Monica Ghirotti

Monitoring systems are essential tools for landslide risk mitigation. Monitoring reduces vulnerability by providing the data required for slope instability characterization and hazard assessment. Moreover, alert and alarm systems rely on the ability of monitoring networks to timely deliver reliable and precise measurements. Remote monitoring systems are not mature enough to ensure reliable early-warning capabilities; therefore, information redundancy is achieved by deploying a set of sensors directly on the landslide body. Commonly used devices include total stations, GPS receivers, automatic inclinometers, and ground-based radar systems. However, the harsh environmental conditions typical of unstable slopes frequently affect the instruments performance and the data availability. The lack of a stable power supply is one of the main limitations of these systems, often preventing their operation precisely during the most critical situations, such as thunderstorms. In addition, adverse atmospheric conditions, including fog or low cloud cover, can compromise the visibility of topographic benchmarks, reducing data availability when it is most needed. To address these limitations a nonlinear parametric model for forecasting landslide displacements based on rainfall input has been developed. The model is trained and continuously updated using displacement data acquired by the monitoring system. In the event of system failure, the model is able to simulate landslide kinematics by means of “virtual sensors”, forecasting displacements and detecting sudden accelerations of the landslide, thereby preserving the early-warning functionality. The approach was successfully tested using data from the monitoring system installed at the Rotolon landslide, located in the municipality of Recoaro Terme (Vicenza, Italy).

Landslides are significant natural hazards frequently triggered by heavy rainfall and earthquakes, representing the most damaging secondary coseismic environmental effects. In geologically active regions like the Northern Apennines (Italy), high seismic hazard often couples with frequent large-scale slope failures. Global evidence suggests a complex interplay between triggers: earthquakes following intense rainfall tend to induce more landslides, while seismically impacted areas often show elevated landslide rates in subsequent years. Analyzing these tectonic-meteorological interactions is crucial for accurate hazard prediction.The central goal of this research is to resolve the intricate interactions among tectonic, meteorological, and surface processes by evaluating the role of seismicity and rainfall (whether concurrent or not) in the evolution of slope failures. This presentation details the conceptual framework and preliminary implementation of a newly initiated project aimed at monitoring these dynamics in real-time. The investigation focuses on the fundamental mechanisms of landslide induction, considering pre- and post-seismic meteorological states to identify crucial triggering parameters.The study utilizes a dedicated, multi-technique monitoring network at the Roncovetro landslide, a relatively young complex-earthflow, with a mean discharge rate of ∼ 0.16 × 105 m3/yr, that serves as a natural laboratory for landslide characterization in the Apennines. To discriminate between induction mechanisms, we integrate:Remote Sensing Tools: Repetitive Unmanned Aerial System (UAS) surveys are employed to conduct high-resolution terrain analysis and quantify volumetric changes. Comparison of digital topography (including historical 1973 data vs. 2014–2025 datasets) allows for the assessment of long-term discharge rates and morphological evolution.Ground-Based Monitoring: An already existing local network of Global Navigation Satellite System (GNSS) stations and Ultra Wide Band (UWB) sensors provides high-frequency displacement data, enabling the correlation of movement with specific triggers. New GNSS stations will be installed in different sectors of the landslides in order to extend the real time analysis of the slope movements.Meteorological Data: Continuous hydro-meteorological parameters are gathered from a nearby weather station managed by the Regione Emilia Romagna, providing the high-resolution rainfall data necessary to establish triggering thresholds.Novel Geophysical Sensing: High-resolution seismic data will be acquired through Distributed Acoustic Sensing (DAS), leveraging fiber optic cables to create a dense linear array of seismic sensors at a 1-meter spatial scale.Field Analysis: Conventional geomorphological mapping and field-based geological surveys validate the remote sensing products and ground-truth the internal boundaries of the landslide body.The availability of this integrated observational network will allow for the spatial and temporal discrimination of landslide sectors triggered by meteorological events versus those sensitive to seismic shaking. Future analysis of the Roncovetro site—an area already characterized by historical data and impacted by both significant earthquakes (e.g., the 1996 Mw 5.4 event) and recent extreme rainfall (2024–2025)—will try to highlight relationships between antecedent moisture conditions and seismic history to define slope stability. This integrated analysis is expected to provide fundamental insights into event timing, shaking intensity, and the ultimate magnitude of landslide movements. Ultimately, the project will offer a robust, multi-sensor framework for multi-hazard risk assessment in complex terrain.

In groundwater flow modelling within mining environments, three main numerical approaches are commonly used: i) the Equivalent Porous Media (EPM), according to which the rock mass and its discontinuities are represented as a continuum; ii) the Discrete Fracture Network (DFN), where hydraulic properties are assigned to natural and/or anthropic discontinuities; iii) the hybrid approach, a combination of the above. To evaluate the advantages and limitations of each method, a selected mine located in the Metalliferous Hills (Colline Metallifere; GR), Tuscany Region, Italy, was selected as a case study. The mine is part of a five-unit complex, joined by a level at −200 m asl, that was active for pyrite extraction until 1981. It was selected due to the availability of hydrogeological and operational data covering a time span of almost 100 years, from around 1920 to 2024. Currently, two 3d transient numerical models have been developed with FEFLOW 10, following the hybrid and EPM approaches. In the former, 1-D anthropic discontinuities (tunnels, shafts, and wells) and 2-D natural discontinuities (major faults) have been represented as discrete features, while in the latter they are implemented as boundary conditions and zones with specific hydrogeological properties, respectively. Hence, the main difference between the two modelling approaches is the representation of geological and anthropic elements. Both models have been successfully used to simulate three managed flooding events that occurred in the period 1997-2014, during which groundwater levels in the mine were raised from −140 m asl to the current −95 m asl by controlling pumping rates of the dewatering system wells active 24/7. At this stage of the research project, the main objective is to evaluate which model better fits observations during managed flooding events. The best fitting model will be used to run predictive simulations of the planned dewatering systems shutdown, which will raise groundwater levels from the current −95 m asl to 70 m asl, where a drainage tunnel is located. In the next stage of the research project, both models will be compared with a DFN model, to evaluate which approach is the most representative of the hydrogeological conditions of the study area and suitable for applications of similar case studies.

Burrows are tunnels or holes excavated into the ground by certain types of animals, to be used as habitation or temporary refuge, or as a by-product of their locomotion. Burrows provide a form of shelter against predation and exposure to the elements, and can be found in nearly every biome and among various biological interaction types. River bank burrowing weakens the soil structure, increases the risk of erosion, and may lead to bank retreat and landslides. Currently, burrow watching, mapping, and prevention are human-only activities, and there are no conventional data or information systems designed for this purpose. In this paper, we design, implement, and test a novel AI-based solution that, starting with drone-acquired imagery, allows the user to automatically identify and map potentially dangerous burrows in the target area, and lays the basis for the digitization and systematic conservation of such information, to be later used for intervention and planning. Our solution contributes to the environmental sustainability of rivers, especially close to densely populated areas.

Rocky coastlines are characterised by steep cliffs, which frequently experience a variety of natural processes that often exhibit intricate interdependencies, such as rainfall, ice and water run-off, and marine actions. The advent of high temporal and spatial resolution data, that can be acquired through remote sensing and geomatics techniques, has facilitated the safe exploration of otherwise inaccessible areas. The datasets that can be gathered from these techniques, typically combined with data from fieldwork, can subsequently undergo analyses employing/applying machine learning algorithms and/or numerical modeling, in order to identify/discern the predominant influencing factors affecting cliff top erosion. This study focuses on a specific case situated at the Conero promontory of the Adriatic Sea in the Marche region. The research methodology entails several steps. Initially, the morphological, geological and geomechanical characteristics of the areas were determined through unmanned aerial vehicle (UAV) and conventional geological/geomechanical surveys. Subsequently, cliff top retreat was determined within a GIS environment by comparing orthophotos taken in 1978 and 2022 using the DSAS tool (Digital Shoreline Analysis System), highlighting cliff top retreat up to 50 m in some sectors. Further analysis was conducted via the use of two Machine Learning (ML) algorithms, namely Random Forest (RF) and eXtreme Gradient Boosting (XGB). The Mean Decrease in Impurity (MDI) methodology was employed to assess the significance of each factor. Both algorithms yielded congruent results, emphasising that cliff top erosion rates are primarily influenced by slope height. Finally, a validation of the ML algorithm results was conducted using 2D Limit Equilibrium Method (LEM) codes. Ten sections extracted from the sector experiencing the most substantial cliff top retreat, as identified by DSAS, were utilised for 2D LEM analysis. Factor of Safety (FS) values were identified and compared with the cliff height of each section. The results from the 2D LEM analyses corroborated the outputs of the ML algorithms, showing a strong correlation between the slope instability and slope height (R² of 0.84), with FS decreasing with slope height.

The integration of multiple data sources, including satellite imagery, aerial photography, and ground-based measurements, represents an important development in the study of landslide processes. The combination of different data sources can be very important in improving our understanding of geological phenomena, especially in cases of inaccessible areas. In this context, the study of coastal areas represents a real challenge for the research community, both for the inaccessibility of coastal slopes and for the numerous drivers that can control coastal processes (subaerial, marine, or endogenic). In this work, we present a case study of the Conero Regional Park (Northern Adriatic Sea, Ancona, Italy) cliff-top retreat, characterized by Neogenic soft rocks (flysch, molasse). In particular, the study is focused in the area between the beach of Portonovo and Trave (south of Ancona), which has been studied using aerial orthophoto acquired between 1978 and 2021, Unmanned Aerial Vehicle (UAV) photographs (and extracted photogrammetric model) surveyed in September 2021 and 2012 LiDAR data. Aerial orthophotos were analyzed through the United States Geological Survey’s (USGS) tool Digital Shoreline Analysis System (DSAS) to identify and estimate the top-cliff erosion. The results were supported by the analysis of wave data and rainfall from the correspondent period. It has been found that for the northernmost sector (Trave), in the examined period of 40 years, an erosion up to 40 m occurred. Furthermore, a Digital Elevation Model (DEM) of Difference (DoD) between a 2012 Digital Terrain Model (DTM) and a UAV Digital Surface Model (DSM) was implemented to corroborate the DSAS results, revealing a good agreement between the retreat areas, identified by DSAS, and the section of coast characterized by a high value of DoD.

Remote sensing and photogrammetry techniques are now widely used in a range of geological applications, including mapping, and monitoring of geological features. Another important de-velopment in geological applications of remote sensing and photogrammetry is the integration of multiple data sources, including satellite imagery, aerial photography, and ground-based meas-urements. By combining these different data sources, it is possible generate more accurate and detailed geological maps, and better understand geological processes. In this work we present a case study of the Conero Regional Park (Northern Adriatic Sea, Ancona, Italy) cliff-top retreat, characterized by Neogenic soft rocks (flysch, molasse). In particular, the study is focused in the area between the beach of Portonovo and Trave (south of Ancona), which has been studied using aerial orthophoto acquired between 1978 and 2021, Unmanned Aerial Vehicle (UAV) photographs (and extracted photogrammetric model) surveyed in September 2021 and 2012 LiDAR data. Aerial or-thophotos were analyzed through the United States Geological Survey’s (USGS) tool Digital Shoreline Analysis System (DSAS), to identify and estimate the top-cliff erosion. The results were supported by the analysis of wave data and rainfall from the correspondent period. It has been found that for the northernmost sector (Trave), in the examined period of forty-years, an erosion up to 40 m occurred. Furthermore, a Digital Elevation Model (DEM) of Difference (DoD) between a 2012 Digital Terrain Model (DTM) and a UAV Digital Surface Model (DSM) was implemented to validate the DSAS results.

Editorial: Developments of remote sensing and numerical modeling applications for landslide analysis

Abu Dhabi is one of the arid regions in the world having less than 100 mm of rainfall per annum. The renewability of freshwater occurs only in the eastern part. The groundwater resources under desirable quality are very concise due to limited dilution/rainfall and higher rate of evaporation. Hence, in recent decades, desalinated water has been introduced for agriculture activities and surplus desalinated water is injected into the aquifer as artificial recharge. This study is conducted to understand the impacts in the aquifer system caused by the introduction of desalinated water for agriculture activities and for aquifer recharge structures. The simulation was carried out from 2000 to 2050 using reported rate of groundwater pumping and of desalinated water with 0.1 g/l, 0.5 g/l, 1 g/l, 1.5 g/l and 2 g/l degrees of salinity. A wide range of decline in the groundwater table is noticed in the western part of the aquifer due to less rainfall recharge. The results confirm that this region demands either reduction in agricultural activities or additional usage of desalinated water by which the pumping of groundwater can be reduced further. The improvement in the groundwater quality is noticed in the aquifer due to the addition of less saline desalinated water into the aquifer. This study confirms the long term suitability of existing aquifer recharge structure. Also, it expresses the need of further management practices in quantifying the desalinated water contribution for agriculture activities.