Geospatial Assessment of Flood Hazard Along the Tamil Nadu Coast

During November–December 2015, very heavy rainfall caused severe flood in Southern Tamil Nadu that resulted in severe damages with huge economic losses as per news agency Times of India. Remote sensing data from Sentinel-1 synthetic aperture radar (SAR) and Landsat-8. Operational land imager (OLI) images together with ancillary information such as rainfall and demographic data were used in the current study to assess the extent and impact of flooding. The SAR data are used to map the flood or inundation zones. Landsat-8 OLI is used to extract built-up area affected by the flood employing three methods: built-up area extraction method (BAEM), BAEM with Enhanced Built-up and Bareness Index (EBBI), and modified Normalized Difference Built-up Index (NDBI) approach. The classification accuracies obtained for these three approaches were 89, 83.5, and 78% for BAEM (using EBBI), BAEM, and NDBI, respectively. Aerial comparison of built-up area extracted using BAEM (using EBBI) shows the best accuracy with respect to the built-up area obtained from very high-resolution imagery. This extracted built-up area BAEM (using EBBI) method was used to estimate the extent of inundation covering the built-up area. Further the flooding risk at village level was assessed using the population density and flooding area. Built-up area extracted was also overlaid with flooding area to highlight actual built-up areas under risk due to flood.