Random Forest Based Flood Monitoring Using Sentinel-1 Images: A Case Study of Flood Prone Regions of North-East India

Abstract

The use of SAR satellite images will be very helpful in flood monitoring as the acquisition of synthetic aperture radar (SAR) images is possible day-night in all weather conditions and are very sensitive to water bodies and the changes in their behaviour. The usage of SAR (like Sentinal-1) images is an added advantage in handling the rescue operations and damage assessments based on images acquired before flood, flood at peak, and after flood effects. This paper discusses flood mapping and results in two different case studies. This is covered in phase-1 with RGB composite images of the cities of Gorakhpur and Ayodhya and phase-2 to analyse the flood situation using an accuracy assessment of Basti city based on the supervised classification method on SAR data. In this paper, random forest classification (RF) technique has been used to identify the flood prone areas by using Sentinel-1 satellite images and interpreted the changes detected for rescue operations. Sentinel-1 images are classified as Crisis image and Archive image, and further analysed to identify the flood prone areas (water bodies due to flood), permanent water bodies, urban (Built-up area), and vegetation.