Skip to main content
U.S. flag

An official website of the United States government

Fine resolution probabilistic land cover classification of landscapes in the southeastern United States

Author(s):

Joseph St. Peter
Jason Drake
Paul Medley

Year:

2018

Publication type:

Scientific Journal (JRNL)

Primary Station(s):

Rocky Mountain Research Station

Source:

ISPRS International Journal of Geo-Information. 7(3): 107.

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.

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.

Cited

Publication Notes

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