Multi‐temporal synthetic aperture radar flood mapping using change detection

Abstract

A change detection and thresholding methodology has been adapted from previous studies to determine the extent of flooding for 13 Sentinel-1 synthetic aperture radar images captured during the floods of winter 2015-2016 in Yorkshire, UK. Both available polarisations, VH and VV, have been processed to allow for a comparison of their respective accuracy for delineating surface water. Peak flood extents are found on 29 December 2015 during the aftermath of storms Eva and Frank. Results have been validated against a Sentinel-2 optical image, with both polarisations producing a total accuracy of 97%. Of the two polarisations, VV produces fewer misclassifications, mirroring the similar results reported in previous research. Mapped results are compared to the Environment Agency Flood Maps for Planning (EA FMP), with good correlation observed for inundation on the floodplains. Differences occur away from the floodplains, with the satellite data identifying pluvial flooding not highlighted by the EA FMP.

Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data

“…For the extraction of TOW, Z-Score VV was determined by a RF classifier as the most reliable time series feature, having the highest contribution in comparison to the VH based time series features. While this finding matches the results of other studies [4,47], some studies [25,37] prefer VH as a basis for the classification. This ambivalence can probably be explained by different sensor characteristics and environmental conditions in the studies.…”

“…For example, wind or heavy rainfall can roughen the water surface and reduce or even erase the typical decrease in backscatter values during the flood event [4,9]. The increase in backscatter at the date of the flood can also be strongly influenced by the interaction between sensor characteristics (wavelength, angle of incidence, and polarisation) [6] and environmental conditions, such as aboveground biomass [72][73][74] or the relation between water level and plant height [10].…”

“…In contrast to methods for the extraction of open water surfaces for flood detection [4,45,46], there is little research for the detection of the entire flood extent comprising TFV besides open water areas [11,25,29]. An example for automatic flood detection of open water in near-real time (NRT) by Sentinel-1 data is given by Twele et al [47].…”

“…Numerous current studies focus on flood mapping using various algorithms that enable the detection of temporary open water [3][4][5]. Besides these areas, the extraction of temporary flooded vegetation (TFV) areas is essential for mapping the entire flood extent.…”

Low cost, multiscale and multi-sensor application for flooded area mapping

“…Copernicus Emergency Management Service (©European Union, 2012Union, -2017). With satellite remote sensing data, it is possible to map flood effects over vast areas at different spatial and temporal resolutions using multispectral (Brakenridge et al, 2006;Gianinetto et al, 2006;Nigro et al, 2014;Wang et al, 2012;Yan et al, 2015;Rahman and Di, 2017) or Synthetic Aperture Radar (SAR) images (Boni et al, 2016;Mason et al, 2014;Schumann et al, 2015;Refice et al, 2014;Pulvirenti et al, 2011;Clement et al, 2017;Brivio et al, 2002). A good description of the main methodologies that are used to map floods with satellite data has been published by Fayne…”