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Article

Applying Multi-Temporal Landsat Satellite Data and Markov-Cellular Automata to Predict Forest Cover Change and Forest Degradation of Sundarban Reserve Forest, Bangladesh

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Key Laboratory of Digital Earth Sciences, Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Beijing 100094, China
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College of Resource and Environmental Studies, University of Chinese Academy of Sciences (UCAS), No. 19A Yuquan Road, Beijing 100049, China
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Department of Geography and Environmental Studies, University of Chittagong, Chittagong 4331, Bangladesh
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Institute of Forestry and Environmental Sciences, University of Chittagong, Chittagong 4331, Bangladesh
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State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
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Key Laboratory of Earth Observation of Hainan Province, Sanya 572029, China
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Department of Economics, University of Chittagong, Chittagong 4331, Bangladesh
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Department of Biology, Norwegian University of Science and Technology, NTNU, 7491 Trondheim, Norway
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Selwyn College, University of Cambridge, Grange Road, Cambridge CB3 9DQ, UK
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Department of Civil, Instituto Superior Técnico, Universidade de Lisboa, Alameda Campus, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal
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Author to whom correspondence should be addressed.
Forests 2020, 11(9), 1016; https://doi.org/10.3390/f11091016
Received: 21 July 2020 / Revised: 12 September 2020 / Accepted: 16 September 2020 / Published: 21 September 2020
Overdependence on and exploitation of forest resources have significantly transformed the natural reserve forest of Sundarban, which shares the largest mangrove territory in the world, into a great degradation status. By observing these, a most pressing concern is how much degradation occurred in the past, and what will be the scenarios in the future if they continue? To confirm the degradation status in the past decades and reveal the future trend, we took Sundarban Reserve Forest (SRF) as an example, and used satellite Earth observation historical Landsat imagery between 1989 and 2019 as existing data and primary data. Moreover, a geographic information system model was considered to estimate land cover (LC) change and spatial health quality of the SRF from 1989 to 2029 based on the large and small tree categories. The maximum likelihood classifier (MLC) technique was employed to classify the historical images with five different LC types, which were further considered for future projection (2029) including trends based on 2019 simulation results from 1989 and 2019 LC maps using the Markov-cellular automata model. The overall accuracy achieved was 82.30%~90.49% with a kappa value of 0.75~0.87. The historical result showed forest degradation in the past (1989–2019) of 4773.02 ha yr−1, considered as great forest degradation (GFD) and showed a declining status when moving with the projection (2019–2029) of 1508.53 ha yr−1 and overall there was a decline of 3956.90 ha yr−1 in the 1989–2029 time period. Moreover, the study also observed that dense forest was gradually degraded (good to bad) but, conversely, light forest was enhanced, which will continue in the future even to 2029 if no effective management is carried out. Therefore, by observing the GFD, through spatial forest health quality and forest degradation mapping and assessment, the study suggests a few policies that require the immediate attention of forest policy-makers to implement them immediately and ensure sustainable development in the SRF. View Full-Text
Keywords: land cover; forest cover change; spatial forest health quality; forest degradation; multi-temporal Landsat satellite image; Markov-cellular automata model; Sundarban Reserve Forest; Bangladesh land cover; forest cover change; spatial forest health quality; forest degradation; multi-temporal Landsat satellite image; Markov-cellular automata model; Sundarban Reserve Forest; Bangladesh
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MDPI and ACS Style

Hasan, M.E.; Nath, B.; Sarker, A.H.M.R.; Wang, Z.; Zhang, L.; Yang, X.; Nobi, M.N.; Røskaft, E.; Chivers, D.J.; Suza, M. Applying Multi-Temporal Landsat Satellite Data and Markov-Cellular Automata to Predict Forest Cover Change and Forest Degradation of Sundarban Reserve Forest, Bangladesh. Forests 2020, 11, 1016. https://doi.org/10.3390/f11091016

AMA Style

Hasan ME, Nath B, Sarker AHMR, Wang Z, Zhang L, Yang X, Nobi MN, Røskaft E, Chivers DJ, Suza M. Applying Multi-Temporal Landsat Satellite Data and Markov-Cellular Automata to Predict Forest Cover Change and Forest Degradation of Sundarban Reserve Forest, Bangladesh. Forests. 2020; 11(9):1016. https://doi.org/10.3390/f11091016

Chicago/Turabian Style

Hasan, Mohammad Emran, Biswajit Nath, A.H.M. Raihan Sarker, Zhihua Wang, Li Zhang, Xiaomei Yang, Mohammad Nur Nobi, Eivin Røskaft, David J. Chivers, and Ma Suza. 2020. "Applying Multi-Temporal Landsat Satellite Data and Markov-Cellular Automata to Predict Forest Cover Change and Forest Degradation of Sundarban Reserve Forest, Bangladesh" Forests 11, no. 9: 1016. https://doi.org/10.3390/f11091016

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