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The USGS Gap Analysis Program maintains four primary data sets: land cover, protected areas, species and aquatic. The GAP Land Cover Data Set is the most complete map ever produced of vegetative associations for the US. Classified into 551 ecological systems, and 32 modified ecological systems (where human impacts have had an effect).
The National Map is part of the USGS National Geospatial Program. [3] The geographic information available includes orthoimagery (aerial photographs), elevation, geographic names, hydrography, boundaries, transportation, structures and land cover. The National Map is accessible via the Web, as products and services, and as downloadable data ...
GAP has produced national land cover and protected areas datasets, which it uses to assess the conservation status of mammal, bird, reptile, and amphibian species in the U.S. A GAP program normally has three principal components: 1. Landcover analysis 2. Vertebrate species distribution prediction 3. Land stewardship database
Land cover disagreement over Africa in croplands and forests available for analysis in Geo-wiki. Global land cover validation exercises are feasible as images less than 2.5 meter resolution provide very detailed information on land cover with global coverage of at least 20% [6] with more high-resolution, up to date images continuously being added.
A national lidar dataset refers to a high-resolution lidar dataset comprising most—and ideally all—of a nation's terrain. Datasets of this type typically meet specified quality standards and are publicly available for free (or at nominal cost) in one or more uniform formats from government or academic sources.
Integrated Surface Database (ISD) is global database compiled by the National Oceanic and Atmospheric Administration (NOAA) and the National Centers for Environmental Information (NCEI) comprising hourly and synoptic surface observations compiled globally from ~35,500 weather stations; it is updated, automatically, hourly.
A supervised classification is a system of classification in which the user builds a series of randomly generated training datasets or spectral signatures representing different land-use and land-cover (LULC) classes and applies these datasets in machine learning models to predict and spatially classify LULC patterns and evaluate classification accuracies.
Currently, the best source for nationwide LiDAR availability from public sources is the United States Interagency Elevation Inventory (USIEI). [1] The USIEI is a collaborative effort of NOAA and the U.S. Geological Survey, with contributions from the Federal Emergency Management Agency, the Natural Resources Conservation Service, the US Army Corps of Engineers, and the National Park Service.