{
 "culture": "en-US",
 "name": "Landcover_2017",
 "guid": "4C9274C1-3C00-4575-91C6-2C23F4DD8C0E",
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 "snippet": "The production of the CBP 1-meter \u201cland cover\u201d 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, \u201cland use\u201d 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\u2019s 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.\nhttps://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/lulc-data-project-2022/",
 "description": "",
 "summary": "The production of the CBP 1-meter \u201cland cover\u201d 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, \u201cland use\u201d 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\u2019s 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.\nhttps://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/lulc-data-project-2022/",
 "title": "Landcover_2017",
 "tags": [
  "ccpa"
 ],
 "type": "Map Service",
 "typeKeywords": [
  "Data",
  "Service",
  "Map Service",
  "ArcGIS Server"
 ],
 "thumbnail": "thumbnail/thumbnail.png",
 "url": "",
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 "spatialReference": "NAD_1983_StatePlane_Pennsylvania_South_FIPS_3702_Feet",
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}