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 (LF) 2001 Environmental Site Potential (ESP) product represents the vegetation that could be supported at a given site based on the biophysical environment. It is important to note that ESP is an abstract concept and represents neither current nor historical vegetation. Map units are named according to NatureServe's Ecological Systems classification, which is a nationally consistent set of mid-scale ecological units (Comer et al 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 LF, 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 product 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). To create the ESP product, LF assigned field plots to one of the ESP map unit classes. Assignments were based on presence and abundance of indicator plant species recorded on the plots, and on the ecological amplitude and competitive potential of these species. Plot locations were then intersected with a series of 30m 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 et al 2002). WX-BGC simulations are based largely on spatially extrapolated weather data from DAYMET (Thornton et al 1997; Thornton and Running 1999) and on soils data in STATSGO (NRCS 1994). Additional indirect gradient layers, such as elevation, slope, and indices of topographic position, were also used. LF used data from plot locations to develop predictive classification tree models, using See5 data mining software (Quinlan 1993; Rulequest Research 1997), for each LF map zone. These decision trees were applied spatially to predict the ESP for every pixel across the landscape. Finally, ESP pixel values were, in some cases, modified based on a comparison with the LF Existing Vegetation Type (EVT) product created with the use of 30m Landsat ETM satellite imagery. LF made modifications only in non-vegetated areas (such as water, rock, snow, or ice) and where information in the EVT layer clearly enabled a better depiction of the ESP concept.

LANDFIRE's (LF) 2022 update (LF 2022) Existing Vegetation Type (EVT) represents the current distribution of the terrestrial ecological systems classification developed by NatureServe for the western hemisphere. In this context, a terrestrial ecological system is defined as a group of plant community types that tend to co-occur within landscapes with similar ecological processes, substrates, and/or environmental gradients. EVT also includes ruderal or semi-natural vegetation types within the U.S. National Vegetation Classification [(NVC) https://usnvc.org/]. See the EVT product page (https://www.landfire.gov/evt.php) for more information about ecological systems and NVC classifications. EVT is mapped using decision tree models, field data, Landsat imagery, topography, and biophysical gradient data. Decision tree models are developed separately for tree, shrub, and herbaceous lifeforms which are then used to produce a lifeform specific EVT product. These models are generated for each Environmental Protection Agency (EPA) Level III Ecoregion (https://www.epa.gov/eco-research/ecoregions). Riparian, alpine, sparse, and other site-specific EVTs are constrained by predetermined masks. Urban and developed areas are derived from the National Land Cover Database (NLCD), and the latest Microsoft Building Footprint dataset. Agricultural lands originate from the 2022 Cropland Data Layer (CDL) and the 2019 California Statewide Crop Mapping layer. Burnable developed classes are identified from building footprint dataset thresholds. LF 2022 retains circa 2016 EVT labels except where shifts in urban, agriculture, and developed classes occur. While Existing Vegetation Cover (EVC) and Height (EVH) are updated using transition rulesets with ST-Sim to account for disturbances, EVT remains unchanged, therefore EVT lifeform is not synchronized to the EVC/EVH lifeform as in some previous versions. LF uses EVT as an input for LF 2022 Fuel Vegetation Type (FVT).

LANDFIRE's (LF) 2022 update (LF 2022) Existing Vegetation Type (EVT) represents the current distribution of the terrestrial ecological systems classification developed by NatureServe for the western hemisphere. In this context, a terrestrial ecological system is defined as a group of plant community types that tend to co-occur within landscapes with similar ecological processes, substrates, and/or environmental gradients. EVT also includes ruderal or semi-natural vegetation types within the U.S. National Vegetation Classification [(NVC) https://usnvc.org/]. See the EVT product page (https://www.landfire.gov/evt.php) for more information about ecological systems and NVC classifications. EVT is mapped using decision tree models, field data, Landsat imagery, topography, and biophysical gradient data. Decision tree models are developed separately for tree, shrub, and herbaceous lifeforms which are then used to produce a lifeform specific EVT product. These models are generated for each Environmental Protection Agency (EPA) Level III Ecoregion (https://www.epa.gov/eco-research/ecoregions). Riparian, alpine, sparse, and other site-specific EVTs are constrained by predetermined masks. Urban and developed areas are derived from the National Land Cover Database (NLCD), and the latest Microsoft Building Footprint dataset. Agricultural lands originate from the 2022 Cropland Data Layer (CDL) and the 2019 California Statewide Crop Mapping layer. Burnable developed classes are identified from building footprint dataset thresholds. LF 2022 retains circa 2016 EVT labels except where shifts in urban, agriculture, and developed classes occur. While Existing Vegetation Cover (EVC) and Height (EVH) are updated using transition rulesets with ST-Sim to account for disturbances, EVT remains unchanged, therefore EVT lifeform is not synchronized to the EVC/EVH lifeform as in some previous versions. LF uses EVT as an input for LF 2022 Fuel Vegetation Type (FVT).

LANDFIRE's (LF) 2023 update (LF 2023) Existing Vegetation Type (EVT) represents the current distribution of the terrestrial ecological systems classification developed by NatureServe for the western hemisphere. In this context, a terrestrial ecological system is defined as a group of plant community types that tend to co-occur within landscapes with similar ecological processes, substrates, and/or environmental gradients. See the EVT product page (https://landfire.gov/vegetation/evt) for more information about ecological systems and NVC classifications. EVT is mapped using decision tree models, field data, Landsat imagery, topography, and biophysical gradient data. Decision tree models are developed separately for tree, shrub, and herbaceous lifeforms which are then used to produce a lifeform specific EVT product. These models are generated for each Environmental Protection Agency (EPA) Level III Ecoregion (https://www.epa.gov/eco-research/ecoregions). Riparian, alpine, sparse, and other site-specific EVTs are constrained by predetermined masks. In LF 2023 Conterminous United States (CONUS) extent, LF will map the lifeform, cover, and height of existing vegetation in areas that were mapped as disturbed over the last twenty years (see LF Annual Disturbance products) using machine learning methods. These disturbed areas were the focus because they are the areas that have changed the most since LF 2016 Remap. To learn more about this new methodology for LF EVC, EVH, and Existing Vegetation Type (EVT) go to https://www.landfire.gov/data/lf2023.

LANDFIRE's (LF) 2023 update (LF 2023) Existing Vegetation Type (EVT) represents the current distribution of the terrestrial ecological systems classification developed by NatureServe for the western hemisphere. In this context, a terrestrial ecological system is defined as a group of plant community types that tend to co-occur within landscapes with similar ecological processes, substrates, and/or environmental gradients. See the EVT product page (https://landfire.gov/vegetation/evt) for more information about ecological systems and NVC classifications. EVT is mapped using decision tree models, field data, Landsat imagery, topography, and biophysical gradient data. Decision tree models are developed separately for tree, shrub, and herbaceous lifeforms which are then used to produce a lifeform specific EVT product. These models are generated for each Environmental Protection Agency (EPA) Level III Ecoregion (https://www.epa.gov/eco-research/ecoregions). Riparian, alpine, sparse, and other site-specific EVTs are constrained by predetermined masks. In LF 2023 Conterminous United States (CONUS) extent, LF will map the lifeform, cover, and height of existing vegetation in areas that were mapped as disturbed over the last twenty years (see LF Annual Disturbance products) using machine learning methods. These disturbed areas were the focus because they are the areas that have changed the most since LF 2016 Remap. To learn more about this new methodology for LF EVC, EVH, and Existing Vegetation Type (EVT) go to https://www.landfire.gov/data/lf2023.

The Existing Vegetation Cover (EVC) product depicts percent canopy cover by life form and is an important input to other LANDFIRE mapping efforts. EVC is generated separately for tree, shrub and herbaceous life forms using training data and a series of geospatial predictor layers. Plots from the Forest Inventory and Analysis (FIA) program of USDA Forest Service (https://www.fia.fs.usda.gov/) were used as the training data for tree canopy cover mapping, with canopy cover of the plots estimated from stem-mapped tree data and calibrated with line intercept field measurements of canopy cover (Toney and others 2009). Shrub and herbaceous canopy cover training data were also derived from plot-level, ground-based visual assessments. More information regarding contributors of field plot data can be found at http://www.landfire.gov/participate_acknowledgements.php. Regression tree models were developed separately for each life form using the training data and a combination of multitemporal Landsat data, terrain data from a digital elevation model, and biophysical gradient data layers. Cubist software was used for modeling. The derived regression tree equations were then applied to the geospatial predictor data to create 30-m resolution, life form specific data layers (i.e., separate data layers are generated for tree, shrub and herbaceous vegetation cover). Each of the derived data layers (tree, shrub, herbaceous) has a potential range of 0-100 percent canopy cover. Tree, shrub and herbaceous values were binned into discrete classes (up to 10 bins at 10 percent intervals for tree, shrub and herbaceous canopy cover). The final EVC layer was evaluated and rectified through a series of QA/QC measures to ensure that the life form of the canopy cover code matched the life form of the LANDFIRE Existing Vegetation Type (EVT) layer. EVC is used in the development of subsequent LANDFIRE data layers. LF 2014 (lf_1.4.0) used modified LF 2010 (lf_1.2.0) data as a launching point to incorporate disturbance and its severity, both managed and natural, which occurred on the landscape 2013 and 2014. Specific examples of disturbance are: fire, vegetation management, weather, and insect and disease. The final disturbance data used in LANDFIRE is the result of several efforts that include data derived in part from remotely sensed land change methods, Monitoring Trends in Burn Severity (MTBS), and the LANDFIRE Events data call. Vegetation growth was modeled where both disturbance and non-disturbance occurs. Urban, agriculture, and wetlands were refined to reflect a 2012 landscape using the National Conservation Easement Database, National Wetlands Inventory (NWI), and Common Land Unit database (CLU) data.

The Existing Vegetation Cover (EVC) product depicts percent canopy cover by life form and is an important input to other LANDFIRE mapping efforts. EVC is generated separately for tree, shrub and herbaceous life forms using training data and a series of geospatial predictor layers. Plots from the Forest Inventory and Analysis (FIA) program of USDA Forest Service (https://www.fia.fs.usda.gov/) were used as the training data for tree canopy cover mapping, with canopy cover of the plots estimated from stem-mapped tree data and calibrated with line intercept field measurements of canopy cover (Toney and others 2009). Shrub and herbaceous canopy cover training data were also derived from plot-level, ground-based visual assessments. More information regarding contributors of field plot data can be found at http://www.landfire.gov/participate_acknowledgements.php. Regression tree models were developed separately for each life form using the training data and a combination of multitemporal Landsat data, terrain data from a digital elevation model, and biophysical gradient data layers. Cubist software was used for modeling. The derived regression tree equations were then applied to the geospatial predictor data to create 30-m resolution, life form specific data layers (i.e., separate data layers are generated for tree, shrub and herbaceous vegetation cover). Each of the derived data layers (tree, shrub, herbaceous) has a potential range of 0-100 percent canopy cover. Tree, shrub and herbaceous values were binned into discrete classes (up to 10 bins at 10 percent intervals for tree, shrub and herbaceous canopy cover). The final EVC layer was evaluated and rectified through a series of QA/QC measures to ensure that the life form of the canopy cover code matched the life form of the LANDFIRE Existing Vegetation Type (EVT) layer. EVC is used in the development of subsequent LANDFIRE data layers. LF 2014 (lf_1.4.0) used modified LF 2010 (lf_1.2.0) data as a launching point to incorporate disturbance and its severity, both managed and natural, which occurred on the landscape 2013 and 2014. Specific examples of disturbance are: fire, vegetation management, weather, and insect and disease. The final disturbance data used in LANDFIRE is the result of several efforts that include data derived in part from remotely sensed land change methods, Monitoring Trends in Burn Severity (MTBS), and the LANDFIRE Events data call. Vegetation growth was modeled where both disturbance and non-disturbance occurs. Urban, agriculture, and wetlands were refined to reflect a 2012 landscape using the National Conservation Easement Database, National Wetlands Inventory (NWI), and Common Land Unit database (CLU) data.

The Existing Vegetation Cover (EVC) product depicts percent canopy cover by life form and is an important input to other LANDFIRE mapping efforts. EVC is generated separately for tree, shrub and herbaceous life forms using training data and a series of geospatial predictor layers. Plots from the Forest Inventory and Analysis (FIA) program of USDA Forest Service (https://www.fia.fs.usda.gov/) were used as the training data for tree canopy cover mapping, with canopy cover of the plots estimated from stem-mapped tree data and calibrated with line intercept field measurements of canopy cover (Toney and others 2009). Shrub and herbaceous canopy cover training data were also derived from plot-level, ground-based visual assessments. More information regarding contributors of field plot data can be found at http://www.landfire.gov/participate_acknowledgements.php. Regression tree models were developed separately for each life form using the training data and a combination of multitemporal Landsat data, terrain data from a digital elevation model, and biophysical gradient data layers. Cubist software was used for modeling. The derived regression tree equations were then applied to the geospatial predictor data to create 30-m resolution, life form specific data layers (i.e., separate data layers are generated for tree, shrub and herbaceous vegetation cover). Each of the derived data layers (tree, shrub, herbaceous) has a potential range of 0-100 percent canopy cover. Tree, shrub and herbaceous values were binned into discrete classes (up to 10 bins at 10 percent intervals for tree, shrub and herbaceous canopy cover). The final EVC layer was evaluated and rectified through a series of QA/QC measures to ensure that the life form of the canopy cover code matched the life form of the LANDFIRE Existing Vegetation Type (EVT) layer. EVC is used in the development of subsequent LANDFIRE data layers. LF 2014 (lf_1.4.0) used modified LF 2010 (lf_1.2.0) data as a launching point to incorporate disturbance and its severity, both managed and natural, which occurred on the landscape 2013 and 2014. Specific examples of disturbance are: fire, vegetation management, weather, and insect and disease. The final disturbance data used in LANDFIRE is the result of several efforts that include data derived in part from remotely sensed land change methods, Monitoring Trends in Burn Severity (MTBS), and the LANDFIRE Events data call. Vegetation growth was modeled where both disturbance and non-disturbance occurs. Urban, agriculture, and wetlands were refined to reflect a 2012 landscape using the National Conservation Easement Database, National Wetlands Inventory (NWI), and Common Land Unit database (CLU) data.