The Great Plains Landscape Conservation Cooperative (GPLCC, https://www.fws.gov/science/catalog) is a partnership that provides applied science and decision support tools to assist natural resource managers conserve plants, fish and wildlife in the mid- and short-grass prairie of the southern Great Plains. It is part of a national network of public-private partnerships — known as Landscape Conservation Cooperatives (LCCs, http://www.fws.gov/science/shc/lcc.html) — that work collaboratively across jurisdictions and political boundaries to leverage resources and share science capacity. The Great Plains LCC identifies science priorities for the region and helps foster science that addresses these priorities to support wildlife conservation throughout the Great Plains region. It also assists partners in building their own capacity to address scientific challenges associated with our rapidly changing environment.These data were compiled because the information did not previously exist as a single resource for the GPLCC area. They are intended to inform local and regional conservation and management strategies with a complete regional perspective. Abstract provided by original data sources: "The LANDFIRE existing vegetation layers describe the following elements of existing vegetation for each LANDFIRE mapping zone: existing vegetation type, existing vegetation canopy cover, and existing 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.DATA SUMMARY: The existing vegetation cover (EVC) data layer is an important input to LANDFIRE modeling efforts. EVC is generated separately for tree, shrub and herbaceous cover life forms using training data and a series of geospatial data layers. Percentage tree canopy cover training data are generated using digital orthophotographs and/or high spatial resolution satellite data for multiple sites. Percentage shrub and herbaceous canopy cover training data are generated using plot-level ground-based visual assessments. Go to http://www.landfire.gov/participate_acknowledgements.php for more information regarding contributors of field plot data. Once the training data are developed, relationships are then established separately for each life form between the training data and the combination of multitemporal Landsat data, digital elevation data and biophysical gradient data layers using regression tree analysis (Cubist). Correlation (R) values are generated through cross-validation using the regression tree software, and provide a means for assessing accuracy. The derived regression tree equations are then applied to the geospatial data to create 30m resolution life form specific data layers (i.e., separate data layers are generated for tree, shrub and herbaceous vegetation cover).After running Cubist, each of the derived data layers (tree, shrub, herbaceous) has a potential range from 0-100 percent. Tree, shrub and herbaceous values are then binned into discrete classes (up to 10 bins at 10 percent intervals for tree, shrub and herbaceous canopy cover data layers). The final EVC layer is then evaluated and rectified through a series of QA/QC measures to ensure that the life-form of the cover code matched the life-form of the existing vegetation type.EVC is used in many subsequent LANDFIRE data layers. Refer to spatial metadata for date ranges of field plot data and satellite imagery for each LANDFIRE map zone."Data were the best available at the time of compilation (2011) with current information represented by a combination of national-scale datasets and state or other regional data (e.g. soils) that could be reasonably aggregated.
