Speaker
Description
Flood events can have devastating consequences on society, economy and ecosystems worldwide. Precise information about the flood extent in the affected areas is therefore an essential foundation for local relief workers, decision-makers from crisis management authorities or insurance companies. Besides the open water, flooded vegetation areas are essential for a detection of the entire flood extent. Synthetic Aperture Radar (SAR) is particularly suitable for mapping large-scale inundations, as this tool allows recording of the affected area regardless of illumination or weather conditions. Continuous monitoring of the Earth's surface is achieved by the Sentinel-1 satellite constellation, which provides C-band SAR time series data that can be used to detect changes over time. Based on this SAR time series data and auxiliary information about land cover and topography, a multi-temporal approach was developed for the extraction of flood extent with a focus on temporary flooded vegetation (TFV).
The method is tested on a three case studies in Namibia, Greece and China, all characterized by a large flood event. Due to the strong dependence of the backscatter values on the different TFV types and other environmental conditions it is revealed that for the individual study areas different time series feature, which are based on different polarisations and their combinations, have been identified as reliable for the classification. It is demonstrated that the SAR time series approach enables the extraction of flood-related classes and the derivation of the entire flood extent by supplementing temporary open water by TFV areas. A quantitative evaluation of the generated inundation maps for the individual case studies shows that all study areas have obtained an overall accuracy between 75.0% and 87.3% for pixel- and object-based classification.
