Predicting land use and land cover change dynamics in the eThekwini Municipality: a machine learning approach with Landsat imagery

Monitoring and providing accurate land use and land cover (LULC) change information is vital for sustainable environmental planning. This study used Landsat imagery from 2002 to 2022 to create updated LULC change maps for the eThekwini Municipality. Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) were used to conduct these LULC classifications, with XGBoost achieving the highest accuracy (80.57%). The generated maps revealed a significant decrease in cropland and an increase in impervious surfaces. As such, this research established a framework for continuous LULC mapping and highlighted Landsat 9's potential in LULC classifications.