This paper represents an advanced assessment of a satellite imagery-based vegetation transition detection method for Bangladesh's southern district of Barguna. Bangladesh has been identified as the most vulnerable country to the effects of climate change due to deteriorating vegetative cover and urbanization pushing forces. This paper aims to use GIS and remote sensing techniques to estimate vegetation change and determine the conversion pattern in the coastal district of Barguna, Bangladesh, from 1989 to 2020. The major methodology used in this research is NDVI differencing and supervised image classification to validate the conversion over time. With a combination of satellite imagery bands, NDVI uses multi-spectral remote sensing techniques to discover vegetation index, water, empty spaces, and forests. To categorize the canopy into distinct kinds, we use NDVI threshold values of 0, 0.1, 0.15, and 0.2. Our findings reveal significant geographical variations in bare regions, sparse, moderate, and thick vegetation types. We discovered that over the last 31 years, a total of 412.90 km 2 (49.25 percent) of land has been deforested, with the transition rate being particularly high in dense vegetation. This research of vegetation cover change can help predict the recurrence of natural disasters, give humanitarian assistance, and enable innovative protection tactics.