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Land Change Monitoring, Assessment, and Projection (LCMAP) Collection 1.1 Annual Land Cover and Land Cover Change Validation Tables (1985-2018) for the Conterminous United States

Published by U.S. Geological Survey | Department of the Interior | Metadata Last Checked: January 27, 2026 | Last Modified: 2021-04-22T00:00:00Z
A validation assessment of Land Cover Monitoring, Assessment, and Projection Collection 1.1 annual land cover products (1985-2019) for the Conterminous United States was conducted with an independently collected reference dataset. Reference data land cover attributes were assigned by trained interpreters for each year of the time series (1984–2018) at to a reference sample of 24,971 Landsat resolution (30m x 30m) pixels. These pixels were randomly selected from a sample frame of all pixels in the ARD grid system which fell within the map area (Dwyer et al., 2018). Interpretation used the TimeSync reference data collection tool which visualizes Landsat images and Landsat data values for all usable images in the time series (1984-2018) (Cohen et al., 2010). Interpreters also referred to air photos and high resolution images available in Google Earth as well as several ancillary data layers. The interpreted land cover attributes were crosswalked to the LCMAP annual land cover classes: Developed, Cropland, Grass/Shrub, Tree Cover, Wetland, Water, Snow/Ice and Barren. Validation analysis directly compared reference labels with annual LCMAP land cover map attributes by cross tabulation. The results of that assessment are reported here as confusion matrices for land cover agreement and land cover change agreement. The standard errors have been calculated using the post stratified estimator (Card, 1982). Land cover class proportions were also estimated from the reference data for each year, 1985-2018, using the post stratified estimator. A cluster sampling formulation (Stehman 1997) was used to calculate standard sampling error for summary tables reporting results for multiple years of data comparison.

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