Geography
Document Type
Article
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%.
Publication Title
Remote Sensing
Publication Date
1-1-2015
Volume
7
Issue
11
First Page
15295
Last Page
15317
ISSN
2072-4292
DOI
10.3390/rs71115295
Keywords
agriculture, classification, cropland, food security, land cover, landsat, logistic regression, multi-temporal, Savanna, spectral mixture analysis
Repository Citation
Sweeney, Sean; Ruseva, Tatyana; Estes, Lyndon; and Evans, Tom, "Mapping cropland in smallholder-dominated savannas: Integrating remote sensing techniques and probabilistic modeling" (2015). Geography. 75.
https://commons.clarku.edu/faculty_geography/75
