Next Article in Journal
Spatio-Temporal Database of Places Located in the Border Area
Next Article in Special Issue
Mapping Forest Characteristics at Fine Resolution across Large Landscapes of the Southeastern United States Using NAIP Imagery and FIA Field Plot Data
Previous Article in Journal
On the Use of Geographic Information in Humanities Research Infrastructure: A Case Study on Cultural Heritage
Open AccessArticle

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.
ISPRS Int. J. Geo-Inf. 2018, 7(3), 107; https://doi.org/10.3390/ijgi7030107
Received: 12 February 2018 / Revised: 8 March 2018 / Accepted: 12 March 2018 / Published: 14 March 2018
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
Show Figures

Figure 1

MDPI and ACS Style

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.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop