Dynamic Changes in Mangrove Forest and Lu/Lc Variation Analysis over Indian Sundarban Delta in West Bengal (India) Using Multi-Temporal Satellite Data

Avinash Kumar Ranjan *, Shruti Kanga **
* M.Tech Scholar, Centre for Land Resource Management, School of Natural Resource Management, Central University of Jharkhand, India.
** Assistant Professor, Centre for Climate Change and Water Research, Suresh Gyan Vihar University, Rajasthan, India.
Periodicity:February - April'2018
DOI : https://doi.org/10.26634/jfet.13.3.14226


Sundarban ecological unit is a well-known world's major mangrove ecosystem, prolonged over Bangladesh and India across the deltaic formation of Ganga, Brahmaputra, and Meghna rivers. Indian Sundarban Delta (ISD) is located over Ganges and Brahmaputra rivers’ delta in West Bengal, India. In the existing epoch, sundarbans delta is prone to ecological pressure caused by various natural as well as anthropogenic activities. The mangroves ecosystems of ISD have been critically disturbing via climate changes such as Sea Level Rise (SLR), gales, flood, shoreline erosion, salinity intrusion, population pressure etc. Aforesaid issues are apprehension for the mangroves forest and can create more socio-economic complications for the local mankind and wildlife of the ISD. The present study has essentially focused on the analysis and assessment of temporal deviation in the spatial pattern of Indian sundarbans delta using chronological satellite imagery since 1972-2017. Digital image processing of multi-temporal Landsat-series satellite data of 1972s, 1987s, 2002s, and 2017s were carefully performed in Geographic Information System (GIS) environment. Loss of Mangroves cover and Land Use / Land Cover (LU/LC) transformation were accomplished using two image processing techniques; Supervised Classification (Random Forest Classification) and Soil Adjusted Vegetation Index (SAVI), and accuracy assessment of the derived LU/LC map was done by preparing a confusion matrix. The impact of climate change on ISD and socio-economic impact of mangrove ecosystem on shelters has also discussed.


Mangrove, ISD, Climate Change, Satellite Data, Random Forest Classification, SAVI, Socio-economic Impact

How to Cite this Article?

Ranjan, A. K., and Kanga, S. (2018). Dynamic Changes in Mangrove Forest and Lu/Lc Variation Analysis over Indian Sundarban Delta in West Bengal (India) Using Multi-Temporal Satellite Data. i-manager’s Journal on Future Engineering and Technology, 13(3), 9-23. https://doi.org/10.26634/jfet.13.3.14226


[1]. Adam, A. H. M., Elhag, A. M. H., & Salih, A. M. (2013). Accuracy assessment of Land Use and Land Cover classification- case study of Shomadi area-Renk country- Upper Nile, South Sudan. International Journal of Scientific and Research Publications, 3(5), 1-6.
[2]. Anderson, J. R., Roach, J. T., & Witmer, R. E. (1976). A Land Use and Land Cover Classification System for Use with Remote Sensor Data (Vol. 964). U.S. Government Printing Office.
[3]. Ardil, E. R., & Wolff, M. (2009). Land use and land cover change affecting habitat Distribution in the Segara Anakanlagoon, Java, Indonesia. Regional Environmental Change, 9(4), 235-243.
[4]. Banko, G. (1998). A Review of assessing the accuracy of classification of remotely sensed data and methods including remote sensing data in forest inventor y. International Institute for Applied Systems Analysis- Interim Report, 1-36.
[5]. Barik, J., & Chowdhury, S. (2014). True mangrove species of Sundarbans delta, West Bengal, Eastern India. Check List, 10(2), 329-334.
[6]. CEGIS. (2006). Coastal Land Use Zoning in the Southwest: Impact of Sea Level Rise on Land use Suitability and Adaptation Options. Centre for Environmental and Geographic Information Services (CEGIS), Dhaka.
[7]. Chatterjee, N., Mukhopadhyay, R., & Mitra, D. (2015). Decadal Changes in Shoreline Patterns in Sundarbans, India, Journal of Coastal Sciences, 2(2), 54-64.
[8]. Cornforth, W. A., Fatoyinbo, T. E., Freemantle, T. P., & Pettorelli, N. (2013). Advanced land observing satellite phased array type L-band SAR (ALOS PALSAR) to inform the conservation of mangroves: Sundarbans as a case study. Remote Sensing, 5(1), 224-237.
[9]. Ellison, J. C., & Stodart, D. R. (1991). Mangrove Ecosystem Collapse during Predicted Sea-Level Rise, Holocene Analogues and implications. Journal of Coastal Research, 7(1), 151-165.
[10]. Foody, G. M. (2002). Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80(1), 185-201.
[11]. Giri, C., Zhu, Z., & Reed, B. (2005). A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets. Remote Sensing of Environment, 94(1), 123-132.
[12]. Haq, S. A. (2010). Impact of climate change on Sundarbans, the largest mangrove forest: ways forward. t h 18 Commonwealth Forestr y Conference 2010, Edinburgh.
[13]. Kanga, S., Sharma, L. K., Pandey, P. C., Nathawat, M. S., & Sinha, S. (2011). Geospatial approach for allocation of potential tourism gradient sites in a part of Shimla District in Himachal Pradesh, India. Journal of GIS Trends, 2(1), 1-6.
[14]. Kanga, S., Sharma, L. K., Pandey, P. C., Nathawat, M. S., & Sharma, S. K. (2013). Forest fire modeling to evaluate potential hazard to tourism sites using geospatial approach. Journal of Geomatics, 7(1), 93-99.
[15]. Kanga, S., Sharma, L. K., Pandey, P. C., & Nathawat M. S. (2014). GIS Modelling approach for forest fire risk assessment and management. International Journal of Advancement in Remote Sensing, GIS, and Geography, 2(1), 30-44.
[16]. Kanga, S., & Singh, S. K. (2017). Forest fire simulation modeling using remote sensing & GIS. International Journal of Advanced Research in Computer Science, 8(5), 326-332.
[17]. Mondal, I., & Bandyopadhyay, J. (2014). Coastal zone mapping through geospatial technology for resource management of Indian Sundarban, West Bengal, India. International Journal of Remote Sensing Applications, 4(2), 103-112.
[18]. Pachauri, R. K., & Reisinger, A. (2007). Climate Change (2007): Synthesis Report. Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC, Geneva, Switzerland.
[19]. Pramanik, M. K. (2015). Changes and Status of Mangrove Habitat in Ganges Delta: Case Study in Indian Part of Sundarbans. Forest Research Open Access, 4(3), 1-7.
[20]. Raha, A. K., Mishra, A., Bhattacharya, S., Ghatak S., Pramanick P., Dey S., …. & Jha, C. (2014). Sea level rise and submergence of sundarban islands: A time series study of estuarine dynamics. Journal of Ecology and Environmental Sciences, 5(1), 114-123.
[21]. Ranjan, A. K., Vallisree, S., & Singh, R. K. (2016a). Role of Geographic information system and remote sensing in monitoring and management of urban and watershed environment: Overview. Journal of Remote Sensing & GIS, 7(2), 1-14.
[22]. Ranjan, A. K., Anand, A., Vallisree, S., & Singh, R. K. (2016b). LU/LC change detection and forest degradation analysis in Dalma Wildlife Sanctuary using 3S Technology: A case study in Jamshedpur-India. AIMS Geosciences, 2(4), 273-285.
[23]. Ranjan, A. K., Vallisree, S., Verma, S. K., Murmu, L., & Kumar, P. B. S. (2017). Spatio-temporal variation in Indian part of Sundarban Delta over the years 1990-2016 using Geospatial Technology. International Journal of Geomatics and Geoscience, 7(3), 275-292.
[24]. Roy, A. K. D., Alam, K., & Gow, J. (2011). A review of the role of property rights and forest policies in the management of the Sundarbans Mangrove Forest in Bangladesh. Forest Policy and Economics, 15, 46-53.
[25]. Singh, S. K., & Pandey, A. C. (2014). Geomorphology and the controls of geohydrology on waterlogging in Gangetic Plains, North Bihar, India. Environmental Earth Sciences, 71(4), 1561- 1579.
[26]. Sinha, S., Pandey, P. C., Sharma, L. K., Nathawat, M. S., Kumar, P., & Kanga, S. (2014). Remote estimation of land surface temperature for different LULC features of a moist deciduous tropical forest region. In Remote Sensing Applications in Environmental Research (pp. 57-68). Springer, Cham.
[27]. Singh, S. K. (2016). Geospatial technique for land use/land cover mapping using multi-temporal satellite images: A case study of Samastipur District (India). Environment & We An International Journal of Science & Technology, 11 (4), 75-85.
[28]. Singh, S. K., Kumar, V., & Kanga, S. (2017a). Land use/land cover change dynamics and river water quality assessment using geospatial technique: A case study of Harmu river, Ranchi (India). International Journal of Scientific Research in Computer Science and Engineering, 5(3), 17-24
[29]. Singh, S. K., Mishra, S. K., & Kanga, S. (2017b). Delineation of groundwater potential zone using geospatial techniques for Shimla city, Himachal Pradesh (India). International Journal for Scientific Research and Development, 5(4), 225-234.
[30]. Thomas, J. V., Arunachalam, A., Jaiswal, R., Diwakar, P. G., & Kiran, B. (2014). Dynamic Land Use and Coastline Changes in active Estuarine Regions-A Study of Sundarban Delta. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(8), 133-139.
[31]. Wikramanayake, E. D., Dinerstein, E., Robinson, J. G., Karanth, U., Rabinowitz, A., Olson, D., ... & Bolze, D. (1998). An Ecology based method for defining priorities for large mammal conservation: The tiger as case study. Conservation Biology, 12(4), 865-878.

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