Mapping Land Use and Land Cover Change Detection Using Supervised Maximum Likelihood Classification of Multi-Temporal Landsat Imagery: A Case Study of Nakuru County

Monitoring land use and land cover (LULC) change is crucial for analyzing the socio-economic and environmental effects of land development and management. This research aims to explore the dynamics of urban growth and LULC changes in Nakuru County, Kenya, over a decade from 2014 to 2024. Supervised Maximum Likelihood Classification (MLC), a popular remote-sensing technique, was utilized for the multi-temporal analysis of Landsat 8 Operational Land Imager (OLI) data acquired from the United States Geological Survey (USGS) Earth Explorer website. Five dominant land-cover classes were distinguished, including built-up areas, bare land, sparse vegetation, dense vegetation, and water bodies. The findings reveal that rapid urbanization and agricultural expansion are the primary forces behind LULC changes, resulting in significant loss of green spaces, forest cover, and water resources. These alterations have led to ecosystem disruption and increased environmental stress throughout Nakuru County. The results underscore the urgent need for sustainable land-use planning and management practices that consider the implications of urban growth. Integrating remote-sensing data into decision-making processes is crucial for formulating policies that effectively mitigate land degradation and promote environmentally sustainable urban development in rapidly expanding regions. The findings provide spatially explicit evidence to guide sustainable land management policies under Kenya's Vision 2030 and United Nations Sustainable Development Goals (SDGs 11 and 15).