Graduate Thesis Or Dissertation
 

Assessment of the accuracy of forested classifications within two broad-scale remotely-sensed vegetation databases in eastern Oregon

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https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/ks65hg637

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  • An accuracy assessment of two broad-scale vegetation databases for eastern Oregon, both developed from satellite imagery, was performed to assess the usefulness of the databases for forest landscape planning and assessment efforts. The two databases were the Oregon Department of Forestry (ODF) vegetation database and the U.S. Geological Survey National Land Cover Database (USGS NLCD) vegetation database, both created from Landsat TM 25m satellite imagery. The ODF vegetation database contained four attributes: crown closure, forest size, forest species, and forest structure. The USGS vegetation database contained a single attribute: forest species. Due to reference data availability problems, the accuracy assessment could only be performed on USDA Forest Service lands, which, however, covered most of the forested landscape of eastern Oregon. Within the ODF vegetation database the accuracy of classifying tree species was good, however, the accuracy of classifying tree species within the USGS database was better. Only one forest size class, the small sawtimber class, was classified moderately well in the ODF vegetation database and neither of the forest structure classes were classified well. While both vegetation databases provide a good representation of forest species as well as non-forested areas, their usefulness for forest landscape planning and assessment is limited. The USGS vegetation database, with only one attribute associated with each pixel, evidently meets the goals of the developers of the map- to provide a reasonably accurate, broad-scale description of forest conditions. The ODF vegetation database, with four attributes associated with each pixel, provides more detailed information about the landscape, albeit at low accuracy levels in many cases.
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