Rapid urbanization significantly impacts land use and land cover (LULC), leading to various socioeconomic and environmental challenges. Effective monitoring and detection of spatial discrepancies are crucial for urban planners and authorities to manage these changes. This study aims to analyze the spatial dynamics of LULC changes and predict future land use patterns. The specific objectives are to classify historical land use from 1990 to 2020, simulate future land use from 2020 to 2050, and interpret the spatial and temporal results. The study utilized remotely sensed images with the semi-automatic classification plugin (SCP) approach for land use classification from 1990 to 2020. Future land use patterns were simulated using the Modules of Land Use Change Evaluation (MOLUSCE)-based Cellular Automata-Artificial Neural Network (CA-ANN) model. The results were then interpreted to comprehend the dynamics of urban expansion. The conclusions direct a significant increase in built-up and grasslands, with a consistent decline in other land use types. From 1990 to 2020, approximately 423.75 km² and 856.97 km² of land were converted into built-up areas and grasslands, respectively. This was accompanied by a decline in rocky bare and bare soil areas, while the proportions of water bodies and mangroves remained steady. Predictions for 2020 to 2050 suggest an additional increase of 561.93 km² in built-up areas, with a progressive decline in other land use classes. The study emphasizes the critical need for spatial planning policies to address challenges arising from rapid urbanization. By analyzing historical land use changes and predicting future patterns this research offers a comprehensive view of urban growth dynamics. The novel application of these techniques provides valuable insights for urban planners to develop informed strategies for managing expansion and mitigating associated socioeconomic and environmental impacts.
