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Land use and land cover (LULC) change is reshaping ecosystems, water resources, and biodiversity across sub-Saharan Africa. In this study, we analyzed LULC dynamics in the Aswa 1 sub-basin of northern Uganda from 1990 to 2024 using multi-temporal Landsat imagery processed in Google Earth Engine. Land use/cover was mapped with machine learning, while spatial statistical models identified biophysical and socio-economic drivers of change. Results show rapid agricultural expansion, with agricultural land increasing more than sevenfold (420 km² in 1990 to 3,069 km² in 2024) largely at the expense of bushland, woodland, and wetlands. Accuracy assessment confirmed reliable classifications (overall accuracy = 82% (1990, 2010, 2024) and 90% (2000), and kappa values ranging from 0.74 to 80, verified by field observations. Importantly, the relationship between vegetation productivity Normalized Difference Vegetation Index (NDVI) and precipitation weakened sharply, with GeoDetector showing explanatory power dropping from q ≈ 1.00 in 1990 to 0.45), reflecting a shift from climate-regulated to human-dominated landscapes. The Land Use Degree Index (LUDI) rose from 3.528 in 1990 to 4.267 in 2024, confirming the intensification of human land use. This shift reflects the growing influence of settlements and agricultural intensification on ecosystem services. The study demonstrates the value of combining Earth observation with spatial analytics in data-scarce regions, highlighting the urgent need for ecological restoration, integrated land-use planning, and community-based resource management. Findings provide critical evidence to guide sustainable land management in rapidly changing East African catchments.
Oketta et al. (Mon,) studied this question.