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

Authors :
Peter, Joseph St.
Hogland, John
Anderson, Nathaniel
Drake, Jason
Medley, Paul
Source :
ISPRS International Journal of Geo-Information; Mar2018, Vol. 7 Issue 3, p107, 14p
Publication Year :
2018

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 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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
22209964
Volume :
7
Issue :
3
Database :
Complementary Index
Journal :
ISPRS International Journal of Geo-Information
Publication Type :
Academic Journal
Accession number :
128780860
Full Text :
https://doi.org/10.3390/ijgi7030107