Name: Chesapeake Conservancy 2017/2018 Land Use Land Cover
Display Field: GeneralLU
Type: Raster Layer
Geometry Type: null
Description: Chesapeake Conservancy, U.S. Geological Survey (USGS) and University of Vermont Spatial Analysis Lab (UVM SAL) are collaborating, with funding from the Chesapeake Bay Program (CBP), to produce 1-meter resolution land cover and land use/land cover datasets for the Chesapeake Bay watershed regional area (206 counties, over 250,000 km2). These data are foundational, authoritative, and transformative looks at the landscape and its management throughout the region.The production of the CBP 1-meter “land cover” data involves the identification and classification of image objects derived from aerial imagery (National Agriculture Imagery Program, NAIP), above-ground height information derived from LiDAR, and other ancillary data. Land cover represents the surface characteristics of the land with classes such as impervious cover, tree canopy, herbaceous, and barren. In contrast, “land use” represents how humans use and manage the land with classes such as turf grass, cropland, and timber harvest. Producing land use from land cover data requires a variety of ancillary datasets combined with spatial rules that leverage the contextual information inherent in the land cover data. The CBP’s land use/land cover (LULC) data are so named because they represent a combination of cover and use classes (e.g., extractive-barren, solar-herbaceous) that are critical for understanding the impact of human activities on the Chesapeake Bay. For example: one land cover class (herbaceous vegetation) encapsulates both the highest polluting land use (e.g., corn production) or one of the lowest (e.g., natural succession). The LULC data contextualize the land cover classes for decision-making, such as informing outcomes in the Chesapeake Bay Watershed Agreement and serving as the basis for developing the next generation of watershed and land change models.For more information, please visit https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/lulc-data-project-2022/