Abstract
Abstract. Flood mapping and estimation of the maximum water depth are essential elements for the first damage evaluation, civil protection intervention planning and detection of areas where remediation is needed. In this work, we present and discuss a methodology for mapping and quantifying flood severity over floodplains. The proposed methodology considers a multiscale and multi-sensor approach using free or low-cost data and sensors. We applied this method to the November 2016 Piedmont (northwestern Italy) flood. We first mapped the flooded areas at the basin scale using free satellite data from low- to medium-high-resolution from both the SAR (Sentinel-1, COSMO-Skymed) and multispectral sensors (MODIS, Sentinel-2). Using very- and ultra-high-resolution images from the low-cost aerial platform and remotely piloted aerial system, we refined the flooded zone and detected the most damaged sector. The presented method considers both urbanised and non-urbanised areas. Nadiral images have several limitations, in particular in urbanised areas, where the use of terrestrial images solved this limitation. Very- and ultra-high-resolution images were processed with structure from motion (SfM) for the realisation of 3-D models. These data, combined with an available digital terrain model, allowed us to obtain maps of the flooded area, maximum high water area and damaged infrastructures.
Highlights
Floods are natural disasters that cause significant damage and casualties (Barredo, 2007).Mapping and modelling the areas affected by floods is a crucial task for (i) identifying the most critical areas for civil protection actions; (ii) evaluating damage and (iii) performing appropriate urban planning (Amadio et al, 2016)
We manually extrapolated the flooded area perimeters considering both satellite data and geomorphological features observed in the hillshade model derived from 5 m digital terrain model (DTM) of the Piedmont Region
For the evaluation of automatic flooded area maps based on satellite data, we applied a GIS query for each map to create boolean rasters of the flooded and non-flooded areas
Summary
Floods are natural disasters that cause significant damage and casualties (Barredo, 2007).Mapping and modelling the areas affected by floods is a crucial task for (i) identifying the most critical areas for civil protection actions; (ii) evaluating damage and (iii) performing appropriate urban planning (Amadio et al, 2016). Advances in remote sensing and technology have introduced the possibility, in the last few years, of generating rapid maps and models during or within a short time after a flood event (e.g. Copernicus Emergency Management Service (©European Union, 2012–2017)). With satellite remote sensing data, it is possible to map flood effects over vast areas at different spatial and temporal resolutions using multispectral (Brakenridge et al, 2006; Gianinetto et al, 2006; Nigro et al, 2014; Wang et al, 2012; Yan et al, 2015; Rahman and Di, 2017) or Synthetic Aperture Radar (SAR) images (Boni et al, 2016; Mason et al, 2014; Schumann et al, 2015; Refice et al, 2014; Pulvirenti et al, 2011; Clement et al, 2017; Brivio et al, 2002). A good description of the main methodologies that are used to map floods with satellite data has been published by Fayne
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