The book was inadvertently published with chapter author’s incorrect family name. This information has been updated from “Del Soldato Matteo” to “Matteo Del Soldato” in the initially published version of chapter “From Satellite Images to Field Survey: A Complete Scheme of Landslide InSAR Monitoring". The correction chapter and the book have been updated.

The analysis of consequence induced by landslides to the built environment is a key step in the landslide risk management process. For this purpose, a thorough knowledge of both the landslide mechanisms and the behaviour/characteristics of the exposed elements turn out to be crucial. Currently, scientists and technicians have access to unprecedented big data sources that provide a variety of useful information on landslide-affected areas. With reference to slow-moving landslides, this paper presents some case studies of an ongoing research aimed at defining and testing original procedures pursuing the exploitation of innovative monitoring/surveying techniques for the analysis and forecasting of vulnerability of buildings and roads.

The Material Point Method (MPM) is an emerging computational tool to simulate the complex dynamic process of rainfall-induced landslide. In this field, hydraulic boundary conditions play an important role. In recent researches, the average relative velocity of pore water with respect to the solid skeleton is considered as Darcy’s velocity usually. Hence, rainfall intensity (mm/h) can be assigned to the node of the mesh as a velocity boundary directly. However, the evolution of true velocities of liquid and solid phases has missed so far in the investigation of the landslide process. In order to keep the information of true velocities in the simulation, this paper provided a new solution to estimate the true velocity of the liquid phase at the node of the boundary layer and has been implemented a coupled hydro-mechanical model using MPM. The validation of such implementation was achieved by simulating a 1D infiltration problem and comparing with the MPM results with those obtained through the commercial software PLAXIS. With the help of this newly implemented boundary condition, rainfall-induced landslides can be better investigated using MPM.

This paper contains the first results of an ongoing research dealing with post-fire mass movements in Italy. Although the attention of the scientific community is increasing worldwide, very few geo-hydrological processes occurring in burned areas are reported for Italy. As the probability of occurrence and magnitude of wildfires is expected to increase in the future because of climate change, more efforts should be made to deepen knowledge on interacting disturbances. Here, we present a case study regarding the erosional response after fire recurrence in a watershed located in NW Italy, where multiple flow processes occurred after six months since the last wildfire, as a consequence of different rainstorms. It contains a description of the geological and geomorphological background, the burn severity assessment together with the analysis of the triggering rainfalls and the outline of the main geomorphic effects that affected people and lifelines.

Each year, rainfall events trigger a large number of landslides causing damage and victims. The study and forecast of rainfall-induced landslides is a field of great importance. Many research activities aim to understand landslide processes and to improve early warning systems. Infiltration processes and underground water circulation have an important role to define failure processes characteristics. In this work, some results from tests performed with a physical slope model are reported. Some experimental tests were conduced, using pyroclastic soil from Sarno area (Southern Italy—near the volcano Vesuvio), affected by landslide events on 5 May 1998. In these places the stratigraphy are composed from limestones covered by layers of pyroclastic deposits. These soils are the product of different eruptions of more volcanoes like Somma-Vesuvius, Flegrei fields and other volcanoes present in the Region no longer active. Generally, they are incoherent deposits with variable granulometry that range from sands, silty sands and silts (ashes) until sands with gravel (pumice) and gravels. Some tests considering both homogeneous and stratified deposits of ash and pumice were carried out. During the tests, both during evaporation and infiltration processes, suction and volumetric water content at different depth were measured by using the appropriate sensors. By comparing and analysing all the collected data it was possible to study the infiltration processes that lead to the failure and the difference between the stratified and the homogeneous deposit.

Slope failures are among the most hazardous natural disasters, causing severe damage to public and private properties. Casualties owing to landslides have been growing in many areas of the world, especially since the increase of climate changes and precipitations. To this, decision-makers need trustworthy information that may be employed to decide the spatial solution plans to protect people. Statistical landslide susceptibility mapping is facing a constant evolution, especially since the introduction of Machine Learning algorithms (ML). A new methodology is here presented, based on the ensemble of Artificial Neural Network, Generalized Boosting Model and Maximum Entropy ML algorithms. Such an approach has been used in Cinque Terre National Park (Northern Italy), severely affected over the years by landslides, following precipitation events, causing extensive damage in a World Heritage Site. Nine predisposing factors were selected and assessed according to the knowledge of the territory, including slope angle, aspect angle, planform curvature, profile curvature, distance to roads, distance to streams, agricultural terraces state of activity, land use and geological information, whilst a database made of ca. 400 landslides was used as input. Four different Ensemble techniques were applied, after the averaging of 150 stand-alone methods, each one providing validation scores such as ROC/AUC curve. Therefore, the results obtained through Ensemble modeling showed improved values, confirming the reliability and the suitability of the proposed approach for decision-makers in land management at local and regional scales.

Landslide dams may collapse within few hours/days after their formation resulting in destructive flooding wave. Due to the limited time available since their formation, forecasting tools able to assess the damming susceptibility over large areas are more advisable for prevention and setting up mitigation measures. A semi-automated GIS-based methodology is proposed in this work to map the spatial damming predisposition over large areas, to analyse consequence and risk scenarios. The procedure is based on a morphological index that use a statistical correlation between morphometric parameters to spatially assess the chance of a river obstruction through the reactivation of an existing landslide. This damming mechanism were tested on the Arno River basin (9116 km2) in Italy, where about 30,000 landslides are mapped. The highest mountain ridges in the Eastern part of the area resulted as the most susceptible to damming in the basin. The concentration of the historical landslide dams endorses the results for this basin.

Landslides are frequent and destructive geo-hydrological events that cause harm to people every year worldwide. We used a catalogue on 1039 landslide fatalities, occurred in Italy in the 50-year period 1970–2019, to determine the dependence of the fatalities on gender, age and circumstance of death. The updated version of the Italian landslide fatalities catalogue includes information on the exact time of the occurrence of the fatal landslide events and the circumstances of death. Possible relations between time of the day and fatalities occurrence are analysed. Males landslides fatalities occurred frequently outdoor, along roads mainly involving drivers or passengers travelling in vehicles, indicating a specific dangerous death contingence preferentially occurring in daylight. Conversely, female landslide fatalities occurred more frequently indoor. To consider the demographic and socio-cultural changes over time, we performed a temporal analysis splitting the catalogue into three overlapping subsets 30-year each. In the three time periods, we estimated the expected fatalities by gender and age, using national census data and a multinomial distribution. Such estimates were compared with the observed landslide fatalities distribution. We identified the age categories over or under represented when the observed fatalities were respectively higher or lower than the modelled expected deaths. The analysis shows that for all the periods landslide male fatalities compared to the female ones are significantly higher than those expected by census data, indicating both a diverse propensity towards the risk taking and a different degree of exposure between males and females.

There has been a significant progress in landslide mitigation strategies in recent years. Recent advancement in physical or experimental and numerical modelling, testing methods to evaluate soil properties, and computing capabilities for slope and deformation analyses as well as landslide risk assessment lead the global scientists to prepare in advance for landslide hazard mitigation. The papers collected in this volume, authored by global leaders in scientific research pertinent to landslide hazard mitigation, provide testament of the progress we made recently on landslide hazard mitigation, specifically on recent development in testing, modelling and risk assessment methods.

Cultivation of grapevines in sloping soils is very widespread all over the world, representing also a fundamental branch of the local economy of several hilly zones. Vineyards can be managed in different ways especially in the inter-rows. These management practices may influence deeply soil properties and grapevine root development. Therefore, this work aims to analyze the effects of different agronomical practices of inter-rows on soil properties, grapevine root systems and proneness towards shallow landslides. We focused on traditional agricultural techniques of tillage and permanent grass cover as well as the alternation of these two practices between adjacent inter-rows. The research was conducted in several test-sites of the Oltrepò Pavese, one of the most important Italian zones for wine production in northern Italian Apennines. Among the examined soil properties, soil hydraulic conductivity was the most influenced one by different soil management practices. Regarding the features of the grapevine root system, vineyards with alternation management of inter-rows had the highest root density and the strongest root reinforcement. As a consequence, slopes with medium steepness were unstable if inter-rows of vineyards were tilled, while vineyards with permanent grass cover or alternation in the inter rows promoted the stability of slopes with higher steepness. The results of this study yielded important information to establish land use managements acting as mitigation measures for shallow landslides susceptibility.

The free availability of Sentinel-1 satellite radar images, combined with the wide-area coverage, the huge benefits-cost ratio, the short revisiting time and the non-invasiveness allows a continuous monitoring of ground displacements. In Italy, Sentinel-1-based continuous monitoring activities are operating in three regions and a possible chain for a practical use of satellite images for local authorities and civil protection operators is presented. A continuous monitoring is set up through the systematic collection of imagery and the repeated processing of each new pair acquired. The Persistent Scatterers (PS) resulting from such chain are subsequently analyzed by means of a data-mining algorithm for highlighting the points with relevant trend variations in the time series. In this way, a huge amount of data and wide areas may be monitored and temporal changes due to various phenomena could be assessed in a short time. The highlighted points, taking advantage of the surrounding PS data, are thus interpreted and classified according to a list of possible causes (e.g. accelerations of landslide or subsidence phenomena). The most relevant cases are notified to local authorities as potential active hazardous phenomena to be verified by means of field survey. A supporting scheme for the operative chain is here proposed and two case studies are presented. This operating workflow is intended to represent a standard procedure which could be applied on a national scale, aiming at the real-time monitoring of hazardous scenarios, and may be used as a guide for civil protection procedures for the risk reduction in large areas.

In this work we exploited Sentinel-1 satellite radar images processed by means of Persistent Scatterers Interferometry (PSI) techniques for the evaluation of landslide geohazard and impact on a mountainous region. In particular, we used PSI data as starting point in a working chain whose final goal is the estimation of the potential worth of loss of the structures involved by slope instability phenomena. We applied this approach on a test area in the Valle d’Aosta Region (North Italy) where more than fifty percent of the territory is above 2000 m a.s.l. and extensively affected by landslides. Firstly, PSI Sentinel-1 data permitted to scan the territory and to highlight the areas characterized by the highest ground motion rates, namely Active Deformation Areas (ADA). These areas were used to derive the intensity of potential landslides in terms of magnitude. Then, for the different elements at risk (EAR) we estimated both the vulnerability, by referring to values already proposed in literature for similar working scale, and the exposure, by considering the current real estate market values of the EAR in the area. We finally derived color-scale maps showing landslide intensity and values of potential loss expressed in quantitative terms (Euros for square meters). This operational methodology can provide useful indications and outputs for landslide risk management at regional scale. Considering the present availability of Sentinel-1 SAR images with 6-days revisiting time, this procedure can represent an example of satellite InSAR monitoring as supporting tool for Civil Protection activities and geohazard mitigation long-term strategies.

Agricultural landscapes cover a significant part of the Earth. In floodplains, we can find large areas dedicated to intensive agriculture. However, also on hills and mountains, agricultural activity can be relevant from the socio-economic point of view. Nowadays, such areas are increasingly under threat because of global environmental changes. Widespread growing rainfall aggressiveness due to climate change, in addition to land abandonment, lack of structural maintenance, and in some cases unsuitable agronomic practices are exposing steep-slope agricultural landscapes to increased hazard of landslides. A suitable hazard assessment and zonation of these phenomena would help better management of such agricultural landscapes. The purpose of this article is to provide an overview of this relevant problem focusing on (i) the contribution of remote sensing technologies (e.g., LiDAR and UAV photogrammetry) in mapping the investigated processes, and (ii) discussing advances and limitations of susceptibility modelling.

Rainfall-induced landslides in Southern Italy often affect the pyroclastic soils produced by the past explosive activity of Vesuvius volcano. Along hilly zones these soils are mostly unsaturated and characterized by a metastable structure, which can experience static liquefaction upon shearing. Several authors studied the mechanical behaviour and the wetting-induced collapse through triaxial tests and direct shear tests. Here, a new series of tests is performed—on remolded specimens—through the Suction Controlled Simple Shear apparatus, which is particularly suitable to reproduce the in-situ stress–strain hillslope conditions and the strain/stress paths induced by rainfall during the failure and post-failure mechanisms. The results are discussed in terms of shear strain, axial strain and saturation degree.

Rainfall-induced shallow landslides of flow-like type are very common in ash-fall pyroclastic soils originated from explosive activity of the Somma-Vesuvius volcano (southern Italy). Over the last few centuries, these phenomena have frequently affected pyroclastic soil-mantled slopes of mountain ranges that surround the volcano causing hundreds of casualties. Many researches have been focused on this topic, especially after the occurrence of the deadly debris flow events of May 1998, which hit Sarno Mountains causing 160 victims. Among the various aspects studied, aimed at the assessment and mapping of hazard to landslide initiation and propagation, the estimation of shear strength of ash-fall pyroclastic soils still deserves to be advanced. This is especially due to the relevant spatial variability of geotechnical properties which are controlled by complex stratigraphic settings. According to such a research focus, the present paper deals with physical and shear strength laboratory characterizations of ash-fall pyroclastic soils and the estimation of the inherent variability. A total number of 97 direct shear tests, supported by grain size and Atterberg’s limits analyses, were carried out. The high number of tests allowed to perform a statistical analysis based on quantile regression approach and aimed at considering the uncertainty related to the high variability of Mohr–Coulomb’s shear strength parameters. The results obtained show values, especially for the drained friction angle (ϕ′), generally higher than those considered in literature. Outcomes of the study and the approach proposed can be conceived as a benchmark for further analyses aimed at the assessment of hazard to initiation of this type of landslides or related physically-based rainfall thresholds.

“From mapping to hazard and risk zonation” is Volume 2 of the book “Understanding and Reducing Landslide Disaster Risk”. The volume collects 54 articles covering five main general topics, including (i) landslide detection, recognition and mapping, (ii) landslide susceptibility assessment and spatial landslide modelling, (iii) landslide size statistics and landslide temporal modelling, (iv) data and information for landslide disaster mitigation, and (v) vulnerability to landslides of people, communities and the built environment. More than 180 authors contributed to the volume, and presented the results of their research conducted in 23 Nations in all continents, except Antarctica. Overall, the 54 articles illustrate a variety of consolidated and innovative methods and techniques, and together represent a contribution of the international landslide community to the long-term effort towards landslide disaster risk reduction.

In this work we describe a first version of the simulation tool developed within the SMART-SED project. The two main components of the SMART-SED model consist in a data preprocessing tool and in a robust numerical solver, which does not require a priori identification of river beds and other surface run-off areas, thus being especially useful to provide accurate input data to more localized landslide and debris-flow models. Furthermore, a geostatistical tool is available to downscale SoilGrids particle size fractions (psf) data to a given resolution. The psf data is employed also within the SCS-CN method, used to model the infiltration process. The results of a complete numerical simulation are reported and possible future developments of the model are discussed.

Landslides of the slide-type movement represent common damaging phenomena in the Italian province of South Tyrol. Up to January 2019, the landslide inventory of the province lists 1928 accurately mapped landslides that required intervention by e.g. the local road service or the provincial geological survey. Thus, this landslide data set mainly includes events that caused damage. The aim of this contribution was to investigate and critically interpret statistical associations between the inventoried slide-type movements and a variety of spatial environmental variables. The assessment of conditional frequencies and the discriminatory power of single variables revealed conditions that are typically present at landslide mapping locations, e.g. topography, land cover, rock types, and proximity to infrastructure. A critical interpretation of the statistical results highlighted the need to consider the landslide data origin (i.e. background information) in order to avoid misleading statements and wrong inferences. The findings of the here presented work show that the availability of detailed landslide information does not always ensure that valid process-related conclusions can be drawn from subsequent statistical analyses (e.g. identification of important landslide controls). Despite considerable methodical advancements in the field of statistical data analysis and machine learning, we conclude that the principle ‘correlation does not necessarily imply (geomorphic) causation’ remains of particular relevance when exploiting available landslide information.

A Deep-Seated Gravitational Slope Deformation (DSGSD) affects the SE slope of the Siah-kuh anticline in its SE periclinal tip in the Ilam region (Zagros Mts., Iran), almost 30 km south of the Seymareh Landslide, which represents the largest landslide on Earth surface. The DSGDS is driven by a Mass Rock Creep (MRC) process and involves an area of about 8 km2. The evolution of such a gravity-induced process is strictly related to the evolution of the of Dowairij River drainage system. River incision originated a stress release at the bottom of the slope which likely caused the initiation of the deformational process. The present study is part of a broader International Programme on Landslide project (Project IPL-237) focused on the role of time-dependent rock mass deformations and landscape evolution rates as predisposing factors for massive rock slope failures. In this regard, the preliminary results of an ongoing research are here presented focusing on the assessment of the present-day landscaping processes. Specifically, a geomorphological survey was carried out in this area firstly through the analysis and interpretation of remote data (Google Earth satellite optical images), which led to the first detection of possible gravity-induced landforms, such as evidence of bulging and lateral release within the deforming slope of the Siah-kuh fold-related ridge. To confirm and quantify the existence of ground displacement due to a MRC process, InSAR techniques were performed for the Siah-kuh slope and surrounding areas by processing 279 satellite Sentinel-1 (A and B) radar images of the ascending and descending orbit spanning from 06 October 2014 to 31 March 2019. Moreover, a quantitative morphometric evaluation was also performed through a morphometric index suitable for predicting the catchment-scale suspended sediment yield on the deformation area produced by the Dowairij River system. We derived the erosion rate of the drainage network responsible for the valley engraving which allows to estimate a starting time for MRC in the order of 101 ka. The comparison between the valley erosion rate and the slope strain rate reveals a difference of almost one order of magnitude allowing to assume that the gravity induced process, identified from remote and field geomorphological survey, evolves faster and originates landforms which can be preserved by the drainage system of the Dowairij River.

The general assumptions and the most popular methods used to assess landslide hazard and for risk evaluation have not changed significantly in recent decades. Some of these assumptions have conceptual weakness, and the methods have revealed limitations. In this work, I deal with populations of landslides i.e. numerous landslides caused in an area by a single trigger (e.g. a rainstorm, an earthquake, a rapid snowmelt event), or by multiple events in a short or long period. Following an introduction on what we need to predict to assess landslide hazard and risk, I introduce the strategies and the main methods currently used to detect and map landslides, to predict populations of landslides in space and time, and to anticipate the numerosity and size characteristics of the expected landslides. For landslide detection and mapping, I consider traditional methods based on the visual interpretation of aerial photographs, and modern approaches that exploit the visual, semi-automatic or automatic analysis of remotely sensed images. For landslide spatial prediction, I discuss the results of a global review of statistical, classification-based methods for landslide susceptibility assessment. For the temporal prediction, leveraging on a global analysis of geographical landslide forecasting and early warning systems, I discuss short term forecast capabilities and their limitations. Next, I discuss long term landslide projections considering the impact of climate variations on landslide projections. For landslide numerosity and size characteristics, I discuss existing statistics of landslide area and volume obtained from large populations of event-triggered landslides. This is followed by an analysis of the landslide consequences, with emphasis on a spatial-temporal model of societal landslide risk in Italy. I end offering recommendations on what I think we should do to make significant progress in our collective ability to predict the hazard posed by populations of landslides, and to mitigate their risk.

Sentinel-1 data on the kinematics of the 2017 Xinmo landslide and its surrounds are studied to understand the precursory failure dynamics of a large region with a historical predisposition to landslides. We perform a systematic spatiotemporal analysis over a period of two years to identify high-risk regions and discriminate between their precursory failure dynamics. We found the 2017 Xinmo landslide source to exhibit a unique kinematic signature which can be distinguished, almost a year in advance, from those of other sites of instabilities. Findings pave the way for the development of a new framework that exploits these differences in the dynamics of motions to accurately predict the location and size of a catastrophic landslide, and distinguish it from false alarms and/or smaller land slips early in the pre-failure regime.

Flow-like landslides, such as debris avalanches, are often dangerous for both structures and human life due to the high velocities and large runout distances. Artificial barriers are proposed to mitigate the landslide threats by reducing the flow velocity and the runout distance as well as diverting the flow towards lateral zones constrained by the barriers. A procedure is here proposed based on multiple steps. The first step focuses on the propagation of debris avalanche along a real topography through the meshless GeoFlow_SPH model in presence of one or more barriers. The second step deals with the behaviour of Deformable Geosynthetics Reinforced Barriers (DGRBs) under horizontal pressures, calculated with reference to literature formulations which include a dynamic component (dependent on landslide velocity at the impact stage), and a static component (height dependent). The time trend of impact pressure is computed from landslide volume and impulse theorem. The third step concerns the evaluation of the performances of the barrier under the impact. The main goal is to present such procedure and to evaluate the performances of DGRBs in: changing the area affected by landslide and dissipating impact energy through deformation and displacement mechanisms.

Earthquake-induced landslide mass mobility is a topic of particular relevance for the analysis of earthquake-induced ground effects scenarios. The landslide masses already existing on the slopes interact with the seismic waves that propagate from the bedrock, giving rise to effects of amplification of the seismic motion at specific frequencies connected to their geometry and their dynamic properties. The quantification of the earthquake-induced displacements expected in landslide masses through numerical models under dynamic conditions highlights how, especially for medium-low energy levels of the seismic input, the displacements thus obtained are generally higher than those computed by conventional approaches (e.g. Newmark method applied to the hypothesis of rigid or deformable block and related semiempirical relations). A series of case studies has also proved that the geometry of significantly dislodged landslide masses (i.e. segmented into kinematically distinct portions, namely “blocks”) due to their geomorphological evolution over time, significantly controls the seismic-induced displacements obtained by numerical models. In particular, the results highlight that the maximum displacements computed through the numerical models do not correspond to seismic inputs whose characteristic periods coincide to those of the resonance or of the length of the landslide mass but are more directly connected to the smaller dimensions of the individual blocks in which the landslide mass is segmented.

In the last years, the use of Unmanned Aerial Vehicles (UAVs) has developed rapidly across several field of earth sciences application, including landslides characterisation and monitoring, therefore providing a strong support for hazard and risk management activities especially with the introduction and advances in the miniaturization of traditional and new generation sensors. The flexibility, low cost, easy operability, and rapidness of intervention in emergency situation gives to these instruments, a strong potential in opening up a vast new area of opportunities in remote sensing for observation, measuring, mapping, monitoring, and management in various landslide environment. Unless initially only air photography was the main application for UAVs, recently new sensors, both passive and active, are being increasingly used. This paper, through some case studies on landslide investigations, aims at giving an overview on several sensors and techniques using UAVs platform addressed to landslide detection, characterization and monitoring.