Abstract
Mangrove ecosystems play an important functional role in providing coastal protection, carbon sequestration, coastal habitat, climate change resilience and socioeconomic services for coastal communities. The present research study investigates spatiotemporal variation, forest health status and predicts the land cover changes in Sundarban mangrove forests of India using multi-temporal satellite images and cellular automata and Markov Chain model. The results revealed that mangrove forest extent has decreased by 3.14% from 1994 and 2019. The image classification resulted in overall accuracy of 74% in 1994, 81% in 2004, 78% in 2014 and 84.5% in 2019 respectively. The satellite-based vegetation indices were analysed for assessing the health of the forests. The findings of present study indicate deteriorating health of the forest and observed significant vegetation stresses over the western to central part of the study region due to anthropogenic activities. The CA Markov model predicted that the extent of mangrove forests could possibly decline from 2011.60 km2 to 1939.24 km2by the period 2029. The results of the present study could foster better decisions, precise mitigation and sustainable development strategies for the region.
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Halder, S., Samanta, K., Das, S. (2021). Monitoring and Prediction of Dynamics in Sundarban Forest using CA–Markov Chain Model. In: Shit, P.K., Pourghasemi, H.R., Das, P., Bhunia, G.S. (eds) Spatial Modeling in Forest Resources Management . Environmental Science and Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-56542-8_18
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