Monitoring of flood water propagation based on microwave and optical imagery

Abstract

The economy of northern part of Bangladesh mainly depends upon agriculture based production. Flood disasters in Dinajpur along the Punarbhaba, Atrai Rivers result in significant loss of life and economic damage. This paper finds the optimum technique to use microwave (Sentinel-1A) and optical (Landsat-8) imagery alternatively along with GIS for fast and effective flood mapping, semi-real-time monitoring and analyzing the propagation of flood water in a flood prone area. Since flooded areas usually are covered by dense clouds, Synthetic Aperture Radar (SAR) has the advantage over optical data by operating at wavelengths not hampered by cloud cover or a lack of illumination. It has capability to continuous observation of flood events for producing rapid, accurate and cost effective flood mapping. This characteristic makes SAR a potential alternative to optical sensors. The focal steps in flood monitoring using optical satellite data are: identifying water bodies efficiently; disregarding some cloud influences; and, monitoring the flood process dynamically. The purpose of this study is to evaluate the effectiveness of using freely available time series of Copernicus Sentinel-1 SAR and Landsat-8 imagery. This study also assess the TRMM data for precipitation measurement in the study area for hydrological analyses of watersheds. The SAR images were first calibrated, geometrically corrected and filtered. Afterward, to extract the inundated areas from the SAR images threshold method was applied. In threshold method and MNDWI index was used to separate the open water and nonwater (land) areas from the images. This research proposed the combination model of SAR and optical data that describes how we can get water extraction result with higher accuracy during flood using optical and microwave data. The results showed that the VH of SAR backscattering coefficient achieved higher accuracy than VV of SAR backscattering coefficient, and the combination model provides the highest accuracy of flood water extraction. The highest number of populations are affected by flood that is around 827822 in 2017. Therefore, the findings of this study will help to require initiative to scale back the flood hazard impact and increase the pliability within the process of flood management. This study proposed a method to automatically generate periodic surface water at 10 m resolution, which may contribute to monitoring surface water in a timely manner. This has also played a significant role in the monitoring and early warning of emerging major natural disasters at home and abroad.