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Remote Sens. 2015, 7(11), 15295-15317; doi:10.3390/rs71115295

Mapping Cropland in Smallholder-Dominated Savannas: Integrating Remote Sensing Techniques and Probabilistic Modeling

1
Center for the study of Institutions, Populations, and Environmental Change (CIPEC), Indiana University, Bloomington, IN 47408, USA
2
Department of Government and Justice Studies, Appalachian State University, Boone, NC 28607, USA
3
Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544, USA
4
Woodrow Wilson School, Princeton University, Princeton, NJ 08544, USA
5
Department of Geography, Indiana University, Bloomington, IN 47408, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Clement Atzberger and Prasad S. Thenkabail
Received: 23 September 2015 / Revised: 31 October 2015 / Accepted: 10 November 2015 / Published: 13 November 2015
View Full-Text   |   Download PDF [1542 KB, uploaded 13 November 2015]   |  

Abstract

Traditional smallholder farming systems dominate the savanna range countries of sub-Saharan Africa and provide the foundation for the region’s food security. Despite continued expansion of smallholder farming into the surrounding savanna landscapes, food insecurity in the region persists. Central to the monitoring of food security in these countries, and to understanding the processes behind it, are reliable, high-quality datasets of cultivated land. Remote sensing has been frequently used for this purpose but distinguishing crops under certain stages of growth from savanna woodlands has remained a major challenge. Yet, crop production in dryland ecosystems is most vulnerable to seasonal climate variability, amplifying the need for high quality products showing the distribution and extent of cropland. The key objective in this analysis is the development of a classification protocol for African savanna landscapes, emphasizing the delineation of cropland. We integrate remote sensing techniques with probabilistic modeling into an innovative workflow. We present summary results for this methodology applied to a land cover classification of Zambia’s Southern Province. Five primary land cover categories are classified for the study area, producing an overall map accuracy of 88.18%. Omission error within the cropland class is 12.11% and commission error 9.76%. View Full-Text
Keywords: cropland; agriculture; savanna; food security; spectral mixture analysis; multi-temporal; logistic regression; land cover; classification; Landsat cropland; agriculture; savanna; food security; spectral mixture analysis; multi-temporal; logistic regression; land cover; classification; Landsat
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Sweeney, S.; Ruseva, T.; Estes, L.; Evans, T. Mapping Cropland in Smallholder-Dominated Savannas: Integrating Remote Sensing Techniques and Probabilistic Modeling. Remote Sens. 2015, 7, 15295-15317.

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