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    Author(s): Joseph St. Peter; John HoglandNathaniel Anderson; Jason Drake; Paul Medley
    Date: 2018
    Source: ISPRS International Journal of Geo-Information. 7(3): 107.
    Publication Series: Scientific Journal (JRNL)
    Station: Rocky Mountain Research Station
    PDF: View PDF  (1.0 MB)

    Description

    Land 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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    Citation

    St. 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.

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    Keywords

    land-cover classification, spectral analysis, NAIP, remote sensing, neural networks, high spatial resolution

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https://www.fs.usda.gov/treesearch/pubs/56142