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North Orissa University Journal Of Science and Technology, Volume 3 & 4, ISSN Number:2319-5142, 48-56

Publish: 15/12/2015

Mangroves are the salt tolerant plant farming the most productive ecosystem located at the interface between land and sea. Survival of this ecosystem is threatened as a result of the expansion of human settlements, boom in commercial aquaculture, impact of tidal waves and storm surges. Thus in the present paper was an assessment on mapping and monitoring of fragile mangrove ecosystem of Bhitarkanika wild life sanctuary in Odisha using RS&GIS approach. The satellite images used for this study includes Landsat TM (Thematic Mapper), Landsat 7 and OLI (Operational Land Imager) onboard on Landsat 8. Supervised digital classification method is used for mapping of vegetation, land use and land cover of the study area. In order to assess the variation of erosion and accreditation rate, supervised classification was carried out on multi-temporal Landsat data corresponding to 2005, 2010 and 2014. The Land use and land cover mapping using satellite imageries provides a detail understanding on the vegetation pattern and vegetation types. Results of the study indicate that the area under dense mangroves continue to decrease between the period 2005 to 2014, whereas the area under sparse mangroves has increased. The erosion and accretion along the coastal areas are continuous processes. So, the findings of the present work can be used for coastal zone management, ecotourism planning and forestry.

I| Food and Agriculture Organization of the United Nations (FAO) (2007). The World's Mangroves1980-2005; FA0: Rome, Italy. [2] Alongi, D.M. The Energetics of Mangrove Forests (2009). 1st ed.; Springer: Midtown, NY, USA. [3] Kairo, J.G; Kivyatu, B.; Koedam, N. (2002). Application of remote sensing and GIS in the management of mangrove forests within and adjacent to Kiunga Marine Protected Area, Lamu, Kenya.Environ. Dev. Sust, 4, 153-166. [4] Vaiphasa, C.; Skidmore, A., Deboer, W. (2006). A post-classifier for mangrove mapping using ecological data. ISPRS J. Photogramm., 61, 1 10. [5) Chadwick, J. (2013). Integrated LiDAR and IKONOS multispectral imagery for mapping mangrove distribution and physical properties. Int. J. Remnote Sens. (2011). 32, 6765 6781.Remote Sens., 5 3577

 

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