Inundation mapping using C- and X-band SAR data: From algorithms to fully-automated flood services

Twele, André; Sandro, Martinis; Wenxi, Cao; Simon, Plank German Aerospace Center (DLR), Germany Since the establishment of the ZKI (Center for Satellite-Based Crisis Information) at the German Aerospace Center (DLR), the development of EO-based methodologies for the rapid mapping of flood situations has been of major concern. This can be especially contributed to the fact that inundations constitute the majority of all ZKI-activations as well as activations of the International Charter ‘Space and Major Disasters’. These requirements have led to the development of dedicated SAR-based flood mapping tools which have been utilized during numerous rapid mapping activities of flood situations. The core of these tools is an automatic tile-based thresholding approach (Martinis et al. 2009, 2011, Martinis and Twele 2010) which allows separating inundated regions from land-areas without any user interaction. Recently, the SAR-based flood detection algorithm has been substantially extended and refined in robustness and transferability to guarantee high classification accuracy under different environmental conditions and sensor configurations with the ultimate goal to allow its implementation in an automatic processing chain (Martinis et al. 2014). The processing chain including SAR data pre-processing, computation and adaption of global auxiliary data, unsupervised initialization of the classification as well as post-classification refinement by using a fuzzy logic-based approach is automatically triggered after new SAR data is available on a delivery server. The dissemination of flood maps resulting from the service is performed through a dedicated web client. With respect to accuracy and computational effort, experiments performed on a data set of >200 different TerraSAR-X scenes acquired during flooding all over the world with different sensor configurations confirmed the robustness and effectiveness of the flood mapping service. The processing chain has recently been adapted to the new European Space Agency’s C-band SAR mission Sentinel-1. The thematic processor has further been enhanced through the integration of the “Height above nearest drainage index” (Rennó et al. 2008) which helps to reduce water look-alikes depending on the hydrologic-topographic setting. In contrast to the current TerraSAR-X based thematic service, Sentinel-1 enables a systematic disaster monitoring with high spatial and temporal resolutions. This is a major advantage since the time-consuming step of tasking new satellite data can be omitted. By minimizing the time delay between data delivery and product dissemination it is expected that the proposed service enhances the value of remote sensing during flood management activities and supports applications in hydrology, where information about the flood extent is systematically assimilated into hydrologic and hydraulic models. The presentation will introduce to the technical concept of the SAR-based fully-automated processing chains, with a focus on the current status of the Sentinel-1 flood service. References: Martinis, S., Twele, A. & Voigt, S. 2009: Towards operational near real-time flood detection using a split-based automatic thresholding procedure on high resolution TerraSAR-X data. Natural Hazards and Earth System Sciences (NHESS), 9, 303-314. Martinis, S. & Twele, A. 2010: A Hierarchical Spatio-Temporal Markov Model for Improved Flood Mapping Using Multi-Temporal X-Band SAR Data. Remote Sensing, 2 (9), 2240-2258. 46 Martinis, S., Twele, A. & Voigt, S. 2011: Unsupervised extraction of flood-induced backscatter changes in SAR data using Markov image modeling on irregular graphs. IEEE Transactions on Geoscience and Remote Sensing, 49 (1), 251-263. Martinis, S., Twele, A. & Kersten, J. 2014 (in press): A fully automated TerraSAR-X based flood service. ISPRS Journal of Photogrammetry and Remote Sensing. Rennó, C. D., Nobre, A. D., Cuartas, L. A., Soares, J. V., Hodnett, M. G., Tomasella, J. & Waterloo, M. J. 2008: HAND, a new terrain descriptor using SRTM-DEM: Mapping terrafirme rainforest environments in Amazonia. Remote Sensing of Environment, 112 (9), 3469-3481