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This paper demonstrates the utility of satellite remote sensing data in water management, and particularly, for ood monitoring and impact analysis. Satellite-derived data can provide timely geographical data from which water body extent in normal and¯ood regimes can be ascertained. Combined with exogenous and historical data, within a GIS, these can provide information useful for¯ood prevention decision making. The recent French Alsation, Camargue and Vaison la Romaine¯oods are taken to illustrate the utility of satellite remote sensing.

This paper introduces a novel methodology for automated detection of buildings from single high-resolution optical images with only visible red, green, and blue bands of data. In particular, we first investigate the shadow evidence to focus on building regions. Then, a novel Markov random field (MRF)-based region growing segmentation technique is proposed. Image is oversegmented into smaller homogeneous regions that can be used to replace the rigid structure of the pixel grid. An iterative classification merging is then applied over this set of regions. At each iteration, regions are classified using a region-level MRF model, then, according to the position of shadows, regions having the same class are merged to produce new regions whose shapes are appropriate to rectangles. The final buildings are determined using a recursive minimum bounding rectangle. The experimental results prove that the proposed method is applicable in various areas (high dense urban, suburban, and rural) and is highly robust and reliable.

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Abstract:Principal components analysis (PCA) is applied to a time series of European Remote Sensing (ERS) synthetic aperture radar (SAR) scenes of the Alzette River floodplain (Grand-Duchy of Luxembourg). These images cover markedly different hydrological conditions during several winter seasons in order to enable the examination of the decrease of the radar backscattering signal during drying-up phases following important flood events. At the floodplain scale, with homogeneous land use and constant topography, the first principal components (PCs) are mainly dominated by the variance related to the changing areas. The PCs are thus mainly controlled by subsurface and surface water dynamics. The field observations of a densely equipped piezometric network in the floodplain are used to calculate a mean soil saturation index (SSI) continuously. A classification scheme, based on the PCs and k-means algorithm, leads to the segmentation of the floodplain into several hydrological behaviour classes with distinctive responses versus changing moisture conditions. To validate this classification method with ground-based estimations, the relation between the mean backscattering values of microplots within each PCA-derived hydrological class and the water table measurements, expressed by means of the SSI, is evaluated. Results show that each class of microplots is characterized by the slope of the 'backscattering-SSI' function and by the SSI threshold value at which groundwater resurgence appears. The water ponding implies very low signal return due to the specular backscattering effect on the water surface. Based on established relationships between measured initial water table depths, runoff coefficients and rainfall-induced water table rises, these results are used to discuss the potential of SAR-derived information in flood management applications.