Exploiting the decrease of costs related to UAV technology, the humanitarian community started piloting the use of similar systems in humanitarian crises several years ago in different application fields, i.e., disaster mapping and information gathering, community capacity building, logistics and even transportation of goods. Part of the author’s group, composed of researchers in the field of applied geomatics, has been piloting the use of UAVs since 2006, with a specific focus on disaster management application. In the framework of such activities, a UAV deployment exercise was jointly organized with the Regional Civil Protection authority, mainly aimed at assessing the operational procedures to deploy UAVs for mapping purposes and the usability of the acquired data in an emergency response context. In the paper the technical features of the UAV platforms will be described, comparing the main advantages/disadvantages of fixed-wing versus rotor platforms. The main phases of the adopted operational procedure will be discussed and assessed especially in terms of time required to carry out each step, highlighting potential bottlenecks and in view of the national regulation framework, which is rapidly evolving. Different methodologies for the processing of the acquired data will be described and discussed, evaluating the fitness for emergency response applications.

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 multisensor 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 mediumhigh-resolution from both the SAR (Sentinel-1, COSMOSkymed) and multispectral sensors (MODIS, Sentinel-2). Using very-and ultra-high-resolution images from the lowcost 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 nonurbanised 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.

Abstract. Landslides leave discernible signs on the land surface, most of which can be captured in remote sensing images. Trained geomorphologists analyse remote sensing images and map landslides through heuristic interpretation of photographic and morphological characteristics. Despite a wide use of remote sensing images for landslide mapping, no attempt to evaluate how the image characteristics influence landslide identification and mapping exists. This paper presents an experiment to determine the effects of optical image characteristics, such as spatial resolution, spectral content and image type (monoscopic or stereoscopic), on landslide mapping. We considered eight maps of the same landslide in central Italy: (i) six maps obtained through expert heuristic visual interpretation of remote sensing images, (ii) one map through a reconnaissance field survey, and (iii) one map obtained through a real-time kinematic (RTK) differential global positioning system (dGPS) survey, which served as a benchmark. The eight maps were compared pairwise and to a benchmark. The mismatch between each map pair was quantified by the error index, E. Results show that the map closest to the benchmark delineation of the landslide was obtained using the higher resolution image, where the landslide signature was primarily photographical (in the landslide source and transport area). Conversely, where the landslide signature was mainly morphological (in the landslide deposit) the best mapping result was obtained using the stereoscopic images. Albeit conducted on a single landslide, the experiment 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. In recent years, the use of unmanned aerial vehicles (UAVs) in civilian/commercial contexts are becoming increasingly common, as well as for applications concerning anthropic and natural disasters. In this paper, we present the first results of a research project aimed at defining a possible methodology for the use of micro-UAVs in emergency scenarios relevant to rockfall phenomena. To develop and support the method presented herein, the results relevant to a rockfall emergency occurred on 7 March 2014 in the San Germano municipality (north-western Italy) are presented and discussed.

Abstract. 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 EarthTM, 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.