Using UAVs to Detect Fine-Scale Signals of Land Degradation and Rehabilitation in West African Drylands

Devon Maloney, Colin Thor West (), Alfredo J. Rojas, Aaron Moody and Gevapaf
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Devon Maloney: Department of Geography and Environment, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
Colin Thor West: Department of Anthropology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
Alfredo J. Rojas: Biobehavioral Health Department, Pennsylvania State University, University Park, PA 16802, USA
Aaron Moody: Department of Geography and Environment, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
Gevapaf: Gestion de l’Environnement et Valorisation des Produits Agropastoraux et Forestiers, Dapong BP184, Togo

Land, 2025, vol. 14, issue 11, 1-15

Abstract: Experts have long associated West Africa’s drylands with extensive and severe land degradation. In fact, the term “desertification” was coined in reference to the great Sahelian droughts of the 1970s and 1980s. Thus, much research has focused on Sahelian countries where there have also been numerous large-scale projects to combat desertification. Wetter, southern Sudanian savannas have received less attention. At the same time, scientific experts and policymakers have seriously questioned desertification as a concept and advocate for a new paradigm of land degradation neutrality (LDN). This entails assessing both land degradation and rehabilitation. The northern Sudanian savannas of Togo had been previously identified as an area with widespread and increasing land degradation based on regional analyses with coarse satellite imagery. Little or no rehabilitation had been either studied or detected. This study sought to follow up on these previous works to investigate local-scale patterns of both land degradation and rehabilitation. Fieldwork entailed a place-based approach using unmanned aerial vehicles (UAVs or drones) and participatory exercises with local stakeholders across nine sites. The spatial analysis incorporated local perceptions to classify the drone imagery. Results indicate that LDN varies markedly among the communities and that patterns of LDN are highly heterogeneous at these local scales.

Keywords: land use/land cover; land degradation neutrality; unmanned aerial vehicle (UAV); remote sensing; West Africa; drylands (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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