Cellular Automata approach in dynamic modelling of land cover changes using RapidEye images in Dhaka, Bangladesh

Satellite images have been used extensively to identify the land use/land cover (LULC) changes in Bangladesh. However, no study has been conducted to classify LULC changes in the Dhaka Metropolitan Development Plan (DMDP) area using high-resolution commercial satellite images. The study aimed to simulate future LULC scenarios using RapidEye commercial images in the fastest-growing DMDP area. Support Vector Machine algorithm was applied to estimate the LULC scenarios for years 2012, 2015, and 2018. Cellular Automata machine learning algorithm was used to simulate the future LULC scenarios for 2025. The study result revealed that a significant net increase in the urban areas (UAs) by 15.52%, a remarkable decrease in sparse vegetation (SV) by 12.48%, and a transformation of 17.83% green cover…