Spatio-Temporal Analysis of Mangrove Loss in Vulnerable Islands of Sundarban World Heritage Site, India

Conference paper
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

Mangroves are unique ecosystem found mainly in tropical coastal region in saline environment and under tidal influence. It has enormous ecological and economic value to the environment and local people. However, the problems are arising in tropical coastal region like Sundarban, where both natural and ever increasing anthropogenic activities have complicated the growth and development of mangroves. Therefore, spatio-temporal monitoring of mangroves has huge importance for their conservation in Sundarban World Heritage Site, the largest mangrove population in the world. Remote sensing has been proven as an important tool to monitor such ecosystem, but the traditional pixel based approach has several drawbacks. Recently, Object-based Image Analysis (OBIA) approach in remote sensing has helped to overcome such drawbacks. The present study attempts to analyse the status of mangroves over the time period of 40 years (1975–2015) in the study area using Landsat time series images through OBIA. The result reveals that the mangroves are gradually reducing over the last 40 years and about 4% mangrove area has been converted into water. It is a major indication of increase in sea water level, making many islands vulnerable. The time series analysis in some islands, like Bhangaduni, Bulchery, Dalhousie and Halliday shows the land area as well as mangroves have been destroyed more than one-third. If the process continues at the same rate, these islands may soon completely disappear.

Keywords

Sundarban Mangroves Landsat OBIA Sea level rise 

Notes

Acknowledgements

Research grant provided by University of Delhi, Research Council is duly acknowledged. Biswajit Mondal is thankful to University Grants Commission for research fellowship.

References

  1. Allison MA (1998) Historical changes in the Ganges—Brahmaputra delta front. J Coast Res 14(4):1269–1275Google Scholar
  2. Alongi DM (2008) Mangrove forests: resilience, protection from tsunamis, and responses to global climate change. Estuar Coast Shelf Sci 76(1):1–13CrossRefGoogle Scholar
  3. Blasco F, Gauquelin T, Rasolofoharinoro M, Denis J, Aizpuru M, Caldairou V (1988) Recent advances in mangrove studies using remote sensing data. Mar Freshw Res 49(4):287–296CrossRefGoogle Scholar
  4. Blasco F, Aizpuru M, Gers C (2001) Depletion of the mangroves of Continental Asia. Wetl Ecol Manag 9(3):245–256CrossRefGoogle Scholar
  5. Census (2011) www.censusindia.gov.in/2011census/dchb/DCHB.html. Accessed 01 Dec 2017
  6. Chavez PS (1988) An improved dark object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sens Environ 24(3):459–479CrossRefGoogle Scholar
  7. Chen G, Hay GJ, Carvalho LMT, Wulder MA (2012) Object based change detection. Int J Remote Sens 33(14):4434–4457CrossRefGoogle Scholar
  8. Church JA, Clark PU, Cazenave A, Gregory JM, Jevrejeva S, Levermann A, Merrifield MA, Milne GA, Nerem RS, Nunn PD, Payne AJ, Pfeffer WT, Stammer D, Unnikrishnan AS (2013) Sea level change. Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. https://doi.org/10.1017/CBO9781107415324.024
  9. Conchedda G, Durieux L, Mayaux P (2008) An object based method for mapping and change analysis in mangrove ecosystem. ISPRS J Photogramm Remote Sens 63(5):578–589CrossRefGoogle Scholar
  10. Dass R, Priyanka, Devi S (2012) Image segmentation techniques. Int J Electron Commun Technol 3(1):66–70Google Scholar
  11. Davis BA, Jensen JR (1998) Remote sensing of mangrove biophysical characteristics. Geocarto Int 13(4):55–64CrossRefGoogle Scholar
  12. District Statistical Handbook (2009) South 24-Parganas, Bureau of Applied Economics and Statistics, Government of West Bengal, IndiaGoogle Scholar
  13. Dornik A, Dragut L, Urdea P (2017) Classification of soil types using geographic object-based image analysis and random forest. Pedosphere.  https://doi.org/10.1016/S1002-0160(17)60377-1 Google Scholar
  14. Ellison JC (1993) Mangrove retreat with rising sea level, Bermuda. Estuar Coast Shelf Sci 37(1):75–87CrossRefGoogle Scholar
  15. Emch M, Peterson M (2006) Mangrove forest cover change in the Bangladesh Sundarbans from 1989–2000: a remote sensing approach. Geocarto Int 21(1):5–12CrossRefGoogle Scholar
  16. Field CD (1999) Mangrove rehabilitation: choice and necessity. Hydrobiologia 413:47–52CrossRefGoogle Scholar
  17. Ghosh A, Schmidt S, Fickert T, Nusser M (2015) The Indian Sundarban mangrove forests: history, utilization, conservation strategies and local perception. Diversity 7(2):149–169CrossRefGoogle Scholar
  18. Gilman EL, Ellison J, Duke NC, Field C (2008) Threats to mangroves from climate change and adaptation options, a review. Aquat Bot 89(2):237–250CrossRefGoogle Scholar
  19. Ha TP, Dijk HV, Visser L (2014) Impacts of changes in mangrove forest management practices on forest accessibility and livelihood: a case study in mangrove-shrimp farming system in Ca Mau Province, Mekong Delta, Vietnam. Land Use Policy 36:89–101CrossRefGoogle Scholar
  20. Hazra S, Mukhopadhyay A, Mukherjee S, Akhand A, Chanda A, Mitra D, Ghosh T (2016) Disappearance of the New Moore Island from the Southernmost Coastal Fringe of the Sundarban delta-a case study. J Indian Soc Remote Sens 44(3):479–484CrossRefGoogle Scholar
  21. Huang H, Jia X (2012) Integrating remotely sensed data, GIS and expert knowledge to update object-based land use/land cover information. Int J Remote Sens 33(4):905–921CrossRefGoogle Scholar
  22. Human Development Report (2009) District Human Development Report: South 24 Parganas. Development and Planning Department, Government of West Bengal, IndiaGoogle Scholar
  23. Im J, Jensen JR (2005) A change detection model based model based on neighbourhood correlation image analysis and decision tree classification. Remote Sens Environ 99(3):326–340CrossRefGoogle Scholar
  24. Islam MA, Mitra D, Dewan A, Akhter SH (2016) Coastal multi-hazards vulnerability assessment along the Ganges deltaic coast of Bangladesh-a geospatial approach. Ocean Coast Manag 127:1–15CrossRefGoogle Scholar
  25. Jusoff K (2006) Individual mangrove species identification and mapping in port Klang using airborne hyperspectral imaging. J Sustain Sci Manag 1(2):27–36Google Scholar
  26. Kamal M, Phinn S, Johansen K (2015) Object—based approach for multi-scale mangrove composition mapping using multi-resolution image data sets. Remote Sens 7(4):4753–4783CrossRefGoogle Scholar
  27. Lewis RR III (2005) Ecological engineering for successful management and restoration of mangrove forests. Ecol Eng 24(4):403–418CrossRefGoogle Scholar
  28. Lugo AE, Snedaker SC (1974) The ecology of mangroves. Annu Rev Ecol Syst 5:39–64CrossRefGoogle Scholar
  29. Mahiny AS, Turner BJ (2007) A comparison of four common atmospheric correction methods. Photogramm Eng Remote Sens 73(4):361–368CrossRefGoogle Scholar
  30. Maltus TJ, Mumby PJ (2003) Remote sensing of the coastal zone: an overview and priorities for future research. Int J Remote Sens 24(13):2805–2815CrossRefGoogle Scholar
  31. Mirza MMQ (1998) Diversion of the Ganges water at Farakka and its effects on salinity in Bangladesh. Environ Manag 22(5):711–722CrossRefGoogle Scholar
  32. Mishra M (2009) Integrated coastal zone management: a case study of selected coastal districts of Orissa. PhD thesis, Jawaharlal Nehru University. http://hdl.handle.net/10603/18140. Accessed 1 Dec 2017
  33. Nabahungu NL, Visser SM (2011) Contribution of wetland agriculture to farmers livelihood in Rwanda. Ecol Econ 71:4–12CrossRefGoogle Scholar
  34. Navulur K (2007) Multispectral image analysis using the object-oriented paradigm. CRC Press Taylor and Francis Group, USAGoogle Scholar
  35. Pramanik MK (2014) Assessment the impact of sea level rise on mangrove dynamics of Ganges delta in India using remote sensing and GIS. J Environ Earth Sci 4(1):117–127Google Scholar
  36. Rahman AF, Dragoni D, El-Masari B (2011) Response of the sundarbans coastline to sea level rise and decreased sediment flow: a remote sensing assessment. Remote Sens Environ 115(12):3121–3128CrossRefGoogle Scholar
  37. Samanta K, Hazra S (2012) Landuse/landcover change study of Jharkhali Island Sundarbans, West Bengal using remote sensing and GIS. Int J Geomat Geosci 3(2):299–306Google Scholar
  38. Snankar D, McCreary JP, Han W, Shetye SR (1996) Dynamics of the East India Coastal Current 1. Analytic solutions forced by interior Ekman pumping and local alongshore winds. J Geophys Res 101(C6):13975–13991CrossRefGoogle Scholar
  39. Stanley DJ, Hait AK (2000) Holocene depositional patterns, neotectonics and Sundarban mangroves in the western Ganges-Brahmaputra delta. J Coast Res 16(1):26–39Google Scholar
  40. Testut L, Duvat V, Ballu V, Fernandes RMS, Pouget F, Salmon C, Dyment J (2016) Shoreline changes in a rising sea level context: the example of Grande Glorieuse Scattered Islands, Western Indian Ocean. Acta Oecologica 72:110–119CrossRefGoogle Scholar
  41. Thom BG (1984) Coastal landforms and geomorphic processes. In: Snedaker SC, Snedaker JG (eds) Mangrove ecosystem: research methods. UNESCO, ParisGoogle Scholar
  42. Wang L, Sousa WP, Gong P (2004) Integration of object based and pixel based classification for mapping mangroves with IKONOS imagery. Int J Remote Sens 25(24):5655–5668CrossRefGoogle Scholar
  43. Wood AL, Butler JRA, Sheaves M, Wani J (2013) Sport fisheries: opportunities and challenges for diversifying coastal livelihoods in the Pacific. Marine Policy 42:305–314CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Geography, Delhi School of EconomicsUniversity of DelhiDelhiIndia

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