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Potential and limists of satellite-derived digital surface model data for assessing flood risks in Southeast Coast of India

Abstract : Flood risk assessment in low-lying coastal areas requires efficient spatial observations of land elevation for the implementation of protection, evacuation and safeguard plans of people and assets. Here we evaluated the potential of Digital Surface Model (DSM) derived from satellite observations to map flood prone areas with the objective of early warning on flood risk in the Cuddalore and Pondicherry region, southeast coast of India. Coastal zone management of this 100 km long coast is particularly challenging. Indeed, the whole region experiences at least two cyclonic storms accompanied with storm surge, heavy rains, flooding and beach erosion every year; the havocs wreaked by the 2004 tsunami, flash floods of 2005 and 2015, and the Thane cyclone in 2011 are still close memories. We analyzed Sentinel-1 Synthetic Aperture Radar (SAR) and ALOS World 3D DSM satellite data, and Google Earth images. All these data are freely available and we compared them to the population census data acquired in 2011. Using Sentinel-1 SAR images, we discriminated flooded from non-flooded areas before comparing maps of low-lying areas derived from ALOS DSM data. The results suggest a good agreement between real flooded areas and low-lying areas. However, the micro-topography reflecting channels and drainage systems could not be captured with important issue for delineating areas with high risks of flooding. We explained that spatial resolution of about 2 m in X, Y and 10 cm in Z directions are necessary for identifying areas with high risk of flooding as demonstrated in many countries of the world. It is time to rethink national Indian spatial policy about high-resolution images in order to prepare safety plans of the property and the lives of populations of Tamil Nadu coasts.
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https://hal.archives-ouvertes.fr/hal-02906734
Contributor : Saravanan Govindaraj <>
Submitted on : Saturday, July 25, 2020 - 1:15:35 PM
Last modification on : Tuesday, July 6, 2021 - 3:43:45 AM

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  • HAL Id : hal-02906734, version 1

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G. Muthusankar, Christophe Proisy, Anaïs Ricout. Potential and limists of satellite-derived digital surface model data for assessing flood risks in Southeast Coast of India. 38th Asian Conference on Remote Sensing Space Applications: Touching Human Lives, Asian Association on Remote Sensing; Indian Society of Remote Sensing; Indian Society of Geomatics; Indian Space Research Organisation, Oct 2017, New Delhi, India. ⟨hal-02906734⟩

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