Landsat classification can cost effectively produce landscape change maps and statistics. The Minneapolis-St.Paul metropolitan area has been used to evaluate the potential of satellite remote sensing for land cover classification and change analysis. The amount of land that was converted from agricultural, forest and wetland use to developed use between 1991 and 1998 was classified using Landsat imagery. The satellite change maps and statistics have accurately captured the patterns and trends of land use change and results have been compared to other traditional surveys. With these techniques we have the capacity to prepare such maps and accompanying data elements within two to three months of the satellite overpass, instead of the more common two to three years for conventional surveys.
Landsat-derived maps of development between 1991 and 1998 (shown here in red), accurately reflect urban expansion in Woodbury, a suburban area east of Saint Paul, Minnesota.
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Upper Midwest Regional Earth Science Applications Center
College of Natural Resources, University of Minnesota
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