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 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.

Flood inundation mapping in data‐scarce areas: A case of Mbire District, Zimbabwe

“…Flood depth is also important because it explains the level of possible damage, thereby helping to estimate the probable losses (Mojtahed et al, 2013; Scorzini & Frank, 2017). Hence, it has a strategic role in assisting emergency response through evaluating the accessibility of the area and crafting appropriate mediation plans (Cian et al, 2018).…”

“…Developing space‐based technology, which includes remote sensing coupled with GIS, has enriched the estimation of flood extents through satellite imagery. Several studies such as Cian et al (2018), Matheswaran et al (2019) and Nharo et al (2019) have furthered the science of flood inundation mapping through satellite images and surface elevation data. For example, Sanyal and Lu (2004) applied remote sensing data for FRM in Monsoon Asia and concluded that the data were more effective for flood extent delineation in developing countries.…”

OmbriaNet—Supervised Flood Mapping via Convolutional Neural Networks Using Multitemporal Sentinel-1 and Sentinel-2 Data Fusion

“…Precipitation events have as a result long-lasting cloud coverage periods and SAR sensors' ability to penetrate clouds both day and night make them more suitable for the task. Lidar technologies can give precise Digital Elevation Models that can be combined with SAR to estimate flood extend along with precise depth [47]. Sentinel-2 multispectral imagery and CNN's have been used in supervised classification producing good results but in limited spatial scale (national) and water segmentation for labeling was produced by visual interpretation with results relative to a human analyst and not in absolute ground truth [48] .…”

Improvement of Flood Extent Representation with Remote Sensing Data and Data Assimilation

“…It aims at discriminating the two classes, namely wet pixels (for flooded areas) and dry pixels in SAR images by estimating their respective backscatter intensity distribution. Cian et al [24] proposed a two-step flood depth estimation using different RS datasets. Firstly, the flood extents were delineated on SAR images using Normalized Difference Flood Index [25], computed based on the SAR images taken before and and after a flood event.…”