Flooding is one of the most destructive natural hazards, accounting for over a third of all disaster damage worldwide. In particular in less developed countries (LDCs) this is typically attributed to poor planning, lack of warning systems and limited awareness of the hazard. A number of flood risk models have been developed, but have as yet contributed little to mapping and quantifying the risk in LDCs, for several reasons. In addition to limited human and technical capacity, these models require considerable amounts of current spatial information that is widely lacking, such as landcover, elevation and elements at risk basedata. Collecting those with ground-based methods is difficult, but remote sensing technologies have the potential to acquire them economically. To account for the variety of required information, data from different sensors are needed, some of which may not be available or affordable. Therefore, data interchangeability needs to be considered. Thus we test the potential of high spatial resolution optical imagery and laser scanning data to provide the information required to run such flood risk models as SOBEK. Using segmentation-based analysis in eCognition, Quickbird and laser scanning data were used to extract building footprints as well as the boundaries of informal settlements. Additionally, a landcover map to provide roughness values for the model was derived from the Quickbird image. These basedata were used in model simulations to assess their actual utility, as well as the sensitivity of the model to variations in basedata quality. The project shows that existing remote sensing data and image analysis methods can match the input requirements for flood models, and that, given the unavailability of one dataset, alternative images can fill the gap.</p
