Analyzing the Land Cover Change and Degradation in Sundarbans Mangrove Forest Using Machine Learning and Remote Sensing Technique

Abstract

The purpose of our research work is to understand the efficiency and advantage of applying machine learning technique on remote sensing data collected from one of the largest mangrove forests in the world, named Sundarbans. Our study area was Sundarbans mangrove forest, and we have detected land cover changes in this area. The images we have used were collected from Landsat 8 OLI, ETM+, TM data. After pre-processing the images, we classified them applying the Maximum Likelihood classifier. We got overall accuracy of 80%, 75%, and 77.1% and kappa efficiency 0.80, 0.62, and 0.69 for the years 2001, 2011, 2021 respectively. To determine the overall accuracy and kappa efficiency, we have used confusion matrix. In the last 20 years, Sundarbans mangrove forest has declined by 0.2% due to human settlements, deforestation, natural calamity, increasing water salinity etc.