Abstract
The present study deals with forest cover classification of Sundarban on the basis of fuzzy c-means algorithm using satellite images from 1975 to 2018. Four features are considered in this region such as dense forest, open forest, open land, and water bodies. The study reveals that during 1975–2018 dense forest and water bodies have gradually increased by 3.75% (113.65 km2) and 4.38% (133.06 km2), respectively, while other features like open forest and open land have progressively declined by 4.68% (142.22 km2) and 3.44% (104.72 km2) correspondingly. The net forest area (dense forest, open forest) has declined by 0.93% (28.57 km2) during the study period. The increasing or decreasing trend is not uniform over the entire study period. Net precision and kappa coefficient are used to validate the classification correctness. The classification results are validate using the net precision (86.29, 82.14, 83.16%) and kappa co-efficient (0.817, 0.761, 0.775) for the year of 1975, 2000, and 2018 respectively. The study reveals that the forest declination rate may be increased over the upcoming years. From this study, policy makers may take appropriate decisions to take proper action to control the declination of forest of Sundarban.
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Kundu, K., Halder, P., Mandal, J.K. (2020). Forest Covers Classification of Sundarban on the Basis of Fuzzy C-Means Algorithm Using Satellite Images. In: Mandal, J., Mukhopadhyay, S. (eds) Proceedings of the Global AI Congress 2019. Advances in Intelligent Systems and Computing, vol 1112. Springer, Singapore. https://doi.org/10.1007/978-981-15-2188-1_40
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DOI: https://doi.org/10.1007/978-981-15-2188-1_40
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