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Remote images are useful tools for detecting and monitoring landslides, including shallow landslides in agricultural environments. However, the use of non-commercial satellite images to detect the latter is limited because their spatial resolution is often comparable to or greater than landslide sizes, and the spectral characteristics of the pixels within the landslide body (LPs) are often comparable to those of the surrounding pixels (SPs). The buried archaeological remains are also often characterized by sizes that are comparable to image spatial resolutions and the spectral characteristics of the pixels overlying them (OBARPs) are often comparable to those of the pixels surrounding them (SBARPs). Despite these limitations, satellite images have been used successfully to detect many buried archaeological remains since the late 19th century. In this research context, some methodologies, which examined the values of OBARPs and SBARPs, were developed to rank images according to their capability to detect them. Based on these previous works, this paper presents an updated methodology to detect shallow landslides in agricultural environments. Sentinel-2 and Google Earth (GE) images were utilized to test and validate the methodology. The landslides were mapped using GE images acquired simultaneously or nearly simultaneously with the Sentinel-2 data. A total of 52 reference data were identified by monitoring 14 landslides over time. Since remote sensing indices are widely used to detect landslides, 20 indices were retrieved from Sentinel-2 images to evaluate their capability to detect shallow landslides. The frequency distributions of LPs and SPs were examined, and their differences were evaluated. The results demonstrated that each index could detect shallow landslides with sizes comparable to or smaller than the spatial resolution of Sentinel-2 data. However, the overall accuracy values of the indices varied from 1 to 0.56 and two indices (SAVI and RDVI) achieved overall accuracy values equal to 1. Therefore, to effectively distinguish areas where shallow landslides are present from those where they are absent, it is recommended to apply the methodology to many image processing products. In conclusion, given the significant impact of these landslides on agricultural activity and surrounding infrastructures, this methodology provides a valuable tool for detecting and monitoring landslide presence in such environments. [ABSTRACT FROM AUTHOR]

Land Surface Temperature (LST) is an important climate factor for understanding the relationship between the land surface and atmosphere. Furthermore, LST is linked to soil moisture and evapotranspiration, which can potentially alter the severity and regime of wildfires, landslide-triggering precipitation thresholds, and others. In this paper, the monthly daytime and nighttime LST products of Moderate Resolution Imaging Spectroradiometer (MODIS) are employed for the period 2000–2023 in order to find areas that have been cooling or warming in a region of great interest in Central Italy, due to its complex geological and geomorphological settings and its recent seismic sequences and landslide events. The annual MODIS land cover images for 2001–2022 are also utilized to investigate the interconnection between LST and land cover change. The results of the non-parametric Mann–Kendall trend test and its associated Sen's slope reveal a significant nighttime warming trend in the region, particularly in July, linked to forest and woodland expansion. Grasslands toward the coastline with low elevation (less than 500 m a.s.l.) have experienced significant heat waves during the summer, with an LST of more than 35 °C. A significant negative correlation between the elevation and LST is observed for each calendar month. In particular, the daytime and nighttime LST have more than 80% correlation with elevation during winter and summer, respectively. In addition, nighttime warming and gradual drainage are noticed in Lake Campotosto. The results of this study could be useful for wildfire and landslide susceptibility analyses and hazard management. [ABSTRACT FROM AUTHOR]

The prediction and prevention of landslide hazard is a challenging topic involving the assessment and quantitative evaluation of several elements: geological and geomorphological setting, rainfalls, and ground motion. This paper presents the multi-approach investigation of the Nevissano landslide (Asti Province, Piedmont, NW Italy). It shows a continuous and slow movement, alongside few paroxysmal events, the last recorded in 2016. The geological and geomorphological models were defined through a field survey. An inventory of the landslide's movements and rainfall records in the period 2000–2016 was performed, respectively, through archive investigations and the application of "Moving Sum of Daily Rainfall" method, allowing for the definition of rain thresholds for the landslide activation (105 mm and 193 mm, respectively, in 3 and 30 days prior to the event). The displacements over the last 8 years (2016–2023) were monitored through an innovative in-continuum monitoring inclinometric system and Earth Observation (EO) data (i.e., relying on Interferometric Synthetic Aperture Radar, or InSAR data): it gave the opportunity to validate the rainfall thresholds previously defined. This study aims to provide information to public authorities for the appropriate management of the site. Moreover, the proposed workflow could be adopted as a guideline for investigating similar situations. [ABSTRACT FROM AUTHOR]

Featured Application: This paper presents a procedure for assessing—at the municipal scale—the level of risk (or attention required) of stretches of roads exposed to the activity of existing or potential slow-moving landslides. This procedure can be applied to support studies of road networks over large areas aimed at the prioritization of risk-mitigation measures and the identification of road sections requiring further investigation. Slow-moving landslides are widespread natural hazards that can affect social and economic activities, causing damage to structures and infrastructures. This paper aims at proposing a procedure to analyze road damage induced by slow-moving landslides based on the joint use of landslide susceptibility maps, a road-damage database developed using Google Street View images and ground-displacement measurements derived from the interferometric processing of satellite SAR images. The procedure is applied to the municipalities of Vaglio Basilicata and Trivigno in the Basilicata region (southern Italy) following a matrix-based approach. First, a susceptibility analysis is carried out at the municipal scale, using data from landslide inventories and thematic information available over the entire municipalities. Then, the susceptibility index, the class of movement and the level of damage are calculated for the territorial units corresponding to the road corridors under investigation. Finally, the road networks are divided into stretches, each one characterized by a specific level of risk (or attention required) following the aggregation of the information provided by the performed analyses. The results highlight the importance of integrating all of these different approaches and data for obtaining quantitative information on the spatial and temporal behavior of slow-moving landslides affecting road networks. [ABSTRACT FROM AUTHOR]

In the future, as a result of global warming, it is possible that rainfall could become more intense and frequent. This could lead to more frequent triggering of damaging phenomena such as floods and landslides (named as a whole damaging hydrogeological events, DHE), and, consequently, to the increase of their impacts on territories, especially in regions where uncontrolled urban sprawl represents a factor that can exacerbate the problem. The analysis of a large quantity of information about both triggering rainfall and triggered phenomena can help to comprehend relationships between triggering precipitation and its related impacts. In this paper, to facilitate the investigation of the relationships between large and complex datasets concerning both rainfall and rainfall-related damage, we propose an index-based approach, illustrated by its application to the Calabria region (Southern Italy). In particular, this manuscript presents some results from a preliminary investigation aimed at assessing the "better" index to describe DHE. Five rainfall indices (RIs) were tested and five composite rainfall indices (CRIs), combinations of two or more RIs, are proposed. We calculated the RIs and the CRIs by means of 1,300,000 daily data registered in the observation period 1980–2020. The CRIs showed the best relationships with the data of damaging hydrogeological events (DHEs). Particularly, better results were obtained with landslides data than with floods data, perhaps due to the hydraulic characteristics of the Calabria rivers, affected by flash floods mainly influenced by very intense hourly rainfall events. [ABSTRACT FROM AUTHOR]

Broad mountainous areas in the western Campania (southern Italy), where young pyroclastic deposits extensively outcrop, frequently experience rainfall-induced slope movements of different degrees of mobility, causing heavy damage and fatalities. Such landslides cannot be easily mitigated, and the implementation of physically based early warning systems is still not able to predict the post-failure evolution of slope movements and the exposed areas at risk. This paper is devoted to overcoming this limit. To this end, the mechanical characterization of pyroclastic soil, carried out through an extensive laboratory testing program, is presented and compared with those of two other ashy soils of different depositional mechanisms. The results show that the depositional mode influences soil properties; to begin with, it affects the unsaturated shear strength, whose intercept of cohesion is up to 5 kPa higher in ashes of flow deposition than in airfall ash deposits. The saturated undrained soil response allowed for the identification of different levels of susceptibility to the liquefaction of pyroclastic deposits, which is one of the main factors governing the post-failure evolution of landslides. Gathering all the acquired information, including saturated and unsaturated soil shear strength, permeability function, and water retention curves, into a soil database, it was possible to present all data under a unitary framework. Finally, the implementation of the proposed flowchart for a simplified assessment of post-failure evolution to be employed in regional early warning systems can enhance our knowledge of the areas at risk. [ABSTRACT FROM AUTHOR]

Cultural heritage is one of the most exceptional resources characterizing the Italian territory. Archaeological heritage, i.e., the archaeological sites with different types of archaeological artifacts, strongly contributes to enriching the national and international cultural heritage. Nevertheless, it is constantly exposed to external factors, such as natural deterioration, anthropic impact, and climate-related hazards, which may compromise its conservation. In Italy, many archaeological areas are affected by significant soil settlements that involve a large part of monuments. This paper focuses on the landslide hazard assessment of the archaeological site of Pietrabbondante (Molise region, Italy). The impact of the expected rainfall regimes, according to the EURO-CORDEX projections, on slope stability conditions were evaluated through the application of a physically based model that couples a hydraulic and a mechanical model to evaluate slope stability evolution due to pore pressure changes. Given the unavoidable lack of knowledge of the geotechnical soil properties in an archaeological heritage area, the proposed method considered the random uncertainty of soil parameters by means of a probabilistic approach in order to assess the stability conditions in terms of probability of occurrence of a landslide. The results of this study provide a reference for the safety assessment and preventive conservation of archaeological areas characterized by high cultural value. [ABSTRACT FROM AUTHOR]

This paper presents a GIS-based approach to create a multilevel data system for detailed knowledge of landslide occurrences in small territorial units such as municipalities. The main aim is to collect all the available data (geological, geomorphological, and climatic data, as well as landslide inventory maps and catalogues) in a structured data management system and perform further analyses to identify the typical landslide scenarios of the study area that can be useful in landslide risk management. We demonstrated the use of the methodology analyzing landslide risk in the municipality of Catanzaro (southern Italy), having a surface of 111.7 km2, 20.5% of which was affected by landslides. The spatial and temporal distribution of landslides highlighted that in several cases, they are reactivations of pre-existing phenomena. In fact, in the municipality, approximately 17% of the buildings fall within landslides-affected areas, 7.9% of which are in areas where landslides are classified as active. Furthermore, active landslides involve 8.1% and 9.5% of the roads and railways, respectively. In the 1934–2020 study period, 53% of activations occurred between October and December and were triggered by daily rain which in the highest percentage of cases (49%) showed values between 50 and 100 mm. The proposed GIS platform can be easily updated in order to preserve the landslide history of the area and can be enriched with further thematic layers (i.e., layers concerning flood events, which often occur simultaneously with major landslide events). The case study demonstrates how the platform can support landslide risk management in terms of monitoring, planning remedial works, and the realization/updating of civil protection plans. [ABSTRACT FROM AUTHOR]

Earth flows are complex gravitational events characterised by a heterogeneous displacement pattern in terms of scale, style, and orientation. As a result, their monitoring, for both knowledge and emergency purposes, represents a relevant challenge in the field of engineering geology. This paper aims to assess the capabilities, peculiarities, and limitations of different remote sensing monitoring techniques through their application to the Pietrafitta earth flow (Southern Italy). The research compared and combined data collected during the main landslide reactivations by different ground-based remote sensors such as Robotic Total Station (R-TS), Terrestrial Synthetic Aperture Radar Interferometry (T-InSAR), and Terrestrial Laser Scanner (TLS), with data being derived by satellite-based Digital Image Correlation (DIC) analysis. The comparison between R-TS and T-InSAR measurements showed that, despite their different spatial and temporal resolutions, the observed deformation trends remain approximately coherent. On the other hand, DIC analysis was able to detect a kinematic process, such as the expansion of the landslide channel, which was not detected by the other techniques used. The results suggest that, when faced with complex events, the use of a single monitoring technique may not be enough to fully observe and understand the processes taking place. Therefore, the limitations of each different technique alone can be solved by a multi-sensor monitoring approach. [ABSTRACT FROM AUTHOR]

This paper deals with the origin of the Pretare clastic deposit (PRA), which crops out along the Morricone fluvial valley in the Central Apennines of Italy. With the aim of deciphering the genesis of the PRA deposit, geological s.l. and geomorphological analyses were carried out allowing for the interpretation of the PRA deposit as a rock avalanche. Furthermore, geological cross sections constrained by well-log and field survey data, together with stratigraphic, sedimentologic, and morphometric analyses, allowed us to assign the deposit to a catastrophic rock slope failure, which occurred during a cold climate of the Late Pleistocene. Several issues concerning the propagation mechanisms were inferred from the mapping of 350 boulders over the entire accumulation area and from the measure of the morphometric parameters of the landslide body. We also performed a restoration of the potential source area by comparing the reconstructed pre- and post-failure DEMs. A missing volume of 8.41 × 106 m3 was estimated on the south-eastern side of the Vettore Mt., which is consistent with the deposit volume computed from the geological interpretation (10.56 × 106 m3). The outcomes of this study provide useful insights for a better understanding of the Quaternary morpho-evolution of the Central Apennines area where analogous rock avalanche events marked the recent evolution of the belt. [ABSTRACT FROM AUTHOR]

This paper investigates the possibility to detect the short-time evolution of the slow-moving Vomice earth flow, located in the northeastern sector of the Calabria region (South Italy), by combining the information obtained from two different drone flights, carried out in February 2019 and June 2022, with field surveys. The obtained results consisted of delimiting all landslide bodies constituting the Vomice earth flow, detecting landslide types and the state of activity, as well as identifying spatial and volumetric changes. The obtained results showed that depletion and transition zones of the Vomice earth flow are active, while the accumulation zone appears prevalently dormant. Particularly, in the analyzed period, the depletion zone was characterized by local collapses of the main scarps where several slides evolving in earth flows caused more than 20 m of retrogressive fail upslope. The maximum elevation changes observed in these zones were about ±5 m. The volume of the material mobilized by mass movements was about 114.2 × 103 m3, whereas the volume of the accumulated material was approximately 92.7 × 103 m3. The transition zone was affected by several slow earthflows that re-mobilized the displaced material located in the middle portion of the landslide and reached the accumulation zone. Overall, the results of this study demonstrated the practicality and feasibility of using UAV tools for detecting the short-time evolution of a large landslide. [ABSTRACT FROM AUTHOR]

Landslides cause fatalities, widespread damages and economic losses. Quite frequently, they are triggered by rainfall. Many studies have investigated the relationships between rainfall characteristics and landslide events. This paper reviews the two main approaches, physical and hydrological, for modelling such relationships. In the physical approach, the influence of rainfall on slope stability is commonly analysed in terms of groundwater infiltration, pore pressure changes and balance between shear stresses and resistances, therefore a considerable amount of hydrogeological, morphological and geotechnical data is required. In the hydrological approach, a statistical-probabilistic study of rainfall series and dates of occurrence of slope movements is instead carried out. Both types of methods are briefly presented, with examples from real applications to study cases in Southern Italy. In particular, the recent reactivations of a large rockslide in Northern Calabria have been modelled by means of physical and hydrological approaches. In addition, shallow landslides in Calabria, Campania and Sicily have been modelled by employing hydrological approaches. Strengths and weaknesses of the adopted methods are discussed, together with the causes that may have hindered better results for the considered cases. For the methods illustrated through real application cases, research perspectives are discussed, as well as their possible use in early warning systems. [ABSTRACT FROM AUTHOR]

Landslide hazard management usually requires time-consuming campaigns of data acquisition, elaboration, and modeling. However, in the post-emergency phase management, time is a factor, and simpler but faster methods of analysis are needed to support decisions even in the short-term. This paper analyzes the Theilly landslide (Western Italian Alps), which was recently affected by a series of reactivations. While some instrumental campaigns are being carried out to support the design of protection measures, simple tools are also needed to assess the hazard of future reactivations and to evaluate the possibility of damming the torrent at the footslope. Therefore, state-of-the-art empirical methods were used and customized for the specific case study: a set of intensity–duration rainfall thresholds depicting increasing hazard levels was defined to monitor and forecast possible reactivations, while a methodology based on hydro-morphometric indices was applied to the case of study, to assess the possible evolution scenarios (landslide that does not dam the river, formation of a stable dam, formation of an unstable dam), based on the landslide volume. The proposed empirical methodologies have the advantage of requiring only ready-available input data and quick elaborations, thus allowing the rapid set up of tools that could be used for hazard management. [ABSTRACT FROM AUTHOR]

Landslides are widespread geological features in Italy's Northern Apennines, with slow-moving earthflows among the most common types. They develop in fine-grained rocks and are subject to periodic rainfall-induced reactivations alternating to phases of dormancy. In this paper, we use radar interferometry (InSAR) and information about landslide activity to investigate deformation signals on an areal basis and to assess the dynamics of recently reactivated earthflows. We use traditional two-pass interferometry by taking advantage of the short revisit time of the Sentinel 1 satellite to characterize 4 years of slope deformations over the 60 km2 study area, where 186 landslides are mapped. Our results show that most intense and sustained deformation signals are associated with phenomena on the verge of reactivation, indicating that radar interferometry may have a potential for early warning purposes. By focusing on three specific earthflow reactivations, we analyze their dynamics through the years that preceded their failure. Despite inherent uncertainties, it was possible to retrieve the deformation signal's temporal evolution, which displayed seasonally recurring accelerations, peaking during the major precipitation episodes in the area. [ABSTRACT FROM AUTHOR]

This paper deals with monitoring systems to manage the risk due to fast slope failures that involve rock masses, in which important elements (such as infrastructures or cultural heritages, among the others) are exposed. Three different approaches for data analysis were here compared to evaluate their suitability for detecting mutual relations among destabilising factors, acting on different time windows, and induced strain effects on rock masses: (i) an observation-based approach (OBA), (ii) a statistics-based approach (SBA) and (iii) a semi-empirical approach (SEA). For these purposes, a test-site has been realised in an abandoned quarry in Central Italy by installing a multi-parametric monitoring sensor network on a rock wall able to record strain effects induced by natural and anthropic forcing actions (like as temperature, rainfall, wind and anthropic vibrations). The comparison points out that the considered approaches allow one to identify forcing actions, responsible for the strain effects on the rock mass over several time windows, regarding a specific size (i.e., rock block dimensional scale). The OBA was more suitable for computing the relations over short- to medium time windows, as well as the role of impulsive actions (i.e., hourly to seasonal and/or instantaneous). The SBA was suitable for computing the relations over medium- to long time windows (i.e., daily to seasonal), also returning the time lag between forcing actions and induced strains using the cross-correlation statistical function. Last, the SEA was highly suitable for detecting irreversible strain effects over long- to very long-time windows (i.e., plurennial). [ABSTRACT FROM AUTHOR]

Landslides recurrently impact the Italian territory, producing huge economic losses and casualties. Because of this, there is a large demand for monitoring tools to support landslide management strategies. Among the variety of remote sensing techniques, Interferometric Synthetic Aperture Radar (InSAR) has become one of the most widely applied for landslide studies. This work reviews a variety of InSAR-related applications for landslide studies in Italy. More than 250 papers were analyzed in this review. The first application dates back to 1999. The average production of InSAR-related papers for landslide studies is around 12 per year, with a peak of 37 papers in 2015. Almost 70% of the papers are written by authors in academia. InSAR is used (i) for landslide back analysis (3% of the papers); (ii) for landslide characterization (40% of the papers); (iii) as input for landslide models (7% of the papers); (iv) to update landslide inventories (15% of the papers); (v) for landslide mapping (32% of the papers), and (vi) for monitoring (3% of the papers). Sixty-eight percent of the authors validated the satellite results with ground information or other remote sensing data. Although well-known limitations exist, this bibliographic overview confirms that InSAR is a consolidated tool for many landslide-related applications. [ABSTRACT FROM AUTHOR]

Rockslides are one of the most dangerous hazards in mountainous and hilly areas. In this study, a rockslide that occurred on 30 November 2022 in Castrocucco, a district located in the Italian municipality of Maratea (Potenza province) in the Basilicata region, was investigated by using pre- and post-event high-resolution 3D models. The event caused a great social alarm as some infrastructures were affected. The main road to the tourist hub of Maratea was, in fact, destroyed and made inaccessible. Rock debris also affected a beach club and important boat storage for sea excursions to Maratea. This event was investigated by using multiscale and multisensor close-range remote sensing (LiDAR and SfM) to determine rockslide characteristics. The novelty of this work lies in how these data, although not originally acquired for rockslide analysis, have been integrated and utilized in an emergency at an almost inaccessible site. The event was analyzed both through classical geomorphological analysis and through a quantitative comparison of multi-temporal DEMs (DoD) in order to assess (i) all the morphological features involved, (ii) detached volume (approximately 8000 m3), and (iii) the process of redistributing and reworking the landslide deposit in the depositional area. [ABSTRACT FROM AUTHOR]

Heterogeneous rock masses that include rhythmic alternations of marl, shale, marly limestone, sandstone, siltstone, and argillite, such as Flysch, are particularly prone to generating colluvial deposits on gentle slopes, which are often subject to failures triggered by heavy rainfall. Flysch-derived colluvial soils are made up of highly heterogeneous sediments ranging from clayey loam to rock fragments, and they have been studied more rarely than homogeneous soils. In this work, we present a geotechnical and hydraulic characterisation performed both in situ and in the laboratory on flysch-derived colluvial soils that were involved in a channelised landslide in the pre-alpine area of the Friuli Venezia Giulia region (NE Italy). The investigated soils were characterised by the average values of the grain size composition of about 25% gravel, 20% sand, 30% silt, and 25% clay. The loamy matrix presented low-to-medium values of the liquid and plastic limits, as well as of the plasticity index (LL = 40%, PL = 23%, and PI = 17%, respectively). The values of the peak friction angle for natural intact samples were 33° < ϕ'p < 38°, whereas the residual friction angle fell to 23–24° at great depths and high vertical stresses, for a prevailing silty–clayey matrix. Variable head permeability tests were performed both in situ and in the laboratory, showing that the values of the vertical and horizontal permeability were very close and in the range 1 × 10−4–1 × 10−6 m/s. The soil permeability measured in the field was generally higher than the hydraulic conductivity calculated on laboratory samples. The proposed geotechnical and hydrological characterisation of flysch-derived colluvial soils can be of fundamental importance before the use of more thorough analyses/models aimed at forecasting the possible occurrence of slope failures and evaluating the related landslide hazard. The reported geotechnical and hydraulic parameters of flysch-derived colluvial materials can represent a useful reference for rainfall infiltration modelling and slope stability analyses of colluvial covers that are subject to intense and/or prolonged precipitation. However, when facing engineering problems involving colluvial soils, particularly those coming from flysch rock masses, the intrinsic variability in their grain size composition, consistency, and plasticity characteristics is a key feature and attention should be paid to the proper assumption of the corresponding geotechnical and hydraulic parameters. [ABSTRACT FROM AUTHOR]

The paper explores the potential of the satellite advanced differential synthetic aperture radar interferometry (A-DInSAR) technique for the identification of impending slope failure. The advantages and limitations of satellite InSAR in monitoring pre-failure landslide behaviour are addressed in five different case histories back-analysed using data acquired by different satellite missions: Montescaglioso landslide (2013, Italy), Scillato landslide (2015, Italy), Bingham Canyon Mine landslide (2013, UT, USA), Big Sur landslide (2017, CA, USA) and Xinmo landslide (2017, China). This paper aimed at providing a contribution to improve the knowledge within the subject area of landslide forecasting using monitoring data, in particular exploring the suitability of satellite InSAR for spatial and temporal prediction of large landslides. The study confirmed that satellite InSAR can be successful in the early detection of slopes prone to collapse; its limitations due to phase aliasing and low sampling frequency are also underlined. According to the results, we propose a novel landslide predictability classification discerning five different levels of predictability by satellite InSAR. Finally, the big step forward made for landslide forecasting applications since the beginning of the first SAR systems (ERS and Envisat) is shown, highlighting that future perspectives are encouraging thanks to the expected improvement of upcoming satellite missions that could highly increase the capability to monitor landslides' pre-failure behaviour. [ABSTRACT FROM AUTHOR]

This paper presents the main results of the GB-InSAR (ground based interferometric synthetic aperture radar) monitoring of the Capriglio landslide (Northern Apennines, Emilia Romagna Region, Italy), activated on 6 April 2013. The landslide, triggered by prolonged rainfall, is constituted by two main adjacent enlarging bodies with a roto-translational kinematics. They activated in sequence and subsequently joined into a large earth flow, channelizing downstream of the Bardea Creek, for a total length of about 3600 m. The displacement rate of this combined mass was quite high, so that the landslide toe evolved with velocities of several tens of meters per day (with peaks of 70-80 m/day) in the first month, and of several meters per day (with peaks of 13-14 m/day) from early May to mid-July 2013. In the crown area, the landslide completely destroyed a 450 m sector of provincial roadway S.P. 101, and its retrogression tendency exposed the villages of Capriglio and Pianestolla, located in the upper watershed area of the Bardea Creek, to great danger. Furthermore, the advancing toe seriously threatened the Antria bridge, representing the "Massese" provincial roadway S.P. 665R transect over the Bardea Creek, the only strategic roadway left able to connect the above-mentioned villages. With the final aim of supporting local authorities in the hazard assessment and risk management during the emergency phase, on 4 May 2013 aerial optical surveys were conducted to accurately map the landslide extension and evolution. Moreover, a GB-InSAR monitoring campaign was started in order to assess displacements of the whole landslide area. The versatility and flexibility of the GB-InSAR sensors allowed acquiring data with two different configurations, designed and set up to continuously retrieve information on the landslide movement rates (both in its upper slow-moving sectors and in its fast-moving toe). The first acquisition mode revealed that the Capriglio and Pianestolla villages were affected by minor displacements (at an order of magnitude of a few millimeters per month). The second acquisition mode allowed to acquire data every 28 seconds, reaching very high temporal resolution values by applying the GB-InSAR technique. [ABSTRACT FROM AUTHOR]

The PARSIFAL (Probabilistic Approach to pRovide Scenarios of earthquake Induced slope FAiLures) method was applied to the survey of post-earthquake landslides in central Italy for seismic microzonation purposes. In order to optimize time and resources, while also reducing errors, the paper-based method of survey data sheets was translated into digital formats using such instruments as Tablet PCs, GPS and open source software (QGIS). To the base mapping consisting of Technical Regional Map (Carta Tecnica Regionale—CTRs) at the scale of 1:10,000, layers were added with such sensitive information as the Inventory of Landslide Phenomena in Italy (Inventario dei Fenomeni Franosi in Italia—IFFI), for example. A database was designed and implemented in the SQLite/SpatiaLite Relational DataBase Management System (RDBMS) to store data related to such elements as landslides, rock masses, discontinuities and covers (as provided by PARSIFAL). To facilitate capture of the datum on the ground, data entry forms were created with Qt Designer. In addition to this, the employment of some QGIS plug-ins, developed for digital surveying and enabling of quick annotations on the map and the import of images from external cameras, was found to be of considerable use. [ABSTRACT FROM AUTHOR]

In this study, we defined screening maps for Italy that classify sites based on their potential for triggering landslides. To this end, we analyzed seismic hazard maps and hazard disaggregation results on a national scale considering four spectral periods (0.01 s, 0.2 s, 0.5 s, and 1.0 s) and three return periods (475, 975, and 2475 years). First, joint distributions of magnitude ( M ) and distance ( R ) from hazard disaggregation were analyzed by means of an innovative approach based on image processing techniques to find all modal scenarios contributing to the hazard. In order to obtain the M - R scenarios controlling the triggering of earthquake-induced landslides at any computation node, mean and modal M - R pairs were compared to empirical curves defining the M - R bounds associated with landslide triggering. Three types of landslides were considered (i.e., disrupted slides and falls, coherent slides, and lateral spreads and flows). As a result, screening maps for all of Italy showing the potential for triggering landslides based on the level of seismic hazard were obtained. The maps and the related data are freely accessible. [ABSTRACT FROM AUTHOR]

The southern Apuan Alps (Italy) are prone to rainfall-induced landslides. A first attempt to calculate rainfall thresholds was made in 2006 using non-statistical and repeatable methods for the 1975–2002 period. This research aims to update, validate, and compare the results of that attempt through different statistical approaches. Furthermore, a new dataset of rainfall and landslides from 2008 to 2016 was collected and analyzed by reconstructing the rainfall events via an automatic procedure. To obtain the rainfall thresholds in terms of the duration–intensity relationship, we applied three different statistical methods for the first time in this area: logistic regression (LR), quantile regression (QR), and least-squares linear fit (LSQ). The updated rainfall thresholds, obtained through statistical methods and related to the 1975–2002 dataset, resulted in little difference from the ones obtained with non-statistical methods and have similar efficiency values among themselves. The best one is provided by the LR, with a landslide probability of 0.55 (efficiency of 89.8%). The new rainfall thresholds, calculated by applying the three statistical methods on the dataset from 2008–2016, are similar to the 1975–2002 ones, except for the LR threshold, which exhibits a higher slope. This result confirms the validity of the thresholds obtained with the old database. [ABSTRACT FROM AUTHOR]

San Nicola Island, pertaining to the Tremiti Archipelago (Southern Adriatic Sea, Italy), is widely affected by cliff retreat and gravitational phenomena which severely threaten its monumental historical and natural value. In this study, geomorphological features of the area were derived following a stepwise approach, combining multitemporal stereoscopic aerial photo interpretations with morphometric analyses and detailed field surveys. A rockfall susceptibility map was created following a heuristic approach based on morphometric and geothematic parameters, accounting for slope, slope aspect, outcropping lithologies, structural discontinuities density, distance from landslide scarps, and presence of anthropic caves. Cliff sectors set on dolomitic limestones feature the highest susceptibility values, especially along the southeastern sector; medium values, instead, are found along the island flanks and along scarps located within the inner sectors; and the lowest values are detected on summit tabular surfaces. The achieved results were compared with historical maps and seismic data derived from local and national archives and catalogues, respectively. These analyses allowed us to define the role played by litho-structural and tectonic features on landslide occurrence and distribution, and their interplay in driving landscape evolution over centuries. Finally, this work represents a valuable scientific tool to support geomorphological studies for landslide hazard assessment and proper territorial planning in any other small insular areas, showing similar geological–geomorphological features and landscape values. [ABSTRACT FROM AUTHOR]

Landslides triggered by heavy rainfall pose significant threats to human settlements and infrastructure in temperate and equatorial climate regions. This study focuses on the development of the Open Landslide Project (OLP), an open source landslide inventory aimed at facilitating geostatistical analyses and landslide risk management. Using a multidisciplinary approach and open source, multisatellite imagery data, more than 3000 landslides triggered by the extreme rainfall of autumn 2019 in northwestern Italy were systematically mapped. The inventory creation process followed well-defined criteria and underwent rigorous validation to ensure accuracy and reliability. The dataset's suitability was confirmed through multivariate correlation and Double Pareto probably density function. The OLP inventory effectiveness in assessing landslide risks was proved by the development of a landslide susceptibility model using binary logistic regression. The analysis of rainfall and lithology revealed that regions with lower rainfall levels experienced a higher occurrence of landslides compared to areas with higher peak rainfall. This was attributed to the response of the lithological composition to rainfalls. The findings of this research contribute to the understanding and management of landslide risks in anthropized climate regions. The OLP has proven to be a valuable resource for future geostatistical analysis. [ABSTRACT FROM AUTHOR]

The Italian territory is subject to a high level of hydrogeological instability that periodically results in the loss of lives, buildings and productive activities. Therefore, the recognition of areas susceptible to hydrogeological instability is the basis for preparing countermeasures. In this context, landslide susceptibility in the mid-Adriatic slope was analyzed using a statistical method, the weight of evidence (WoE), which uses information from several independent sources to provide sufficient evidence to predict possible system developments. Only flows, slides, debris flows and mud flows were considered, with a total of 14,927 landslides obtained from the IFFI (Inventory of Franous Phenomena in Italy) database. Seven climatic–environmental factors were used for mapping landslide susceptibility in the study area: slope, aspect, extreme precipitation, normalized difference vegetation index (NDVI), CORINE land cover (CLC), and topographic wetness index (TWI). The introduction of these factors into the model resulted in rasters that allowed calculation by GIS-type software of a susceptibility map. The result was validated by the ROC curve method, using a group of landslides, equal to 20% of the total, not used in the modeling. The performance of the model, i.e., the ability to predict the presence or absence of a landslide movement correctly, was 0.75, indicating a moderately accurate model, which nevertheless appears innovative for two reasons: the first is that it analyzes an inhomogeneous area of more than 9000 km2, which is very large compared to similar analyses, and the second reason is the causal factors used, which have high weights for some classes despite the heterogeneity of the area. This research has enabled the simultaneous introduction of unconventional factors for landslide susceptibility analysis, which, however, could be successfully used at larger scales in the future. [ABSTRACT FROM AUTHOR]

In Italy, most of the destructive landslides are triggered by rainfall, particularly in central Italy. Therefore, effective monitoring of rainfall is crucial in hazard management and ecosystem assessment. Global precipitation measurement (GPM) is the next-generation satellite mission, which provides the precipitation measurements worldwide. In this research, we employed the available monthly GPM data to estimate the monthly precipitation for the twenty administrative regions of Italy from June 2000 to June 2021. For each region, we applied the non-parametric Mann–Kendall test and its associated Sen's slope to estimate the precipitation trend for each calendar month. In addition, for each region, we estimated a linear trend and the seasonal cycles of precipitation with the antileakage least-squares spectral analysis (ALLSSA) and showed the annual precipitation variations using box plots. Lastly, we compared machine-learning models based on the auto-regressive moving average for monthly precipitation forecasting and showed that ALLSSA outperformed them. The findings of this research provide a significant insight into processing climate data, both in terms of trend-season estimates and forecasting, and can potentially be used in landslide susceptibility analysis. [ABSTRACT FROM AUTHOR]

The relationships between hillslope and fluvial processes were studied in a mountainous area of the Northern Apennines (Italy) where intermittent landslide activity has interacted for a long time with river morphodynamics. The aim of the study was to analyse such relationships in two study sites of the Scoltenna catchment. The sites were analysed in detail and monitored through time. A long-term analysis was carried out based on multitemporal photointerpretation of aerial photos. Slope morphological changes and land use modifications since 1954 were detected and compared with the evolution of the channel morphology. A short-term analysis was also performed based on two monitoring campaigns accomplished in 2021 and 2022 in order to detect possible slope displacements and channel-bed-level changes. The techniques used are global navigation satellite systems and drone photogrammetry accompanied by geomorphological surveys and mapping. The multitemporal data collected allowed us to characterise slope surface deformations and quantify morphological changes. The combination of various techniques of remote and proximal sensing proved to be a useful tool for the analysis of the surface deformations and for the investigation of the interaction between slope and fluvial dynamics, showing the important role of fluvial processes in the remobilisation of the landslide toe causing the displacement of a significant volume of sediment into the stream. [ABSTRACT FROM AUTHOR]

Large-scale slow-moving deep-seated landslides are complex and potentially highly damaging phenomena. The detection of their dynamics in terms of displacement rate distribution is therefore a key point to achieve a better understanding of their behavior and support risk management. Due to their large dimensions, ranging from 1.5 to almost 4 km2, in situ monitoring is generally integrated using satellite and airborne remote sensing techniques. In the framework of the EFRE-FESR SoLoMon project, three test-sites located in the Autonomous Province of Bolzano (Italy) were selected for testing the possibility of retrieving significant slope displacement data from the analysis of multi-temporal airborne optic and light detection and ranging (LiDAR) surveys with digital image correlation (DIC) algorithms such as normalized cross-correlation (NCC) and phase correlation (PC). The test-sites were selected for a number of reasons: they are relevant in terms of hazard and risk; they are representative of different type of slope movements (earth-slides, deep seated gravitational slope Deformation and rockslides), and different rates of displacement (from few cm/years to some m/years); and they have been mapped and monitored with ground-based systems for many years (DIC results can be validated both qualitatively and quantitatively). Specifically, NCC and PC algorithms were applied to high-resolution (5 to 25 cm/px) airborne optic and LiDAR-derived datasets (such as hillshade and slope maps computed from digital terrain models) acquired during the 2019–2021 period. Qualitative and quantitative validation was performed based on periodic GNSS surveys as well as on manual homologous point tracking. The displacement maps highlight that both DIC algorithms succeed in identifying and quantifying slope movements of multi-pixel magnitude in non-densely vegetated areas, while they struggle to quantify displacement patterns in areas characterized by movements of sub-pixel magnitude, especially if densely vegetated. Nonetheless, in all three landslides, they proved to be able to differentiate stable and active parts at the slope scale, thus representing a useful integration of punctual ground-based monitoring systems. [ABSTRACT FROM AUTHOR]

The growing volume of synthetic aperture radar (SAR) imagery acquired by satellite constellations creates novel opportunities and opens new challenges for interferometric SAR (InSAR) applications to observe Earth's surface processes and geohazards. In this paper, the Parallel Small BAseline Subset (P-SBAS) advanced InSAR processing chain running on the Geohazards Exploitation Platform (GEP) is trialed to process two unprecedentedly big stacks of Copernicus Sentinel-1 C-band SAR images acquired in 2014–2020 over a coastal study area in southern Italy, including 296 and 283 scenes in ascending and descending mode, respectively. Each stack was processed in the GEP in less than 3 days, from input SAR data retrieval via repositories, up to generation of the output P-SBAS datasets of coherent targets and their displacement histories. Use-cases of long-term monitoring of land subsidence at the Capo Colonna promontory (up −2.3 cm/year vertical and −1.0 cm/year east–west rate), slow-moving landslides and erosion landforms, and deformation at modern coastal protection infrastructure in the city of Crotone are used to: (i) showcase the type and precision of deformation products outputting from P-SBAS processing of big data, and the derivable key information to support value-adding and geological interpretation; and (ii) discuss potential and challenges of big data processing using cloud/grid infrastructure. [ABSTRACT FROM AUTHOR]

Steep slopes covered by loose unsaturated pyroclastic deposits widely dispersed in Campania, Southern Italy, are often subjected to shallow landslides that turn into fast debris flows causing a large amount of damage and many casualties, triggered by heavy and persistent precipitation. The slope of Cervinara, located around 40 km northeast of Naples, was involved in a destructive flowslide between 15 and 16 December 1999, triggered by a rain event of 325 mm in 48 h. Hydrometeorological monitoring activities have been carried out near the landslide scarp of 1999 since 2017 to assess the water balance and to identify major hydrological processes involving the cover and the shallow groundwater system developing in the upper part of the underlying limestone fractured bedrock. Since 1 December 2022, a remotely accessible low-cost network has been installed to expand the field hydrological monitoring. The use of a network of low-cost capacitive sensors, communicating within the domain of Internet of Things (IoT) technology, aiming at dispersed monitoring of soil moisture, has been tested. Specifically, the tested prototype network allows measurements of the soil water content at two different points, communicating through a Wi-Fi-based IoT system using ESP32 boards. The ThingSpeakTM IoT platform has been used for remote field data visualization. Based on the obtained results, the prototype of this IoT-based low-cost network shows the potential to expand the amount of hydrological data, suitable for setting up early warning systems in landslide-prone areas. [ABSTRACT FROM AUTHOR]

Landslides are catastrophic natural phenomena occurring as a consequence of climatic, tectonic, and human activities, sometimes combined among them. Mostly due to climate change effects, the frequency of occurrence of these events has quickly grown in recent years, with a consequent increase in related damage, both in terms of loss of human life and effects on the involved infrastructures. Therefore, implementing properly actions to mitigate consequences from slope instability is fundamental to reduce their impact on society. Satellite systems, thanks to the advantages offered by their global view and sampling repetition capability, have proven to be valid tools to be used for these activities in addition to traditional techniques based on in situ measurements. In this work, we propose an advanced multitemporal technique aimed at identifying and mapping landslides using satellite-derived land cover information. Data acquired by the Multispectral Instrument (MSI) sensor aboard the Copernicus Sentinel-2 platforms were used to investigate a landslide affecting Pomarico city (southern Italy) in January 2019. Results achieved indicate the capability of the proposed methodology in identifying, with a good trade-off between reliability and sensitivity, the area affected by the landslide not just immediately after the event, but also a few months later. The technique was implemented within the Google Earth Engine Platform, so that it is completely automatic and could be applied everywhere. Therefore, its potential for supporting mitigation activities of landslide risks is evident. [ABSTRACT FROM AUTHOR]

Pyroclastic soils are widespread in the world. In particular, they cover a great part of Campania, a densely populated country of Southern Italy, where some distinct volcanic centers are present. In these soils, precipitations can trigger fast flow-like landslides causing destruction and loss of human lives. The movement style, the high velocity and the long run-out of these landslides are an indication of the occurrence, in the saturated soil mass, of mechanisms of undrained instability due to the inability of soil to sustain the deviator stress related to the slope condition. This paper reports the results of a wide experimental laboratory program carried out on a volcanic ash, which recently has been the seat of a killer landslide, stressing the factors that govern the undrained response of these materials. [ABSTRACT FROM AUTHOR]

In this paper, the preliminary results of an integrated geomorphological study carried out in a 1.6 ha catchment area located on the eastern side of the Crati River valley (northern Calabria, South Italy) have been presented. An orthophoto and shaded relief map of the study catchment, obtained by 288 unmanned aerial vehicle (UAV) images, integrated with field geomorphological surveys have been used to produce a detailed map of landslides and water erosion phenomena. The study area is characterized by active morphodynamic processes that result in the occurrence of water erosion phenomena and several landslides. In particular, 29 slides and 37 earth slides that evolve into earth flows have been recognized. Spatial and temporal development of geomorphic processes (erosion/depletion and sedimentation/accumulation) have affected the catchment area in the last seven years. Indeed, the comparison between light detection and ranging digital terrain models (LiDAR-DTM) of 2012 and UAV-DTM of 2019 showed depletion values between −0.01 and −5.76 m, with a mean value of −0.96 m; whereas for the accumulation the mean value is 0.94 m, with a maximum thickness of the deposited material of about 2.98 m. The results obtained highlight the usefulness of the methodology to provide detailed information on geomorphic processes and related short-term landscape development in a small drainage basin. [ABSTRACT FROM AUTHOR]

Earth observation data are useful to analyze the impact of climate-related variables on geomorphological processes. This work aims at evaluating the impact of rainfall on slow-moving landslides, by means of a quantitative procedure for identifying satellite-based displacement clusters, comparing them with rainfall series, and applying statistical tests to evaluate their relationships at the regional scale. The chosen study area is the Basento catchment in the Basilicata region (southern Italy). Rainfall series are gathered from rain gauges and are analyzed to evaluate the presence of temporal trends. Ground displacements are obtained by applying the P-SBAS (Parallel Small BAseline Subset) to three datasets of Sentinel-1 images: T146 ascending orbit, and T51 and T124 descending orbits, for the period 2015–2020. The displacement series of the pixels located in areas mapped as landslides by the Italian Landslide Inventory and sited within rain gauge influence regions (defined as 10 km circular buffers) are studied. Those displacement series are analyzed and compared to the rainfall series to search for correlations, by employing statistical and non-parametric tests. In particular, two landslides are selected and investigated in detail. Significant results were obtained for the T124 descending orbit for both landslides, for a 3-day cumulative rainfall and a 7-day delay of the slope response. Challenges in the whole procedure are highlighted and possible solutions to overcome the raised problems are proposed. Given the replicability of the proposed quantitative procedure it might be applied to any study area. [ABSTRACT FROM AUTHOR]

In mountain areas, anthropic pressure is growing while, concurrently, landslide frequency in most of the mountain regions of the world is increasing due to a more extreme precipitation pattern and permafrost deglaciation. Because of budget constraints, the need to investigate innovative and low-cost countermeasures for landslide risk is becoming more and more pressing. In this context, the Passo della Morte area (North-East Italy) is a perfect benchmark case. It comprises an extensive, long-term database of monitoring data that allows for testing hypotheses and validating them. Based on this data, a strong correlation between the velocity of a displacement of a landslide and the discharge of the Rio Verde stream was found. According to this evidence, local authorities have started the construction of a completely innovative mitigation strategy. It is focused on the triggering factor by identifying a significant component of the flow rate of the stream that cuts through the landslide. In addition, aiming to reduce the cost of construction and maintenance, this mitigation strategy is coupled with a micro-hydropower plant that can provide economic revenue by exploiting the discharge itself to produce electricity. Considering the active monitoring system that will be used to verify the effectiveness of the countermeasure, the Passo della Morte case study could become a starting point for implementing this pioneering and low-cost mitigation solution in similar morphologies. [ABSTRACT FROM AUTHOR]

The design of countermeasure works to mitigate landslide risk needs to deal with the multiple unknowns that are linked with soil properties, distribution and rheology. Most of the time, the degree of definition of all these elements is low. Through landslide monitoring, it is possible to acquire signals from the landslide that carry synthetic information about its dynamic. Thus, if it is possible to define a model that is able to link the landslide displacements with the triggering factors and to predict them consistently, that model may be used to evaluate the effect of a countermeasure work directly, bypassing the geomechanical uncertainty. In this paper, an example application of this approach is described. The displacements of a landslide located in North East Italy are connected with the water discharge of the small stream the crosses the landslide body. A countermeasure work that intercepts the discharge of the torrent is expected to reduce the landslide displacements of approximately 70%, with lower costs of construction and smaller impacts on the environment and landscape with respect of other types of structural mitigation works such as slope reprofiling and large retaining walls. [ABSTRACT FROM AUTHOR]

The identification of the triggering mechanism of rainfall-induced, shallow landslides requires a complete understanding of the hydro-mechanical response of soil, which can be represented through the trends of the degree of soil saturation. In this paper, multiple annual cycles of soil saturation obtained through field monitoring were used to validate an empirical model based on climate data. Both field measurements and model outputs were used to conduct simplified slope stability analysis to evaluate the model chain capability in predicting the temporal occurrence of shallow failures. Field data were collected on a testsite slope located in Oltrepò Pavese (Northern Italy), where a shallow landslide occurred during the monitoring period. The experimental trends of the degree of saturation at various depths in the soil profile were compared with the calculated values and showed good agreement. Landslide triggering is reached when the soil is completely saturated. Both measured and modeled trends of soil saturation correctly identified the triggering time of the shallow landslide and the depth of the sliding surface, 1.0 m below the ground surface, in the test slope. The obtained results indicated the possibility of extending this approach for theassessment of the initiation time and the depth of shallow landslides, particularly for preliminary susceptibility evaluations, based on widely available climate data. [ABSTRACT FROM AUTHOR]

Natural disasters have a significant impact on urban areas, resulting in loss of lives and urban services. Using satellite and aerial imagery, the rapid and automatic assessment of at-risk located buildings from can improve the overall disaster management system of urban areas. To do this, the definition, and the implementation of models with strong generalization, is very important. Starting from these assumptions, the authors proposed a deep learning approach based on the U-Net model to map buildings that fall into mapped landslide risk areas. The U-Net model is trained and validated using the Dubai's Satellite Imagery Dataset. The transferability of the model results are tested in three different urban areas within Calabria Region, Southern Italy, using natural color orthoimages and multi-source GIS data. The results show that the proposed methodology can detect and predict buildings that fall into landslide risk zones, with an appreciable transferability capability. During the prevention phase of emergency planning, this tool can support decision-makers and planners with the rapid identification of buildings located within risk areas, and during the post event phase, by assessing urban system conditions after a hazard occurs. [ABSTRACT FROM AUTHOR]

Landslide mapping techniques have had many improvements in recent decades, the main field of development has been on traditional cartographic techniques and to a lesser extent on indirect numerical cartography. As for Direct Numerical Cartography (DNC), only a few improvements have been made due to the complexity and economic cost of the new technologies. To meet this lack in DNC techniques GOGIRA (Ground Operative-system for GIS Input Remote-data Acquisition), a new system following the GIS (Geographic Information System) scheme, was developed. It is a suite of hardware and software tools, algorithms, and procedures for easier and cheaper DNC. Initial tests conducted on the Quincinetto landslide system (north-western Italy) demonstrated good results in terms of morphometric coherence and precision. A geomorphological map made with GOGIRA was compared with a highly detailed geomorphological map developed with modern tested methods. In conclusion GOGIRA proved to be a valid system for geomorphological DNC when applied to a complex landslide system, considering the early stage of developing results for linear and point mapping was excellent, as for polygonal elements more studies must be conducted to improve accuracy and precision. [ABSTRACT FROM AUTHOR]

Central Italy is characterized by complex orography. The territorial response to heavy precipitation may activate different processes in terms of hydrogeological hazards. Floods, flash floods, and wet mass movements are the main ground effects triggered by heavy or persistent rainfall. The main aim of this work is to present a unique tool that is based on a distributed hydrological model, able to predict different rainfall-induced phenomena, and essential for the civil protection early warning activity. The Cetemps Hydrological Model is applied to the detection of hydrologically stressed areas over a spatial domain covering the central part of Italy during a weather event that occurred in 2014. The validation of three hydrological stress indices is proposed over a geographical area of approximately 64,500 km2 that includes catchments of varying size and physiography. The indices were used to identify areas subject to floods, flash floods, or landslides. Main results showed very high accuracies (~90%) for all proposed indices, with flood false alarms growing downstream to larger basins, but very close to zero in most cases. The three indices can give complementary information about the predominant phenomenon and are able to distinguish fluvial floods from pluvial floods. Nevertheless, the results were influenced by the presence of artificial reservoirs that regulated flood wave propagation, therefore, indices timing slightly worsen downstream in larger basins. [ABSTRACT FROM AUTHOR]

Rockfalls are widespread, rapid, and high-energy landslide phenomena that could potentially affect large portions of populated lands. The preliminary identification of the most rockfall-prone zones is a challenging task, especially in times of extreme and unpredictable climate change. Even slight environmental modifications can produce significant consequences in terms of exposure, hazard, and risk. Therefore, a timely risk assessment is paramount for territorial administrators to plan and prioritize adequate countermeasures. Risk assessment is crucial to guaranteeing the safety of human lives, the integrity of structures and infrastructures, the preservation of historic and environmental heritage, and the safeguard of economic activities. Hence, new and rapid evaluation methods for rockfall hazard, vulnerability, and risk are needed to identify the most critical areas where more indepth analyses aimed at the design of protective works should be carried out. This study proposes a quick, innovative, and completely GIS-based procedure to preliminarily assess rockfall time-independent hazard and risk in large areas. Propagation analysis is performed by integrating powerful QGIS plugin QPROTO, which can estimate rockfall energy within the invasion area in a simplified way, with the slope units polygons of the Italian territory for the definition of the input parameters. The quantification of risk was obtained by the application of the multidisciplinary IMIRILAND methodology, again within a free and open QGIS environment. Lastly, to test the capabilities of the method, the procedure was applied to a case study of the Sorba Valley (Piemonte, Italy), a tourist region in the northwestern Italian Alps. The findings offer an important contribution to the field of land-planning activities and risk-management strategies. [ABSTRACT FROM AUTHOR]

In recent years, space-borne InSAR (interferometric synthetic aperture radar) techniques have shown their capabilities to provide precise measurements of Earth surface displacements for monitoring natural processes. Landslides threaten human lives and structures, especially in urbanized areas, where the density of elements at risk sensitive to ground movements is high. The methodology described in this paper aims at detecting terrain motions and building deformations at the local scale, by means of satellite radar data combined with in situ validation campaigns. The proposed approach consists of deriving maximum settlement directions of the investigated buildings from displacement data revealed by radar measurements and then in the cross-comparison of these values with background geological data, constructive features and on-field evidence. This validation permits better understanding whether or not the detected movements correspond to visible and effective damages to buildings. The method has been applied to the southwestern sector of Volterra (Tuscany region, Italy), which is a landslide-affected and partially urbanized area, through the use of COSMO-SkyMed satellite images as input data. Moreover, we discuss issues and possible misinterpretations when dealing with PSI (Persistent Scatterer Interferometry) data referring to single manufactures and the consequent difficulty of attributing the motion rate to ground displacements, rather than to structural failures. [ABSTRACT FROM AUTHOR]

Among the various predisposing factors of rainfall-induced shallow landslides, land use is constantly evolving, being linked to human activities. Between different land uses, improper agricultural practices can have a negative impact on slope stability. Indeed, unsustainable soil tillage can modify the mechanical properties of the soils, leading to a possible increase of the instability phenomena. However, the effects of soil tillage on slope stability are poorly investigated. To address this topic, the PG_TRIGRS model (a probabilistic, geostatistic-based extension of TRIGRS) was applied to a cultivated, landslide-prone area in central Italy, thoroughly studied and periodically monitored through systematic image analysis and field surveys. A heuristic approach was adopted to quantitatively evaluate the effect of soil tillage on the mechanical properties of the soil: after a first run of the model with unbiased parameters, the slope stability analysis was carried out assuming several percentages of reduction of the effective soil cohesion to mimic an increasing impact of soil tillage on the strength conditions. Then, a comparison between observed landslides and the spatial distribution of the probability of failure derived from the application of PG_TRIGRS was carried out. A back analysis with contingency matrix and skill scores was adopted to search for the best compromise between correct and incorrect model outcomes. The results show that soil tillage caused a 20 to 30% reduction in soil cohesion in the analyzed area. [ABSTRACT FROM AUTHOR]

A procedure aimed at forecasting the velocity trend of a landslide for a period of some hours to one or two days is proposed here together with its MATLAB implementation. The method is based on continuous wavelet transform (CWT) and convolutional neural network (CNN) applied to rainfall and velocity time series provided by a real-time monitoring system. It is aimed at recognizing the conditions that induce a strong increase, or even a significant decrease, in the average velocity of the unstable slope. For each evaluation time, the rainfall and velocity scalograms related to the previous days (e.g., two weeks) are computed by means of CWT. A CNN recognizes the velocity trend defined in the training stage corresponds to these scalograms. In this way, forecasts about the start, persistence, and end of a critical event can be provided to the decision makers. An application of the toolbox to a landslide (Perarolo di Cadore landslide, Eastern Alps, Italy) is also briefly described to show how the parameters can be chosen in a real case and the corresponding performance. [ABSTRACT FROM AUTHOR]

Different soil cover saturation has a significant effect in influencing slope stability conditions of weathered covers under earthquake-induced shaking. Here we analyze the Montecilfone, Italy (2018), case history, an Mw 5.1 earthquake that revealed an exceptionality in the spatial distribution of the surveyed earthquake-induced shallow landslides. This feature can be justified as intense rainfall occurred in the epicentral area before the seismic event, contributing to increasing the saturation and the weight of the soil covers. To verify the effective influence of antecedent rainfall as a preparatory factor in the earthquake triggering of soil covers, stability conditions for both static and dynamic scenarios were validated by reconstructing different saturation conditions related to a rainfall event that occurred before the earthquake. Soil cover surveying was performed within a 150 km2 area to output its spatial distribution in terms of their compositional features and thickness, whose variability was constrained through empirical models. Based on laboratory test results, 1D infiltration numerical models were performed through the Hydrus-1D free domain software to estimate the saturation degree of the soil cover and the water infiltration depth, taking as a reference the intensity of the rainfall event. Soil cover sequential charts of water content were obtained at different depths and times up to those recorded at the time of earthquake occurrence by the performed numerical modelling. Safety factors (SFs) of the slope covers were quantified assuming an unsaturated condition in the slope stability equation. The outputs reveal that pore pressure spatial distribution in the unsaturated medium infers on the earthquake-induced scenario of shallow landsliding, demonstrating its role as a preparatory factor for earthquake-induced shallow landslides. [ABSTRACT FROM AUTHOR]

Italy is one of the European countries most affected by landslides. In order to mitigate the risk, the analysis of such phenomena should involve a broad spectrum of studies to understand the geological and geomorphological properties of the unstable areas, the geometric features of the landslides and the causes of their trigger, the evolution over time, and the works of risk mitigation taken as well as their effectiveness over time. This article is concerned with multidisciplinary investigations on a historical earth flow occurred in Montemurro (Basilicata, Southern Italy) in 1907. We analyse unpublished archive sources strictly coupled with new geological and geomorphological surveys. Furthermore, to gain information on the geometrical features of the landslide body, geophysical prospections (ERT) is used alongside the field surveys. Lastly, to gain insight on the landslide triggering factors, we employed historical–climatological analysis: in particular, we made use of the monthly simple daily intensity index (SDII) to evaluate extreme events and the standardised precipitation index (SPI) to consider previous wetness conditions. The earth flow was triggered on 26 February 1907 and the main movement lasted about one week, involving several buildings, including those of cultural interest. Historical documentary investigations and historical climatological analysis both indicate that the earth flow was triggered by a preceding heavy rain period, which independent historical sources suggest also caused the activation of landslides over a wider area around Montemurro. Currently, the earth flow is NE–SW oriented, extends for a length of ~1.1 km, and has an average width of ~220 m. The landslide is in a dormant activity phase. From a methodological point of view, the research stresses the importance of integrated approaches to investigate natural hazards, particularly by the use of historical data. This research may be of interest to academics, practitioners, and policymakers for both the methodological approach followed and results gained, useful in view of both risk mitigation and territorial planning of landslide-prone areas. [ABSTRACT FROM AUTHOR]

This contribution exposes the relative uncertainties associated with prediction patterns of landslide susceptibility. The patterns are based on relationships between direct and indirect spatial evidence of landslide occurrences. In a spatial database constructed for the modeling, direct evidence is the presence of landslide trigger areas, while indirect evidence is the presence of corresponding multivariate context in the form of digital maps. Five mathematical modeling functions are applied to capture and integrate evidence, indirect and direct, for separating landslide-presence areas from the areas of landslide assumed absence. Empirical likelihood ratios are used first to represent the spatial relationships. These are then combined by the models into prediction scores, ordered, equal-area ranked, displayed, and synthesized as prediction-rate curves. A critical task is assessing how uncertainty levels vary across the different prediction patterns, i.e., the modeling results visualized as fixed, colored groups of ranks. This is obtained by a strategy of iterative cross validation that uses only part of the direct evidence to model the pattern and the rest to validate it as a predictor. The conducted experiments in a mountainous area in northern Italy point at a research challenge that can now be confronted with relative rank-based statistics and iterative cross-validation processes. The uncertainty properties of prediction patterns are mostly unknown nevertheless they are critical for interpreting and justifying prediction results. [ABSTRACT FROM AUTHOR]

During recent years, the availability of innovative monitoring instrumentation has been a fundamental component in the development of efficient and reliable early warning systems (EWS). In fact, the potential to achieve high sampling frequencies, together with automatic data transmission and elaboration are key features for a near-real time approach. This paper presents a case study located in Central Italy, where the realization of an important state route required a series of preliminary surveys. The monitoring system installed on site included manual inclinometers, automatic modular underground monitoring system (MUMS) inclinometers, piezometers, and geognostic surveys. In particular, data recorded by innovative instrumentation allowed for the detection of major slope displacements that ultimately led to the landslide collapse. The implementation of advanced tools, featuring remote and automatic procedures for data sampling and elaboration, played a key role in the critical event identification and prediction. In fact, thanks to displacement data recorded by the MUMS inclinometer, it was possible to forecast the slope failure that was later confirmed during the following site inspection. Additionally, a numerical analysis was performed to better understand the mechanical behavior of the slope, back-analyze the monitored event, and to assess the stability conditions of the area of interest. [ABSTRACT FROM AUTHOR]

An extensive investigation of more than 90 landslides affecting a small river basin in Central Italy was performed by combining field surveys and remote sensing techniques. We thus defined the geomorphological setting of slope instability processes. Basic information, such as landslides mapping and landslides type definition, have been acquired thanks to geomorphological field investigations and multi-temporal aerial photos interpretation, while satellite SAR archive data (acquired by ERS and Envisat from 1992 to 2010) have been analyzed by means of A-DInSAR (Advanced Differential Interferometric Synthetic Aperture Radar) techniques to evaluate landslides past displacements patterns. Multi-temporal assessment of landslides state of activity has been performed basing on geomorphological evidence criteria and past ground displacement measurements obtained by A-DInSAR. This step has been performed by means of an activity matrix derived from information achieved thanks to double orbital geometry. Thanks to this approach we also achieved more detailed knowledge about the landslides kinematics in time and space. [ABSTRACT FROM AUTHOR]