Abstract
A new automatic, free and open-source python toolbox for the mapping of floodwater is presented. The output of the toolbox is a binary mask of floodwater at a user-specified time point within geographical boundaries. It exploits the high spatial (10m) and temporal (6 days per orbit over Europe) resolution of Sentinel-1 GRD intensity time series and is based on four processing steps. In the first step, a selection of Sentinel-1 images related to pre-flood (baseline) state and flood state is performed. In the second step, the preprocessing of the selected images is performed in order to create a co-registered stack with all the pre-flood and flood images. In the third step, a statistical temporal analysis is performed and a t-score map that represents the changes due to a flood event is calculated. Finally, in the fourth step, a classification procedure based on the t-score map is performed to extract the final flood map. A thorough analysis based on several flood events is presented to demonstrate the main benefits, limitations and the potential of the proposed methodology. The validation was performed using Copernicus Emergency Management Service (EMS) products. In all case studies, overall accuracies were higher than 0.95 with Kappa scores higher than 0.76. We believe that the end-user community can benefit by exploiting the flood maps of the proposed methodological pipeline by using the provided open-source toolbox.
Highlights
Mapping the spatial extent of surface waters is considered an important step for many initiatives related to water sustainability and natural hazards such as floods [1]
In the particular flood event, Pinios River and all its tributaries have overflowed since 24 February 2018 and hundreds of acres of rural and urban areas have been affected by flooding [37]
The FLOMPY results and the validation with Emergency Management Service (EMS) product are preIn Figure 4a, an optical image that covers a part of the area of interest is presented
Summary
Mapping the spatial extent of surface waters is considered an important step for many initiatives related to water sustainability and natural hazards such as floods [1]. Fast responses from decision makers and emergency managers can mitigate casualties and damages For this reason, near-real time spaceborne remote sensing data could be exploited in order to provide accurate and rapid maps of affected area by floods [4]. Near-real time spaceborne remote sensing data could be exploited in order to provide accurate and rapid maps of affected area by floods [4] These maps can be used for calibration and validation of hydrological models [5]. They can help to better set intervention priorities in order to form a loss mitigation plan [6] and even improve flood forecasting [7]
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