Flood depth estimation by means of high-resolution SAR images and lidar data

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.

Urban Ecosystem Services Quantification through Remote Sensing Approach: A Systematic Review

“…However, current RS methods can vary greatly. For instance, different applications include the LULC change detection [39] and forest disturbance history [40], data fusion of optical and radar data for precisely machine learning supervised mapping of LULC [41,42], the evaluation of water quality index with machine learning algorithms [43], the use of ALOS-2 PALSAR-2 and Sentinel-2A imagery to estimate aboveground biomass [44], and the use of synthetic aperture radar (SAR) and light detection and ranging (LiDAR) data to evaluate the flood depth through the application of a normalized difference index [45].…”

Redefining Agricultural Insurance Services Using Earth Observation Data. The Case of Beacon Project

“…BEACON uses a methodology for flood mapping based on multi-temporal SAR data analysis and the computation of two indices, i.e. the Normalized Difference Flood Index (NDFI) for highlighting flooded areas, and the Normalized Difference Flood in Vegetated areas Index (NDFVI) for highlighting shallow water in short vegetation [15,16]. According to the method, two SAR multi-temporal layer stacks are created.…”

A Review of the Internet of Floods: Near Real-Time Detection of a Flood Event and Its Impact

“…As an example, Cian et al (2018) presented a methodology in which flood maps (including information about water depth) could be derived with the aid of high-resolution SAR imagery and Light Detection And Ranging (LiDAR) digital elevation models (DEMs) [65]. By estimating the water surface elevation through a statistical analysis of terrain elevation along the boundary lines of the detected flood extent, it becomes possible to assess the water depth of a specific region accurately [66,67].…”