Research Article: A Comparison of Land Use and Land Cover Data in Watersheds of the Mid-Atlantic Region, USA

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Abstract
We compared different sources of land use/land cover data using the percentages of forest, agriculture, urban, and mined lands in approximately 400 watersheds of the U.S. Mid-Atlantic region. The primary data sources were digital Land Use/Land Cover maps (LULC) from the U.S. Geological Survey (USGS) and data derived from Landsat Thematic Mapper (TM) imagery obtained by the Multi-Resolution Land Characteristics (MRLC) Consortium and interpreted at the Earth Resources Observation Systems (EROS) Data Center (version 2). The latter is referred to as MRLC-TM. We also used aerial photographs (AP), topographic maps, field comments, and qualitative watershed assessments as ancillary information. The LULC and MRLC-TM data differed in age, source material, classifications, format (vector vs. raster), and resolution. However, Pearson correlation coefficients between LULC and MRLC-TM were high for broad (composite) forest, agriculture, and urban categories (0.95, 0.96, and 0.92, respectively). Correspondence varied by forest type and by percentage. Where deciduous forests comprised less than 50% of the watershed, MRLC-TM data depicted greater amounts than did LULC, while the reverse was true where deciduous forests were dominant. Agriculture and forest percentages were inversely related in all cases. Urban land percentages were consistently lower for MRLC-TM than LULC. Relatively few watersheds contained strip mines, quarries, or gravel pits and the MRLC-TM vs. LULC relationship was weak (correlation coefficient = 0.41). Detection of reclaimed mines seemed problematic for LULC and MRLC-TM, but these were readily discerned on AP. A single “snapshot” of land cover data will inadequately characterize a watershed if rapid landscape changes occur (e.g., forest clearcuts). Each data type has strengths and weaknesses, depending on a study's objectives, but individual limitations may be alleviated by using multiple data sources.