use the USA Contiguous Albers Equal Area Conic coordinate reference system (USGS version). RDNBR must be alligned (same origin) as RAVG raster data. References: Stewart, J., van Mantgem, P., Young, D., Shive, K., Preisler, H., Das, A., Stephenson, N., Keeley, J., Safford, H., Welch, K., Thorne, J., 2020. Effects of postfire climate and seed availability on postfire conifer regeneration. Ecological Applications. Wright, M.C., Stewart, J.E., van Mantgem, P.J., Young, D.J., Shive, K.L., Preisler, H.K., Das, A.J., Stephenson, N.L., Keeley, J.E., Safford, H.D., Welch, K.R., and Thorne, J.H. 2021. poscrptR. R package version 0.1.3.

LANDFIRE’s (LF) Annual Disturbance products provide temporal and spatial information related to landscape change. Annual Disturbance depicts areas of 4.5 hectares (11 acres) or larger that have experienced a natural or anthropogenic landscape change (or treatment) within a given year. For the creation of the Annual Disturbance product, information sources include national fire mapping programs such as Monitoring Trends in Burn Severity (MTBS), Burned Area Reflectance Classification (BARC) and Rapid Assessment of Vegetation Condition after Wildfire (RAVG), 18 types of agency-contributed "event" perimeters (see LF Public Events Geodatabase), and remotely sensed Landsat imagery. To create the LF Annual Disturbance products, individual Landsat scenes are stacked and made into composites representing the 50th percentile of all stacked pixels (band-by-band) to reduce data gaps caused by clouds or other anomalies. Composite imagery from the specified mapping year, the two prior years, and the following year serve as the base data from which change products such as the Normalized Differenced Vegetation Index (dNDVI), the Normalized Burn Ratio (dNBR), and the Multi-Index Integrated Change Algorithm (MIICA) (Jin et al. 2013) are derived. Image analysts collectively use these datasets (separately or in combination) to isolate the true change from false change (commission errors). False changes can be attributed to many anomalies but are mostly caused by differences in annual or seasonal phenology, and/or artifacts in the image composites. Fire-caused disturbances sourced from MTBS may contain data gaps where clouds obscure the full burn scar from being mapped. Models trained from pre-fire and post-fire Landsat data are used to fill these gaps. The result is gap-free continuous severity and extent information for all MTBS fire disturbances. MTBS pixels derived from gap filling techniques, such as modeling, are noted as such in the Annual Disturbance attribute table. Smaller fires that do not meet the size criteria set forth by MTBS may be attributed using Burned Area (BA), informed from Landsat Level-3 science products and only available in the lower 48 states. Causality and severity information assigned to a disturbance are prioritized by source, with the highest priorities reserved for fire mapping programs (MTBS, BARC, and RAVG) followed by user-contributed events contained in the LF Events Geodatabase, and lastly, Landsat image-based change.

This 30 meter raster represents burn severity for fires in CA that burned between 2012 and 2022. If a pixel burned more than once during the period, it is assigned a burn severity value from the more recent date of fire. Data for fires between 2012 and 2020 came from the Monitoring Trends in Burn Severity (MTBS) dataset, which represents classified difference in Landsat normalized burn ratio (dNBR). Data for fires in 2021 and 2022 came from Rapid Assessment of Vegetation Conditions after Wildfire (RAVG), which is based on a composite burn index (CBI). Burn severity is mapped from low to high, with the following classes:Unburned or low (pixel value = 1)Low (pixel value = 2)Moderate (pixel value = 3)High (pixel value =4)Increase in Greenness (pixel value = 5, MTBS only)Non Processing Area Mask (pixel value = 6, MTBS only)Unmappable (pixel value = 9, RAVG only)

LANDFIRE’s (LF) Annual Disturbance products provide temporal and spatial information related to landscape change. Annual Disturbance depicts areas of 4.5 hectares (11 acres) or larger that have experienced a natural or anthropogenic landscape change (or treatment) within a given year. For the creation of the Annual Disturbance product, information sources include national fire mapping programs such as Monitoring Trends in Burn Severity (MTBS), Burned Area Reflectance Classification (BARC) and Rapid Assessment of Vegetation Condition after Wildfire (RAVG), 18 types of agency-contributed “event” perimeters (see LF Public Events Geodatabase), and remotely sensed Landsat imagery. To create the LF Annual Disturbance products, individual Landsat scenes are stacked and made into composites representing the 50th percentile of all stacked pixels (band-by-band) to reduce data gaps caused by clouds or other anomalies. Composite imagery from the specified mapping year, the two prior years, and the following year serve as the base data from which change products such as the Normalized Differenced Vegetation Index (dNDVI), the Normalized Burn Ratio (dNBR), and the Multi-Index Integrated Change Algorithm (MIICA) (Jin et al. 2013) are derived. Image analysts collectively use these datasets (separately or in combination) to isolate the true change from false change (commission errors). False changes can be attributed to many anomalies but are mostly caused by differences in annual or seasonal phenology, and/or artifacts in the image composites. Fire-caused disturbances sourced from MTBS may contain data gaps where clouds obscure the full burn scar from being mapped. Models trained from pre-fire and post-fire Landsat data are used to fill these gaps. The result is gap-free continuous severity and extent information for all MTBS fire disturbances. MTBS pixels derived from gap filling techniques, such as modeling, are noted as such in the Annual Disturbance attribute table. Smaller fires that do not meet the size criteria set forth by MTBS may be attributed using Burned Area (BA), informed from Landsat Level-3 science products and only available in the lower 48 states. Causality and severity information assigned to a disturbance are prioritized by source, with the highest priorities reserved for fire mapping programs (MTBS, BARC, and RAVG) followed by user-contributed events contained in the LF Events Geodatabase, and lastly, Landsat image-based change.

The purpose of the Colorado All-Lands Quantitative Wildfire Risk Assessment (COAL) for the USFS Rocky Mountain Region (R2) is to provide foundational information about wildfire hazard and risk to highly valued resources and assets across all land ownerships in the state of Colorado. Such information supports fuel management planning decisions, as well as revisions to land and resource management plans. A wildfire risk assessment is a quantitative analysis of assets and resources and how they would be potentially impacted by wildfire. The COAL analysis considers several different components, each resolved spatially across the project area, including:likelihood of a fire burning;the intensity of a fire if one should occur;the exposure of assets and resources based on their locations;the susceptibility of those assets and resources to wildfire. This data is part of the COAL 'fuelscape', which is a data stack or collection of raster files that describe the fuel and physical environment for a given spatial extent used in fire behavior modeling. The fuelscape is also referred to as the Farsite/FlamMap data sandwich, Farsite/FlamMap landscape files, (LCP) and similar notations.This data was customized to ameliorate discrepancies between Landfire map zones for the same dominant cover type and to account for disturbances through the 2020 field season and other related fuel characteristic issues identified by stakeholders in the State of Colorado during calibration workshops. The COAL 2021 fuelscape was updated to include wildfire and other disturbances from the year 2020 and is intended for use in the 2021 fire season. Data may be applicable beyond 2021 or may be updated to reflect disturbances occurring after 2020. The COAL 2021 fuelscape consists of geospatial data layers representing surface fuel model, canopy cover, canopy height, canopy base height, canopy bulk density, and topography characteristics (slope, aspect, elevation). The original COAL fuelscape was developed from Landfire Remap 2016 30-m raster data and edited based on feedback from interagency fuels and fire staff across Colorado. The original COAL fuelscape was updated using RAVG, MTBS, and fire perimeter datasets where available to account for wildfire disturbances that occurred between 2017 and 2019. The fuelscape was also updated with Forest Service Activity Tracking System (FACTS) and National Fire Plan Operations and Reporting System (NFPORS) treatment data. Landfire disturbance records reported a large fire perimeter (approximately 30,000 acres) in the Breckenridge, CO area from 2015. During the fuelscape calibration workshop, this was identified as an error in the FACTS database carried forward in Landfire Remap. This record was removed from the fuel disturbance grid to prevent misrepresentation of fuel reduction within the original treatment polygon. For more information regarding quantitative wildfire risk assessment, please refer to GTR-315: https://www.fs.usda.gov/rm/pubs/rmrs_gtr315.pdf. For information about the COAL QWRA, please refer to the COAL report: https://pyrologix.com/download. For details about Landfire treated map logic, refer to Landfire Rulesets Database: https://www.landfire.gov/fuel_rulesets_db.php.Data was developed for the Rocky Mountain Region of the USDA Forest Service by Pyrologix LLC.

Compliance Status is determined using a combination of fixed measurements from Defra’s Automatic Urban Rural Network and supplementary assessment. Supplementary assessment includes modelled background and roadside pollutant values from Defra’s Pollution Climate Mapping Model.

The purpose of the Colorado All-Lands Quantitative Wildfire Risk Assessment (COAL) for the USFS Rocky Mountain Region (R2) is to provide foundational information about wildfire hazard and risk to highly valued resources and assets across all land ownerships in the state of Colorado. Such information supports fuel management planning decisions, as well as revisions to land and resource management plans. A wildfire risk assessment is a quantitative analysis of assets and resources and how they would be potentially impacted by wildfire. The COAL analysis considers several different components, each resolved spatially across the project area, including:likelihood of a fire burning;the intensity of a fire if one should occur;the exposure of assets and resources based on their locations;the susceptibility of those assets and resources to wildfire. This data is part of the COAL 'fuelscape', which is a data stack or collection of raster files that describe the fuel and physical environment for a given spatial extent used in fire behavior modeling. The fuelscape is also referred to as the Farsite/FlamMap data sandwich, Farsite/FlamMap landscape files, (LCP) and similar notations.In addition to canopy base height, the COAL 2021 fuelscape was updated to include wildfire and other disturbances from the year 2020 and is intended for use in the 2021 fire season. Data may be applicable beyond 2021 or may be updated to reflect disturbances occurring after 2020. The COAL 2021 fuelscape consists of geospatial data layers representing surface fuel model, canopy cover, canopy height, canopy base height, canopy bulk density, and topography characteristics (slope, aspect, elevation). The original COAL fuelscape was developed from Landfire Remap 2016 30-m raster data and edited based on feedback from interagency fuels and fire staff across Colorado. The original COAL fuelscape was updated using RAVG, MTBS, and fire perimeter datasets where available to account for wildfire disturbances that occurred between 2017 and 2019. The fuelscape was also updated with Forest Service Activity Tracking System (FACTS) and National Fire Plan Operations and Reporting System (NFPORS) treatment data. Landfire disturbance records reported a large fire perimeter (approximately 30,000 acres) in the Breckenridge, CO area from 2015. During the fuelscape calibration workshop, this was identified as an error in the FACTS database carried forward in Landfire Remap. This record was removed from the fuel disturbance grid to prevent misrepresentation of fuel reduction within the original treatment polygon.For more information regarding quantitative wildfire risk assessment, please refer to GTR-315: https://www.fs.usda.gov/rm/pubs/rmrs_gtr315.pdf. For information about the COAL QWRA, please refer to the COAL report: https://pyrologix.com/download. For details about Landfire treated map logic, refer to Landfire Rulesets Database: https://www.landfire.gov/fuel_rulesets_db.php.Data was developed for the Rocky Mountain Region of the USDA Forest Service by Pyrologix LLC.

Compliance Status is determined using a combination of fixed measurements from Defra’s Automatic Urban Rural Network and supplementary assessment. Supplementary assessment includes modelled background and roadside pollutant values from Defra’s Pollution Climate Mapping Model.

Fixed measurements of Ozone AOT40 averaged over 5 years 2017-2021 from Defra’s Automatic Urban Rural Network.