Mapping and assessing spatial extent of floods from multitemporal synthetic aperture radar images: a case study over Adyar watershed, India

Abstract

Urban oods are more concerned in recent days due to their substantial effect in loss of human lives and properties. Due to climate change, urban oods are frequently observed in many parts of the world. Flood events in Chennai city is a frequent scenario due to rapid increase in the density of population. Adyar river watershed and surrounding urban cover is focused in the present study. The present study aims at mapping ooded region using Sentinel 1 datasets over Chennai city. Series of Sentinel-1A image is collected before, during and after oods for mapping the extent of ood and mapping risk zones in Adyar watershed. Methodologies such as ISODATA Technique, Multi-Temporal Analysis, Thresholding Method, PCA and ICA Analysis and Grey Level Co-Occurrence Matrix are adopted for the extraction of ooded extent from the SAR datasets. Analysis performed over the Chennai city provided promising results in the extraction of ooded extent with Thresholding Method and Grey Level Co-Occurrence Matrix being the dominant of all the methods. Though higher accuracy is obtained in the extraction of ooded extent, limitation of layover, foreshortening and shadow is experienced in the built up region for the extraction of ooded pixels.

Application of Gated Recurrent Unit Neural Network for Flood Extraction from Synthetic Aperture Radar Time Series

“…For example, Sentinel-1 can provide satellite images with a revisit period of 6-12 days, and some high-latitude areas can even have revisit periods of 3-4 days. Google Earth Engine (GEE) and other platforms can provide a large amount of preprocessed SAR data, offering powerful assistance for real-time flood monitoring [52][53][54][55]. These long-term remote sensing data include numerical dimensions, temporal dimensions, and spatial dimensions, forming a four-dimensional information space.…”