Detection and evaluation of long-term Land Use/Cover Changes using Landsat satellite data. A case in the Ahafo Ano Southwest District of Ghana

Abstract This study examines Land Use and Land Cover (LULC) changes over a 30-year period (1994–2024) in the Ahafo Ano Southwest District of Ghana using Landsat imagery from 1994, 2004, 2014, and 2024. Supervised classification based on the Maximum Likelihood algorithm was applied to classify the images into five LULC categories: dense vegetation, sparse vegetation, wetland, bare land, and built-up area. The classification results achieved overall accuracies above 95% with Kappa coefficients of approximately 0.92. The results indicate a substantial expansion of built-up areas, sparse vegetation, and bare land, occurring at the expense of dense vegetation and wetlands. These changes are primarily associated with increasing human activities, particularly agricultural expansion and urbanization, which corresponds with population growth in the study area. The findings provide important insights for land-use planning and the formulation of sustainable development policies aimed at enhancing forest cover, conserving ecosystems, and mitigating carbon stock loss.