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Landslide hazards have significant social, economic, and environmental impact. This work provides a critical review of the main existing literature using satellite data for mapping landslides. We created and examined an extensive bibliographic database from Web of Science (WoS) consisting in 291 outputs from > 1,000 authors who studied almost 700,000 landslides across all continents, for a total of 52 countries represented with China and Italy on top of the list with more authors. The outputs are equivalent to ~ 5% of the whole landslide-related production for the period 1996–2022, with a 600% increase in the number of papers after 2014 driven by the availability of Sentinel-1 and Sentinel-2 data. Analysis of the geographical location across the 66 different countries analysed shows that, within the total number of contributions, the satellite imagery was used to detect and map two main types of landslides: flows and slides. When specified in the manuscripts, the events have been triggered by rainfall (104 cases), earthquakes (32 cases), or both (17 cases). Slope instabilities in these areas were predominantly identified through manual detection (40%); but since 2020, the advent of artificial intelligence is suppressing all other techniques. Despite the undisputed progress of EO-based landslide mapping over the last 26 years, which makes it a consolidated tool for many landslide-related applications, challenges still remain for an effective and operational use of EO images for landslide detection and mapping, and we provide a perspective for future applications considering the existing and the planned SAR satellite missions. [ABSTRACT FROM AUTHOR]

Thickness and stratigraphic settings of soils covering slopes potentially control susceptibility to initiation of rainfall-induced shallow landslides due to their local effect on slope hydrological response. Notwithstanding the relevance of the assessment of hazard to shallow landsliding at a distributed scale by approaches based on a coupled modelling of slope hydrological response and slope stability, the spatial variability of soil thickness and stratigraphic settings are factors poorly considered in the literature. Under these premises, this paper advances the well-known case study of rainfall-induced shallow landslides involving ash-fall pyroclastic soils covering the peri-Vesuvian mountains (Campania, southern Italy). In such a unique geomorphological setting, the soil covering is formed by alternating loose ash-fall pyroclastic deposits and paleosols, with high contrasts in hydraulic conductivity and total thickness decreasing as the slope angle increases, thus leading to the establishment of lateral flow and an increase of pore water pressure in localised sectors of the slope where soil horizon thickness is less. In particular, we investigate the effects, on hillslope hydrological regime and slope stability, of irregular bedrock topography, spatial variability of soil thickness and vertical hydraulic heterogeneity of soil horizons, by using a coupled three-dimensional hydrological and a probabilistic infinite slope stability model. The modelling is applied on a sample mountain catchment, located on Sarno Mountains (Campania, southern Italy), and calibrated using physics-based rainfall thresholds derived from the literature. The results obtained under five simulated constant rainfall intensities (2.5, 5, 10, 20 and 40 mm h−1) show an increase of soil pressure head and major failure probability corresponding to stratigraphic and morphological discontinuities, where a soil thickness reduction occurs. The outcomes obtained from modelling match the hypothesis of the formation of lateral throughflow due to the effect of intense rainfall, which leads to the increase of soil water pressure head and water content, up to values of near-saturation, in narrow zones of the slope, such as those of downslope reduction of total soil thickness and pinching out of soil horizons. The approach proposed can be conceived as a further advance in the comprehension of slope hydrological processes at a detailed scale and their effects on slope stability under given rainfall and antecedent soil hydrological conditions, therefore in predicting the most susceptible areas to initiation of rainfall-induced shallow landslides and the related I-D rainfall thresholds. Results obtained demonstrate the occurrence of a slope hydrological response depending on the spatial variability of soil thickness and leading to focus slope instability in specific slope sectors. The approach proposed is conceived to be potentially exportable to other slope environments for which a spatial modelling of soil thickness would be possible. [ABSTRACT FROM AUTHOR]

The term debris flow is associated with a wide variety of phenomena as debris slides, debris torrents, debris floods, mudflows, mudslides, hyper-concentrated flows and lahars (in volcanic slopes). In historical times, they frequently occurred in Campania Region (Southern Italy) producing damage and casualties. In this paper, more than 200 events described as "debris flow" between 1924 and 2020 were collected. They are mainly located in the central sector of the region, which is characterized by pyroclastic deposits resting on steep carbonate and volcanic slopes and highly prone to hydrogeological hazards. Debris flow events registered in the region were classified as "gravity processes" (flow-like landslides, FL) or "fluvial processes" (flash floods, FF). This classification is fundamental for designing effective early warning systems and risk mitigation plans, since the two phenomena are triggered by different types of rainfall events and are responsible for damages of different magnitude. In this study, a large rainfall database was explored to identify the time scales and seasonality of the two classes of rainfall events, respectively triggering the two phenomena. [ABSTRACT FROM AUTHOR]

In the Campania region (southern Italy), sloping pyroclastic surfaces in partially saturated conditions are frequently affected by flow-like landslides and shallow slip. In this context, safeguarding human life is entrusted to early warning systems (EWSs), typically based on empirical precipitation intensity-duration thresholds that may suffer from missed and false alarms. Indeed, the consequences of heavy rainfall depend on the hydraulic conditions in the soil before the rainstorm. If this aspect is neglected, precipitation intensity-duration thresholds can fail. However, although the current hydraulic condition in the soil is a discriminating factor, it is rarely measured and taken into account. In this regard, the present study focuses on the assessment of the role of the groundwater conditions for the initiation of shallow landslides, and it may be the first step towards the implementation of an innovative early warning system. At this aim, a numerical approach to reproduce the current hydraulic condition in the slope is presented. This study is based on the back-analysis of two debris flows that occurred in the Campania region by means of uncoupled hydromechanical numerical modelling. The stratigraphy and hydromechanical characteristics of the soil layers are known due to surveys and investigations carried out at an experimental test site set up a few kilometres from the two debris flows that occurred. The results show that in the slopes examined, the hydraulic state in the soil before landslide triggering was correctly reproduced through an infiltration analysis starting a few months before, in which rainfall recorded on site was imposed as an entering flow at ground level. Moreover, it was shown that the water storage in the entire loose soil cover was an effective indicator of the average hydraulic state of the slope; more than local variables, such as matric suction and water content. [ABSTRACT FROM AUTHOR]

Landslide risk assessment is fundamental in identifying risk areas, where mitigation measures must be introduced. Most of the existing methods are based on susceptibility assessment strongly site-specific and require information often unavailable for damage quantification. This study proposes a simplified methodology, specific for rainfall-induced shallow landslides, that tries to overcome both these limitations. Susceptibility assessed from a physically-based model SLIP (shallow landslides instability prediction) is combined with distance derived indices representing the interference probability with elements at risk in the anthropized environment. The methodology is applied to Gioiosa Marea municipality (Sicily, south Italy), where shallow landslides are often triggered by rainfall causing relevant social and economic damage because of their interference with roads. SLIP parameters are first calibrated to predict the spatial and temporal occurrence of past surveyed phenomena. Susceptibility is then assessed in the whole municipality and validated by comparison with areas affected by slide movements according to the regional databases of historical landslides. It is shown that all the detected areas are covered by points where the SLIP safety factor ranges between 0 and 2. Risk is finally assessed after computation of distances from elements at risk, selected from the land use map. In this case, results are not well validated because of lack of details in the available regional hydrogeological plan, both in terms of extension and information. Further validation of the proposed interference indices is required, e.g., with studies of landslide propagation, which can also allow considerations on the provoked damage. [ABSTRACT FROM AUTHOR]

The redaction of landslide inventory is a fundamental task for risk management and territorial planning activities. The availability of synthetic aperture radar imagery, especially after the launch of Sentinel-1 mission, enables to systematically update landslide inventories covering wide areas in a reduced time frame and at different scales of analysis. In this work, SAR data processed from the fully automatic P-SBAS pipeline have been adopted to update the Italian national landslide database. Specifically, a matrix has been introduced by comparing past landslide state of activity obtained with Envisat data (2003–2010) and that computed with Sentinel-1 (2014–2018). The state of activity was defined by obtaining the projected velocity along the slope dip direction. The analysis involved about 56,000 landslides which showed at least one Sentinel-1 measurement point, of which 74% were classified as dormant, having annual average velocity < 7 mm/year (considering a value of two times the standard deviation) and 26% as active (mean velocity > 7 mm/year). Furthermore, a landslide reliability matrix was introduced on the landslide inventory updated with S1 data, using the measurement point (MP) density within each landslide and the standard deviation of the mean Vslope value of each landslide. In this case, the analysis revealed that more than 80% of landslides has values of reliability from average to very high. Finally, the 2D horizontal and vertical components were computed to characterize magnitude and direction of every type of landslides included in this work, showing that spreadings, deep-seated gravitation slope deformations, and slow flows showed a main horizontal movement, while complex and translational/rotational slides had more heterogeneity in terms of deformation direction. Hence, the work demonstrated that the application of fast and automatically nationwide Sentinel-1 MTInSAR (multi-temporal interferometry SAR) may provide a fundamental aid for landslide inventory update. [ABSTRACT FROM AUTHOR]

Earthquake-induced landslides represent a significant seismic hazard since they can largely increase the damage and losses due to a seismic event, an issue that must be considered in land-use and risk management purposes. However, it can be difficult to consider all the natural variables, such as geotechnical parameters, that predispose the occurrence of landslides under a specific dynamic triggering, especially for wide areas. Among these, the most important and critical ones to quantify at large scale, are represented by the hydraulic conditions in both unsaturated and saturated media. For this reason, in this work we present a newly developed GIS tool that was specifically designed for the automation of a pseudo-dynamic Newmark model to estimate the co-seismic displacements over wide areas. The tool takes into account reactivations of landslides under different rupture mechanisms and parametrically weighs the role of variable initial soil moisture or pressure head conditions, as well as the influence of ground shaking resulting from local amplification effects. The proposed tool was tested in the Molise region (central-southern Italy), where almost 23,000 existing landslides have been selected for evaluating potential reactivations. The obtained results point out the importance of local conditions on the displacement amount, even by considering a unique return period of the seismic action. Strengths and weaknesses of the proposed model have been also highlighted in view of potential future applications in the framework of co-seismic landslide risk assessment and mitigation measures. [ABSTRACT FROM AUTHOR]

Triggering mechanisms and causative processes of deep-seated gravitational slope deformations (DSGSD) in Alpine and high mountain areas include, among others, post-glacial de-buttressing, earthquake-induced ground shaking, or co-seismic surface faulting. Distinguishing between climatic or tectonically driven factors is challenging since faults and fracture systems can play both an active and/or passive role in the process initiation. We applied an integrated approach, including morpho-structural analysis, geologic field survey, a paleoseismological approach applied to trenching, radiocarbon dating and detailed sedimentological analysis, to a DSGSD located in the Cavargna Valley (N Italy), an area that was never occupied by extensive ice tongues in the Upper Pleistocene and Holocene. We were able to document at least two phases of movement from the Middle Holocene onwards and related triggering factors. Thanks to the comparison with dated lacustrine turbidites in the Alpine Lakes, we argue a seismic triggering for the landslide onset, with a possible source located in southern Switzerland, close to the Insubric Line. The later evolution of the landslide was instead driven by climatic predisposing conditions (humid and cold), instead, corresponding to a phase of enhanced slope instability. [ABSTRACT FROM AUTHOR]

Landslides represent a serious worldwide hazard, especially in Italy, where exposure to hydrogeological risk is very high; for this reason, a landslide quantitative risk assessment (QRA) is crucial for risk management and for planning mitigation measures. In this study, we present and describe a novel methodological approach of QRA for slow-moving landslides, aiming at national replicability. This procedure has been applied at the basin scale in the Arno River basin (9100 km2, Central Italy), where most landslides are slow-moving. QRA is based on the application of the equation risk = hazard (H) × vulnerability (V) × exposure (E) and on the use of open data with uniform characteristics at the national scale. The study area was divided into a grid with a 1 km2 cell size, and for each cell, the parameters necessary for the risk assessment were calculated. The obtained results show that the total risk of the study area amounts to approximately 7 billion €. The proposed methodology presents several novelties in the risk assessment for the regional/national scale of the analysis, mainly concerning the identification of the datasets and the development of new methodologies that could be applicable over such large areas. The present work demonstrates the feasibility of the methodology and discusses the obtained results. [ABSTRACT FROM AUTHOR]

Rockslide-debris flow may cause catastrophic damages because of its high speed and long run-out distance. The influences of slope gradient and gully channel on the entrainment, deposition, and run-out behavior of this type of landslide were not sufficiently investigated by previous studies. To achieve a better understanding toward this type of landslide, a recent rockslide-debris flow that occurred in the Northern Apennines of Italy is studied through field investigation and numerical simulation. The run-out process of this landslide is simulated by an improved depth-averaged model, paying special attention to analyzing the influence of slope gradient and gully channel. The simulation results are compared with the detailed field data of entrainment and deposition distributions. It shows that the depth-averaged model can reasonably simulate the entrainment and deposition characteristic of this landslide by adopting different basal friction strengths for the rockslide region and debris flow region. Entrainment occurs in both high and low slope gradient zones. However, entrainment can only be observed in the high slope gradient zones, while in the low gradient zones, the post-failure topography shows accumulation and deposition. The simulation results also demonstrate that the presence of a gully channel is a key factor in determining landslide mobility and run-out distance. In comparison to a landslide with similar size and geological settings but without a gully channel, the run-out distance is much less, and the landslide does not develop into a flow. [ABSTRACT FROM AUTHOR]

Nowadays, several systems to set up landslide inventories exist although they rarely rely on automated or real-time updates. Mass media can provide reliable info about natural hazard events with a relatively high temporal and spatial resolution. The news publication about a natural disaster inside newspaper or crowdsourcing platforms allows a faster observation, survey, and classification of these phenomena. Several techniques have been developed for data mining inside social media for many natural events, but they have been rarely applied to the automatic extraction of "landslide events". This source of information allows continuous feedback from real world, and news concerning landslide events can be rapidly collected. In this work, the newspaper articles about landslides in Italy are automatically collected by an existing data mining algorithm, based on a semantic engine. The news has been analysed to assess their distribution over the territory and to verify the possibility of using them for hazard mapping purpose. In 10 years, from 2010 to 2019, the algorithm identified and geolocated 184322 articles referring to 32525 generical events ("news"). At first, the collected data underwent to a manual verification, followed by a classification based on news relevance, localization accuracy and time of publication. Then, these data have been used to identify the areas and the periods most affected by landslide phenomena. The analyses show that almost 42% of Italian municipalities have been affected by landslide. According to the results, the use of data mining is helpful for the creation of landslide databases where the day and the approximative location (municipality) of the possible landslide triggers are known. This database, in turn, can be used for scientific purposes, as the definition of the meteorological condition associated with landslide initiation, the validation of risk maps. It can also be used for a proper land use or risk mitigation planning, since the most landslide-prone municipalities can be defined. [ABSTRACT FROM AUTHOR]

This study presents the employment of automatic deep drainage systems to stabilize the Mendatica landslide, one of the largest relict landslides of western Liguria (north-western Italy). Despite the relict state of activity of the main landslide body, a residual kinematic still affects the sector of accumulation zone over which the small village of Mendatica is located and where sharp reactivations of the movements were frequently recorded after severe rainstorms. The aim of this research is to investigate both the efficiency and effectiveness of Electropneumatic Drains® during an extreme rainstorm based on geotechnical monitoring data. On the one hand, the continuous groundwater level measurements allowed to examine the performance of two alignments of wells equipped with electropneumatic pumps during a severe 5-day rainfall event occurred in November 2016, when cumulative rainfalls equalled approximately 800 mm at Mendatica. On the other hand, the subsurface displacements measured from inclinometers before and after the activation of the two draining diaphragms enabled the evaluation of the influence of the automatic deep drainage on the landslide kinematic. The analysis of the hydraulic response of the two drainage lines reveals that, despite the severity of the recorded rainfall values, the pumping wells efficiently worked during the rainstorm since they successfully controlled the increase of hydraulic heads. Furthermore, the outcomes of this study show that, under severe rain conditions, the presence of the Electropneumatic Drains® has mitigated the magnitude of the landslide displacements preventing the development of rapid accelerations of the slope movements. [ABSTRACT FROM AUTHOR]

Landslides hazard assessment requires the combination of spatial and temporal probabilities. In this work, we combine spatial modelling and regionalization of debris rainfall thresholds for assessing both these probabilities and map debris flows initiation hazard over 15 × 103 km2 of the Emilia-Romagna Apennines (Italy). In this area, debris flows are spatially and temporally less frequent than earth slides and earth flows. However, more than a hundred debris flows occurred in October 2014 and September 2015 during two large rainstorms clusters in Parma in Piacenza provinces; some tens of debris flows are reported to have occurred in the past and few others have occurred recently. Since landslides inventory maps used for land use planning only consider some large debris flows accumulation fans, and substantially no information is given on the slopes along which these phenomena might occur, this study aims to fill this gap by creating a hazard map using the evidences collected after the recent abovementioned multi-occurrence events. Different spatial statistical models (Frequency Ratio [FR], Weight of Evidence [WOE] and Logistic Regression [LR]), set up with various combinations of geo-environmental causal factors, have been trained using 60% of debris flow initiation points mapped after the 2014 and 2015 events. The predictive performances of the models have been compared by success rate curves using the remaining 40% of initiation points of the 2014 and 2015 events. The model with the best predictive capability (area under the curve > 0.96) has been further validated using the spatial distribution of other debris flows occurred in the period 1972–2016, and its outputs have been classified into spatial probability classes. Furthermore, the annual exceedance probability of recently published debris flows 3 h cumulated rainfall triggering thresholds has been calculated for in 185 rain gauges and regionalized by spatial interpolation. Finally, spatial and temporal probability maps ranked in a 0–1 range have been combined into a regional debris flows initiation hazard map that, on the basis of the return periods, is associated to different yearly probability values. The resulting hazard map classifies 0.87% of the area as high hazard, 2.83% as medium hazard, 0.5% as low hazard and the remaining 95.81% as null hazard. The spatial distribution of hazardous zones is quantitatively and qualitatively consistent with the spatial distribution of past debris flows. Furthermore, it is coherent with geomorphological common sense and it has proven sufficiently accurate in discriminating as hazardous the debris flows initiation zones of phenomena occurred after it was produced. On such a basis, despite its limitations, we consider the debris flows hazard map produced sufficiently reliable to integrate existing inventory maps in land-use regulation and emergency planning. [ABSTRACT FROM AUTHOR]

Slope stability is influenced by a number of factors that modify the resisting/acting force ratio control landslide initiation and movement velocity. Among these, artificial vibrations have been identified as an important degrading factor for soil strength, but it is not fully clear if they can trigger or modulate movements of clay landslides. To contribute to a better understanding of the potential effect of vibrations on landslide movement, also in terms of boundary conditions, we analyzed monitoring data acquired at the toe of the Pietrafitta landslide in southern Italy. This landslide adjoins the SS87 national road that suffered periodic closure due to landslide activity and in April 2016 operates daytime only for risk mitigation purpose. This condition promoted a better identification of a potential cause-effect relation between traffic vibration and landslide movement. Results from data analysis and landslide modeling suggest that in condition of incipient movement, artificial vibrations, also of limited amplitude, are able to directly initiate clay landslide movement that due to the viscous nature of the involved material exhibit a specific displacement pattern that is not consistent with a sliding block model. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]