Skip to Main Content
Due to a lapse in federal funding, this USDA website will not be actively updated. Once funding has been reestablished, online operations will continue.
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
View PDF (1.0 MB)
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.
- You may send email to email@example.com to request a hard copy of this publication.
- (Please specify exactly which publication you are requesting and your mailing address.)
- We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
- This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
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
- Mapping forest characteristics at fine resolution across large landscapes of the southeastern United States using NAIP imagery and FIA field plot data
- Vegetation cover in relation to socioeconomic factors in a tropical city assessed from sub-meter resolution imagery
- The investigation of classification methods of high-resolution imagery
XML: View XML