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Flood mapping and estimation of maximum water depth are essential elements for a first damages evaluation, civil protection interventions planning and detection of areas where remedial are more needed. In this work, we present and discuss a methodology for mapping and quantifying flood severity over plain areas. The proposed methodology considers a multiscale and multi-sensor approach using free or low-cost data/sensors. We applied this method to November 2016 Piemonte (NW Italy) flood. We first mapped flooded areas at basin scale using free satellite data from low to medium-high resolution using both 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 we detected the most damaged sector. The presented method considers both urbanized and not urbanized areas. Nadiral images have several limitations in particular in urbanized areas, where the use of terrestrial images solved this limitation. Very- and ultra-high resolution images have been processed with Structure from Motion (SfM) for the realization of 3-D models. These data, combined with available digital elevation model, allowed us to obtain maps of flooded area, maximum water high and damaged infrastructures. [ABSTRACT FROM AUTHOR]

We executed an experiment to determine the effects of image characteristics on event landslide mapping. In the experiment, we compared eight maps of the same landslide, the Assignano landslide, in Umbria, central Italy. Six maps were obtained through the expert visual interpretation of monoscopic and pseudo-stereoscopic (2.5D), ultra-resolution (3 × 3 cm) images taken on 14 April 2014 by a Canon EOS M photographic camera flown by an CarbonCore 950 hexacopter over the landslide, and of monoscopic and stereoscopic, true-colour and false-colour-composite, 1.84 × 1.84 m resolution images taken by the WorldView-2 satellite also on 14 April 2014. The seventh map was prepared through a reconnaissance field survey aided by a pre-event satellite image taken on 8 July 2013, available on Goggle Earth™, and by colour photographs taken in the field with a hand-held camera. The images were interpreted visually by an expert geomorphologist using the StereoMirrorTM hardware technology combined with the ERDAS IMAGINE® and Leica Photogrammetry Suite (LPS) software. The eighth map, which we considered our reference showing the ground truth, was obtained through a Real Time Kinematic differential GPS survey conducted by walking a GPS receiver along the landslide perimeter to capture geographic coordinates every about 5 m, with centimetre accuracy. The eight maps of the Assignano landslide were stored in a GIS, and compared adopting a pairwise approach. Results of the comparisons, quantified by the error index E, revealed that where the landslide signature was primarily photographical (in the landslide source and transport area) the best mapping results were obtained using the higher resolution images, and where the landslide signature was mainly morphometric (in the landslide deposit) the best results were obtained using the stereoscopic images. The ultra-resolution image proved very effective to map the landslide, with results comparable to those obtained using the stereoscopic satellite image. Conversely, the field-based reconnaissance mapping provided the poorest results, measured by large mapping errors, and confirmed the difficulty in preparing accurate landslide maps in the field. Albeit conducted on a single landslide, we maintain that our results are general, and provide useful information to decide on the optimal imagery for the production of event, seasonal and multi-temporal landslide inventory maps. [ABSTRACT FROM AUTHOR]