Recent research trends in landslide science highlight a growing diffusion of automated techniques for the detection and mapping of landslides at different spatial and temporal scales. Although emerging techniques based on AI algorithms and remote sensing techniques can facilitate the creation of landslide inventory, conventional geomorphological methods of production of landslide maps still play a central role in the compilation of reliable census of landslide processes at a regional scale. After a synoptic view of the limitations and advantages of the new techniques of landslide mapping, this work focused on the statistical analysis of a 1:10,000 scale landslide inventory map of a large sector of the southern Apennine belt, which has been created by extensive visual interpretation of stereoscopic aerial photography, supported by field surveys. GIS-based statistical analysis of the landslide inventory map provided a clear picture of the main predisposing factors that controlled the distribution, size and pattern of landslide processes within the different morpho-structural units of the chain. The non-random spatial distribution of landslide processes is strongly controlled by lithological and morpho-structural factors and the resulting zonation represents an effective basis for landscape planning purposes and a key tool for more advanced analyses based on more innovative techniques such as InSAR monitoring, slope stability models or definition of rainfall thresholds. More specifically, landslide-dominated landscapes prevail in sectors with a relevant tectonic activity and Quaternary relief growth. Finally, the work explored several case studies where the integration of conventional and innovative methods provided relevant results on the surface and subsurface characterization of mass movements and the estimation of displacement fields and mobilized volumes.
How to cite: Gioia, D.: Distribution, statistics and control factors of landslide processes in the mountain landscape of southern Apennines, Italy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11106, https://doi.org/10.5194/egusphere-egu25-11106, 2025.
The Rudraprayag district in the Uttarakhand Himalayas, India, is highly prone to landslides, exacerbated by a combination of natural and anthropogenic factors. This study employs a Random Forest classification algorithm to create a time-stamped landslide inventory using Sentinel-2 satellite images (2019–2023) and ancillary datasets, including ALOS PALSAR DEM. Landslide locations were validated through visual interpretation of high-resolution Google Earth imagery and field visits. The results identify 196 confirmed landslide locations, with most occurrences concentrated near road networks and influenced by rainfall and anthropogenic activities.
Topographic metrics such as elevation, slope, aspect, and ruggedness emerged as significant predictors of landslides, while other features like Topographic Wetness Index and curvature had minimal influence. Rainfall analysis revealed no statistically significant correlation with landslide occurrence timing, though extreme rainfall events, such as in July 2023, contributed to gradual landslide expansions. Seismic analysis showed a weak correlation with landslides, suggesting the need for denser seismic monitoring networks for further exploration.
This inventory supports the development of susceptibility maps and disaster management strategies. The study underscores the importance of integrating geological, hydrological, and anthropogenic factors for comprehensive landslide risk assessments, with implications for expanding such analyses across the broader Himalayas.
How to cite: Mukherjee, K., Rana, N., Rao, P. B., and Rivas Casado, M.: Time stamped landslide inventory and its causal factors in Rudraprayag, India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4637, https://doi.org/10.5194/egusphere-egu25-4637, 2025.
Landslides pose a significant threat to community safety globally, with Italy being particularly vulnerable. In the Campania Region (Southern Italy), nearly all municipalities are classified as high geo-hydrological risk areas, necessitating focused attention on these natural hazards. From a geological point of view, the Campania Region is characterised by a high complexity, presenting lithologies affected by both rapid (debris flow) and slow (earthflow) landslides, almost all of which are triggered by rainfall, sometimes by earthquakes. This concern is underscored by requests from rail transport authorities in Campania to enhance monitoring systems to identify landslide-prone areas that may impact railway operations.
This study investigates the use of unsupervised machine learning techniques for the automatic identification of landslide-prone areas in the western region of Caiazzo, Caserta (Southern Italy). The research addresses the frequent disruptions of the Naples-Caiazzo-Piedimonte Matese railway line due to severe hydrogeological instability. An automatic procedure was developed to identify areas at higher risk, utilizing a dataset comprising 12 geomorphological parameters relevant to landslide susceptibility. The analysis involved dimensionality reduction through principal component analysis and clustering using the K-Means algorithm. The clustering results segmented the area into twelve zones, highlighting three critical zones with the highest landslide risk. Comparison with a landslide inventory map indicated that most triggering points fell within these clusters, offering valuable insights for targeted monitoring and risk management strategies.
How to cite: Di Martire, D., Piegari, E., Ramaglietti, M., Cascella, E., Carotenuto, F., and Graziano, M. D.: Automated Identification of Landslide-Prone Areas in Southern Italy: A Case Study from Caiazzo , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18891, https://doi.org/10.5194/egusphere-egu25-18891, 2025.
Following spatially expansive landslide events, rapid remote sensing data acquisition is perhaps the most efficient means of capturing the nature and extent of landsliding. This is particularly true in the Appalachian Mountains of eastern North America, where high annual rainfall, humidity, and vegetation can obscure landslide features within a single growing season. In July 2022, a convective rainfall event with an annual exceedance probability of 0.1–0.2% caused record-breaking flooding and widespread landslides throughout about 1,800 km2 of the Appalachian Plateau in eastern Kentucky. In the immediate weeks following the storm, field and remote-sensing reconnaissance mapping by the Kentucky Geological Survey identified approximately 1,065 landslides triggered during the event. In January 2023, the state of Kentucky acquired a lidar dataset over the impacted region, complimenting previous acquisitions from 2012 and 2017. We used point cloud alignment and surface-normal comparison techniques to compare 2012 and 2017 lidar point clouds to post-storm 2023 point clouds. This resulted in a lidar change detection dataset with a limit of detection of +/- 13 cm over an area of 1,800 km2. By using this dataset as a basis for our inventorying, we are finding more numerous and smaller landslides compared to state-of-practice mapping methods (e.g., aerial photo interpretation, hillshade comparisons, field-based inspections). Additionally, we can compute statistics on volume balance within landslides, thereby providing insight into landslide mechanics at scale that is difficult to impossible to understand without such data. Inventorying is ongoing, however, as of January 2025, we have inventoried over 2,000 landslides that occurred between 2017-2023 in 10% of the impacted area. This presentation will discuss how high-fidelity lidar change detection methods influence landslide inventory mapping, statistical characterizations of the landslide event, and ongoing efforts to advance AI-driven landslide inventory mapping.
How to cite: Scheip, C., Crawford, M., Bibbins, E., Koch, H., Graham, A., Winters, S., Hsiao, V., Weidner, L., Zellman, M., and Anderson, S.: Improving Landslide-Event Inventories Using High-Fidelity Lidar Change Detection in Eastern Kentucky, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10018, https://doi.org/10.5194/egusphere-egu25-10018, 2025.
Tropical mountains are particularly vulnerable to landslides due to their susceptibility to climate warming combined with changes in land use driven by development and social transformations. Therefore, landslides in these regions pose a serious challenge to local land management, infrastructure development, and the conservation of soil and water resources. The Colombian Andes is a region where landslides are widespread, which, combined with the dense population, makes it prone to geohazards.
Numerous studies focused on national and regional inventories of landslides; however, small landslides (<1 km2) are often neglected despite having strong consequences for local communities. A detailed inventory of past landslides is essential for analysing the geomorphological processes related to landslide initiation and for calibrating and validating landslide susceptibility models. This study focuses on the impact of land cover and geomorphology on the distribution of small landslides (less than 1 km²). To minimise the influence of other factors, we concentrated on a single catchment area characterised by relatively uniform geology and precipitation. The main objectives of the study were (1) to document and analyse the spatial distribution of landslides; (2) to investigate factors potentially responsible for their development, specifically examining the differences in the frequency of landslides between forested and non-forested areas in a local spatial scale.
Landslides were identified using high-resolution satellite imagery from Ikonos, WorldView, and Pleiades from 2000/2003, 2013/2014, and 2019/2020. Landslides were visually interpreted from the images based on factors such as image tone, texture, vegetation cover, and visible disturbances of the surface. The identified landslides were vectorised as polygons, and a point representing the centre of the headscarp was also added for each landslide. The mapping results were verified during fieldwork in 2017, 2018, and 2019. Basic morphometric and descriptive parameters were attributed to each landslide, including area, type of landslide, and land cover. In the final step, frequency ratio modelling was employed to investigate the relationship between topographical and land cover factors and the distribution of landslides.
We mapped more than 900 small landslides ranging in size from 102 m² to 104 m². Most of these landslides were found in cultivated areas, such as pastures, farms, and plantations, or along local roads. Our findings revealed four potential scenarios for landslide activity: (1) an intensification of landslide processes and an increase in the overall landslide area; (2) active landslides that remain stable in terms of size; (3) the activation of new landslides; and (4) deactivation of existing landslides accompanied by vegetation succession. The activation of new landslides and the intensification of existing ones were primarily linked to direct human modifications of the terrain, mainly through constructing new roads or repairing existing ones. The results indicate that the spatial distribution of landslides at a local scale is marked by significant clustering, with the zone of pastures characterised by the biggest concentration of landslides.
The research was funded by the Polish National Science Centre, Poland (Project number 2015/19/D/ST10/00251)
How to cite: Tomczyk, A. and Ewertowski, M.: Effect of geomorphology and land cover on landslide distribution at a local spatial scale: An example of the central Andes, Colombia , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7425, https://doi.org/10.5194/egusphere-egu25-7425, 2025.
Landslides in mountainous regions are key processes shaping the landscape and pose significant challenges to human activities, particularly due to their potential impact on infrastructures. Even dormant deep-seated landslides remain a persistent threat, as heavy rainfall events can often trigger catastrophic reactivations. The Cà di Sotto landslide in San Benedetto Val di Sambro (BO), Italy is a well-documented large phenomenon (> 45 hectares) that in 1994 destroyed some buildings and occluded the stream below, necessitating extensive drainage systems to mitigate flood risks. This landslide is classified as a complex movement, originating as a rotational slide and evolving into an approximately 2 km-long earthflow. The affected material, the Monte Venere Formation, consists of tectonized calcareous-marly turbidites interbedded with arenaceous-pelitic strata. After 30 years of dormancy in October 2024, following a heavy rainfall event, the entire body underwent a new catastrophic failure reaching peak velocities of several meters per day and disrupting previously established mitigation measures. Multi-temporal InSAR techniques (PS and DS-InSAR) are widely used to monitor slow-moving landslides, but the targeted phenomena strongly exceeded their maximum detectable velocity (Vmax ~ 100 mm/yr). All analysis were consequently performed through the two-pass DInSAR technique using Sentinel-1 A/B C-band SAR images, acquired with a minimum acquisition interval of six days, from 2015 to early 2025. This method grants higher territorial coverage in mountainous areas and increases the maximum detectable velocities (Vmax ~ 20 mm/week). Our results show signs of activity in the crown area in the period preceding the catastrophic failure while no clear deformation signals were detected in the landslide body. During the failure event, the quality of InSAR data varied depending on the perpendicular baseline, atmospheric disturbances and vegetation cover. Peak deformation (V > 10 m/day) exceeded the detection capabilities of InSAR, requiring ground-based monitoring techniques for effective tracking. However, low-noise interferograms clearly delineated the spatial distribution of the active area with frequent phase jumps and decorrelation soon after the failure. During the later post-failure stage interferograms have high enough coherence to map the deformation field. The comparison between InSAR data and on-site ground measurement (including subsequent UAV surveys and topographical data) helped to understand and interpret the remotely sensed information and highlights the potential and the limits of standard interferometry to identify and monitor active landslides in mountainous regions.
How to cite: Mercurio, A., Bayer, B., Franceschini, S., Ciccarese, G., Bartola, M., Dal Seno, N., Rani, R., Zuccarini, A., and Simoni, A.: The potential of two-pass DInSAR to investigate the spatial and temporal evolution of a large landslide in the Northern Apennines of Italy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11110, https://doi.org/10.5194/egusphere-egu25-11110, 2025.
Deep-seated landslides represent a major issue in geomorphology and engineering geology due to their complexity and potential impact on infrastructure and settlements. This study focuses on terrain analysis and monitoring of two large, complex deep-seated rock slides located in the Northern Apennines, in the municipality of Ferriere (Piacenza province, Italy). Both landslides (namely Colla di Gambaro and Brugneto) extend for more than 1 km in length and are characterized by roto-translational sliding of stratified arenaceous and silty rock masses (down to depths of more than 40 m), evolving into earth slides at the landslide toe. This makes the combination of conventional and remote sensing techniques essential for unreveal their characteristics and dynamics at the slope scale. An integrated approach was therefore adopted, using both existing and newly collected data. High-resolution DEMs from UAV surveys with LiDAR technologies and field surveys were integrated to delineate main landslide units and subunits based on combined geomorphological and kinematic criteria. The distinction of units affected by different movement rates and the evolution and propagation of movements downslope was greatly supported by InSAR displacement time series (obtained by both Permanent/Distributed Scatterers and Interferogram Stacking of Sentinel-1 satellite datasets) as well as continuous GNSS monitoring in some key points. Seismic surveys and inclinometers/piezometers, contributed to the identification of main sliding surfaces at depths and of the groundwater conditions. The integration of these techniques, improved the delineation of landslide boundaries, enhanced understanding of spatial variability in movement rates, and increased the accuracy of landslides mapping. Furthermore, it supported the construction of reference cross-sections that highlight the complexity of movements and movements rates at the slope scale, the transition from rock sliding mechanisms to earth sliding downslope. Maps and cross sections, ultimately, exemplify the geological and geotechnical model of these phenomena and demonstrate the added values of the combined use of conventional and remote sensing tools for enhancing our understanding of complex landslide phenomena, thus providing a basis for risk assessment and structural or non-structural mitigation strategies.
How to cite: Fabbiani, C., Mulas, M., Bayer, B., Critelli, V., Franceschini, S., Ghiselli, I., Iannacone, J. P., Lelli, F., Tondo, M., Truffelli, G., and Corsini, A.: Terrain Analysis and Monitoring of Large Deep-Seated Rock Slides in the Northern Apennines Using Integrated Ground-Based and Remote Sensing Techniques, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11751, https://doi.org/10.5194/egusphere-egu25-11751, 2025.
The increasing occurrence of geohazards such as landslides, rockfalls, and slope instabilities, often exacerbated by climate change and human activities, highlights the urgent need for innovative tools to assess and monitor these phenomena effectively. While conventional techniques in the field of Remote Sensing such as laser scanning (LiDAR), aerial photogrammetry, satellite interferometry (InSAR), have proven invaluable for geohazard analysis, they often require significant financial and technical resources. In this context, Gigapixel imaging emerges as a promising, cost-effective alternative, providing ultra-high-resolution visual data capable of supporting geohazard assessment and fostering awareness among stakeholders and the general public. This work explores the use of Gigapixel imaging - a technique based on the capture of ultra-high-definition optical images composed of billions of pixels - for geohazard assessment and analysis. This approach, coupled with traditional photogrammetric techniques (e.g. Structure from Motion) enables the generation of detailed visual representations, both in two and three dimensions, of geological features and processes. The practical implications of this research extend to geotechnical monitoring, early warning systems, geoscience education and public awareness campaigns. For example, the detailed visualizations produced by Gigapixel imaging can be used to communicate geohazard risks to policymakers and local communities, fostering better understanding and preparedness. Additionally, the system’s affordability and ease of use make it accessible to a wide range of users, including researchers, professionals, public entities, and NGOs.
How to cite: Romeo, S., Fraccica, A., and Vitale, V.: From pixels to prevention: gigapixel imaging for landslide assessment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20341, https://doi.org/10.5194/egusphere-egu25-20341, 2025.
Digitization of rock outcrops (e.g., LiDAR, SfM, SLAM) and digitalization of rock fracture data (e.g., Coltop, DSE, CloudCompare) have recently been greatly improved; however, disciplines related to rock mechanics, such as engineering geology, geomechanics, hydrogeology, reservoir engineering, and structural geology, still face two critical limitations (Elmo and Stead, 2021; Yang et al., 2022). First, geological and geotechnical data collection and processing methods remain largely unchanged for decades (e.g., Markland, 1976; ISRM, 1981; ASTM D5878-19, 2019). These methods are often qualitative, prone to significant biases, and reliant on outdated classification and characterization systems, such as manual scanline measurements, photo interpretation, and indices like RQD, RMR, and GSI. Second, despite the shift towards digital approaches, there is still a lack of standardized and statistically robust digital workflows for analyzing fractured rock masses (Yang et al., 2022). For this reason, in this study, we propose an open-source workflow for characterizing fracture networks and analyzing rock slope stability. Our approach integrates UAV-based digital photogrammetry with Digital Outcrop Models (DOM), utilizing CloudCompare software alongside DICE and ROKA algorithms. This workflow was applied to a steep granite slope in Southern Alps near Monte Montorfano, Italy. Manual digitalization in CloudCompare produced a robust dataset of discontinuities—including faults, fractures, and dikes—that influence slope stability. DICE enabled calculations of areal (P21) and volumetric (P32) fracture intensity, as well as intersection density/intensity (I20, I30, and I31). Spatial analysis revealed a general increase in fracture damage with distance from the main fault, though this trend displayed abrupt variations better modeled by an oscillatory pattern than by a simple exponential or power law. ROKA identified critical discontinuities prone to planar sliding, flexural toppling, and wedge sliding, offering more reliable results than traditional kinematic analyses (e.g., Markland test). By visualizing discontinuity planes, intersection metrics, and failure mechanisms directly on DICE and ROKA point clouds, the workflow enabled detailed geometric characterization of the fractured rock slope. High-resolution 3D maps produced through this workflow facilitate robust and user-friendly slope zoning, delivering high-quality, timely information essential for planning effective mitigation strategies.
How to cite: Menegoni, N., Giordan, D., Corvò, S., Bonazzi, M., Petagine, A., Guerra, M., Foletti, M., Arese, E., Perotti, C., and Maino, M.: Fracture Network and Rock Slope Stability Analysis of Quarry Areas by Digital Outcrop Modelling and open-sources Algorithms: an example from Montorfano (Southern Alps, Italy), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6906, https://doi.org/10.5194/egusphere-egu25-6906, 2025.
Predicting the likelihood of collapses and landslides on the German Baltic Sea coast cliffs requires a wide range of geological, hydrological, and climate data. Point clouds and images from drone surveys constitute a significant part of the data.
The cliffs predominantly consist of Quaternary sediments of glacial origin with highly variable properties, often intricately interwoven. The glacial processes that contributed to these sediments' formation, shaping, and modification left heterogeneous deposits and various glacial-tectonic structures such as joints, shear planes, and oriented stones. These structures are crucial for assessing failure probabilities in cliff areas and are necessary for engineering geological slope stability analysis.
CloudCompare is an open-source software supporting various point cloud analyses. It includes a FACETS plugin for extracting planes from 3D point clouds of rock bodies. The identification of discontinuities has been performed and validated by various authors using the FACETS plugin on hard rock exposures. We are testing the plugin for mapping discontinuities in unconsolidated sediments.
Unconsolidated sediments like glacial till and varved silts reveal glacial discontinuities in cliff exposures. These can be documented in the field but require significant time. In point clouds, facets can be calculated using the plugin in a single step. However, unlike joints measured with a compass, these are always open surfaces on the cliff. While their formation may relate to joint systems, additional factors such as flaking, rolling, erosion, drying, frost wedging, and root growth may contribute to or independently cause the formation of these facets.
We use point clouds generated from drone surveys of three cliff locations. These sites differ significantly in their geological structure and glacial deformation history. The facets are calculated from the point clouds and validated using structural data from engineering geological coastal surveys conducted in the past and our recent fieldwork. The FACETS plugin is suitable for capturing open joint surfaces on cliffs in unconsolidated sediments. However, care must be taken to ensure that the exposure of the steep coastal section does not dominate the measured discontinuity data. Slope-parallel planar surfaces in unconsolidated sediments are not always open joints. Also, shear planes and oriented stones are challenging to detect. Shear planes rarely form open surfaces due to frost wedging, and the long axes of stones cannot be calculated with the plugin method due to their rounding.
The method is well-suited for rapid and reliable documentation of joints. Given the considerable annual coastal retreat of several meters at some locations, the FACETS method makes it possible to create a time series for joints to find potential changes in orientation, dip, and joint density. These structural datasets are particularly valuable for engineering geological slope stability calculations. Specifically, these data could be integrated into training deep learning algorithms as additional features to support the automatic identification of sediments forming the cliffs.
How to cite: Fuchs, M., Schütze, K., Schüßler, N., Torizin, J., and Kuhn, D.: Structural Data of Unconsolidated Sediments from Point Clouds on Coastal Cliffs of Mecklenburg-Western Pomerania, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4146, https://doi.org/10.5194/egusphere-egu25-4146, 2025.
The C2R-IA project (www.c2r-ia.fr) is aimed to better account for the influence of weather conditions on the level of rockfall hazards and to anticipate temporary increases in hazard levels during storms and other specific weather conditions, in order to implement risk mitigation systems (access restrictions, monitoring, mobilization of emergency kits, predictive maintenance). To achieve this, a database of rockfall events is built to train AI predictive models of rockfalls based on weather conditions. One of the monitoring technologies used is a terrestrial laser scanner with a RIEGL VZ-2000i long range 3D laser scanning system. Lidar point clouds are thus used to provide at several time intervals the 3D surface of the study site: the Saint-Eynard cliff, located northeast of Grenoble in the french Alps. From the lidar point cloud series, the goal is to compare the clouds to detect changes and identify rockfall events (Manceau et al, EGU 2025, oral presentaion). For a large and rich database, it is important to achieve very precise alignement between lidar point clouds to detect the smallest possible changes in our point clouds series (small rockfall volumes).
In this context, a basic ICP (Iterative Closest Point) alignement reveals artefacts that need to be treated in a special way to achieve high-precision alignement. Geometric distortions are thus observed within the point clouds in the form of vertical strips. This phenomenon occurs at two scales:
- Low frequency: observations of decimetric to multi-decimetric jumps with strip widths ranging from 10 to 100 meters during acquisitions from a tripod, a flexible support.
- High frequency: observations of centimetric jumps with narrower strip widths (ranging from one to several meters) during acquisitions from a rigid base (reinforced concrete post).
Several hypotheses are put forward and tested to explain the existence of these strips: machine-related mechanical issues, independent or dependent on time, interaction between the ground, support, and machine, changes in atmospheric conditions during the acquisition period (lasting 40 minutes), the geometry of the cliff and its local orientation relative to the lidar's line of sight.
A processing method is proposed to overcome these geometric distortions during acquisition and maintain a low detection threshold when comparing two point clouds: this involves a new strip-based alignment of the two clouds before change detection. The first step is the extraction of strips from the compared cloud, then an independent alignment of each strip to the reference cloud is performed using the ICP method. Finally, the aligned strips are merged to form the new compared cloud : we reach a detection threshold of less than 10 cm (i.e. 10-4 times the measurement distance) whereas 40 cm has been previously used on the same site in the literature.
How to cite: Chanut, M.-A., Manceau, L., Lévy, C., Dewez, T., and Amitrano, D.: Rockfall detection using lidar point clouds: identification of geometric distortions during acquisition and proposed processing to enable a low detection threshold, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2535, https://doi.org/10.5194/egusphere-egu25-2535, 2025.
The ANR C2R-IA (anrc2ria.fr) project aims to develop reliable decision-making tools for dynamic rockfall risk management, such as restricting access to hazardous zones during critical periods. To achieve this, we aim to develop a predictive model for observed rockfall events that relates them to weather conditions history using Artificial Intelligence tools. Training an artificial neural network requires a comprehensively labelled dataset of rockfall events. To build this dataset, we deployed various instruments, including a Permanent LiDAR Scanner (PLS), whose data is processed by an automated workflow to handle the large volume of hourly-acquired point clouds.
The workflow started with a pre-processing step that includes point cloud alignment (registration), quality control, cropping the area studied, and vegetation removal. During the processing phase, changes are identified using a multi-step approach:
- First, pairs of point clouds are aligned either globally or by spatial strips (Chanut et al. EGU 2025, poster session).
- Then, M3C2 distances (Lague et al, 2013) are calculated. For a pair of point clouds (N1, N2), the distance computation is made twice from N1 to N2 and from N2 to N1 to identify significant changes.
- Dense clusters of significant changes are extracted using DBSCAN clustering, and a spatial association between clusters from the two clouds is performed to track corresponding zones and ensure accurate changes in output.
- To refine block characterization, a local registration and comparison is further performed, followed by alphashape surface reconstruction for volume estimation.
The workflow was developed in Python, primarily using the CloudComPy library, and requires minimal operator intervention thanks to integrated quality metrics at each processing step.
This optimized workflow combined with a fixed point of acquisition (a reinforced concrete pillar) has significantly improved the detection threshold at the St. Eynard site (Grenoble, France), allowing for identifying rockfalls as shallow as 10 cm in depth and 0.01 m³ volume — an improvement from the previous 40 cm and 0.1 m³ (Verdier-Legoupil, 2023; Le Roy, 2020). Catalog completeness has also been improved, with the number of detected events increased thresholds from less than 50 events/month/km² to about 150 events/month/km². However, numerous false positives are generated, primarily due to persistent vegetation artifacts despite the vegetation removal step. To address this issue, future work will focus on integrating an automatic change validation method using criteria such as morphology, scalar field information, and additional point cloud comparisons to check the temporal persistence of changes.
Chanut, M.-A., Manceau, L., Levy, C., Dewez, T., Amitrano, D., 2025. Rockfall detection using lidar point clouds: identification of geometric distortions during acquisition and proposed processing to enable a low detection threshold. EGU 2025, Poster session.
Lague, D., Brodu, N., Leroux, J., 2013. Accurate 3D comparison of complex topography with terrestrial laser scanner: Application to the Rangitikei canyon (N-Z). ISPRS Journal of Photogrammetry and Remote Sensing 82, 10–26. URL https://doi.org/10.1016/j.isprsjprs.2013.04.009
Le Roy, G., 2020. Rockfalls multi-methods detection and characterization. Université Grenoble Alpes.
Verdier-Legoupil, M., 2023. Etude des chutes de blocs par la photogrammétrie, cas du St Eynard. Université Grenoble Alpes.
How to cite: Manceau, L., Chanut, M.-A., Levy, C., Dewez, T., and Amitrano, D.: Enhancing Rockfall Detection Using Permanent LiDAR Scanner (PLS) Data and Automated Workflows at St. Eynard Cliff (Grenoble, France), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6312, https://doi.org/10.5194/egusphere-egu25-6312, 2025.
Located in the western part of the Garhwal Himalaya, India, the upper Bhagirathi region of District Uttarkashi is subject to numerous tectonic events, viz., earthquakes, landslides, and subsidence. Extreme rainfall in monsoon and the area's shifting land use pattern combined with tectonic instability cause frequent landslides especially along the highway stretches. In this study a field survey for rock slope kinematic analysis was conducted along National Highway No. 108 connecting Dharasu to Gangotri where eleven heavily jointed rock slopes were examined and the most susceptible slopes were identified based on the joints, their spacing, aperture, roughness, filling type, and weathering state were recorded for Rock Quality Designation (RQD), Geological Strength Index (GSI), Rock Mass Rating (RMR), and Slope Mass Rating (SMR). Rock slopes at a few locations have shown a high propensity towards planar and wedge failure where the slope face and the joints are dipping in the same direction with a high dip amount for the slope i.e. up to 70° whereas in other slopes the joint set intersections have indicated wedge failure probability. Further temporal landslide inventories, from 2012 to 2018 and 2018 to 2022 were used in the spatial analytic tools to create a database for the major causative elements (topographic roughness index, slope units, slope angles, fault and lineament density, slope curvature, topographic wetness index, proximity of slopes to the highway, proximity of slopes to the stream) through ensemble GIS-based models (Shannon Entropy, Information Value, and Frequency Ratio Assessment). Comparative landslide hazard evaluation (LHE) was performed for pre-2018 (before highway expansion) and LHE post-2018 when the national highway expansion started. The southward-oriented slope units with an inclination > 45°, concave curvature, and proximity of 130 m from the highway stretch have shown more association with landslide pixels. Also, the total landslide pixels have shown a considerable increase from 11391 (up to 2018) to 17999 (post-2018), which mostly fall along National Highway 108. We deciphered the dominance of litho-structural factors that contribute to the extremely brittle nature of the rock slopes in the Dharasu region based on field and remote sensing studies.
How to cite: Sharma, S., Puniya, N., Mukherjee, S., and Patidar, A. K.: Terrain Analysis and Landslide Hazard Evaluation from Garhwal Himalaya: Contribution from conventional and remote sensing tools, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16324, https://doi.org/10.5194/egusphere-egu25-16324, 2025.
Large-scale landslides pose a significant threat to both population and infrastructure. Among various slope movements, deep-seated gravitational slope deformations (DSGSDs) are landslides affecting large portions of slopes, occurring in several mountain regions in the Alps and worldwide. These processes are evolving in a long-term dynamic, and their kinematics are characterized by relatively low displacement rates (mm-cm/yr) compared to the spatial extent of the affected slope. However, these phenomena should not be neglected when assessing potential hazards in a specific area. Continuous evolution of DSGSDs may cause damages to infrastructure and, in some cases, evolve into secondary, faster landslide processes and invoke a substantial risk for critical infrastructure. This becomes of major importance when the infrastructure is essential for local communities, commuters and cross-border transportation. Therefore, it is important to investigate and better understand ongoing processes.
This study presents preliminary findings from the investigation of a known DSGSD in the bottleneck area of the Brenner Corridor between Italy and Austria. In this region, the occurrence of DSGSDs is controlled by the tectonic setting, combined with the presence of lithologies with structural weaknesses (e.g., schistosity). These slope instabilities not only affect entire valley flanks, potentially involving massive unstable volumes in case of collapse, but also threaten the Brenner corridor, a key transportation route linking northern and southern Europe across the Alps. Our investigation focuses on the characterization of the upper scarp of the Padauner Berg slope (2230 m a.s.l.) in Austria, which shows surface evidence of ongoing deformation. The research combines close-range remote sensing using a commercial UAV device (DJI Phantom 4 Pro) and field observations across an area of 0.10 km2. Images captured during the UAV survey were processed using a standard Structure from Motion (SfM) workflow to generate a high-resolution 3D point cloud. The point cloud was georeferenced using ground control points (GCPs), equally distributed across the study area and surveyed with a high-precision GNSS device. Approximately one-third of the GCPs were used as checkpoints to assess the accuracy of the georeferenced point cloud.
The results of this study contribute to identifying terrain morphologies and mapping distinct morphostructures on the slope, such as ridges and uphill-facing scarps. These findings provide a preliminary assessment of the potential extent and enlargement of the slope instability, aiming to bridge the gap between remote sensing outputs and conventional geomorphological analysis to understand DSGSD dynamics at a local scale. Additionally, this study evaluates the possibility of complementing previous DEMs as well as orthoimagery to calculate surface changes and quantitatively assess the temporal evolution of the investigated DSGSD. However, while UAV-based surveys offer a practical solution for spatial representation of potentially hazardous processes in high-alpine areas, the study highlights certain methodological limitations, such as flight altitude and terrain accessibility, that must be considered when planning flight missions to ensure consistent and comparable results across repeated surveys.
How to cite: Carraro, E., Wenzel, T., Andlinger, H., and Marr, P.: High-resolution, UAV-based mapping of the DSGSD scarp at Padauner Berg (Brenner Pass, Austria), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18377, https://doi.org/10.5194/egusphere-egu25-18377, 2025.
Climate change has among its effects the increasing frequency and intensity of both natural and anthropic hazard, such as landslides, floods, erosion, sea level rise, weathering and fires (Fatorić and Seekamp, 2017). These phenomena pose significant threats to archaeological heritage, as highlighted in scenarios outlined by the IPCC (Intergovernmental Panel on Climate Change).
Ancient sites, especially the archaeological settlements dispersed across rural landscapes, are particularly vulnerable to climate-related hazards due to their limited protection compared to cultural heritage present in urban contexts. This is particular significant for buried sites, which can be reasonably identified through surface traces or remote sensing techniques.
In this work, we propose a method based on spatial analysis and remote sensing, to assess the progression of the erosion hazard, that can affect both visible and unexcavated sites (Minervino et al. 2024). The USPED (Unit Stream Power-based Erosion Deposition) model was used to obtain the erosion risk/deposition map of the entire Basilicata region. This was then overlayed with the archaeological site locations in order to assess erosion risk map specifically for archaeological sites of interest
The result is a predictive risk map for the chosen case study that can forecast the future erosion risk in the archaeologically sensitive areas.
The area analysed for the archaeological risk assessment is the Basilicata Region and the sites considered are related to medieval rural settlements. A comprehensive census of these sites - some abandoned and others still inhabited - was carried out based on documentary sources and satellite and LiDAR data.
The work was carried out within the framework of Project PE 0000020 CHANGES, - CUP [B53C22003890006], Spoke 5, PNRR Mission 4 Component 2 Investment 1.3, funded by the European Union - NextGenerationEU.*
Reference
Fatorić, S.; Seekamp, E. Are cultural heritage and resources threatened by climate change? A systematic literature review. Climatic Change 2017, 142, 227-254, doi:10.1007/s10584-017-1929-9.
Minervino Amodio, A.; Danese, M.; Gioia, D. Past, Present and Climate Change Scenarios: Investigating Erosion Risk on Archaeological Heritage in the Sinni Valley (Basilicata, Italy). In Proceedings of the Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2024; pp. 412-428.
How to cite: Frisetti, A., Minervino Amodio, A., Abate, N., Corrado, G., Gioia, D., Masini, N., and Danese, M.: Assessing erosion risk and its relationships to climate change on archaeological heritage: medieval sites in the Basilicata region, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12559, https://doi.org/10.5194/egusphere-egu25-12559, 2025.
Pissouri village, located in Limassol, Cyprus, has been experiencing an active and fast-moving landslide, resulting in significantly accelerated displacement rates in recent years. The devastating consequences of the landslide include the continuous evacuation of houses, severe damage to properties, and transformations of the wider landscape. To improve national disaster preparedness and resilience to geological threats, the Cyprus University of Technology Laboratory of Geodesy established CyCLOPS (Cyprus Continuously Operating Natural Hazards Monitoring and Prevention System) in 2021, focusing on Pissouri as a critical case study. Since 2022, a number of ten geodetic-grade GNSS receivers have been installed in the broader area to enable continuous monitoring of the landslide.
This study presents an integration of multi-sensor data to investigate displacement rates and advance the understanding of landslide dynamics, utilizing the CyCLOPS strategic infrastructure. Sentinel-1 acquisitions in ascending and descending mode, covering the period from August 2022 to August 2024, were processed using GAMMA software. The data reveal significantly increased displacement patterns compared to earlier analyses, which detected only millimeters of movement per year. Concurrently, GNSS monitoring was performed using CyCLOPS equipment, indicating notable local movements and providing continuous ground-truth measurements. Additionally, rainfall data from the Cyprus Meteorological Department stations were integrated into a GIS framework, correlating intense precipitation events with rapid displacement trends. Two novel additions to this monitoring effort include: (a) the installation of a rain gauge within the study area to improve the reliability and accuracy of precipitation data, and (b) the use of Laser Scanning technology to detect and map structural cracks and landscape changes within the affected zone. These approaches provide localized insights into the landslide’s impact.
This comprehensive multi-sensor approach offers a robust framework for understanding and monitoring active landslides. The findings underscore the critical role of data integration and the use of a multi-sensor strategy in assessing displacement rates, correlating environmental triggers, and accurately evaluating hazards. Collectively, these measures support improved hazard mitigation strategies and enhance resilience.
Acknowledgments: The authors would like to acknowledge the "CyCLOPS+" (RIF/SMALL SCALE INFRASTRUCTURES/1222/0082) project, which is co-financed by the European Regional and Development Fund and the Republic of Cyprus through the Research and Innovation Foundation in the framework of the Cohesion Policy Programme "THALIA 2021-2027" and by national resources. The authors would like to acknowledge the ‘CyCLOPS’ (RIF/INFRASTRUCTURES/1216/0050) project, which was funded by the European Regional and Development Fund and the Republic of Cyprus through the Research and Innovation Foundation in the framework of the RESTART 2016-2020 program.
How to cite: Fotiou, K., Kakoullis, D., Kotsakis, C., Hatzinikos, M., and Danezis, C.: Comprehensive Monitoring of the Active and Fast-moving Landslide of Pissouri village, Cyprus: Integrating SAR, GNSS, Rainfall and Laser Scanner Data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9566, https://doi.org/10.5194/egusphere-egu25-9566, 2025.
Due to Taiwan's high seismicity and heavy rainfall, numerous landslides have occurred, causing severe damage. These landslides pose long-term threats to human life, property, and the environment. As a result, significant research has focused on assessing landslide hazards and developing mitigation methods. Key areas of study include the size, volume, recurrence, and evolution of landslides. The rapid advancement of geospatial information technology has greatly improved land monitoring and expanded into other applications, including hazard monitoring. Geospatial data, obtained through surveying and mapping, allows for the quantitative evaluation of debris production, migration, and deposition over time and space at the catchment scale. In recent years, MEMS (Micro-Electro-Mechanical Systems) technology has played a key role in advancing Unmanned Aerial Systems (UAS) for measurements, offering advantages such as efficiency, timeliness, low cost, and ease of use in harsh weather. Real-time, high-resolution aerial images provide essential spatial information for research. This study used UASs to monitor a landslide area in Baolai Village, southern Taiwan, which was severely affected by a catastrophic landslide triggered by Typhoon Morakot in 2009. To assess hazards, the study combined UASs, field surveys, terrestrial LiDAR, and UAS LiDAR for data collection beginning in 2015. Since early 2018, UAS LiDAR technology has been used to scan the area. Changes in the landscape were measured and verified using Ground Control Points (GCPs) and Check Points (CPs). The results showed that the most active regions are on the eastern side of the landslide. Significant elevation changes were detected before mid-2017, but activity increased again in 2018 and intensified after 2021.The study provides valuable geospatial datasets for hazardous areas, as well as essential geomorphological data and methods that can support future research, hazard mitigation, and planning.
How to cite: Chang, K.-J. and Huang, M.-J.: Landslide displacement and activity monitoring based on UAS multi-sensors, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2477, https://doi.org/10.5194/egusphere-egu25-2477, 2025.
Deep learning has been successfully used in landslide detection, with convolutional neural networks (CNNs) being the most widely used framework. The characteristics of the terrain are often the best predictors in such tasks. However, it can be difficult to choose which geomorphometric variables to use as inputs for the deep learning model. A small area in the Moldavian Plateau, a region where landslides are often present in the landscape, was used to benchmark the performance of more than 30 geomorphometric variables in a binary classification task. The area was split into raster tiles of 100x100 pixels, each being labeled as either having a landslide present or not. To generate the geomorphometric variables, a high-resolution LiDAR DEM was used. Three CNN architectures were tested (AlexNet, ResNet, and ConvNeXt), and the model performance metrics were reported. Expectedly, ConvNeXt was the best-performing architecture, with over 20 of the variables having an F1-score of more than 0.8. The hillshade, the digital elevation model, and the profile curvature were the best-performing variables.
How to cite: Cuvuliuc, A.-A., Ursu, D.-E., and Niculiță, M.: Evaluating the performance of geomorphometric variables for landslide detection using convolutional neural networks, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12939, https://doi.org/10.5194/egusphere-egu25-12939, 2025.
The deformation monitoring of the ring rockfill dam in pumped storage power stations is of great significance. Traditional monitoring techniques such as geodetic survey, GPS, and multi-point displacement meters have high precision and reliability but are limited by point monitoring in terms of layout density and range. InSAR technology has advantages like high precision, large range, all-weather, non-contact, and low cost, yet faces challenges from factors like spatio-temporal decorrelation, atmospheric delay, and vegetation cover.
This research utilized the permanent scatterer InSAR processing technology with multi-reference point baseline network adjustment and high-precision DEM data to monitor the surface deformation of the ring rockfill dam in the upper reservoir of Zhanghewan Pumped Storage Power Station. It analyzed the impact of DEM resolution on PSInSAR monitoring accuracy and verified the accuracy of InSAR deformation monitoring using ground synchronous monitoring data from a high-precision measuring robot.
The results indicate that the dam and slope of Zhanghewan Power Station's upper reservoir showed an overall uplift trend during the observation period, which was preliminarily judged to be caused by the temperature rise from winter to summer. The correlation coefficient between the monitoring point deformation rate obtained by the InSAR technology and the ground synchronous observation result was 0.838, with an RMSE of ±7.24mm/yr. The higher the precision of the external DEM, the higher the InSAR monitoring accuracy, with an improvement range of 2 - 3mm.
By combining the ground and satellite monitoring results with the water level and temperature observation data, it was found that for the ring rockfill dam, the cumulative displacement of the monitoring points was significantly correlated with the temperature, but the displacement change was not significantly correlated with the temperature change. The influence of temperature on the displacement of monitoring points was slow and nonlinear, and different monitoring points had different responses. The cumulative displacement of monitoring points had a weak correlation with the water level, while the displacement change had a stronger correlation with the water level change. The water level had a greater impact on the upstream and downstream displacement of specific points. This study provides an important reference for the research and application of InSAR deformation monitoring of large-area structures such as ring rockfill dams.
How to cite: Wan, P., Han, X., and Ding, B.: Deformation Monitoring of Ring Rockfill Dam in Pumped Storage Power Station Based on Spaceborne InSAR, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2745, https://doi.org/10.5194/egusphere-egu25-2745, 2025.
The aim of this contribution is to develop a procedure for monitoring stone-retaining walls using terrestrial laser scanning (TLS). Compared to classical point-based deformation analysis, however, areal deformation analysis is still less common and has more limitations in terms of quality information for the results because the stochastic model is still incomplete for TLS measurements. The procedure is therefore intended to show, in a methodologically correct way, how the monitoring of a stone-retaining wall with TLS is nevertheless successful.
To this end, the methodological basis for the procedure is developed according to the state of the art and research and the four phases of the procedure are defined for the structure of the work. In phase 1, the monitoring task is analysed and planned, in phase 2 the reference system is implemented with a geodetic network, in phase 3 the TLS measurements and evaluations are carried out and finally, in phase 4, the TLS deformation analysis is performed. As part of the procedure, the TLS instrument is first tested in accordance with ISO 17123-9:2018 (E) 2018 and found to be suitable for use. This is followed by the first practical development step of the procedure for monitoring individual stones under laboratory conditions. A point-based deformation analysis is carried out as a control. When comparing M3C2, feature matching and virtual targets, the latter method, with its robustness and high quality of results, proves to be the most suitable method for the procedure. Building on these findings, the monitoring of a rock face with TLS on the Kitzsteinhorn was also successful. The task was clearly defined, a reference system was realised as a frame of reference and a new zero epoch was created with TLS measurements. On this basis, past epochs were then successfully transformed into the stable reference system using a stable range method. The TLS deformation analysis with virtual targets then succeeded, and large movements could be determined, albeit without associated quality data. Glacier melt was also identified as a possible cause of the movements.
The developed procedure for monitoring rock retaining walls with TLS is finally presented in a flow chart, and the individual process steps are described in it. In addition, an objective evaluation of the procedure is carried out using methodological elements of engineering geodesy.
How to cite: Eitler, L., Kostjak, V., and Neuner, H.: Development of a procedure for monitoring stone retaining walls with terrestrial laser scanning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4370, https://doi.org/10.5194/egusphere-egu25-4370, 2025.
Landslide-dammed lakes are formed by natural blockages of river channels. These lakes pose significant hydrological risks downstream, especially under changing climate conditions. Monitoring the surface area extent and modelling the hydrology of such lakes is important to assess the stability of landslide dams and the downstream flood risk. In this study we aim to link observed lake water balances changes with the driving hydrological processes in the upstream catchment.
The present study focuses on two lakes in Pakistan, formed by landslide dams, both still standing many years after their formation: Attabad Lake (formed in 2010 by a rockfall triggered by rainfall) and Zalzal Lake (formed in 2005 by a landslide triggered by earthquake). Over the years, after an initial phase of increasing trend and large fluctuations, both lakes have seen a consistent decline in area and volume, apart from some remaining seasonal fluctuations. Remote sensing images from Landsat 5, 7, and 8 were integrated to determine lake surface area based on the Normalized Difference Water Index (NDWI). Data Gap filling techniques were applied to estimate missing months with cloudy images. Digital elevation models (DEM) prior to lake formation were used to derive volume over time for the two lakes.
The estimated variations of lake volumes were subsequently modelled based on the water balance of the upstream catchments. We considered precipitation, snowfall, snow accumulation, snowmelt, ice melt, springs, and groundwater recharge. Hydrometeorological data (including precipitation, snowfall, snowmelt, temperature, runoff, and actual evapotranspiration) was collected from various sources (GRACE, TerraClimate, ERA5-Land) by utilizing Google Earth Engine. Groundwater recharge was calculated by analyzing variations in terrestrial water storage collected from GRACE data for both lakes. Additionally, we used lumped hydrological models (such as the Budyko framework) to quantify the interplay between climatic inputs and hydrological fluxes.
We conclude that using hydrological models helps understand the role of hydrological processes in lake inflows, outflows and storage changes. This approach facilitates the assessment of the sensitivity of lake hydrology to changes in climatic variables. The analysis showed seasonal variations in lake inflow and outflows driven by snowmelt, and precipitation. This study will contribute to the assessment of the hydrology of landslide-dammed lakes in data scarce catchments.
Keywords: Landslide dams, hydrological modeling, water balance, climate change, remote sensing
How to cite: Shazil, M. S., Marino, P., Damiano, E., Bogaard, T., and Greco, R.: Analyzing hydrological dynamics for water balance estimation of landslide dammed lakes in Pakistan, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4469, https://doi.org/10.5194/egusphere-egu25-4469, 2025.
Landslide inventory mapping is a critical component of landslide susceptibility analysis and prediction. The mapping process has been carried out based on field surveys and comparisons of aerial or satellite imagery, which are both time-consuming and labor-intensive. Therefore, recent studies have utilized artificial intelligence models to identify landslide locations. However, the accuracy of these approaches remains limited due to dense vegetation, the low spectral resolution, and seasonal spectral variations in forested regions. Consequently, there have been efforts to enhance the accuracy of landslide inventory mapping through the integration of landslide conditioning factors.
The objective of this study is to enhance landslide detection through the utilization of Sentinel-2 satellite imagery prior to and following landslide occurrences, in conjunction with landslide conditioning factors. The analysis is divided into two phases: a change detection phase and a post-processing phase. In the change detection phase, Sentinel-2 L2A images from before and after landslide events were analyzed using a multi-layer perceptron model, with changes in NDVI and surface reflectance across bands 2 to 12. In the post-processing phase, the frequency ratio technique was applied to calculate the conditioning factor grades. These grades were then used to weight the result of the change detection phase. The conditioning factors encompassed effective soil depth, timber age, elevation, slope, geological lithology, and land cover. To validate and compare the results, the area under the curve (AUC) was computed based on receiver operating characteristic (ROC) curves. The model's training and validation were carried out using data from Jecheon-si, a region that experienced a high incidence of landslides in 2020. In addition, the model's performance was evaluated using the data from the study area. The proposed integrated approach integrates change detection using satellite imagery with landslide conditioning factors to enhance the accuracy of landslide detection models. The proposed model is expected to contribute to the enhancement of landslide hazard management and prevention by providing more reliable detection techniques.
Acknowledgement
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government(MSIT) (RS-2023-00222563)
How to cite: Song, W.-J., Lee, J.-H., and Park, H.-J.: A semi-automatic landslide detection model combining spatial statistical analysis and change detection, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7884, https://doi.org/10.5194/egusphere-egu25-7884, 2025.
Landslides are one of the most critical natural hazards in the world and can be extremely destructive. Campania region (southern Italy) is particularly susceptible to these phenomena, in particular to the flowslides, due to the presence of pyroclastic deposits, related to the eruption of volcanic complexes (Vesuvius and the Phlegraean Fields), on steep slopes made of carbonate or volcanic bedrock.
This study deals with experimental site in Salerno; it was chosen as it is geologically and geotechnically representative of the Lattari Mts, an area historically affected by this type of landslides. Furthermore, this choice allows for bridging the knowledge gap on these landslides between the northern slope, which has been extensively studied, and the southern slope, which has been less investigated.
This study proposes the geological characterization of the site through a multidisciplinary approach integrating boreholes, thickness logs and Electrical Resistivity Tomography (ERT) surveys; it is possible to define the stratigraphic section of the area and to determine the pyroclastic thickness, information that will then be crucial for a slope stability analysis.
In addition, the site will be equipped for the measurement and monitoring of soil hydraulic parameters (suction and volumetric water content), as they are preparatory factors for the triggering of these landslides. In fact, continuous monitoring of hydraulic and mechanical soil parameters is an important tool to improve the Early Warning Systems (EWS) and, thus, the risk mitigation.
Finally, preliminary results of UAV remote sensing data, using thermal and multispectral cameras, will be shown to estimate the hydraulic soil parameters measured in situ.
How to cite: De Luise, S., Forte, G., and Pirone, M.: Geological characterization and stability analysis for Salerno test site, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21387, https://doi.org/10.5194/egusphere-egu25-21387, 2025.
Upper Mustang, central Nepal, is a dry valley located between the Tibetan plateau and the High Himalayas. The Thakkhola fault system, which bounds the Thakkhola half-graben, gave its orientation to the valley, which is nearly perpendicular to the main Himalayan range. The Kali Gandaki River rises here and flows south through the high Himalayan peaks of Annapurna and Dhaulagiri, influencing the valley's landscape with its cycles of sediment aggradation and erosion. In the current phase of incision, the river has generated steep slopes that are further destabilized by the altered Tethyan shales below, creating a perfect setup for the emergence of large-scale slope deformations.
Although they have been recognized for a long time, these major deep-seated slope deformations have never been thoroughly investigated, and their activity has never been studied. Despite the area's low population, landslides have affected several settlements, including Muktinath, a significant Hindu pilgrimage destination, where deformations are destroying houses and roadways. At present, there are still questions concerning the relocation of some villages, including the monastery complex.
The landslides may be driven by spatial factors (aspect, elevation), climate factors (permafrost, snow melt, precipitation) and anthropogenic activity (irrigation). Using both remote sensing data and in-situ observations, this project aims to determine the rates and patterns of slope deformation in the Upper Mustang region and assess the possible temporal and spatial controls on the deformations.
In order to monitor landslides across a range of velocities, we use both correlation of optical satellite images from Sentinel-II (2016-2023), and InSAR time-series processing from Sentinel-I images (2015-2024). Initial mapping of the region indicated six significant deformation zones moving at varying rates, all located in the area where the Tethyan shale bedrock is found. We generate time series of displacement at finer resolution using correlation of Planet images (2016-2024), concentrating on specific landslides. On the Dhe landslide, a period of faster movement in early 2019 is found. Field observations have revealed numerous water sources in the landslides that could impact its kinematics. To supplement the kinematic analysis, seismic ambient noise from a single station seismometer is analysed to better characterize the subsurface properties of the landslides. We will present the first analyses and results from this multi-source dataset.
How to cite: Maury, L., Cook, K., Raj Adhikari, B., and Lacroix, P.: Deep Seating Slow-moving Landslides in Upper Mustang: Mapping, Kinematics and Triggering Factors, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10114, https://doi.org/10.5194/egusphere-egu25-10114, 2025.
Landslides are among the most common natural hazards in mountainous regions, with substantial impacts on infrastructure, ecosystems, and communities. The landslide in Kotrupi and near the Prashar Lake, Mandi, Himachal Pradesh, India, has been actively evolving for the past few years, posing significant challenges to the region. This study combines UAV-based remote sensing with a novel computational approach using open-source MATLAB code to analyse the landslide's failure surface and quantify its volume.
Using high-resolution UAV data, detailed 3D models and Digital Elevation Models (DEMs) of the sites are developed. This method was applied to estimate the landslide failure surface and volume using spline curves and transversal vertical profiles derived from the high-resolution DEMs. The model assumes tangent values of the failure surface, calculates the depth of the probable failure surface at each grid point, and plots it using a 2D grid function. By employing MATLAB code, the process is fully automated, requiring minimal data inputs, such as a DEM and KML file of the contour limits. The model generates a 3D failure surface, enabling rapid and precise volume calculations.
Preliminary results highlight a significant volume release, offering insights into landslide dynamics and potential downstream hazards. The model's simplicity and adaptability are valuable tools for predicting hazard zones and defining mitigation strategies. By imposing additional constraints based on field measurements, this approach further refines predictions and enhances disaster preparedness. This study underscores the utility of combining UAV technology with advanced computational modelling to address landslide monitoring and risk assessment challenges effectively.
How to cite: Manocha, A. R. and Riaz, M. T.: Integrating UAV Mapping and Spline-Based Modeling for Landslide Volume Estimation: A Case Study of Landslides in Himachal Pradesh , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13202, https://doi.org/10.5194/egusphere-egu25-13202, 2025.
Modern natural hazard monitoring systems, utilizing various platforms and sensors, support risk management and Early Warning Systems (EWSs). A crucial aspect of hazard prediction is detecting spatial and temporal changes in landslide areas and identifying their precursors. Despite the rapid development of modern measurement techniques, such as remote sensing, accurately monitoring landslide areas remains challenging. These challenges arise from the diversity of landslide types, the nature and density of vegetation cover, and the limitations associated with the spatial resolution of the acquired data, which may affect the detection of changes in the study areas. This study presents an analysis of optical images and radar interferograms for selected landslide areas to identify precursors and characterize landslide dynamics.
The analyses included a time series of changes in normalized vegetation indices and radar interferogram coherence. Vegetation indices such as the Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index (NDMI), Moisture Stress Index (MSI), and Normalized Moisture Stress Index (NMSI) were examined, along with radar image coherence. Integrating these data types enhances monitoring efficiency by combining information from different measurement techniques, providing complementary insights, and enabling a better understanding of landslide dynamics.
The conducted analysis of high-frequency measurement data revealed that normalized vegetation indices in many cases showed significant changes in landslide-prone areas before the landslides occurred. Decreases in coherence coefficient values over the same period also indicated significant changes in the analyzed areas, further confirming the occurrence of displacement in these areas. The observed correlation between the decrease in coherence and changes in vegetation index values suggests that landslide processes affected both the terrain structure and vegetation cover. Integrating optical and radar satellite data shows the potential for identifying landslide precursors and evaluating landslide activity. Such analyses can significantly support the development of landslide risk assessment tools and EWSs.
How to cite: Januchta, K.: Time series analysis of vegetation indices and radar coherence as precursors of landslide occurrence, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16035, https://doi.org/10.5194/egusphere-egu25-16035, 2025.
Posters virtual: Mon, 28 Apr, 14:00–15:45 | vPoster spot 3
EGU25-1357 | Posters virtual | VPS12
Differences in applicability of mudslide scars estimation methods due to different spatial resolutionsMon, 28 Apr, 14:00–15:45 (CEST) | vP3.14
In recent years, the variety of satellite data that can be used for analysis in the event of a disaster has increased. At the same time, there is a need to process different satellite data using a unified analysis method, especially when extracting mudslide scars that have been newly exposed after a sediment disaster. Nonetheless, comparative studies focusing on spatial resolution, a potential factor affecting applicability and accuracy, have been lagging. Therefore, this study targeted the area surrounding Murakami City, Niigata Prefecture, which was the site of extensive sediment outflows due to heavy rainfall in August 2022. Specifically, the mudslide scar was estimated by calculating NDVI difference values (ΔNDVI) for four types of optical satellite data with different spatial resolutions. The data was extracted over a wide area and the effects of differences in spatial resolution on the applicability of the extraction method and the extraction rate were clarified. The relationship between precision and recall can be approximated by the quadratic equation y=ax2+bx+c, and there was a trade-off relationship between the two metrics; as the threshold value rose, precision increased while recall decreased. The optimal NDVI threshold for maximizing the F-measure ranged from 0.20 to 0.25. The medium-resolution satellite platforms Planet and Sentinel-2 had higher F-measure values, and the efficacy of NDVI extraction was not proportional to the fineness of the spatial resolution. The reason for this was that the area distribution of the mudslide scar in the target area was dominated by relatively small areas with a mode of 42 m2 and a median of 253 m2, which were considered to increase precision and recall. Consequently, selecting a spatial resolution that matches the area of the mudslide scar in the target area is considered to be effective.
How to cite: Akita, H.: Differences in applicability of mudslide scars estimation methods due to different spatial resolutions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1357, https://doi.org/10.5194/egusphere-egu25-1357, 2025.
EGU25-8988 | Posters virtual | VPS12
Populating a catalogue with displacement vs. time data: a tool for typifing landslides kinematic and a support for sustainable risk managementMon, 28 Apr, 14:00–15:45 (CEST) | vP3.15
Collection of data from landslides monitoring is crucial for a sustainable risk management. With this aim, the integrated monitoring systems combining in situ and remote sensing techniques provide a comprehensive understanding of landslide activity. One of the tasks of the Innovation Ecosystem "Tech4You - Technologies for Climate Change Adaptation and Quality of Life Improvement" focuses on analysing case studies to compare different landslide types, their associated monitoring networks and the displacements entity.
A key objective is to create a catalogue of displacements for typifying landslides. To achieve this goal, a comprehensive literature review was conducted. Only landslides with displacement data over time were considered. The catalogue records the landslide type, location, monitoring system, sensor type, installation year, monitoring period, and main dimensions.
A notable challenge in this research was the limited availability of raw displacement data. Many studies present monitoring results in graphical form, often as images, making numerical data extraction difficult. To overcome this, software tools and artificial intelligence (AI) methods have been employed to analyse graph images and extract numerical values. However, AI often encounters limitations in accurately interpreting and extracting numerical values from diverse graph formats. While AI offers rapid initial analyses, the use of dedicated software guarantees precision in data extraction. The combined workflow of inspection, validation, and software application ensures reliable outcomes, making the process more efficient than manual or traditional methods.
The catalogue now includes more than 60 classified landslides, and research on new case studies is always ongoing. For this reason, and to overcome the limitation of the reduce number of studies with associated data, this work serves as encouragement to increase the number of cases registered in the database.
A specialized digital tool will be developed to integrate in a general platform and utilize collected landslide displacement data. This platform aims to: i) support local and national public institutions, ii) facilitate widespread access to and utilization of the data for monitoring and mitigating landslide risk, and iii) assist in the identification and classification of landslides with characteristics similar to those catalogued in the database.
ACKNOWLEDGEMENTS
This work was funded by the Next Generation EU - Italian NRRP, Mission 4, Component 2, Investment 1.5, call for the creation and strengthening of Innovation Ecosystems', building 'Territorial R&D Leaders' (Directorial Decree n. 2021/3277) - project Tech4You – Technologies for climate change adaptation and quality of life improvement, n. ECS0000009. This work reflects only the authors’ views and opinions, neither the Ministry for University and Research nor the European Commission can be considered responsible for them.
How to cite: Vennari, C., Coscarelli, R., and Gullà, G.: Populating a catalogue with displacement vs. time data: a tool for typifing landslides kinematic and a support for sustainable risk management, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8988, https://doi.org/10.5194/egusphere-egu25-8988, 2025.
