Abstract. Extreme natural events, like e.g. tsunamis or earthquakes, regularly lead to catastrophes with dramatic consequences. In recent years natural disasters caused hundreds of thousands of deaths, destruction of infrastructure, disruption of economic activity and loss of billions of dollars worth of property and thus revealed considerable deficits hindering their effective management: Needs for stakeholders, decision-makers as well as for persons concerned include systematic risk identification and evaluation, a way to assess countermeasures, awareness raising and decision support systems to be employed before, during and after crisis situations. The overall goal of this study focuses on interdisciplinary integration of various scientific disciplines to contribute to a tsunami early warning information system. In comparison to most studies our focus is on high-end geometric and thematic analysis to meet the requirements of smallscale, heterogeneous and complex coastal urban systems. Data, methods and results from engineering, remote sensing and social sciences are interlinked and provide comprehensive information for disaster risk assessment, management and reduction. In detail, we combine inundation modeling, urban morphology analysis, population assessment, socioCorrespondence to: H. Taubenböck (hannes.taubenboeck@dlr.de) economic analysis of the population and evacuation modeling. The interdisciplinary results eventually lead to recommendations for mitigation strategies in the fields of spatial planning or coping capacity.

Application of conventional elevation static corrections and migration to wavefield data recorded on irregular surfaces may result in poor reconstructions of complex subsurface features. Particulary poor images may be obtained at locations where the depths to target structures are comparable to undulations in the surface topography. For example, topographic relief of only 1-2 m may be important for the processing of georadar data. We describe an algorithm that allows georadar data to be migrated directly from gently to highly irregular acquisition surfaces. When applied to a variety of complicated synthetic data sets, topographically migrated images are observed to be markedly superior to those produced by two standard processing schemes. Extensive tests demonstrate that topographic migration should be considered in regions characterized by surface gradients 10% (i.e., dips 6 • ). For effective topographic migration, lateral and vertical coordinates of the georadar antennas should be determined to better than 10% of the dominant georadar wavelength, and velocities should be known to within 10-20% (e.g., 0.01-0.02 m/ns) of their true values. When applied to data collected across a moderately dipping (∼14 • ) rock glacier in the Swiss Alps, georadar sections resulting from two standard processing schemes have reflectors with depths and dips that differ by a significant 10-15% from those in the topographically migrated images.

Under favorable conditions, georadar techniques can provide vivid images of the shallow subsurface (<10–50 m). Although the significant advantages of 3-D georadar surveying strategies are well documented, they generally require much greater expenditures than traditional 2-D approaches. We introduce an efficient, semiautomated 3-D georadar acquisition and processing scheme that does not jeopardize data quality. A standard georadar acquisition unit is integrated with an innovative self‐tracking laser theodolite with automatic target recognition capabilities. Georadar and coordinate data are recorded simultaneously as the georadar antennae are transported steadily across a survey area. While tracking a target prism mounted between the antennae, the theodolite provides real‐time coordinate information with high accuracy in all directions. At ∼1 m/s (moderate walking speed) the coordinates are determined to better than ±0.04 m, and at ∼3 m/s they are better than ±0.07 m. Subsequent to acquisition, semiautomatic processing allows the georadar data to be static corrected, transferred to a regular grid using a novel 2-D Fourier transform method, amplitude modulated, filtered, and displayed. The acquisition component of the new scheme is five to ten times faster than standard step‐mode georadar techniques, and the semiautomatic processing component allows initial 3-D images to be viewed in the field. Typically, a 3-D georadar data set covering a 25 m × 25 m area may be collected and processed in less than 3 hours. One such data set recorded across a former glaciofluvial environment allows reflections from complex river channel sediments and surface features to be readily identified and interpreted.

The ability of airborne hyperspectral remote sensing methods to detect hydrocarbons was investigated by the Federal Institute of Geosciences and Natural Resources. Reference areas of de ned geometry and chemical properties were prepared, e.g. sandy soil, oil-contaminated soil, grass, plastic tarpaulins. The aim of the study was to collect hyperspectral data from these areas and simultaneously determine their spectra with the infrared intelligent spectroradiometer GER Mark V IRIS. The data corrections and further processing were based on data provided by a eld spectrometer.This study showed that airborne hyperspectral remote sensing can be used to detect hydrocarbons e Y ciently. Hydrocarbon-bearing substances are characterized by typical absorption features in the spectra. The availability of the high signal-to-noise-ratio HyMap hyperspectral imaging system permits these features to be recognized in the pixel spectra even if they are not very pronounced. Oilcontaminated soil and other materials containing hydrocarbons can be detected and located directly and unambiguously by image processing focused on the spectral characteristics of hydrocarbons. By this procedure, atmospheric correction of the HyMap data is not necessary.

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