Unveiling and modelling: Land degradation, poverty nexus in Nigeria's semi-arid (jigawa state) using cloud-based geospatial data

Abstract

Land degradation and multidimensional poverty represent global challenges whose interconnected dynamics are poorly understood and hinder effective interventions. The aim of this study was to address these issues by integrating the RULSE model, the Vegetation Condition Index (VCI), and the Multidimensional Poverty Index (MPI). Using high-resolution geospatial data from Sentinel-2 via the Google Earth Engine, land-use changes were identified as indicators of degradation, with VCI critical for assessing vegetation health. A holistic poverty assessment using MPI data from the Nigerian National Bureau found strong evidence of land degradation in these regions, with approximately 60–75% experiencing multidimensional poverty. Spatial overlap highlights the close connection between severe land degradation and high multidimensional poverty rates. The correlation analysis provided insights into the relationships between land degradation variables (vegetation status, soil loss, and digital elevation) and poverty patterns (population dynamics). This study highlights how soil erosion negatively impacts agriculture and perpetuates the vicious cycle of poverty. This study illuminates the complex relationship between land degradation and poverty, and argues for further exploration, technological integration, and a nuanced understanding of the components of poverty. Advanced assessments can guide targeted policies and interventions to break the cycle of land degradation and poverty.