Remote sensing approach to simulate the land use/land cover and seasonal land surface temperature change using machine learning algorithms in a fastest-growing megacity of Bangladesh

Rapid urbanization across many regions in the world is altering the existing land use/land cover (LULC), which is significantly raising the land surface temperature (LST). The present study aims to estimate future LULC and seasonal (summer and winter) LST scenarios in one of the fastest-growing megacities and the business capital of Bangladesh, named as Chattogram. Support Vector Machine (SVM) algorithm and Landsat thermal bands were used to retrieve LULC and LST changing patterns for 1999, 2009 and 2019, respectively. The Cellular Automata (CA) and the Artificial Neural Network (ANN) machine learning algorithms were applied to simulate the LULC and seasonal LST scenarios for 2029 and 2039. The CA and ANN model were validated using simulated and estimated LULC and LST…