SREL Reprint #1921





Forest Mapping at Lassen Volcanic National Park, California, Using Landsat TM Data and a Geographical Information System

Joseph D. White, Glenn C. Kroh, and John E. Pinder III

Knowledge of forest species composition is an integral part of designing and implementing resource management policies in a national park. Managers must rely on cost-effective methods of vegetation mapping, namely, use of remotely sensed data coupled with digital geographic data, to help them meet their management goals. In this study, we demonstrate that genus-level maps can be generated from unsupervised classifications of Landsat TM data at an accuracy level of 73 percent. Species-level maps can be created to an accuracy level of 58 percent by post-stratification of the spectral classification with topographic data in a geographic information system (GIS). This modification method is a rule-based system whereby spectral forest classes are sorted based on elevation and soil-mioisture gradients established for each species through ecological research. Our observations illustrate that spectral classification is optimized by using all six reflective TM bands and that classification accuracy is affected by canopy cover and understory vegetation. Modifying spectral classifications by environmental data in a GIS is a useful way of defining species composition of forests in an area where access to forests is limited but need for map information is great.

SREL Reprint #1921

White, J.D., G.C. Kroh, and J.E. Pinder III. 1995. Mapping forest at Lassen Volcanic National Park, California, using Landsat Thematic Mapper Data and a Geographical Information System. Photogrammetric Engineering and Remote Sensing 61:299-305.

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