Multi-Method Tracking of Monsoon Floods Using Sentinel-1 Imagery

Abstract

Floods cause great losses in terms of human life and damages to settlements. Since the exposure is a proxy of the risk, it is essential to track flood evolution. The increasing availability of Synthetic Aperture Radar (SAR) imagery extends flood tracking capabilities because of its all-water and day/night acquisition. In this paper, in order to contribute to a better evaluation of the potential of Sentinel-1 SAR imagery to track floods, we analyzed a multi-pulse flood caused by a typhoon in the Camarines Sur Province of Philippines between the end of 2018 and the beginning of 2019. Multiple simple classification methods were used to track the spatial and temporal evolution of the flooded area. Our analysis indicates that Valley Emphasis based manual threshold identification, Otsu methodology, and K-Means Clustering have the potential to be used for tracking large and long-lasting floods, providing similar results. Because of its simplicity, the K-Means Clustering algorithm has the potential to be used in fully automated operational flood monitoring, also because of its good performance in terms of computation time.

A Modeling-Based Approach for Soil Frost Detection in the Northern Boreal Forest Region With C-Band SAR

“…Floods in boreal forests were mapped by Voormansik, et al, (2014), who concluded that additional testing of the X-band sensor should be carried out with more detailed information regarding tree species, stand age, height and density. ESA's Sentinel-1 satellite pair offers a valuable SAR data source suitable for monitoring flood conditions (Twele, et al, 2016;Clement, et al, 2018;Ruzza, et al, 2019;Tsyganskaya, et al, 2019;Uddin, et al, 2019), especially in northern latitudes due to nearly daily temporal resolution. However, the land areas are regularly imaged only with VV-and VH-polarizations, which are less useful for detecting floods under forest canopy compared to HH-polarization (see section 2.2)…”

Country‐wide flood exposure analysis using Sentinel‐1 synthetic aperture radar data: Case study of 2019 Iran flood

“…Based on the backscattering intensity of the SAR image where water appears as a dark area resulting in a low backscatter recording as incident radar signals are reflected away from the radar antenna (Henderson & Lewis, 1998), one can visualize and interpret flooding based on various classification methods. This includes a simple visual interpretation approach (Matgen et al, 2007;Oberstadler et al, 1997;Sanyal & Lu, 2004), image change detection (Bazi et al, 2005;Clement et al, 2018;Nico et al, 2000), region growing algorithms (Malnes et al, 2002;Mason et al, 2012), supervised classification (Pulvirenti et al, 2013;Townsend, 2002), histogram thresholding (Chini et al, 2012;Elkhrachy et al, 2021;Pulvirenti et al, 2016), and clustering algorithm (Ruzza et al, 2019). Further, many flood detection approaches have used a combination of thresholding, region growing, and change detection utilizing a single SAR image Matgen et al, 2011).…”

Improving Situational Awareness for Floods: Monitoring Using Satellite Data in the Boreal Region

“…The increasing amount of satellite sensors providing freely available and continuous observations have encouraged investigations assessing their suitability for flood monitoring (Notti, et al, 2018;DeVries, et al, 2017;Twele, et al, 2016;Tsyganskaya, et al, 2019;Ruzza, et al, 2019;Reksten, et al, 2019;Kordelas, et al, 2018;Clement, et al, 2018;Uddin, et al, 2019;Du, et al, 2016). Considering spatial and temporal resolution, Sentinel-1 and Sentinel-2 have the potential to significantly improve flood monitoring capabilities.…”