Improved Forest Classification in the Northern Lake States Using Multi-Temporal Landsat ImageryPhotogrammetric Engineering & Remote Sensing (1995)
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 DateSeptember, 1995
Citation InformationPeter 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/