Team: Natural Hazards

The demand for crisis information in the context of natural disasters such as floods, earthquakes, volcanic eruptions, landslides, etc. is increasing since several years. During such crisis situations, one major challenge is to rapidly gather relevant information and to achieve the cooperation of all the parties involved. In the meantime, up-to-date spatial information on the extent and severity of damages can be made available through the analysis of remote sensing data.

In particular, up-to-date and accurate spatio-temporal information on the extent of damages over large areas can be rapidly provided after serious incidents to support disaster management activities. Remote sensing data can also be an important information source for disaster prevention and monitoring reconstruction activities.

The research focus of the work group is on developing, implementing and applying semiautomatic as well as fully automatic processing chains and analytical methods for the derivation of remote sensing parameters for georisks, in particular for identifying damage and its extent caused by natural disasters using low, medium and high resolution radar, optical and thermal satellite data.

Priority topics are the derivation of the extent, duration and temporal dynamic of flood situations, the mapping of burned areas, fire hot spot and active volcanoes, the detection of drought events as well as the derivation of abrupt changes on the earth's surface (e.g. in the context of earthquakes, tsunamis, volcanic eruptions, landslides, etc.). For this purpose, pixel- and object-based classification approaches, automatic thresholding algorithms, generic change detection methodologies, machine learning and GIS analysis procedures are used.

The developments of the team directly support the operational activities of the Center for Satellite Based Crisis Information (ZKI). Further, the team coordinates the provision of DLR's technical and operational contribution to the International Charter “Space and Major Disasters”.

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