journal article
Open Access
Sep 21, 2020
Applying Multi-Temporal Landsat Satellite Data and Markov-Cellular Automata to Predict Forest Cover Change and Forest Degradation of Sundarban Reserve Forest, Bangladesh
Abstract
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.
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References
149
[1]
Chen "Recent progresses in mangrove conservation, restoration and research in China" J. Plant Ecol. (2009) 10.1093/jpe/rtp009
[2]
Malik "Mangrove forest decline: Consequences for livelihoods and environment in South Sulawesi" Reg. Environ. Chang. (2017) 10.1007/s10113-016-0989-0
[3]
Abdullah "The impact of the expansion of shrimp acquaculture on livelihoods in coastal Bangladesh" Environ. Dev. Sustain. (2017) 10.1007/s10668-016-9824-5
[4]
Mukhtar "Constrains on mangrove forests and conservation projects in Pakistan" J. Coast. Conserv. (2012) 10.1007/s11852-011-0168-x
[5]
Sandilyan "Decline of mangroves—A threat of heavy metal poisoning in Asia" Ocean Coast. Manag. (2014) 10.1016/j.ocecoaman.2014.09.025
[6]
WWF (2017). Sundarban in a Global Perspective: Long Term Adaptaton and Development, WWF-India Secretariat. Available online: https://wwfin.awsassets.panda.org/downloads/sundarbans_discussion_paper_nitisha.pdf.
[7]
Oswell, A. (2020, January 05). Mangrove Forests: Threats. Available online: http://wwf.panda.org/about_our_earth/blue_planet/coasts/mangroves/mangrove_threats/.
[8]
CEGIS Center for Environmental and Geographic Information Services (2019, December 19). Effect of Cyclone Sidr on the Sundarbans: A Preliminary Assessment, Available online: http://www.lcgbangladesh.org/derweb/cyclone/cyclone_assessment/effect%20of%20cyclone%20sidr%20on%.
[9]
Gilman "Threats to mangroves from climate change and adaptation options: A review" Aquat. Bot. (2008) 10.1016/j.aquabot.2007.12.009
[10]
Son "Mangrove mapping and change detection in Ca Mau Peninsula, Vietnam, using Landsat data and object-based image analysis" IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. (2015) 10.1109/jstars.2014.2360691
[11]
Shamsuddoha, M., and Chowdhury, R.K. (2007). Climate Change Impact and Disaster Vulnerabilities in the Coastal Areas of Bangladesh, COAST Trust and Equity and Justice Working Group (EJWG).
[12]
Meles, K.H. (2008). Temporal and Spatial Changes in Land Use Patterns and Biodiversity in Relation to Farm Productivity at Multiple Scales in Tigray, Ethiopia, Wageningen Universiteit.
[13]
Hamad, R., Kolo, K., and Balzter, H. (2018). Land Cover Changes Induced by Demining Operations in Halgurd-Sakran National Park in the Kurdistan Region of Iraq. Sustainability, 10.
10.3390/su10072422
[14]
Luetz, J. (2008). Planet Prepare: Preparing Coastal Communities in Asia for Future Catastrophes, World Vision International. Asia Pacific Disaster Report.
[15]
Al-Amin, M. (2013). Vulnerability and Adaptation to Climate Change, University of Chittagong.
[16]
Mohal, N., Khan, Z.H., and Rahman, N. (2006). Impact of Sea Level Rise on Coastal Rivers of Bangladesh, Coast, Port & Estuary Division, Institute of Water Modelling (IWM). Available online: http://archive.riversymposium.com/2006/index.php?element=06MOHALNasreen.
[17]
Payo "Projected changes in area of the Sundarban mangrove forest in Bangladesh due to SLR by 2100" Clim. Chang. (2016) 10.1007/s10584-016-1769-z
[18]
Rahman "The causes of deterioration of Sundarban mangrove forest ecosystem of Bangladesh: Conservation and sustainable management issues" AACL Bioflux (2010)
[19]
Dezhkam "Performance evaluation of land change simulation models using landscape metrics" Geocarto Int. (2017)
[20]
Regmi "Geospatial analysis of land use land cover change predictive modeling at Phewa Lake Watershed of Nepal" Int. J. Curr. Eng. Technol. (2014)
[21]
Kirui "Mapping of mangrove forest land cover change along the Kenya coastline using Landsat imagery" Ocean Coast. Manag. (2013) 10.1016/j.ocecoaman.2011.12.004
[22]
Omar, N.Q., Sanusi, S.A., Hussin, W.M., Samat, N., and Mohammed, K.S. (2014). Markov-CA model using analytical hierarchy process and multiregression technique. IOP Conference Series: Earth and Environmental Science, IOP Publishing.
10.1088/1755-1315/20/1/012008
[23]
Roy "Monitoring and prediction of land use/land cover change using the integration of Markov chain model and cellular automation in the Southeastern Tertiary Hilly Area of Bangladesh" Int. J. Sci. Basic Appl. Res. (2015)
[24]
Sang "Simulation of land use spatial pattern of towns and village based on CA-Markov model" Math. Comput. Model. (2011) 10.1016/j.mcm.2010.11.019
[25]
Kumar "Land use change modelling using a Markov model and remote sensing" Gemat. Nat. Hazards Risk (2014) 10.1080/19475705.2013.795502
[26]
Yagoub "Prediction of land cover change using Markov and cellular automata models: Case of Al-Ain, UAE, 1992–2030" J. Indian Soc. Remote. (2014) 10.1007/s12524-013-0353-5
[27]
Halmy "Land use/land cover change detection and prediction in the north-western coastal desert of Egypt using Markov-CA" Appl. Geogr. (2015) 10.1016/j.apgeog.2015.06.015
[28]
APPLICATION OF CA-MARKOV MODEL AND LAND USE/LAND COVER CHANGES IN MALACCA RIVER WATERSHED, MALAYSIA
A. K HUA
Applied Ecology and Environmental Research
2017
10.15666/aeer/1504_605622
[29]
Hamad, R., Balzter, H., and Kolo, K. (2017). Multi-criteria Assessment of Land Cover Dynamic Changes in Halgurd Sakran National Park (HSNP), Kurdistan Region of Iraq, Using Remote Sensing and GIS. Land, 6.
10.3390/land6010018
[30]
Hamad, R., Balzter, H., and Kolo, K. (2018). Predicting Land Use/Land Cover Changes Using a CA-Markov Model under Two Different Scenarios. Sustainability, 10.
10.3390/su10103421
[31]
Gidey "Cellular automata and Markov Chain (CA_Markov) model-based predictions of future land use and land cover scenarios (2015–2033) in Raya, northern Ethiopia" Model. Earth Syst. Environ. (2017) 10.1007/s40808-017-0397-6
[32]
Nath, B., Niu, Z., and Singh, R.P. (2018). Land Use and Land Cover Changes, and Environment and Risk Evaluation of Dujiangyan City (SW China) Using Remote Sensing and GIS Techniques. Sustainability, 10.
10.3390/su10124631
[33]
Nath, B., Wang, Z., Ge, Y., Islam, K., Singh, R., and Niu, Z. (2020). Land Use and Land Cover Change Modeling and Future Potential Landscape Risk Assessment Using Markov-CA Model and Analytical Hierarchy Process. ISPRS Int. J. Geo-Inf., 9.
10.3390/ijgi9020134
[34]
Song "Remote sensing of mangrove wetlands identification" Proc. Environ. Sci. (2011) 10.1016/j.proenv.2011.09.357
[35]
Jones, T.G., Glass, L., Gandhi, S., Ravaoarinorotsihoarana, L., Carro, A., Randriamanatena, D., and Cripps, G. (2016). Madagascar’s Mangroves: Quantifying Nation-Wide and Ecosystem Specific Dynamics, and Detailed Contemporary Mapping of Distinct Ecosystems. Remote Sens., 8.
10.3390/rs8020106
[36]
Giri "Status and distribution of mangrove forests of the world using earth observation satellite data" Glob. Ecol. Biogeogr. (2011) 10.1111/j.1466-8238.2010.00584.x
[37]
Sulaiman, N.A., Ruslan, F.A., Tarmizi, N.M., Hashim, K.A., and Samad, A.M. (2013, January 19–20). Mangrove forest changes analysis along Klang coastal using remote sensing technique. Proceedings of the IEEE 3rd International Conference on System Engineering and Technology (ICSET), Shah Alam, Malaysia.
10.1109/icsengt.2013.6650190
[38]
Dale "Local and landscape effects on spatial patterns of mangrove forest during wetter and drier periods: Moreton Bay, Southeast Queensland, Australia" Estuar. Coast. Shelf Sci. (2010) 10.1016/j.ecss.2010.05.011
[39]
Heumann "An object-based classification of mangroves using a hybrid decision tree-support vector machine approach" Remote Sens. (2011) 10.3390/rs3112440
[40]
Kanniah "Satellite images for monitoring mangrove cover changes in a fast growing economic region in southern Peninsular Malaysia" Remote Sens. (2015) 10.3390/rs71114360
[41]
Milani "Satellite-based assessment of the area and changes in the mangrove ecosystem of the Qeshm Island" Iran. J. Environ. Res. Dev. (2012)
[42]
Pham, T.D., and Yoshino, K. (2015, January 17–19). Mangrove mapping and change detection using multi-temporal Landsat imagery in hai phong city, Vietnam. Proceedings of the Inter-National Symposium on Cartography in Internet and Ubiquitous Environments, Tokyo, Japan.
[43]
Shi, C. (2020, March 10). An Analysis Comparing Mangrove Conditions under Different Management Scenarios in Southeast Asia. Available online: http://dukespace.lib.duke.edu/dspace/handle/10161/14138.
[44]
Team, R.C. (2012). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing.
[45]
Zhang "Mapping mangrove forests using multi-tidal remotely-sensed data and a decision-tree-based procedure" Int. J. Appl. Earth Obs. Geoinf. (2017)
[46]
Chen "Multi-decadal mangrove forest change detection and prediction in Honduras, Central America, with Landsat imagery and a Markov chain model" Remote Sens. (2013) 10.3390/rs5126408
[47]
Mujiono, I.T.L., Harmantyo, D., Rukmana, I.P., and Nadia, Z. (1862). Simulation of Land Use Change and Effect on Potential Deforestation Using Markov Chain-Cellular Automata. American Institute of Physics (AIP) Conference Proceeding, AIP Publishing.
[48]
Liao, J., Zhen, J., Zhang, L., and Metternicht, G. (2019). Understanding Dynamics of Mangrove Forest on Protected Areas of Hainan Island, China: 30 Years of Evidence from Remote Sensing. Sustainability, 11.
10.3390/su11195356
[49]
Ghosh, M.K., Kumar, L., and Roy, C. (2016). Mapping Long-Term Changes in Mangrove Species Composition and Distribution in the Sundarbans. Forests, 7.
10.3390/f7120305
[50]
Ghosh "The Indian Sundarban Mangrove Forests: History, Utilization, Conservation Strategies and Local Perception" Diversity (2015) 10.3390/d7020149
Showing 50 of 149 references
Cited By
38
Urban growth modeling using earth observation datasets, Cellular Automata-Markov Chain model and urban metrics to measure urban footprints
Remote Sensing Applications: Societ...
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Metrics
38
Citations
149
References
Details
- Published
- Sep 21, 2020
- Vol/Issue
- 11(9)
- Pages
- 1016
- License
- View
Authors
Funding
the CAS Earth Big Data Science Project of China
Award: XDA19060303
Climate-Resilient Ecosystems and Livelihoods (CREL) project for John D. Rockefeller 3rd Scholars Program (Mangrove Research Team), Winrock International, U.S. Agency for International Development (USAID), Bangladesh Mission, Dhaka, Bangladesh
Award: AID-388-A-12-00007
Cite This Article
Mohammad Emran Hasan, Biswajit Nath, A.H.M. Raihan Sarker, et al. (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), 1016. https://doi.org/10.3390/f11091016
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