데이터셋 상세
미국
Data from: Not just crop or forest: building an integrated land cover map for agricultural and natural areas (tabular files)
,Due to our increasing understanding of the role the surrounding landscape plays in ecological processes, a detailed characterization of land cover, including both agricultural and natural habitats, is ever more important for both researchers and conservation practitioners. Unfortunately, in the United States, different types of land cover data are split across thematic datasets that emphasize agricultural or natural vegetation, but not both. To address this data gap and reduce duplicative efforts in geospatial processing, we merged two major datasets, the LANDFIRE National Vegetation Classification (NVC) and USDA-NASS Cropland Data Layer (CDL), to produce an integrated land cover map. Our workflow leveraged strengths of the NVC and the CDL to produce detailed rasters comprising both agricultural and natural land-cover classes. We generated these maps for each year from 2012-2021 for the conterminous United States, quantified agreement between input layers and accuracy of our merged product, and published the complete workflow necessary to update these data. In our validation analyses, we found that approximately 5.5% of NVC agricultural pixels conflicted with the CDL, but we resolved a majority of these conflicts based on surrounding agricultural land, leaving only 0.6% of agricultural pixels unresolved in our merged product.,Spatial data,Technical validation data,,
연관 데이터
Data from: Not just crop or forest: building an integrated land cover map for agricultural and natural areas (spatial files)
공공데이터포털
,Due to our increasing understanding of the role the surrounding landscape plays in ecological processes, a detailed characterization of land cover, including both agricultural and natural habitats, is ever more important for both researchers and conservation practitioners. Unfortunately, in the United States, different types of land cover data are split across thematic datasets that emphasize agricultural or natural vegetation, but not both. To address this data gap and reduce duplicative efforts in geospatial processing, we merged two major datasets, the LANDFIRE National Vegetation Classification (NVC) and USDA-NASS Cropland Data Layer (CDL), to produce integrated ‘Spatial Products for Agriculture and Nature’ (SPAN). Our workflow leveraged strengths of the NVC and the CDL to produce detailed rasters comprising both agricultural and natural land-cover classes. We generated SPAN for each year from 2012-2021 for the conterminous United States, quantified agreement between input layers and accuracy of our merged product, and published the complete workflow necessary to update SPAN. In our validation analyses, we found that approximately 5.5% of NVC agricultural pixels conflicted with the CDL, but we resolved a majority of these conflicts based on surrounding agricultural land, leaving only 0.6% of agricultural pixels unresolved in the final version of SPAN.,Spatial data,Resources in this dataset:,,SCINet users: The files can be accessed/retrieved with valid SCINet account at this location: /LTS/ADCdatastorage/NAL/published/node455886/,See the SCINet File Transfer guide for more information on moving large files: https://scinet.usda.gov/guides/data/datatransfer,Globus users: The files can also be accessed through Globus by following this data link. The user will need to log in to Globus in order to retrieve this data. User accounts are free of charge with several options for signing on. Instructions for creating an account are on the login page.,,
Land use change and fragmentation of Rocky Mountain Greater Wildland Ecosystems (GWE) using LANDFIRE data
공공데이터포털
Fragmentation extent of six ecosystem types after European Settlement was analyzed using LANDFIRE data. The ecosystem types includes: Grassland, Shrubland, Conifer, Riparian, Hardwood and Sparse ecosystems. The land use change and fragmentation extents have been analyzed by delineating nine Greater Wildland Ecosystems (GWEs) across NCCSC.