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LANDFIRE Environmental Site Potential

Metadata Updated: July 6, 2024

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 by LANDFIRE map zone. Second, each ESP map unit was stratified by an ESP life form classification layer that incorporated NLCD 2001 data, LF2001 EVC data, a Vegetation Change Tracker (VCT) dataset (Huang, 2010), and the National Wetlands Inventory (NWI) data. Each layer was leveraged against each other to determine areas of stable Sparse, Upland Herb, Upland Shrub, Upland Woodland, Upland Forest, Wetland Shrub-herb, Wetland Forest, Wetland Shrub, and Wetland Herb. Areas mapped as agriculture, urban, barren, snow-ice, and water were described as “Undetermined”.

Access & Use Information

Public: This dataset is intended for public access and use. License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

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Dates

Metadata Created Date August 1, 2023
Metadata Updated Date July 6, 2024

Metadata Source

Harvested from DOI EDI

Additional Metadata

Resource Type Dataset
Metadata Created Date August 1, 2023
Metadata Updated Date July 6, 2024
Publisher U.S. Geological Survey
Maintainer
@Id http://datainventory.doi.gov/id/dataset/4024f55a3b3d79016ee0e8f600bdf6a0
Identifier USGS:64bea9b1d34e70357a31c4d9
Data Last Modified 20230928
Category geospatial
Public Access Level public
Bureau Code 010:12
Metadata Context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
Metadata Catalog ID https://datainventory.doi.gov/data.json
Schema Version https://project-open-data.cio.gov/v1.1/schema
Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
Harvest Object Id 623a0dfc-f818-4cc9-912c-d17769a80220
Harvest Source Id 52bfcc16-6e15-478f-809a-b1bc76f1aeda
Harvest Source Title DOI EDI
Metadata Type geospatial
Old Spatial -127.9878,22.7657,-65.2544,51.6202
Publisher Hierarchy White House > U.S. Department of the Interior > U.S. Geological Survey
Source Datajson Identifier True
Source Hash 314cd820fb5409c5df243d7a97cd1d6658e26cc26c7cf18f6ae356b4a965df5c
Source Schema Version 1.1
Spatial {"type": "Polygon", "coordinates": -127.9878, 22.7657, -127.9878, 51.6202, -65.2544, 51.6202, -65.2544, 22.7657, -127.9878, 22.7657}

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