Abstract
The present study deals with change detection analysis of forest cover in Sundarban delta during 1975–2015 using remote sensing data and GIS. Supervised maximum likelihood classification techniques are needed to classify the remote sensing data, and the classes are water body, barren land, dense forest, and open forest. The study reveals that forest cover areas have been increased by 1.06% (19.28 km2), 5.80% (106.82 km2) during the periods of 1975–1989 and 1989–2000, respectively. The reversed tendency has been observed during 2000–2015, and its areas have been reduced to 5.77% (111.85 km2). The change detection results show that 63%–80% of dense forest area and 66%–70% of open forest area have been unaffected during 1975–2015 and 1975–2000, respectively, while during the interval 2000–2015, only 36% of open forest area has been unaltered. The overall accuracy (86.75%, 90.77%, 88.16%, and 85.03%) and kappa statistic (0.823, 0.876, 0.842, and 0799) have been achieved for the year of 1975, 1989, 2000, and 2015 correspondingly to validate the classification accuracy. Future trend of forest cover changes has been analyzed using the fuzzy logic techniques. From this study, it may be concluded that in future, the forest cover area has been more declined. The primary goal of this study is to notify the alteration of each features, and decision-maker has to take measurement, scrutinize, and control the natural coastal ecosystem in Sundarban delta.
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Acknowledgement
This research activity has been carried out in the Dept. of CSE, University of Kalyani, Kalyani, India. The authors acknowledge the support provided by the DST PURSE Scheme, Govt. of India at the University of Kalyani.
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Kundu, K., Halder, P., Mandal, J.K. (2020). Forest Cover Change Analysis in Sundarban Delta Using Remote Sensing Data and GIS. In: Mandal, J., Sinha, D. (eds) Intelligent Computing Paradigm: Recent Trends. Studies in Computational Intelligence, vol 784. Springer, Singapore. https://doi.org/10.1007/978-981-13-7334-3_7
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DOI: https://doi.org/10.1007/978-981-13-7334-3_7
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