Journal
REMOTE SENSING OF ENVIRONMENT
Volume 179, Issue -, Pages 210-221Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.rse.2016.03.010
Keywords
Land cover; Agriculture; Sub-Saharan Africa; Computer vision; Machine learning
Funding
- Princeton Environmental Institute through the Walbridge Fund
- Mary and Randall Hack '69 Research Fund
- Program in Science, Technology, and Environmental Policy (PEI-STEP) Fellowship
- NASA Jet Propulsion Laboratory Strategic University Partnerships (JPL SURP) [1524338]
- National Science Foundation [SES-1360463, BCS-1026776]
- NASA New Investigator Program [NNX15AC64G]
- NASA [809418, NNX15AC64G] Funding Source: Federal RePORTER
- Divn Of Social and Economic Sciences
- Direct For Social, Behav & Economic Scie [1534544, 1360463] Funding Source: National Science Foundation
- Divn Of Social and Economic Sciences
- Direct For Social, Behav & Economic Scie [1830752] Funding Source: National Science Foundation
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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. (C) 2016 Elsevier Inc. All rights reserved.
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