12 December 2006 Classification of rice crops based on submergence due to tropical cyclone using remotely sensed data: an Indian case study
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Tropical cyclones are one of the most destructive natural disasters occurring frequently in coastal India. The socio economic impacts of these tropical cyclones are high as they result in enormous loss of life and property every year. In the present study, pre event visible-near IR images and post event Radarsat images were procured and used to identify completely submerged landcovers temporally. The methodology is developed considering a case study on the Kendrapara district of Orissa state, which was hit by a cyclone on 29-30th October 1999. The pre event IRS 1D LISS III (resolution = 22m) image of Kendrapara district was procured geometrically corrected and classified into several landuse and landcover classes. For landuse/landcover classification, supervised classification technique was used. This georeferenced landuse/landcover map provided the baseline information for the district. Next step involved procurement of immediate temporal post-event SAR images of the cyclone-affected district. These images were geometrically corrected and cleaned for speckle noise. Deterministic approach was used to set up threshold for classifying pixel as completely submerged under water or non submerged for Radarsat SAR images i.e. Radarsat SAR images exactly delineated areas completely submerged under water due to cyclonic floods. This type of analysis will help policy makers in determining the extent of submergence and damage. This methodology would be used as a rapid tool to assess damage. Further, this will help in expediting the release of relief funds as well as aid proper allocation of funds to the affected areas/people.
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Abhijat A. Abhyankar, Abhijat A. Abhyankar, Anand Patwardhan, Anand Patwardhan, Arun Inamdar, Arun Inamdar, } "Classification of rice crops based on submergence due to tropical cyclone using remotely sensed data: an Indian case study", Proc. SPIE 6412, Disaster Forewarning Diagnostic Methods and Management, 641206 (12 December 2006); doi: 10.1117/12.696598; https://doi.org/10.1117/12.696598

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