The LANDFIRE Annual Disturbance products for 1999-2007 are included within the LF 2001 version and are developed through a multistep process. Inputs to this process include (but are not limited to): Landsat imagery derived Normalized Burn Ratio (NBR) data (in CONUS only), polygon data developed by local agencies, fire data obtained from Monitoring Trends in Burn Severity (MTBS), Burned Area Reflectance Classification (BARC), and Rapid Assessment of Vegetation Condition after Wildfire (RAVG) fire mapping efforts, and Protected Area Database (PAD) data. Event polygon data are provided to LANDFIRE by various local, regional, and national agencies and organizations. Disturbance type and year information is included as attributes for each polygon and transferred to the disturbance grids. Severity is determined by using dNBR (difference Normalized Burn Ratio) data classified into high, medium, and low severity levels based on a statistical comparison with the MTBS, BARC, and RAVG fire severity. Vegetation Tracker (VCT, Huang, et. al. 2008) algorithms are used to identify disturbances outside of LF 2001 Events. VCT data are developed for each year identifying disturbed areas and severity. Since disturbance type (i.e. causality) is not determined in the VCT process, a spatial analysis was done comparing the VCT output to buffered (1 kilometer) LF 2001 Events and PAD GAP land use characteristics. While not providing a precise type of disturbance, this analysis provides information useful for narrowing down the types of disturbance that could or could not typically occur. Each zone has ten disturbance grids, one for each year 1999 to 2007. Each grid is attributed with year, disturbance type (if known, otherwise a description of possible types), severity, and the data sources used to create the data.

The LANDFIRE vegetation layers describe the following elements of existing and potential vegetation for each LANDFIRE mapping zone: environmental site potentials, biophysical settings, existing vegetation types, canopy cover, and vegetation height. Vegetation is mapped using predictive landscape models based on extensive field reference data, satellite imagery, biophysical gradient layers, and classification and regression trees. The environmental site potential (ESP) data layer represents the vegetation that could be supported at a given site based on the biophysical environment. Map units are named according to NatureServe's Ecological Systems classification, which is a nationally consistent set of mid-scale ecological units (Comer and others 2003). Usage of these classification units to describe environmental site potential, however, differs from the original intent of Ecological Systems as units of existing vegetation. As used in LANDFIRE, map unit names represent the natural plant communities that would become established at late or climax stages of successional development in the absence of disturbance. They reflect the current climate and physical environment, as well as the competitive potential of native plant species. The ESP layer is similar in concept to other approaches to classifying potential vegetation in the western United States, including habitat types (for example, Daubenmire 1968 and Pfister and others 1977) and plant associations (for example, Henderson and others 1989). It is important to note that ESP is an abstract concept and represents neither current nor historical vegetation. To create the ESP data layer, we first assign field plots to one of the ESP map unit classes. Go to http://www.landfire.gov/participate_acknowledgements.php for more information regarding contributors of field plot data. Assignments are based on presence and abundance of indicator plant species recorded on the plots and on the ecological amplitude and competitive potential of these species. We then intersect plot locations with a series of 30-meter spatially explicit gradient layers. Most of the gradient layers used in the predictive modeling of ESP are derived using the WX-BGC simulation model (Keane and Holsinger, in preparation; Keane and others 2002). WX-BGC simulations are based largely on spatially extrapolated weather data from DAYMET (Thornton and others 1997; Thornton and Running 1999; http://www.daymet.org/) and on soils data in STATSGO (NRCS 1994). Additional indirect gradient layers, such as elevation, slope, and indices of topographic position, are also used. We use data from plot locations to develop predictive classification tree models, using See5 data mining software (Quinlan 1993; Rulequest Research 1997), for each LANDFIRE map zone. These decision trees are applied spatially to predict the ESP for every pixel across the landscape. Finally, ESP pixel values are, in some cases, modified based on a comparison with the LANDFIRE existing vegetation type (EVT) layer created with the use of 30-meter Landsat ETM satellite imagery. We make such modifications only in non-vegetated areas (such as water, rock, snow, or ice) and where information in the EVT layer clearly enables a better depiction of the environmental site potential concept. Although the ESP data layer is intended to represent current site potential, the actual time period for this data set is variable. The weather data used in DAYMET were compiled from 1980 to 1997. Refer to spatial metadata for date ranges of field plot data and satellite imagery for each LANDFIRE map zone. A number of changes were implemented for the LF2010 ESP product that worked with this original data. LF2010 updates to mapping EVT map units for Barren, Snow-Ice, and Water were translated to the LF2010 ESP product so those map units will coincide with the EVT. Subsequent to that, each ESP map unit was stratified spatially two different ways. First, each ESP map unit was stratified

The LANDFIRE vegetation layers describe the following elements of existing and potential vegetation for each LANDFIRE mapping zone: environmental site potentials, biophysical settings, existing vegetation types, canopy cover, and vegetation height. Vegetation is mapped using predictive landscape models based on extensive field reference data, satellite imagery, biophysical gradient layers, and classification and regression trees. The environmental site potential (ESP) data layer represents the vegetation that could be supported at a given site based on the biophysical environment. Map units are named according to NatureServe's Ecological Systems classification, which is a nationally consistent set of mid-scale ecological units (Comer and others 2003). Usage of these classification units to describe environmental site potential, however, differs from the original intent of Ecological Systems as units of existing vegetation. As used in LANDFIRE, map unit names represent the natural plant communities that would become established at late or climax stages of successional development in the absence of disturbance. They reflect the current climate and physical environment, as well as the competitive potential of native plant species. The ESP layer is similar in concept to other approaches to classifying potential vegetation in the western United States, including habitat types (for example, Daubenmire 1968 and Pfister and others 1977) and plant associations (for example, Henderson and others 1989). It is important to note that ESP is an abstract concept and represents neither current nor historical vegetation. To create the ESP data layer, we first assign field plots to one of the ESP map unit classes. Go to http://www.landfire.gov/participate_acknowledgements.php for more information regarding contributors of field plot data. Assignments are based on presence and abundance of indicator plant species recorded on the plots and on the ecological amplitude and competitive potential of these species. We then intersect plot locations with a series of 30-meter spatially explicit gradient layers. Most of the gradient layers used in the predictive modeling of ESP are derived using the WX-BGC simulation model (Keane and Holsinger, in preparation; Keane and others 2002). WX-BGC simulations are based largely on spatially extrapolated weather data from DAYMET (Thornton and others 1997; Thornton and Running 1999; http://www.daymet.org/) and on soils data in STATSGO (NRCS 1994). Additional indirect gradient layers, such as elevation, slope, and indices of topographic position, are also used. We use data from plot locations to develop predictive classification tree models, using See5 data mining software (Quinlan 1993; Rulequest Research 1997), for each LANDFIRE map zone. These decision trees are applied spatially to predict the ESP for every pixel across the landscape. Finally, ESP pixel values are, in some cases, modified based on a comparison with the LANDFIRE existing vegetation type (EVT) layer created with the use of 30-meter Landsat ETM satellite imagery. We make such modifications only in non-vegetated areas (such as water, rock, snow, or ice) and where information in the EVT layer clearly enables a better depiction of the environmental site potential concept. Although the ESP data layer is intended to represent current site potential, the actual time period for this data set is variable. The weather data used in DAYMET were compiled from 1980 to 1997. Refer to spatial metadata for date ranges of field plot data and satellite imagery for each LANDFIRE map zone. A number of changes were implemented for the LF2010 ESP product that worked with this original data. LF2010 updates to mapping EVT map units for Barren, Snow-Ice, and Water were translated to the LF2010 ESP product so those map units will coincide with the EVT. Subsequent to that, each ESP map unit was stratified spatially two different ways. First, each ESP map unit was stratified

The LANDFIRE vegetation layers describe the following elements of existing and potential vegetation for each LANDFIRE mapping zone: environmental site potentials, biophysical settings, existing vegetation types, canopy cover, and vegetation height. Vegetation is mapped using predictive landscape models based on extensive field reference data, satellite imagery, biophysical gradient layers, and classification and regression trees. The environmental site potential (ESP) data layer represents the vegetation that could be supported at a given site based on the biophysical environment. Map units are named according to NatureServe's Ecological Systems classification, which is a nationally consistent set of mid-scale ecological units (Comer and others 2003). Usage of these classification units to describe environmental site potential, however, differs from the original intent of Ecological Systems as units of existing vegetation. As used in LANDFIRE, map unit names represent the natural plant communities that would become established at late or climax stages of successional development in the absence of disturbance. They reflect the current climate and physical environment, as well as the competitive potential of native plant species. The ESP layer is similar in concept to other approaches to classifying potential vegetation in the western United States, including habitat types (for example, Daubenmire 1968 and Pfister and others 1977) and plant associations (for example, Henderson and others 1989). It is important to note that ESP is an abstract concept and represents neither current nor historical vegetation. To create the ESP data layer, we first assign field plots to one of the ESP map unit classes. Go to http://www.landfire.gov/participate_acknowledgements.php for more information regarding contributors of field plot data. Assignments are based on presence and abundance of indicator plant species recorded on the plots and on the ecological amplitude and competitive potential of these species. We then intersect plot locations with a series of 30-meter spatially explicit gradient layers. Most of the gradient layers used in the predictive modeling of ESP are derived using the WX-BGC simulation model (Keane and Holsinger, in preparation; Keane and others 2002). WX-BGC simulations are based largely on spatially extrapolated weather data from DAYMET (Thornton and others 1997; Thornton and Running 1999; http://www.daymet.org/) and on soils data in STATSGO (NRCS 1994). Additional indirect gradient layers, such as elevation, slope, and indices of topographic position, are also used. We use data from plot locations to develop predictive classification tree models, using See5 data mining software (Quinlan 1993; Rulequest Research 1997), for each LANDFIRE map zone. These decision trees are applied spatially to predict the ESP for every pixel across the landscape. Finally, ESP pixel values are, in some cases, modified based on a comparison with the LANDFIRE existing vegetation type (EVT) layer created with the use of 30-meter Landsat ETM satellite imagery. We make such modifications only in non-vegetated areas (such as water, rock, snow, or ice) and where information in the EVT layer clearly enables a better depiction of the environmental site potential concept. Although the ESP data layer is intended to represent current site potential, the actual time period for this data set is variable. The weather data used in DAYMET were compiled from 1980 to 1997. Refer to spatial metadata for date ranges of field plot data and satellite imagery for each LANDFIRE map zone. A number of changes were implemented for the LF2010 ESP product that worked with this original data. LF2010 updates to mapping EVT map units for Barren, Snow-Ice, and Water were translated to the LF2010 ESP product so those map units will coincide with the EVT. Subsequent to that, each ESP map unit was stratified spatially two different ways. First, each ESP map unit was stratified

The LANDFIRE Annual Disturbance products for 1999-2007 are included within the LF 2001 version and are developed through a multistep process. Inputs to this process include (but are not limited to): Landsat imagery derived Normalized Burn Ratio (NBR) data (in CONUS only), polygon data developed by local agencies, fire data obtained from Monitoring Trends in Burn Severity (MTBS), Burned Area Reflectance Classification (BARC), and Rapid Assessment of Vegetation Condition after Wildfire (RAVG) fire mapping efforts, and Protected Area Database (PAD) data. Event polygon data are provided to LANDFIRE by various local, regional, and national agencies and organizations. Disturbance type and year information is included as attributes for each polygon and transferred to the disturbance grids. Severity is determined by using dNBR (difference Normalized Burn Ratio) data classified into high, medium, and low severity levels based on a statistical comparison with the MTBS, BARC, and RAVG fire severity. Vegetation Tracker (VCT, Huang, et. al. 2008) algorithms are used to identify disturbances outside of LF 2001 Events. VCT data are developed for each year identifying disturbed areas and severity. Since disturbance type (i.e. causality) is not determined in the VCT process, a spatial analysis was done comparing the VCT output to buffered (1 kilometer) LF 2001 Events and PAD GAP land use characteristics. While not providing a precise type of disturbance, this analysis provides information useful for narrowing down the types of disturbance that could or could not typically occur. Each zone has ten disturbance grids, one for each year 1999 to 2007. Each grid is attributed with year, disturbance type (if known, otherwise a description of possible types), severity, and the data sources used to create the data.

The LANDFIRE Annual Disturbance products for 1999-2007 are included within the LF 2001 version and are developed through a multistep process. Inputs to this process include (but are not limited to): Landsat imagery derived Normalized Burn Ratio (NBR) data (in CONUS only), polygon data developed by local agencies, fire data obtained from Monitoring Trends in Burn Severity (MTBS), Burned Area Reflectance Classification (BARC), and Rapid Assessment of Vegetation Condition after Wildfire (RAVG) fire mapping efforts, and Protected Area Database (PAD) data. Event polygon data are provided to LANDFIRE by various local, regional, and national agencies and organizations. Disturbance type and year information is included as attributes for each polygon and transferred to the disturbance grids. Severity is determined by using dNBR (difference Normalized Burn Ratio) data classified into high, medium, and low severity levels based on a statistical comparison with the MTBS, BARC, and RAVG fire severity. Vegetation Tracker (VCT, Huang, et. al. 2008) algorithms are used to identify disturbances outside of LF 2001 Events. VCT data are developed for each year identifying disturbed areas and severity. Since disturbance type (i.e. causality) is not determined in the VCT process, a spatial analysis was done comparing the VCT output to buffered (1 kilometer) LF 2001 Events and PAD GAP land use characteristics. While not providing a precise type of disturbance, this analysis provides information useful for narrowing down the types of disturbance that could or could not typically occur. Each zone has ten disturbance grids, one for each year 1999 to 2007. Each grid is attributed with year, disturbance type (if known, otherwise a description of possible types), severity, and the data sources used to create the data.

The LANDFIRE Annual Disturbance products for 1999-2007 are included within the LF 2001 version and are developed through a multistep process. Inputs to this process include (but are not limited to): Landsat imagery derived Normalized Burn Ratio (NBR) data (in CONUS only), polygon data developed by local agencies, fire data obtained from Monitoring Trends in Burn Severity (MTBS), Burned Area Reflectance Classification (BARC), and Rapid Assessment of Vegetation Condition after Wildfire (RAVG) fire mapping efforts, and Protected Area Database (PAD) data. Event polygon data are provided to LANDFIRE by various local, regional, and national agencies and organizations. Disturbance type and year information is included as attributes for each polygon and transferred to the disturbance grids. Severity is determined by using dNBR (difference Normalized Burn Ratio) data classified into high, medium, and low severity levels based on a statistical comparison with the MTBS, BARC, and RAVG fire severity. Vegetation Tracker (VCT, Huang, et. al. 2008) algorithms are used to identify disturbances outside of LF 2001 Events. VCT data are developed for each year identifying disturbed areas and severity. Since disturbance type (i.e. causality) is not determined in the VCT process, a spatial analysis was done comparing the VCT output to buffered (1 kilometer) LF 2001 Events and PAD GAP land use characteristics. While not providing a precise type of disturbance, this analysis provides information useful for narrowing down the types of disturbance that could or could not typically occur. Each zone has ten disturbance grids, one for each year 1999 to 2007. Each grid is attributed with year, disturbance type (if known, otherwise a description of possible types), severity, and the data sources used to create the data.

LANDFIRE’s (LF) 2001 Canopy Cover (CC) product describes the percent cover of tree canopy in a stand. A spatially-explicit map of canopy cover supplies information for fire behavior models such as FARSITE (Finney 1998) to determine surface fuel shading for calculating dead fuel moisture and for calculating wind reductions. In FARSITE, canopy characteristics are used to compute shading, wind reduction factors, spotting distances, crown fuel volume, spread characteristics of crown fires and incorporate the effects of ladder fuels for transitions from a surface to crown fire. CC is derived from the LF Existing Vegetation Cover (EVC) product using the LANDFIRE Total Fuel Change Tool (LFTFC). Forested EVC values are reclassified from the nine EVC codes to represent the midpoint of the classification. CC values are represented in 10 percent increments starting with fifteen and ending at ninety-five. Where EVC is not forested or the tree cover is considered to be part of the surface fuel CC receives a value of 0 percent.

LANDFIRE's (LF) 2001 Biophysical Settings (BPS) product represents the vegetation system that may have been dominant on the landscape prior to Euro-American settlement. BPS is based on both the current biophysical environment and an approximation of the historical disturbance regime. Map units are based on NatureServe's Ecological Systems classification and represent the natural plant communities that may have been present during the reference period (Comer et al 2003). Each BPS map unit is matched with a model of vegetation succession which is used to estimate reference fire frequency and severity. LANDFIRE uses BPS to depict reference conditions of vegetation systems across landscapes and should not be regarded as a representation of existing vegetation.

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).