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

文献类型：期刊论文

作者Hasan, Mohammad Emran2,3; Nath, Biswajit4; Sarker, A. H. M. Raihan5; Wang, Zhihua1,2; Zhang, Li6; Yang, Xiaomei1,2; Nobi, Mohammad Nur7; Roskaft, Eivin8; Chivers, David J.9; Suza, Ma10
刊名FORESTS
出版日期2020-09-01
卷号11期号:9页码:35
关键词land cover forest cover change spatial forest health quality forest degradation multi-temporal Landsat satellite image Markov-cellular automata model Sundarban Reserve Forest Bangladesh
DOI10.3390/f11091016
通讯作者Wang, Zhihua(zhwang@lreis.ac.cn)
英文摘要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%similar to 90.49% with a kappa value of 0.75 similar to 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.
WOS关键词MANGROVE FORESTS ; SPATIAL-PATTERNS ; NEURAL-NETWORK ; TIME-SERIES ; CLASSIFICATION ; CHINA ; MODEL ; DYNAMICS ; IMAGERY ; CONSERVATION
资助项目National Key Research and Development Program of China[2016YFC1402003] ; CAS Earth Big Data Science Project of China[XDA19060303] ; Climate-Resilient Ecosystems and Livelihoods (CREL) project ; Winrock International ; U.S. Agency for International Development (USAID), Bangladesh Mission, Dhaka, Bangladesh[AID-388-A-12-00007] ; CAS-TWAS President's Fellowship-2017 - University of the Chinese Academy of Sciences (UCAS)[2017CTF099]
WOS研究方向Forestry
语种英语
WOS记录号WOS:000580261100001
出版者MDPI
资助机构National Key Research and Development Program of China ; CAS Earth Big Data Science Project of China ; Climate-Resilient Ecosystems and Livelihoods (CREL) project ; Winrock International ; U.S. Agency for International Development (USAID), Bangladesh Mission, Dhaka, Bangladesh ; CAS-TWAS President's Fellowship-2017 - University of the Chinese Academy of Sciences (UCAS)
源URL[http://ir.igsnrr.ac.cn/handle/311030/157067]  
专题中国科学院地理科学与资源研究所
通讯作者Wang, Zhihua
作者单位1.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci UCAS, Coll Resource & Environm Studies, 19A Yuquan Rd, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Key Lab Digital Earth Sci, Aerosp Informat Res Inst, 9 Dengzhuang South Rd, Beijing 100094, Peoples R China
4.Univ Chittagong, Dept Geog & Environm Studies, Chittagong 4331, Bangladesh
5.Univ Chittagong, Inst Forestry & Environm Sci, Chittagong 4331, Bangladesh
6.Key Lab Earth Observat Hainan Prov, Sanya 572029, Peoples R China
7.Univ Chittagong, Dept Econ, Chittagong 4331, Bangladesh
8.Norwegian Univ Sci & Technol, NTNU, Dept Biol, N-7491 Trondheim, Norway
9.Univ Cambridge, Selwyn Coll, Grange Rd, Cambridge CB3 9DQ, England
10.Univ Lisbon, Inst Super Tecn, Dept Civil, Alameda Campus,Av Rovisco Pais 1, P-1049001 Lisbon, Portugal
推荐引用方式
GB/T 7714		 Hasan, Mohammad Emran,Nath, Biswajit,Sarker, A. H. M. Raihan,et al. Applying Multi-Temporal Landsat Satellite Data and Markov-Cellular Automata to Predict Forest Cover Change and Forest Degradation of Sundarban Reserve Forest, Bangladesh[J]. FORESTS,2020,11(9):35.
APA Hasan, Mohammad Emran.,Nath, Biswajit.,Sarker, A. H. M. Raihan.,Wang, Zhihua.,Zhang, Li.,...&Suza, Ma.(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(9),35.
MLA Hasan, Mohammad Emran,et al."Applying Multi-Temporal Landsat Satellite Data and Markov-Cellular Automata to Predict Forest Cover Change and Forest Degradation of Sundarban Reserve Forest, Bangladesh".FORESTS 11.9(2020):35.

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来源：地理科学与资源研究所

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