Geography
Document Type
Article
Abstract
Smallholder farms dominate in many parts of the world, particularly Sub-Saharan Africa. These systems are characterized by small, heterogeneous, and often indistinct field patterns, requiring a specialized methodology to map agricultural land cover. Using a variety of sites in South Africa, we present a new approach to mapping agricultural fields, based on efficient extraction of a vast set of simple, highly correlated, and interdependent features, followed by a random forest classifier. We achieved similar high performance across agricultural types, including the spectrally indistinct smallholder fields as well as the more easily distinguishable commercial fields, and demonstrated the ability to generalize performance across large geographic areas. In sensitivity analyses, we determined multi-temporal information provided greater gains in performance than the addition of multi-spectral bands available in DigitalGlobe Worldview-2 imagery.
Publication Title
Remote Sensing of Environment
Publication Date
6-15-2016
Volume
179
First Page
210
Last Page
221
ISSN
0034-4257
DOI
10.1016/j.rse.2016.03.010
Keywords
agriculture, computer vision, land cover, machine learning, Sub-Saharan Africa
Repository Citation
Debats, Stephanie R.; Luo, Dee; Estes, Lyndon; Fuchs, Thomas J.; and Caylor, Kelly K., "A generalized computer vision approach to mapping crop fields in heterogeneous agricultural landscapes" (2016). Geography. 72.
https://commons.clarku.edu/faculty_geography/72
Copyright Conditions
The available download for this article is a pre-print copy.
