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Improved Forest Classification in the Northern Lake States Using Multi-Temporal Landsat Imagery
Photogrammetric Engineering & Remote Sensing (1995)
  • Peter T. Wolter, University of Minnesota - Duluth
  • David J. Mladenoff, University of Minnesota - Duluth
  • George E. Host, University of Minnesota - Duluth
  • Thomas R. Crow, United States Department of Agriculture Forest Service
Abstract

Forest classifications using single date Landsat TM data have been only moderately successful in separating forest  cover types in the northern Lake States region. Few regional forest classifications have been presented  that achieve genus or species level accuracy. We developed a more specific forest cover classification using TM data from early summer in con­junction with four MSS dates to capture phenological changes of different tree species. Among the 22 forest  types classified, multi-temporal image analysis aided in separating 13 types. Of greatest significance, trembling aspen, sugar maple, north­ern red oak, northern pin oak, black ash, and tamarack were successfully classified. The overall classification accuracy was 83.2 percent and the forest  classification accuracy was 80.1 percent. This approach may be useful for broad-scale forest cover monitoring in other areas, particularly where an­cillary data layers are not available.
Publication Date
September, 1995
Publisher Statement
Works produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted.
Citation Information
Peter T. Wolter, David J. Mladenoff, George E. Host and Thomas R. Crow. "Improved Forest Classification in the Northern Lake States Using Multi-Temporal Landsat Imagery" Photogrammetric Engineering & Remote Sensing Vol. 61 Iss. 9 (1995) p. 1129 - 1143
Available at: http://works.bepress.com/peter-wolter/8/