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

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 OPEN ACCESSRemote Sens. 2015, 7 15296 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%.

Statistical Machine Learning Methods and Remote Sensing for Sustainable Development Goals: A Review

“…An example of the combination of different methods for improved classification is given by [55]. The authors combined probabilistic modelling, in the form of logistic regression, with traditional remote sensing approaches to obtain maps of small-scale cropland.…”

National-scale cropland mapping based on spectral-temporal features and outdated land cover information

“…Similarly, accuracies obtained by [ 78 ] were always higher than 80% for sites of intensive farming and stalled at around 50% for sites dominated by smallholder agriculture. While dependable information on commercial farming systems is critical to reduce uncertainty in the global commodity markets, traditional smallholder farming systems dominate the savanna range countries of sub-Saharan Africa and provide the foundation for the region’s food security [ 79 ]. More generally, estimates suggest that in the rural areas of developing countries around half of the population is smallholder farmers with up to three hectares of cropland [ 80 ].…”

Classification of Land Use on Sand-Dune Topography by Object-Based Analysis, Digital Photogrammetry, and GIS Analysis in the Horqin Sandy Land, China

“…Table 2 shows that the unrealistic land-use types (which we have classified as "N/A") rarely exist: "paddy on flat sandy land" and "paddy on sand dunes". As mentioned in the introduction, detailed land-use maps are still missing in various regions of the world, including the Horqin Sandy Land, but some remote-sensing researchers have recently worked on the classification of agricultural land uses in some areas, such as Mediterranean and savanna regions [29,[43][44][45][46]. Their research areas and ours share some common land-surface characteristics, e.g., grassland interspersed with trees, shrubs, and crops, which cannot be differentiated by traditional pixel-based classification or vegetation indices [43].…”