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The present study highlights the application of satellite remote sensing in seasonal change monitoring and mapping of coastal vegetation types along Midnapur-Balasore coast, Bay of Bengal, using multi-temporal satellite images of Landsat ETM? of 2000, 2001 and 2002 of pre-monsoon and post-monsoon seasons. Two types of image analysis techniques were applied in this study to identify different species of coastal vegetations, health and their areal extent. RGB-NDVI was computed to detect and monitor the changes of vegetation health during pre and post-monsoon period. Another technique is supervised digital classification was used for mapping of different coastal vegetations and to determine their spatial extent of the study area. Five major coastal vegetation types were delineated using satellite data viz., dune vegetation, mangroves, salt marsh, agricultural lands and other vegetations. An attempt has been made to analyze seasonal change monitoring and mapping of different coastal vegetations communities including mangroves, salt marsh, and dune vegetation from 2000 to 2002 using Landsat 7 ETM? data. From 2000 to 2002 it is observed that the areal extent of salt marsh and dune vegetation is changed seasonally along each littoral cell. It is increased during post-monsoon season and decrease during pre-monsoon season. In this period, the gain of 704.34 ha area of salt marsh and 2153.88 ha area of dune vegetations cover was noticed. During post-monsoon period the mangrove vegetation covers along all littoral cells have increased this may be due to supply of huge amount of sediment and increase of salinity condition after monsoon. But over the entire 3 years period from pre-monsoon 2000 to post-monsoon 2002 the net change was negative, which was quantified as 61.83 ha. The depletion of mangroves may be due to high anthropogenic pressure, commercial aquaculture, highly eroding nature of the coast and tidal activities. The NDVI derived from ETM? images provide useful information to monitor coastal vegetation changes, especially the natural plants growing on sand dunes, mangroves and salt marsh vegetations. The analysis of NDVI variations showed the qualitative information about the vegetation. On the other hand classified image provide quantitative information about the coastal vegetation.

Shoreline is one of the quickly fluctuating linear landscapes of the coastal zone which is active in nature. In the present study, the analysis of remote sensing data sets covering Midnapur-Balasore coast, with an average time span of 6 months, has shown that they can be used to evaluate the short-term shoreline oscillations. In the present study, multi temporal satellite images of Landsat have been used to demarcate the short-term position of the shoreline changes. The techniques such as littoral cells, shore line change rate and beach recovery and devastation concept has been applied in this study. Finally the use of remote sensing data has proven as a good technique to estimate and quantify short-term shoreline oscillations along Midanpur-Balasore coast.