No abstract

Abstract. When floods hit inhabited areas, great losses are usually registered in terms of both impacts on people (i.e., fatalities and injuries) and economic impacts on urban areas, commercial and productive sites, infrastructures, and agriculture. To properly assess these, several parameters are needed, among which flood depth is one of the most important as it governs the models used to compute damages in economic terms. This paper presents a simple yet effective semiautomatic approach for deriving very precise inundation depth. First, precise flood extent is derived employing a change detection approach based on the normalized difference flood index computed from high-resolution synthetic aperture radar imagery. Second, by means of a high-resolution lidar digital elevation model, water surface elevation is estimated through a statistical analysis of terrain elevation along the boundary lines of the identified flooded areas. Experimental results and quality assessment are given for the flood that occurred in the Veneto region, northeastern Italy, in 2010. In particular, the method proved fast and robust and, compared to hydrodynamic models, it requires sensibly less input information.

The sub-Saharan African coast is experiencing fast-growing urbanization, particularly around major cities. This threatens the equilibrium of the socio-ecosystems where they are located and on which they depend: underground water resources are exploited with a disregard for sustainability; land is reclaimed from wetlands or lagoons; built-up areas, both formal and informal, grow without adequate urban planning. Together, all these forces can result in land surface deformation, subsidence or even uplift, which can increase risk within these already fragile socio-ecosystems. In particular, in the case of land subsidence, the risk of urban flooding can increase significantly, also considering the contribution of sea level rise driven by climate change. Monitoring such fast-changing environments is crucial to be able to identify key risks and plan adaptation responses to mitigate current and future flood risks. Persistent scatterer interferometry (PSI) with synthetic aperture radar (SAR) is a powerful tool to monitor land deformation with high precision using relatively low-cost technology, also thanks to the open access data of Sentinel-1, which provides global observations every 6 days at 20-m ground resolution. In this paper, we demonstrate how it is possible to monitor land subsidence in urban coastal areas by means of permanent scatterer interferometry and Sentinel-1, exploiting an automatic procedure based on an integration of the Sentinel Application Platform (SNAP) and the Stanford Method for Persistent Scatterers (StaMPS). We present the results of PSI analysis over the cities of Banjul (the Gambia) and Lagos (Nigeria) showing a comparison of results obtained with TerraSAR-X, Constellation of Small Satellites for the Mediterranean Basin Observation (COSMO-SkyMed) and Environmental Satellite advanced synthetic aperture radar (Envisat-ASAR) data. The methodology allows us to highlight areas of high land deformation, information that is useful for urban development, disaster risk management and climate adaptation planning.

Abstract. When floods hit inhabited areas, great losses are usually registered both in terms of impacts on people (i.e., fatalities and injuries) as well as economic impacts on urban areas, commercial and productive sites, infrastructures and agriculture. To properly assess these, several parameters are needed among which flood depth is one of the most important as it governs the models used to compute damages in economic terms. This paper presents a simple yet effective semi-automatic approach for deriving very precise inundation depth. First, precise flood extent is derived employing a change detection approach based on the Normalized Difference Flood Index computed from high resolution Synthetic Aperture Radar imagery. Second, by means of a high-resolution Light Detection And Ranging Digital Elevation Model, water surface elevation is estimated through a statistical analysis of terrain elevation along the boundary lines of the identified flooded areas. Experimental results and quality assessment are given for the flood occurred in the Veneto region, North-Eastern Italy, in 2010. In particular, the method proved fast and robust and, compared to hydrodynamic models, it requires sensibly less input information.