Improved estimation of flood parameters by combining space based SAR data with very high resolution digital elevation data

Abstract

Abstract. Severe flood events turned out to be the most devastating catastrophes for Europe's population, economy and environment during the past decades. The total loss caused by the August 2002 flood is estimated to be 10 billion Euros for Germany alone. Due to their capability to present a synoptic view of the spatial extent of floods, remote sensing technology, and especially synthetic aperture radar (SAR) systems, have been successfully applied for flood mapping and monitoring applications. However, the quality and accuracy of the flood masks and derived flood parameters always depends on the scale and the geometric precision of the original data as well as on the classification accuracy of the derived data products. The incorporation of auxiliary information such as elevation data can help to improve the plausibility and reliability of the derived flood masks as well as higher level products. This paper presents methods to improve the matching of flood masks with very high resolution digital elevation models as derived from LiDAR measurements for example. In the following, a cross section approach is presented that allows the dynamic fitting of the position of flood mask profiles according to the underlying terrain information from the DEM. This approach is tested in two study areas, using different input data sets. The first test area is part of the Elbe River (Germany) where flood masks derived from Radarsat-1 and IKONOS during the 2002 flood are used in combination with a LiDAR DEM of 1 m spatial resolution. The other test data set is located on the River Severn (UK) and flood masks derived from the TerraSAR-X satellite and aerial photos acquired during the 2007 flood are used in combination with a LiDAR DEM of 2 m pixel spacing. By means of these two examples the performance of the matching technique and the Correspondence to: H. Zwenzner (hendrik.zwenzner@dlr.de) scaling effects are analysed and discussed. Furthermore, the systematic flood mapping capability of the different imaging systems are examined. It could be shown that the combination of high resolution SAR data and LiDAR DEM allows the derivation of higher level flood parameters such as flood depth estimates, as presented for the Severn area. Finally, the potential and the constraints of the approach are evaluated and discussed.

Social media as an information source for rapid flood inundation mapping

“…In combination with a DEM, the approximation of flood water levels, and thus the estimation of inundation depth, is feasible by detecting the flood boundary and extracting height information from the DEM (Zwenzner and Voigt, 2009;Mason et al, 2012). However, image acquisition is largely dependent on the revisiting time of orbital platforms, which in turn is inversely related to spatial resolution .…”

“…Schnebele and Cervone (2013) show the complementary value of information extracted from photos and videos which have been compiled from a search on the internet for flood extent mapping. In urban areas, the additional micro-level evidence of the flooding situation is valuable since there are difficulties in utilizing remotely sensed information and flood inundation models in these areas (Zwenzner and Voigt, 2009;Apel et al, 2009). Despite these obvious opportunities of social media for rapid flood damage estimation, there are a number of challenges to overcome.…”

Flood risks in urbanized areas – multi-sensoral approaches using remotely sensed data for risk assessment

“…Thus, critical information for coordinating and directing rescue and recovery operations as a basis for supporting, managing and coordinating relief activities is promptly available. The technique presented here is based on the concept of locally fitting a carefully processed flood mask into high resolution digital elevation data sets (Meinel et al, 2003;Zwenzner & Voigt, 2009). For the initial parameterization of the flood plain, cross sections were established perpendicularly to the river centerline.…”

“…However, it must be considered that each flood mask carefully derived even from high resolution satellite data still includes some residual geometric and/or thematic classification errors. Such errors have to be minimized when the flood masks are intersected with high resolution DEM (Zwenzner and Voigt, 2009). …”

Estimating flooded area and mean water level using active and passive microwaves: the example of Paraná River Delta floodplain

“…Among them, it is worth mentioning direct measuring techniques based on scatterometer data (Prigent et al, 2007), interferometric techniques (Alsdorf et al, 2007) and the fusion of remote sensing-derived Correspondence to: F. Grings (verderis@iafe.uba.ar) flood boundaries with topographic data (Pappenberger et al, 2007;Matgen et al, 2007;Zwenzner and Voigt, 2009, all based on SAR data) and the estimation of flooded areas with passive microwave signatures (Sippel et al, 1994).…”