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The National Cooperative Soil Survey Program (NCSS) in the United States is a nationwide partnership of federal, regional, state, and local agencies and institutions. This partnership works together to cooperatively investigate, inventory, document, classify, and interpret soils and to disseminate, publish, and promote the use of information about the soils of the United States and its trust ...
Name Description; Maps and GIS of the Great Lakes Region: Great Lakes regional GIS datasets on a variety of topics hosted by the Great Lakes Commission. Regional Ecosystem Office Information Center/Library: Datasets developed for the Northwest Forest Plan: DEM, digital raster graphic, land use, watersheds, old growth, habitat, etc.
a detailed map with specific soil series outlined and indexed; a section on the use and management of soils; tables describing the physical features and environment of the county; tables containing land use suitability based on standards set by the Natural Resources Conservation Service.
The name of a map unit is usually named after the dominant component within the mapping unit. [2] For example, the dominant component of the mapping unit LhE—Lily sandy loam, 15 to 35 percent slopes, very stony in the Greenbrier County, West Virginia soil survey is the Lily series, which comprises 80% of the mapping unit.
Natural Resources Conservation Service (NRCS), formerly known as the Soil Conservation Service (SCS), is an agency of the United States Department of Agriculture (USDA) that provides technical assistance to farmers and other private landowners and managers. Its name was changed in 1994 during the presidency of Bill Clinton to reflect its ...
In United States conservation policy, Major Land Resource Areas (MLRA) are geographically associated land resource units delineated by the Natural Resources Conservation Service and characterized by a particular pattern that combines soils, water, climate, vegetation, land use, and type of farming.
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.
Soil color is quantitatively described using the Munsell color system, which was developed in the early 20th century by Albert Munsell. Munsell was a painter and the system covers the entire range of colors, though the specially adapted Munsell soil color books commonly used in field description only include the most relevant colors for soil. [10]