In the autumn of 2014, a series of rainfall events affected several sectors of the Liguria region, triggering many shallow landslides and causing three casualties and severe structural damages. The most intensely unstable area covered 385 km 2 , in which more than 1600 landslides have been identified. After these events, an airborne Light Detection and Ranging survey was carried out. The survey yielded a high-resolution digital terrain model (DTM) and aerial images that provided a means of identifying and mapping all the occurred landslides. The distribution analysis of slope instabilities highlighted the link with various human activities. In fact, the majority of the detected landslides occurred in man-modified areas. Geospatial and statistical analyses provided the identification of three main anthropic factors: terraces, their level of maintenance and road network. Moreover, they quantified their role in landslide triggering. These factors were not analysed as separate elements, but as a continuous process, overlapping in time, in man-made influence on landscape. The identification of such factors is a key element for a correct behaviour characterization of this landscape towards extreme flash floods events.
This paper presents a methodology taking advantage of the GPOD-SBAS service to study the surface deformation information over high mountain regions. Indeed, the application of the advanced DInSAR over the arduous regions represents a demanding task. We implemented an iterative selection procedure of the most suitable SAR images, aimed to preserve the largest number of SAR scenes, and the fine-tuning of several advanced configuration parameters. This method is aimed at minimizing the temporal decorrelation effects, principally due to snow cover, and maximizing the number of coherent targets and their spatial distribution. The methodology is applied to the Valle d'Aosta (VDA) region, Northern Italy, an alpine area characterized by high altitudes, complex morphology, and susceptibility to different mass wasting phenomena. The approach using GPOD-SBAS allows for the obtainment of mean deformation velocity maps and displacement time series relative to the time period from 1992 to 2000, relative to ESR-1/2, and from 2002 to 2010 for ASAR-Envisat. Our results demonstrate how the DInSAR application can obtain reliable information of ground displacement over time in these regions, and may represent a suitable instrument for natural hazards assessment.
Structure from Motion (SfM) is a powerful tool to provide 3D point clouds from a sequence of images taken from different remote sensing technologies. The use of this approach for processing images captured from both Remotely Piloted Aerial Vehicles (RPAS), historical aerial photograms, and smartphones, constitutes a valuable solution for the identification and characterization of active landslides. We applied SfM to process all the acquired and available images for the study of the Champlas du Col landslide, a complex slope instability reactivated in spring 2018 in the Piemonte Region (north-western Italy). This last reactivation of the slide, principally due to snow melting at the end of the winter season, interrupted the main road used to reach Sestriere, one of the most famous ski resorts in north-western Italy. We tested how SfM can be applied to process high-resolution multisource datasets by processing: (i) historical aerial photograms collected from five diverse regional flights, (ii) RGB and multi-spectral images acquired by two RPAS, taken in different moments, and (iii) terrestrial sequences of the most representative kinematic elements due to the evolution of the landslide. In addition, we obtained an overall framework of the historical development of the area of interest, and distinguished several generations of landslides. Moreover, an in-depth geomorphological characterization of the Champlas du Col landslide reactivation was done, by testing a cost-effective and rapid methodology based on SfM principles, which is easily repeatable to characterize and investigate active landslides.
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