In November 2016, an extreme rainfall event affected the Ligurian Alps (NW Italy). Consequently, several landslides and debris flows occurred in the upper Tanarello stream basin. In particular, the village of Monesi di Mendatica was severely damaged by two landslide phenomena: the activation of a rotational landslide, which caused the total collapse of two buildings and part of the main road, and the reactivation of a deep-seated planar massive and a complex landslide, which widely fractured most of the buildings in the village. The latter phenomenon was mostly unknown and had never been monitored prior to the 2016 event. Due to the extensive damage, the village of Monesi was completely evacuated, and the road connecting a ski resort area in the upper part of the valley was closed. Furthermore, a potentially dangerous situation related to the eventual progressive evolution of this landslide that could cause a temporary occlusion of the Tanarello stream still remains. For this reason, we defined the landslide behaviour, triggering conditions and chronological evolution leading to the 2016 event using a multidisciplinary approach. This approach consisted of field surveys, satellite DInSAR time series analyses, digital image correlation techniques, rainfall records analyses, postevent monitoring campaigns and subsurface investigation data analyses, and numerical modelling. This multidisciplinary approach enhanced our understanding of this landslide, which is fundamental to better comprehend its behaviour and possible evolution.

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.

In the Ferret Valley (NW Italy), anthropic activities coexist close to the Grandes Jorasses massif’s glaciological complex. In the past, break-off events have caused damage to people and infrastructure. These events concerned two specific sectors: the Montitaz Lobe (Planpincieux Glacier) and the Whymper Serac (Grandes Jorasses Glacier). Since 2010, permanent and discontinuous survey campaigns have been conducted to identify potential failure precursors, investigate the glacier instability processes, and explore different monitoring approaches. Most of the existing terrestrial apparatuses that measure the surface kinematics have been adopted in the Grandes Jorasses area. The monitoring sites in this specific area are characterized by severe weather, complex geometry, logistic difficulties, and rapid processes dynamics. Such exceptional conditions highlighted the limitations and potentialities of the adopted monitoring approaches, including robotic total station (RTS), GNSS receivers, digital image correlation applied to time-lapse imagery, and terrestrial radar interferometry (TRI). We examined the measurement uncertainty of each system and their monitoring performances. We discussed their principal limitations and possible use for warning purposes. In the Grandes Jorasses area, the use of a time-lapse camera appeared to be a versatile and cost-effective solution, which, however is not suitable for warning applications, as it does not guarantee data continuity. RTS and GNSS have warning potentialities, but the target installation and maintenance in remote environments remain challenging. TRI is the most effective monitoring system for early warning purposes in such harsh conditions, as it provides near-real-time measurements. However, radar equipment is very costly and requires extreme logistic effort. In this framework, we present data integration strategies to overcome the abovementioned limits and we demonstrate that these strategies are optimal solutions to obtain data continuity and robustness.

Over the last decades, technological development has strongly increased the number of instruments suitable for landslide monitoring. For large landslides, monitoring systems are organized in complex and multi-instrumental networks aimed at controlling several representative physical variables. The management of these networks is often a complicated task that must consider technological aspects, data-sets processing, and results publication. We developed a new hybrid system focused on capturing and elaborating data-sets from monitored sites and on disseminating monitoring results to support decision makers. With respect to other available monitoring solutions, we emphasized the importance of technological aspects and a correct communication strategy, which represents the last fundamental step for a correct use of collected data. Monitoring results are often published in a difficult and not user-friendly way because they are intended for technicians with adequate background. Such an approach may be inefficient, especially during emergencies, when also non-expert people are involved. Additionally, this system consists of early warning application, which integrates a threshold-based approach and a failure forecasting modeling. The presented approach represents a possible improvement for a more sustainable management of active landslides that could have a strong impact on population and infrastructures in particular in highly urbanized areas.

Active landslide risk assessment and management are primarily based on the availability of dedicated studies and monitoring activities. The establishment of decision support for the efficient management of active landslides threatening urban areas is a worthwhile contribution. Nowadays, consistent information about major landslide hazards is obtained through an interdisciplinary approach, consisting of field survey data and long-time monitoring, with the creation of a high populated dataset. Nevertheless, the large number and variety of acquired data can generate some criticalities in their management. Data fragmentation and a missing standard format of the data should represent a serious hitch in landslide hazard management. A good organization in a standard format can be a good operative solution. Based on standardized approaches such as the ICAO (International Civil Aviation Organization), we developed a standard document called operative monography. This document summarizes all available information by organizing monitoring data and identifying possible lacks. We tested this approach in the Aosta Valley Region (NW Italy) on five different slow moving landslides monitored for twenty years. The critical analysis of the available dataset modifies a simple sequence of information in a more complex document, adoptable by local and national authorities for a more effective management of active landslides.