Identification of Land Use, Land Cover Change, and Land Surface Temperature (LST) in Ethiopia using Landsat and MODIS Data, East Africa

This study investigated land use and land cover (LULC) dynamics and their influence on land surface temperature (LST) using multi-temporal Landsat and MODIS satellite imagery. Supervised classification employing the Maximum Likelihood Algorithm was applied to classify LULC patterns, and classification accuracy was assessed following standard validation procedures to ensure reliability for environmental monitoring, water resource management, and climate change assessment. Trend and change detection analyses were conducted to evaluate the spatial and temporal variability of LULC and LST within the study area. The results identified 15 major LULC classes and revealed substantial expansion and contraction among different land cover types over time, accompanied by significant variations in LST. Grassland was the dominant land cover category, accounting for more than 36.53 percent of the total area. In contrast, the evergreen needle-leaf forest was the least extensive class, covering less than 0.0014 percent of the study area. The observed changes in LULC and associated thermal characteristics are primarily attributed to both anthropogenic activities and natural environmental processes, which directly influence hydrological conditions, including precipitation, evaporation, streamflow, and water quality dynamics. The findings highlight the strong interrelationship between land-cover transformation and surface thermal responses, emphasising the implications of uncontrolled land-use changes for hydrological balance and ecosystem sustainability. Therefore, the study recommends continuous monitoring and effective management of LULC changes, particularly settlement expansion and deforestation, to mitigate potential environmental degradation and hydrological imbalance within the catchment.