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ISPRS Int. J. Geo-Inf. 2018, 7(3), 107; https://doi.org/10.3390/ijgi7030107

Fine Resolution Probabilistic Land Cover Classification of Landscapes in the Southeastern United States

1
College of Forestry and Conservation, University of Montana, Missoula, MT 59812, USA
2
Rocky Mountain Research Station, U.S. Forest Service, Missoula, MT 59801, USA
3
U.S. Forest Service, Tallahassee, FL 32303, USA
*
Author to whom correspondence should be addressed.
Received: 12 February 2018 / Revised: 8 March 2018 / Accepted: 12 March 2018 / Published: 14 March 2018
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Abstract

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 1 m resolution 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. View Full-Text
Keywords: land-cover classification; spectral analysis; NAIP; remote sensing; neural networks; high spatial resolution land-cover classification; spectral analysis; NAIP; remote sensing; neural networks; high spatial resolution
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St. Peter, J.; Hogland, J.; Anderson, N.; Drake, J.; Medley, P. Fine Resolution Probabilistic Land Cover Classification of Landscapes in the Southeastern United States. ISPRS Int. J. Geo-Inf. 2018, 7, 107.

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