Submitted to: Photogrammetric Engineering and Remote Sensing
Publication Type: Peer reviewed journal
Publication Acceptance Date: 8/20/2010
Publication Date: 2/1/2011
Citation: Goslee, S.C. 2011. National land-cover data and national agricultural census estimates of agricultural land use in the northeastern United States. Photogrammetric Engineering and Remote Sensing. 77:141-147. Interpretive Summary: The National Land-Cover Data (NLCD) project mapped the land uses of the United States in 1992 and 2001. This spatial information is needed to address urgent social and environmental issues related to agriculture in the northeastern United States. NLCD estimates of cropland area and pasture/hay area for each county in the Northeast were compared to USDA Census of Agriculture values for the same dates. Total agriculture area matched well, but cropland areas were less accurate, and agricultural grassland area estimates were poor. NLCD classification accurately identified agricultural areas, but did not clearly distinguish types within that broad class. It is the best-available regional land cover map, but may not be usable for projects requiring detailed spatial data.
Technical Abstract: The landscape of the northeastern United States is diverse and patchy, a complex mixture of forest, agriculture, and developed lands. Many urgent social and environmental issues require spatially-referenced information on land use, a need filled by the National Land-Cover Data (NLCD). The accuracy of the NLCD data from 1992 and 2001 was assessed by aggregating it by county and comparing proportions of cropland and pasture/hay area to those recorded in the USDA Census of Agriculture (CoA) for the corresponding years. Estimates of total agricultural area corresponded closely between the two data sources (r2 = 0.91 for 1992 and 0.85 for 2001/2), but NLCD performed poorly at distinguishing between cropland and agricultural grassland (r2 = 0.64 and 0.80 for cropland, and only 0.50 and 0.44 for agricultural grassland). High forest cover reduced accuracy. Landscape complexity and patch size may also impede classification accuracy. Accuracy varied greatly by state, even for states within the same mapping regions. The NLCD is the best-available source of spatial information on agricultural land uses, but may not be suitable for projects requiring that agricultural lands be divided into subclasses.