Abstract
The C-band Sentinel-1 satellite constellation enables the continuous monitoring of the Earth’s surface within short revisit times. Thus, it provides Synthetic Aperture Radar (SAR) time series data that can be used to detect changes over time regardless of daylight or weather conditions. Within this study, a time series classification approach is developed for the extraction of the flood extent with a focus on temporary flooded vegetation (TFV). This method is based on Sentinel-1 data, as well as auxiliary land cover information, and combines a pixel-based and an object-oriented approach. Multi-temporal characteristics and patterns are applied to generate novel times series features, which represent a basis for the developed approach. The method is tested on a study area in Namibia characterized by a large flood event in April 2017. Sentinel-1 times series were used for the period between September 2016 and July 2017. It is shown that the supplement of TFV areas to the temporary open water areas prevents the underestimation of the flood area, allowing the derivation of the entire flood extent. Furthermore, a quantitative evaluation of the generated flood mask was carried out using optical Sentinel-2 images, whereby it was shown that overall accuracy increased by 27% after the inclusion of the TFV.
Highlights
Flooding affects societies, economies, and ecosystems worldwide and can have a devastating impacts
The multi-temporal behaviour of the NDVI and NDWI values for the same time-period is displayed in Figures 5d and 6d and in Figures 5e and 6e, which serve as a comparison to the Synthetic Aperture Radar (SAR) time series data
The light blue areas represent confusion between temporary open water (TOW) in the classification results and Dry Land in the validation image. These misclassifications may be caused by an insufficient decrease of the backscatter values in the VV polarisation at the analysed date of the flood event, which could not be detected by the generated threshold value
Summary
Economies, and ecosystems worldwide and can have a devastating impacts. In the framework of the Center for Satellite Based Crisis Information (ZKI) located at the German Aerospace Center (DLR), this method is designed to provide a fully automatic web-based Sentinel-1 Flood Service (S-1FS) for rapid provision of open flood extent information for humanitarian relief activities and civil security issues worldwide [48,49].
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