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Fine resolution probabilistic land cover classification of landscapes in the southeastern United StatesAuthor(s): Joseph St. Peter; John Hogland; Nathaniel Anderson; Jason Drake; Paul Medley
Source: ISPRS International Journal of Geo-Information. 7(3): 107.
Publication Series: Scientific Journal (JRNL)
Station: Rocky Mountain Research Station
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DescriptionLand cover classification provides valuable information for prioritizing management and conservation operations across large landscapes. Current regional scale land cover geospatial products within the United States have a spatial resolution that is too coarse to provide the necessary information for operations at the local and project scales. This paper describes a methodology that uses recent advances in spatial analysis software to create a land cover classification over a large region in the southeastern United States at a fine (1 m) spatial resolution. This methodology used image texture metrics and principle components derived from National Agriculture Imagery Program (NAIP) aerial photographic imagery, visually classified locations, and a softmax neural network model. The model efficiently produced classification surfaces at 1mresolution across roughly 11.6 million hectares (28.8 million acres) with less than 10% average error in modeled probability. The classification surfaces consist of probability estimates of 13 visually distinct classes for each 1 m cell across the study area. This methodology and the tools used in this study constitute a highly flexible fine resolution land cover classification that can be applied across large extents using standard computer hardware, common and open source software and publicly available imagery.
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CitationSt. Peter, Joseph; Hogland, John; Anderson, Nathaniel; Drake, Jason; Medley, Paul. 2018. Fine resolution probabilistic land cover classification of landscapes in the southeastern United States. ISPRS International Journal of Geo-Information. 7(3): 107.
Keywordsland-cover classification, spectral analysis, NAIP, remote sensing, neural networks, high spatial resolution
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