Monitoring spatio-temporal changes in land use, land cover, and NDVI using MODIS data in Ethiopia’s Gambela region

Abstract Understanding spatiotemporal changes in land use, land cover (LULC), and vegetation dynamics is crucial for sustainable environmental management and planning. This study investigated LULC and vegetation changes in the Gambela region of Ethiopia using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data from 2004 to 2024. This study relied on MOD13A3 (NDVI, 1 km, monthly) to track vegetation changes from to 2004–2024, as well as Landsat image classification was used to LULC estimation. The IGBP was refined using a random forest with NDVI thresholding to identify shifts. The accuracy was 87% through Sentinel-2 and ground truth, and NDVI deviations were associated (0.80) with yields. Geospatial and statistical techniques were employed to detect and quantify transitions between land cover classes and fluctuations in greenness in the study area. Six LULC classes, namely forest, agricultural land, grassland, irrigated land, built‑up area, and water bodies, were mapped and analyzed. Between 2004 and 2024, forest cover declined by 2 693.9 km 2 (from 74.2% to 65.3%), agricultural land expanded by 4 618.4 km 2 (from 5.3% to 20.6%), and grasslands contracted by 2 397.8 km 2 (from 19.5% to 11.5%). Irrigated areas more than tripled (0.4% to 1.2%), and built‑up extent grew nearly five‑fold (0.2% to 0.9%), whereas water bodies remained largely stable during this period. NDVI analysis revealed a 12% reduction in high-greenness areas, typically corresponding to NDVI values ≥ 0.6 (often 0.6–0.8), and a mean NDVI drop from 0.62 to 0.59 in non-forest zones, indicating declining vegetation health in converted landscapes. The study found significant LULC changes driven by agricultural expansion, settlement growth, and climate variability, with declining natural vegetation and increasing cultivated and built-up areas in the western and central regions. MODIS data are valuable for environmental monitoring, offering insights into land management and climate adaptation.