Detecting Density Changes of Mangrove Forest in India using Remotely Sensed Images - Chakraborty, Sunanda; Nandi, Soumyajit; Ahmed, Shakir; Adhikari, Nilanjana; Sultana, Mahamuda; Bhattacharya, Suman (International Journal of Computer Sciences and Engineering, 2026)
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