Flash flood is disastrous; it losses property and life. Its effect is intensified while it occurs in semiarid region because of less preparedness. The present case conferred about a flash flood in semiarid region in Gujarat which was affected by flood in 2015 and 2017. Massive loss of lives and properties has been observed after the event. Now, recuperating the region against flood losses, it was a prime requirement to distribute the flood relief packages to the flood-susceptible areas. To identify the flood hazards and flood risk and assess the flood vulnerability in Rel River catchment, the region is divided into 52 micro-watersheds using RS and GIS techniques. The morphology of the Rel River catchments has been explored using the morphometric analysis. The priority rank and category for each micro-watershed were assigned based on compound factor values, whereas compound factor was calculated using weighted sum analysis techniques. Flood hazard zone map was prepared, and flood vulnerability has been characterized from very low to very high. Furthermore, the multicriteria analysis was used to calculate the risk factor for the basin and AHP-MCE method was used to find the normalized weights of each factor (LU/LC, CF, soil, slope, drainage density) that were significant to the flood disaster. The integration of flood hazard map along with these parameters helped to understand the sensitivity of flash floods at different locations within the study area. Flood risk map was further analyzed at village level, and it has been identified that 17 out of 39 villages were at high risk, 12 villages were at moderate risk and 10 villages were at low risk. The study helped to clearly identify villages vulnerable to flood risk where more relief and flood insurance packages need to be allotted. Thus, the present method and integrated approach would be a useful tool for the decision maker to distribute the flood relief package in flash flood-prone area.
The potential of Synthetic Aperture Radar (SAR) to detect surface and subsurface characteristics of land, sea, and ice using polarimetric information has long piqued the interest of scientists and researchers. Traditional strategies include employing polarimetric information to simplify and classify SAR images for various earth observation applications. Deep learning (DL) uses advanced machine learning algorithms to increase information extraction from SAR datasets about the land surface, as well as segment and classify the dataset for applications. The chapter highlights several problems, as well as what and how DL can be utilized to solve them. Currently, improvements in SAR data analysis have focused on the use of DL in a range of current research areas, such as data fusion, transfer learning, picture classification, automatic target recognition, data augmentation, speckle reduction, change detection, and feature extraction. The study presents a small case study on CNN for land use land cover classification using SAR data.
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