Land Cover change assessment based on Normalized Difference Vegetation Index (NDVI) in relation to river morphological dynamics: The case of Lower Most Omo River-Sub Watershed (LMOR-SW), Ethiopia

Hydrology, land cover changes, and societal factors affect flood risk and erosion along riverbanks. Lower Most Omo River-Sub Watershed (LMOR-SW) illustrates this trend over three decades, with back flooding from Lake Turkana currently threatening Dasenech woreda residents. This study employs a non-signature based classification to assess land cover change using NDVI for LMOR sub-basin, categorizing pixels into water body (NDVI < 0), bare land (0 ≤ NDVI < 0.2), shrub land (0.2 ≤ NDVI < 0.5), and forest (NDVI ≥ 0.5).The study aims to relate changes in land cover (LC) to channel alterations. Classified map accuracy was 92.8%, 89.67%, and 92.40% for 1995, 2015, and 2025, with Kappa Coefficients of 91%, 86.40%, and 90%, respectively. The NDVI value range and non-signature based supervised classification effectively modeled land cover changes among water bodies, bare land, shrub land, and forest while adhering to Landsat pre-processing. Over 30 years, water bodies increased by 153.76 km², bare land decreased by 392.59 km², shrub land increased by 529.59 km², and forest cover decreased by 290.77 km². From 1995 to 2025, 27.8 km² of bare land, 72.94 km² of shrub land, and 62.27 km² of forest cover converted to water bodies, increasing it finally from 20.39 km² to 174.15 km².Analysis report warns that without prompt action, Omorate town could flood totally in about 3.07 years and Omo River bridge in 8.26 years due to 5.125km2/yr flooding rate. Plan properly, manage resources sustainably, and use Landsat NDVI with remote sensing and GIS in arid regions to prevent hazards.