Land cover change across 45 years in the world’s largest mangrove forest (Sundarbans): the contribution of remote sensing in forest monitoring

This study explored the land use cover (LULC) change over 45 years (1975 -2020) in world’s largest mangrove forest, Sundarbansusing Landsat imagery. LULC maps were created with same-season imagery lowest cloud at four intervals: 1975, 1990, 2005, and 2020. Maximum likelihood classification (MLC) was applied to assign five classes: dense moderate sparse barren land, water body. Accuracy assessment carried out 250 control points for each year resulting overall accuracy kappa coefficient ranging from 84.8% 90.0% 0.81 0.87, respectively. Results show forest its highest 1975 then decreasing by an estimated annual rate of 1.3% 2020, but not consistently. Dense class mostly turned sparse; most land. Most lands located near boundary between human settlement, these two classes more frequent Indian part Sundarbans than Bangladesh part. The conclusion is that time-series remote sensing data can validly support effective management identifying space time changes biodiversity Sundarbans.