The use of deep learning technology in the domain of biodiversity has been expanding over the past few years, with applications in wildlife and vegetation monitoring. The Convolutional (CNN) is a powerful tool that has enabled new feature extraction methods in computer vision. Remote sensing techniques, such as satellite and drone-assisted images, have also contributed to the development of vegetation cover assessment. This study focuses on detecting changes in vegetation cover in the Sundarbans mangrove forest, which is the world`s largest mangrove and a heritage site that supports over 4.37 million people and reduces 45 million tons of CO2. The study Neural Network used a deep learning model to analyze time-series data and achieved an accuracy score of 99.85% and a value of 1 for the other three metrics - precision, recall, and F1-Score. The study also includes a review of previous work in the field and proposes a novel model for vegetation cover assessment. This study emphasizes the importance of sustaining the ecology of the Sundarbans and provides valuable insights for future research in this field.

Mangrove Forest; Deep Learning; Convolutional Neural Network; Remote Sensing Images.

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