Orals: Tue, 29 Apr | Room 1.15/16
Ground-Based Doppler Radar (GBDR) is an innovative technology designed to address hazards in steep mountainous terrains. Rockfalls, rockslides, ice and snow avalanches pose significant risks to human lives, infrastructures and ecosystems. These rapid phenomena sometimes exhibit minimal deformation prior to failure, making early detection challenging. GBDR offers a promising solution for real-time, long-range, and wide-area monitoring of such rapid slope hazards. This technology enables timely alerts once the phenomenon has been triggered, allowing for an instantaneous response to mitigate risks in vulnerable areas. Additionally, GDBR data allows for the reconstruction of runout trajectories, which is crucial for calibrating mitigation measures and prioritizing structural interventions to protect the elements at risk.
In Italy and around the world, GBDR has been successfully deployed at a limited number of sites, addressing various types of gravitational deformation, from rockslides (e.g., Ruinon landslide) to ice-rock avalanches (e.g., Marmolada glacier). Recently it has been installed to monitor a sub-vertical granitic slope, 500 meters high, above the Gallivaggio sanctuary (Central Italian Alps), specifically to detect rockfalls ranging in size from thousands of cubic meters to approximately one cubic meter. To our knowledge, this is the first instance of GBRD being deployed to monitor such a steep, acute-angled slope alongside an existing Ground-Based Interferometric Synthetic Aperture Radar (GB-InSAR) monitoring system.
In this work preliminary results of these monitoring activities are presented, highlighting the potential of GBDR technology to enhance slope monitoring and risk mitigation strategies in mountainous regions.
How to cite: Szakolczai, I., Carlá, T., Magrin, A., Nocentini, M., and Gigli, G.: Real-Time Monitoring of rapid slope hazards through Radar Doppler in Italy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16775, https://doi.org/10.5194/egusphere-egu25-16775, 2025.
The geological conditions in the alpine gorge regions of western China are complex, with widespread and frequent disasters that result in hundreds to thousands of deaths and billions of yuan in direct economic losses annually. Currently, nearly 300,000 potential geological hazard sites have been identified in China, yet over 70% of the geological hazards that lead to disastrous consequences occur outside these known potential hazard areas. Therefore, conducting early and precise identification of large-scale landslide hazards is of great significance for enhancing China's geological disaster prevention and control capabilities. Interferometric Synthetic Aperture Radar (InSAR), due to its characteristics of large coverage, all-weather measurement, and non-contact measurement, has been widely used and valued in the early identification and monitoring of landslide hazards. However, in engineering applications of early landslide hazard identification using InSAR technology, there are issues such as geometric distortions caused by SAR satellite oblique viewing that are unclear in terms of how to accurately identify them on a large scale and their impact on InSAR monitoring, as well as the quantitative relationship between detected Line of Sight (LOS) deformation and true deformation, and the unclear removal methods for atmosphere-related and spatially heterogeneous atmospheric effects caused by unique topographies in alpine gorge regions when external data are not available.
This study reviews the current application status of InSAR technology in the early identification and monitoring of landslide hazards, and clearly and innovatively addresses the key issues in its engineering applications, such as limitations in spatial detection capabilities, LOS detection sensitivity, and atmospheric correction methods in mountainous areas. It also summarizes the application characteristics and future prospects of InSAR technology, which is of great significance for effectively conducting engineering applications of InSAR technology in geological disaster prevention and control.
How to cite: Dai, K.: Early Identification, Monitoring, and Warning of Landslide Hazards in Steep Mountainous Areas using InSAR, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5109, https://doi.org/10.5194/egusphere-egu25-5109, 2025.
Geohazards pose a significant and growing threat to human populations and critical infrastructure worldwide. The impact of these hazards is further exacerbated by climate change, which is intensifying the frequency and magnitude of events, making the need for effective monitoring tools more critical than ever.
Satellite radar technology, particularly Synthetic Aperture Radar (SAR) interferometry (InSAR), has emerged as a powerful tool for geohazard monitoring. By providing high-resolution, wide-area, and all-weather monitoring capabilities, InSAR enables geoscientists and engineers to detect subtle ground movements that may precede catastrophic events. However, the effectiveness of InSAR is intrinsically linked to the characteristics of the radar signal, particularly its frequency band.
Satellite radar data is typically acquired in three different frequency bands: X-band (3 cm wavelength), C-band (6 cm wavelength), and L-band (24 cm wavelength). The European Ground Motion Service (EGMS) has made InSAR data derived from the Sentinel-1 satellite constellation (operating at C-band) freely available to a wide range of users, significantly advancing the accessibility of this technology for geohazard monitoring. EGMS Sentinel-1's medium resolution data can provide a synoptic view of a wide range of phenomena, and it proved to be extremely effective in the detection of deformations on a regional scale.
Our experience with diverse geohazards highlights the value of integrating EGMS Sentinel-1 data with data from other satellite missions operating in different frequency bands, such as TerraSAR-X, PAZ, COSMO-SkyMed, COSMO Second Generation (all operating at X-band) and SAOCOM or ALOS-2 (operating at L-band). Each frequency band possesses unique characteristics that make it complementary to the others in the context of InSAR monitoring. In fact, X-band offers high spatial resolution and sensitivity to small displacements, making it ideal for monitoring localized phenomena or monitoring individual assets, while L-band, with its longer wavelength, has greater penetration capacity through vegetation compared to both X and C-band data, making it particularly useful for monitoring movements in densely vegetated areas.
The integration of data from multiple sensors enhances our ability to monitor and predict geohazards through:
- Improved Spatial Coverage and Resolution: This allows for detailed mapping of hazard-prone areas, facilitating informed land-use planning and infrastructure design decisions.
- Increased Temporal Density of Observations: More frequent data enables the detection of incipient movements and improved prediction of geohazard evolution, which is crucial for rapidly evolving hazards.
- Improved Accuracy of Measurements: Integrating multiple data sources reduces uncertainties and yields more accurate estimates of ground deformations, which is vital for reliable hazard assessment and risk management.
This paper explores the benefits of a synergistic, multi-band InSAR monitoring approach for risk mitigation. Using a gallery of examples of how complementary data sources improve InSAR analysis, we aim to contribute to the design of more powerful decision support systems. These systems can enable timely interventions that should protect communities and infrastructure from geohazards, particularly in a changing climate.
How to cite: Spreafico, M., Ferretti, A., and Passera, E.: Mitigating Geohazard Risk through Synergistic, Multi-Band InSAR Monitoring, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15846, https://doi.org/10.5194/egusphere-egu25-15846, 2025.
The study of coastal areas represents a real challenge for the research community, due to the numerous drivers that can control coastal processes (subaerial, marine or endogenous). In particular, the analysis of cliffs is fundamental for the assessment and management of coastal landslide hazard and risk.
In cliff stability studies, the integration of multiple data sources, including satellite imagery, aerial photography and LIDAR, represents an important development and advancement. The integration of various data sources can significantly improve our understanding of geological phenomena, as well as the accuracy of monitoring data and forecasting systems.
The aim of this work is to integrate the PS-InSAR technique and UAV LIDAR and photogrammetric surveys to improve cliff stability evaluations. UAV LIDAR/photogrammetry and PS-InSAR are remote sensing techniques that allow to improve information about slope geometry, even in hard-to-reach areas. LIDAR acquisitions in this study have been undertaken through a DJI Matrice 350 + Zenmuse L2 LIDAR system and are processed in high-resolution DTMs (in this work the cell resolution is ca 20 cm). With regard to PS-InSAR, Sentinel 1 Single Look Complex radar images have been processed through Sarproz software to extract Persistent Scatter points. The time series of the Persistent Scatter points are then used to monitor surface displacements in selected coastal cliffs. The combination of UAV and PS-InSAR data were then utilized to create detailed 3D slope models and validate the results of cliff stability simulations, verifying the main drivers controlling cliff stability and retrogression. Stability numerical cliff simulations could be in future a very powerful tool to potentially predict the future processes in relation to climate variations.
The combination of these advanced methodologies offers a comprehensive approach that improves the quality of cliff monitoring and the precision of the forecasting systems. By leveraging the strengths of both PS-InSAR and UAV data, detailed insights into reef dynamics can be obtained, ultimately leading to more informed decisions for coastal management and risk mitigation.
How to cite: Ottaviani, F., Ahmed, M., Brunetti, A., Guidi, E., Marini, R., and Francioni, M.: Combining InSAR and UAV data to analyze the stability of coastal cliffs, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18695, https://doi.org/10.5194/egusphere-egu25-18695, 2025.
Ground deformations, such as landslides, subsidence, and mining-related deformations, pose significant risks to communities and infrastructure. Accurate classification of these deformations is crucial for hazard management and land use planning. Existing classification methods primarily rely on thresholding or traditional machine learning models, failing to fully capture the rich temporal and spatial information available from spaceborne remote sensing data.
This study proposes a deep learning method that integrates both ground motion time series (European Ground Motion Service - EGMS) and geospatial data (spaceborne optical imagery, and morphological features) to classify ground motions. The method employs a dual-branch model, where 1D CNNs extract temporal features from ground motion time series, and 2D CNNs capture spatial characteristics from corresponding satellite imagery and topographic data. The features extracted by both branches are fused and fed to a multilayer perceptron to classify deformation processes, i.e., landslides, deep-seated gravitational slope deformations (DSGSD), subsidence, and mining-related deformations. To inform the model, we used a dataset over 26,000 Active Deformation Areas (ADAs), defined with the ADA finder tool(Navarro et al., 2020). We annotate each ADA by crossing it with existing inventories such as the Italian Landslide Inventory (IFFI) and CORINE Land Cover map. Corresponding time series data and imagery were subsequently extracted for each and fed to the model. Results, using cross-validation, show that the model achieves an overall accuracy of over 90%. This demonstrates its effectiveness and robustness in handling diverse deformation types. We finally deployed the validated model and classify all the ADAs generated for the entire Italy.
This research provides a scalable and automated framework for ground motion classification, and the classification achieved can lead to better-targeted risk mitigation strategies, and improved ground motion forecasting and early warning systems.
References: Navarro, J. A., Tomás, R., Barra, A., Pagán, J. I., Reyes-Carmona, C., Solari, L., Vinielles, J. L., Falco, S., & Crosetto, M. (2020). ADAtools: Automatic Detection and Classification of Active Deformation Areas from PSI Displacement Maps. ISPRS International Journal of Geo-Information, 9(10), 584. https://doi.org/10.3390/ijgi9100584
How to cite: Dong, Y., Nava, L., Palama, R., Monserrat, O., Festa, D., Floris, M., and Catani, F.: Integrating Temporal and Spatial Data for Deep Learning-Based Classification of Slow-Moving Ground Deformations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15532, https://doi.org/10.5194/egusphere-egu25-15532, 2025.
Landslides are among the most destructive geologic hazards, causing significant damage to infrastructure such as buildings, roads, and bridges, and often resulting in loss of life. These events pose a significant risk, especially to communities living near steep slopes. Satellite optical remote sensing images are widely used in geohazard studies due to their detailed content and high resolution. Interferometric Synthetic Aperture Radar (InSAR) is effective in monitoring subtle deformations over large areas and is particularly suitable for quantifying deformations and accurately measuring slope instability. Combining optical data with Synthetic Aperture Radar (SAR) data provides a more comprehensive understanding of landslide dynamics, leading to improved monitoring and analysis.
The Kakrud landslide occurred in Gilan Province, northern Iran, in June 2018, resulting in fatalities, property damage, and the destruction of key access roads. This study used multi-source remote sensing data, including Planet and Sentinel-2 optical images and Sentinel-1 SAR data, to analyze the life cycle of this catastrophic failure. Precipitation, snowmelt, and soil moisture data were also incorporated to identify the causes and influencing factors of the landslide. Cross-correlation of high-resolution optical images from Planet and Sentinel-2 revealed significant displacement between June 14 and 18, 2018, with a maximum horizontal > 50 m. InSAR analysis of Sentinel-1 data from October 2014 to June 2018 revealed pre-landslide instability, with an average deformation rate of 2 mm/year. Precipitation data indicate that rainfall in June 2018 was 10 mm above the average for the same period from 2014 to 2017, when the region experienced a dry cycle with an average annual rainfall of 1,400 mm; 2018 marked the onset of a wet cycle, with total rainfall reaching 2,000 mm. The initial failure of the landslide occurred on its lower left side, triggered by river undercutting, which washed debris into the channel and obstructed the valley. This increased water flow exacerbated erosion at the landslide toe, leading to further collapse. MODIS snowmelt data show a negative correlation between snow cover and temperature, with snowmelt intensifying from spring (March–May) and peaking in summer (June–August) as temperatures rose and snow cover diminished. Combined with soil moisture data, the cumulative effect of snowmelt in June significantly increased pore water pressure and reduced soil shear strength. A combination of these factors ultimately triggered the landslide.
In conclusion, this study explores the kinematic changes in the Kakrud landslide over a long time series throughout its life cycle using multi-source remote sensing techniques.
Keywords: Landslide; Remote Sensing; Multi-temporal InSAR; Cross-correlation
How to cite: Li, J., Motagh, M., Jiang, H., Akbari, B., Rezaei, M., and Roessner, S.: The June 2018 Kakrud landslide in northern Iran: Process understanding using satellite remote sensing data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17975, https://doi.org/10.5194/egusphere-egu25-17975, 2025.
Following the Chi-Chi earthquake in Taiwan, the surface soil and rocks were fractured and loosened, making the geology more unstable. As typhoons continued to batter the mountainous regions, the resulting heavy rainfall—in terms of total accumulation and intensity—exacerbated the situation. This caused an increase in the size of the collapsed and exposed upstream catchment areas, leading to more damage from rainwater erosion. The migration of soil and sand into the reservoir, triggering landslides of varying scales or other soil-related disasters, has severely worsened reservoir siltation. Traditional dredging methods are ineffective in solving this issue. Therefore, addressing the migration of soil and sand has become crucial in mitigating and slowing down the process of reservoir siltation.
This study focuses on the Wushe Reservoir catchment area, which is experiencing significant siltation. According to the Water Resources Administration of the Ministry of Economic Affairs, the current reservoir capacity is less than 25% of its original design. The study primarily analyzes historical data and ongoing monitoring of the area. By observing the impact of rainfall on the slopes, the study aims to gain a deeper understanding of soil and sand migration in the catchment area.
Additionally, the study employs Synthetic Aperture Radar (SAR) images for Small Baseline Subset (SBAS) analysis to detect potential slope sliding within the study area. Landslide potential is identified through SAR data, and GNSS (Global Navigation Satellite System) is used to confirm whether slope sliding is occurring. Data from existing on-site single-frequency and dual-frequency GNSS monitoring equipment are also analyzed for verification.
The study uses NDVI (Normalized Difference Vegetation Index) and GNDVI (Green Normalized Difference Vegetation Index) to identify bare land affected by landslides and river channels. The accuracy of these interpretations is evaluated using precision analysis metrics. The average accuracy for bare land identification is 73.91%, with an average Kappa coefficient of 69.94%. Rainfall events are categorized to map landslides caused by different rainfall conditions and a landslide mapping model is established. The amount of debris is estimated based on the collapsed area, and soil loss is calculated using the Universal Soil Loss Equation (USLE). These results are cross-verified with changes in reservoir capacity over the years to validate the study's findings.
How to cite: Wang, K.-L., Jhang, J.-Y., Hsieh, Y.-C., Lin, M.-L., and Lin, C.-W.: Investigation of Landslide Debris Migration in the Wushe Reservoir Catchment Area, Taiwan, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10718, https://doi.org/10.5194/egusphere-egu25-10718, 2025.
The European Ground Motion Service (EGMS), part of the Copernicus program, provides free, pan-European Sentinel-1 InSAR (Interferometric Satellite Aperture Radar) products to support ground deformation analysis at continental scales. Despite its potential, managing the large volume of EGMS data can be challenging, especially for non-expert users. To address these challenges, the EGMStream webapp was developed aimed at enhancing the download and conversion of EGMS products. Built in Python and JavaScript, the webapp improves the first EGMStream stand-alone tool enhancing its accessibility, functionality and performance. By leveraging server-side processing through Docker containers, the webapp avoids the need for software installation and reliance on user personal computer performance, enabling efficient handling of large datasets with parallel processing. The EGMStream webapp allows for automatic downloading and conversion of EGMS data into different formats, i.e. Shapefile, GeoPackage, and GeoJSON. Users interact with a simple, user-friendly interface to upload a text (.txt) file from the EGMS Explorer, containing links to bursts (for L2, LoS and calibrated, data) and/or tiles (for L3, ortho, data) of EGMS data for their area of interest. In addition, users can upload a specific area of interest to crop data on it and they can also customize data conversion, such as including time series and selecting the output format. At the end of the process, users will receive an email with a link to download processed data. In contrast with the format downloadable by the EGMS Explorer (.csv format), the outputs of the EGMStream webapp conversion allow a simpler inclusion and management in GIS environmental or WebGIS platforms.
The webapp allows for reaching a wider audience to use the EGMS data, improving the dissemination and usability of ground motion data for urban planning, natural hazard monitoring, and environmental management. Future enhancements will focus on integrating advanced analysis tools, real-time visualization capabilities, and in-app post-processing features. These developments aim to meet the increasing needs of the geospatial and geological communities, ensuring the platform’s adaptability to emerging challenges.
How to cite: Becattini, F., Medici, C., and Del Soldato, M.: A fast solution for downloading and converting the EGMS data: EGMStream webapp, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19815, https://doi.org/10.5194/egusphere-egu25-19815, 2025.
Spaceborne interferometric SAR (InSAR) has been proven to provide displacement measurements with millimeter-per-year precision over large areas. Since the start of operations of the Copernicus European Ground Motion Service (EGMS) in 2022 these InSAR products have been routinely produced and made freely available for Europe. These products consist of millions of measurement points, making visual inspection challenging.
Within the EU Horizon project GoldenRAM, InSAR post-processing techniques are investigated. The goal is to improve mining safety by providing an easy-to-use open-source service that facilitates timely monitoring of open pit and tailings dam stability at active and closed mines, utilising the EGMS products.
The aim of this work is to develop a workflow for i) ingestion of EGMS data, ii) post-processing of EGMS data to automatically extract relevant information, and iii) visualisation of the results on an online platform. To demonstrate this work, examples are provided from an active multi-metal mine in Kevitsa, located in northern Finland.
The GoldenRAM project is funded by the European Union under Grant Agreement No. 101138153.
How to cite: Reichstein, A. and Andre Cahyadi, K.: Post-processing based on EGMS Sentinel-1 InSAR products for mining applications, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17504, https://doi.org/10.5194/egusphere-egu25-17504, 2025.
The failure of mining voids and the formation of sinkholes is a major hazard at both active and dismissed mining areas that may cause important social and economic losses for the communities leaving nearby. Monitoring ground motions at mining area has been proved to successfully reduce the occurrence and the consequences of sudden sinkhole collapses. However, while most active mines are constantly monitored today, the ground instabilities around dismissed mining areas often remain disregarded. In southern Tuscany (Italy), the Gavorrano area was among the biggest pyrite (FeS2) mines in Europe during its period of activity (1898-1981). According to mining reports, the pyrite extraction was accompanied by the failures of underground mining voids and some of them were followed by the formation of fractures at the surface. Today, the area shows significant evidence of sinkhole activity, with the major Monte Calvo sinkhole dominating the landscape of Gavorrano. However, the spatio-temporal evolution of the sinkhole phenomena, the relationship with mining, and the potential ongoing sinkhole activity in the area remained unclear. In this study we combined InSAR measurements from the European Ground Motion Service (EGMS) with historical mining reports and maps, aerial images, high-resolution Digital Surface Models (DSMs) and field surveys to reconstruct the long-term spatial and temporal evolution of ground deformation around the mining area of Gavorrano and to explore the possible relationship with the mining activity. Three sinkholes were identified: Monte Calvo, Valsecchi, and Ravi; the latter two have never been reported in the literature. The sinkholes have a spatial correlation with the mining voids and galleries. InSAR revealed that an area of ~ 700 m × 400 m around the Monte Calvo sinkhole has been subsiding with rates of ~5 mm/yr between 2016-2022. Conversely, no evidence of deformation is observed at Valsecchi, Ravi, and the nearby city of Gavorrano. The collected data suggest that the sinkhole activity has been induced by the past mining activity (until 1981) in the area. Possible scenarios to explain the observed deformation could envisage for: 1) a constant long-term subsidence; 2) an evolution characterised by multiple sudden collapses punctuated by periods of gradual subsidence; 3) a gradual stabilization of the area. Slowing down surface velocities respect to the past suggest that the Monte Calvo sinkhole is stabilizing. However, future sinking episodes cannot be ruled out if the underground stability conditions change, for example, for further mining voids failures.
How to cite: Tinagli, L., La Rosa, A., and Paoli, G.: Decadal spatio-temporal reconstruction of mining-induced sinkholes activity in Gavorrano (Italy) using remote sensing and field data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5077, https://doi.org/10.5194/egusphere-egu25-5077, 2025.
The expansion of tension cracks, step-like terrain, and other features associated with landslide reactivation is prominent phenomena within the landslide area, making the investigation of their temporal development essential for understanding landslide dynamics. In this study, we aim to examine the temporal development of an NW-SE trending counter scarp with a height up to approximately 3 m within the Kamitokitozawa landslide in Akita prefecture, Japan, using a combination of multiple approaches. The approaches include dendrogeomorphological analyses, such as analyzing tree-ring eccentricity, the recovery age of stem wounding caused by landslides in 11 disks from Cryptomeria japonica, and the establishment ages of shade-intolerant tree species, along with interpretations of multi-temporal Google Earth imagery and topographic data derived from a laser-equipped UAV. These approaches allow us to reconstruct the multiple stages of scarp development, which may have initially formed on its southeast side, creating a forest gap in 2010, based on Google Earth imagery and subsequent expansions of the scarp. Dendrogeomorphological analyses indicate expansions during 2016–2017 and 2020–2021, based on the recovery age of stem wounding, as well as during 2019–2023, based on the establishment ages of shade-intolerant tree species. Additionally, 13 events spanning from 1995 to 2021 were identified from tree-ring eccentricity, with a notable clustering around 2018–2021. Additionally, expansions of the scarp were captured in 2019, 2021, 2022, and 2023 based on the UAV topographic data.
How to cite: Kawakami, R., Tsou, C.-Y., Ishikawa, Y., Ogita, S., Hayashi, K., Kuriyama, D., and Ito, K.: Multi-Temporal Analysis of Scarp Expansion in the Kamitokitozawa Landslide: Insights from Tree-Ring, UAV Data, and Google Earth Imagery, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2231, https://doi.org/10.5194/egusphere-egu25-2231, 2025.
Landslides are among the most destructive geohazards and commonly interact with other hazards, amplifying impacts and leading to cascading and compounding events (Shugar et al., 2021). For example, earthquakes can trigger widespread landsliding, and landslides can induce floods by failing into lakes (e.g. GLOFs) or damming rivers. Identifying the location of potential landslides pre-failure can help understand and mitigate these multihazard events. One possible approach is tracking the precursory failure signals, such as subtle ground displacement, that many landslides exhibit pre-collapse. Enhancing the identification of unstable areas and monitoring their displacement over space and time is therefore critical to understanding their role in multi-hazard chains and mitigating their impacts.
Spatially resolved ground motion monitoring over large areas is only possible with remote sensing techniques, with radar interferometry (InSAR) being the most widely used method. While InSAR is sensitive to small deformations (millimetres to centimetres), it struggles to capture rapid ground motions and is less reliable in regions with dense vegetation. Offset tracking techniques offer an alternative for monitoring faster ground velocities and remain applicable in heavily vegetated areas and for NS-oriented displacements.
In this abstract, we introduce an open-source, cloud-based, end-to-end optical offset tracking tool for ground motion monitoring. Building on previous implementations (Provost et al., 2022; Van Wyk de Vries et al., 2024), the tool leverages Google Earth Engine and Sentinel-2 imagery, allowing users to interactively define the area of interest, automatically download and pre-process satellite data, and compute displacements using different offset tracking techniques. Outputs include velocity maps and time series, with customizable filters to refine results for different use cases and scales. The tool can operate entirely in the Google Colaboratory cloud environment. Hence, it removes the need for local computational resources, avoids software conflicts, and is accessible even to those with limited experience in Python programming. We validated the tool on cases with independent displacement measurements, including the Slumgullion landslide, showing that its results are consistent with existing estimates.
Owing to its ease of use and versatility, the tool is a valuable resource for the multihazard and landslide research communities, complementing InSAR for monitoring surface motion in space and time. The tool can estimate motion in near real time, making it an asset for early warning systems that rely on velocity thresholds or predictive modelling of future motion. Furthermore, its ability to identify unstable slopes can guide targeted, detailed investigations into landslide dynamics, enhancing situational awareness and supporting proactive risk mitigation.
References:
Shugar, D. H., et al. (2021). A massive rock and ice avalanche caused the 2021 disaster at Chamoli, Indian Himalaya. Science, 373(6552), 300-306.
Van Wyk de Vries, M., et al. (2024). Detection of slow‐moving landslides through automated monitoring of surface deformation using Sentinel‐2 satellite imagery. Earth Surface Processes and Landforms, 49(4), 1397-1410.
Provost, F., et al. (2022). Terrain deformation measurements from optical satellite imagery: The MPIC-OPT processing services for geohazards monitoring. Remote Sensing of Environment, 274, 112949.
How to cite: Nava, L., Van Wyk de Vries, M., and Bell, L. E.: A Workflow for Monitoring Ground Deformations through Spaceborne Optical Offset Tracking, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12171, https://doi.org/10.5194/egusphere-egu25-12171, 2025.
Landslides cause severe damage to the built environment and communities, requiring effective hazard management. Landslide catalogs, which provide essential data on past landslide occurrences, are the primary data sources for this purpose. Furthermore, they enable training and validation of predictive landslide models. Although landslide catalogs are largely compiled through manual mapping based on expert judgement, various advanced techniques using optical Earth observation (EO) imagery have been developed to automate and enhance the creation of such inventories. These methods, however, are mostly tested in specific case studies and they are not put into operation to detect landslides on a regular basis. Moreover, they rely on cloud-free imagery that can be time-consuming to gather, resulting in delays in the timely detection of landslides. This is especially true in regions with frequent rainfall, such as mountainous areas, where landslides are more prevalent.
The Landslide Hunter is a prototype online platform designed to reduce the gap by addressing cloud-cover-related omission in optical imagery, reducing delays in landslide detection, and providing an environment for testing and benchmarking of different EO-based landslide detection methods through a simple plug-and-play method. The platform monitors online resources for events that could trigger landslides, such as major earthquakes, and identifies regions where landslides are likely to have occurred in their aftermath. It then collects and analyzes consecutive optical EO images for these areas to identify visible landslide extents using various landslide detection models, ranging from simple index-based approaches (e.g., NDVI) to advanced machine learning techniques utilizing image segmentation. Proximity to cloud cover is used to assess whether a landslide extent is partially visible, with partial extents being marked for further tracking until complete landslide coverage is achieved through successive analyses. This enables the timely first detection and effective monitoring of landslides, even under cloudy conditions. The results are made available in an open-access landslide catalog through a user-friendly web portal, offering faster updates than traditional catalogs. Users are notified when new landslides are detected, facilitating rapid damage assessment efforts that can ultimately enhance the safety of communities and the built environment.
We present a detailed overview of the design principles and operational framework of the Landslide Hunter platform, highlighting its core features, functionalities, and user interface. We also provide a thorough explanation of the data access methods developed to improve interoperability and ensure seamless integration with other systems. A live demonstration will illustrate how the platform automatically identifies and tracks landslides under cloudy conditions, enabling timely detection and monitoring of landslide progression.
How to cite: Girgin, S., Özbakır, A., and Tanyaş, H.: Cataloging and mapping of landslides rapidly by using an Earth observation-based innovative platform – the Landslide Hunter, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16112, https://doi.org/10.5194/egusphere-egu25-16112, 2025.
Times series of VHR optical imagery (SPOT6-7, SPOT-HRS Pléiades, PNEO), with their high spatial resolution (<0.5 to 2 m) and stereoscopic capabilities are offering huge potential for monitoring surface deformation using Optical Image Correlation (OIC) techniques. Very-High spatial resolution allows to enhance both the sensitivity and the accuracy of the measurements leading to the detection of small changes in deformation rates (possibly close to 0.10 m in theory) for Pléiades imagery. However, the exploitation of these VHR satellite image time series remains challenging because of errors associated with the image acquisition geometry, which are potentially high in mountain regions with complex and string topography.
We propose an automated and generic processing chain, based on the GDM-OPT workflow (Provost et al., 2022) initially tailored for Sentinel-2 (10 m spatial resolution) image time series in order to process time series of VHR imagery, taking into account Pléiades Panchromatic monoscopic and stereoscopic data products.
The approach consists first in the generation of intermediary DSM by a classical stereo-photogrammetric process. Second, in order to compensate for the planimetric and vertical errors, we correct the generated DSMs through an alignment to a reference topography. We then compute the ground coordinates of tie points of the image system taking into account the newly aligned topography. Considering these points as GCPs (Ground Control Points) and by performing a new bundle adjustment forced to fit to them, the alignment step is integrated in the stereo-photogrammetric process. Then, a new DSM and an ortho-image mosaïc consistent with the reference topography are calculated. Finally, the ortho-image mosaïcs are correlated using a specific pairing network (Stumpf et al., 2017). At the end of this step, all the displacement maps obtained (North-South, East-West) are inverted into a displacement time series.
The processing workflow is tested on the two landslides of La Valette and Aiguilles/Pas de l’Ours (where time series of 8 Pléiades imagery are available) allowing to retrieve the mean velocity and the ground displacement time series for each pixel. We validate the proposed workflow by comparing the results of the processing chain and in-situ dataset (GNSS, LiDAR and photogrammetry). We show that the proposed methodology allows the monitoring of large landslides displacement, with velocity larger than 0.07 m/year.
How to cite: Wirtz, B., Provost, F., Malet, J.-P., Méric, O., and Rupnik, E.: Tracking landslide terrain motion with Very High Resolution optical image time series., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17664, https://doi.org/10.5194/egusphere-egu25-17664, 2025.
The Eastern Cordillera of the Central Andes in Northwestern Argentina and Southern Bolivia is an actively deforming mountain front with elevations ranging from ~1 km in the foreland to ~4 km and higher on the Central Andean Plateau. The orographic barrier induces a strong climatic and environmental east-west gradient with peak rainfall in the steep eastward-facing slopes. Frequent rainstorms during the South American summer monsoon in combination with fault-weakened lithologies drive mass-movement processes. The resulting debris flows and landslides pose a serious threat to the local infrastructure.
In this study, we integrate satellite-based optical remote sensing data over the last 10 years to characterize the long-term dynamics of slow-moving landslides in the eastern Central Andes in Argentina. In this way, we aim to establish potential relationships between climatic seasonality, seismic activity and landslide deformation signals. We apply a combination of pixel and feature-based tracking approaches to a data set comprising a network of medium-resolution Sentinel-2 and Landsat 8, and high-resolution SPOT7 optical images. The final ground displacement time series in east-west and north-south directions were reconstructed through time-series inversion. Vertical variations were obtained by comparing high-resolution Digital Surface Models (DSM) produced from tri-stereo SPOT7 images. We attempt to improve the detection of very slow-moving landslides with velocities below 0.5 m/yr by stacking multiple matching pairs and relying on feature-based tracking approaches. In some examples, the displacement time series reveal metric ground displacements following earthquake events observed in the region, changing the dynamics of the landslide.
This study emphasizes the usefulness of large-scale, decadal-long time series of optical satellite imagery and presents a novel GPU-based approach of combining computer vision feature tracking methods with classic correlation based block matching.
How to cite: Leder, F., Mueting, A., Rheinwalt, A., and Bookhagen, B.: Dynamics of slow-moving landslides in the Eastern Cordillera of the Central Andes derived from optical satellite imagery, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11254, https://doi.org/10.5194/egusphere-egu25-11254, 2025.
Landslides represent a significant geological hazard globally, with over 635,000 landslides identified in Italy alone. Despite their prevalence, only a fraction of these phenomena is actively monitored. Advancements in monitoring technologies offer promising tools for improving landslide management, but their application requires further validation and dissemination within the technical community.
This study, conducted under the PNRR “GeosciencesIR” project, investigates the use of photomonitoring techniques across fifteen landslide sites in Italy, where continuous or periodic monitoring activities are conducted. Monitoring setups feature ten ground-based cameras, while periodic drone-based acquisitions or photographic surveys provide supplementary observations for the remaining sites. The landslides encompass diverse mechanisms and kinematics, offering a robust basis for comparative analysis and evaluation of technique applicability.
The deployed monitoring systems utilize various hardware configurations, including optical cameras, robotic heads with Reflex cameras, and mobile devices. Images are predominantly captured in RGB format, and analyses are performed using the proprietary software “IRIS”, developed by NHAZCA S.r.l., employing change detection and digital image correlation algorithms. The techniques allow to identify variations (e.g., appearance or disappearance of objects in the FOV) or the track of object motion caused by landslide displacements between successive images over time. Additionally, time series of displacements have been extracted, providing insights into temporal evolution and supporting comparative validation against other monitoring data.
These sites have been continuously monitored since early 2024 and to date, over 140,000 images have been acquired, amounting to a dataset of more than 370 GB. Preliminary results include the identification of rockfalls, their size and timing, and the detection of retrogressive failure processes. For landslides with complex or flow mechanisms, the estimated 2D velocities provide consistent insights into motion trends, acknowledging the optimal performance and the inherent limitations of 2D analyses compared to 3D measurements. Project allows continuous feedback and data sharing with geological regional services, optimizing system operations and validation of results.
Challenges encountered during the project include ensuring the stability of monitoring equipment in remote locations and addressing environmental factors such as extreme weather conditions. Despite these hurdles, the collaboration with local technicians has facilitated knowledge exchange, fostering the development of photomonitoring techniques and their application in diverse geomorphological contexts.
This research advances monitoring methodologies, improving accuracy in displacement measurements and promoting cost-effective, accessible solutions. By promoting collaboration within the scientific and technical community, it aims to increase the number of monitored landslides and support innovative strategies for landslide risk mitigation.
How to cite: Stefanini, C. A., Marmoni, G. M., Molinari, A., Cosentino, A., Santicchia, G., and Mazzanti, P.: Enhancing Photomonitoring techniques in landslide studies in the frame of Geosciences IR project, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17207, https://doi.org/10.5194/egusphere-egu25-17207, 2025.
The Baldiola landslide (Panaro river valley, Northern Italy) is an active earthflow that has experienced continuous toe advancement and source area widening/retrogression for over 40 years, as evidenced by archive aerial and satellite imagery. This long-term evolution now poses a potential hazard to a residential area near the crown and for slope-river interaction, emphasizing the need for innovative monitoring strategies to assess displacement dynamics and support risk management. For such an objective, during 2024, we have implemented high-frequency (i.e. from weekly to bi-weekly) UAV-based LiDAR & Photogrammetric surveys, in order to obtain a detailed characterization of landslide kinematics.
More specifically, the UAV-derived datasets collected throughout 2024, i.e. Digital Elevation Models (DEMs) and high-resolution Orthomosaics, were processed in order to obtain spatially distributed slope displacement values across the entire landslide by using Digital Image Correlation (DIC) & Homologous Point Tracking (HPT) for horizontal displacement and DEM of Difference (DoD) for vertical variations. Results have been validated in specific key points by using time series from continuous Robotic Total Station (RTS) monitoring.
Results of archive aerial and satellite imagery analysis showed more than 120 meters retrogression of the main scarp since 1978, with 30 to 50 meters occurring between 2006 and 2024). Results of DIC and HPT evidence differential movement patterns across the landslide body, with higher displacement rates from 5 to 10 m/month recorded along the main channel, particularly in the middle-lower channel and toe area, and extensive retrogression recorded in part of the source area (14 meters between April and November 2024). The comparison between RTS and HPT-derived displacements showed a strong correlation (R² > 0.99 in most cases), confirming the reliability of UAV-based tracking methods. Additionally, DIC analysis successfully captured displacement trends comparable to RTS and HPT, demonstrating the potential of automated image processing for large-scale motion detection. The DoD analysis was essential for tracking and monitoring local reactivations, particularly in the source area, where a depletion of several meters was observed. Furthermore, and altogether, the results unravel mass transfer processes at the slope scale mand the spatial and temporal pattern of progressive acceleration of the landslides from the source area, down into the channel and finally to the toe zone, as well as the peculiar pattern of progressive deceleration of the phenomenon.
This integrated approach allowed a detailed assessment of the landslide’s kinematics, providing valuable insights into its spatial variability and temporal evolution and, ultimately, the processes governing earthflows. The high-frequency UAV dataset proved particularly useful in detecting small-size accelerations and minor reactivations that were not always evident in RTS data alone. Future research will focus on examining the relationship between rainfall events and acceleration phases, aiming to improve the understanding of triggering mechanisms and short-term response dynamics.
How to cite: Lelli, F., Mulas, M., Tondo, M., Fabbiani, C., Critelli, V., Aleotti, M., and Corsini, A.: High-frequency UAV LiDAR survey for monitoring active earthflows at the slope scale by using Digital Image Correlation, Homologous Point Tracking and DEM of Differences (Baldiola landslide, Northern Italy), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12194, https://doi.org/10.5194/egusphere-egu25-12194, 2025.
Moving landforms, such as active rock glaciers and landslides, can pose significant hazards, particularly in densely populated regions such as the European Alps. Traditional techniques used to monitor landform kinematics, including in-situ differential Global Navigation Satellite System (GNSS) and georeferenced Total Station (TS) measurements, face limitations in capturing the rapid and localized movements due to environmental constraints and restricted spatial coverage. Remote sensing methods provide improved spatial resolution but often fall short in temporal resolution, limiting their ability to capture sub-seasonal dynamics.
This study presents a novel methodology that integrates Artificial Intelligence (AI) and monoscopic time-lapse imaging to address these challenges, enabling high-temporal-resolution velocity estimation for dynamic landform processes. Focusing on the Grabengufer site in the Swiss Alps, we applied our approach to time-lapse datasets capturing a fast-moving landslide and rock glacier. Key innovations include the Persistent Independent Particle tracking (PIPs++, Zheng et al., 2023) model for 2D image-based point tracking and a robust image-to-geometry registration process that transfers 2D measurements into 3D object space, facilitating velocity analysis. These processes are supported by GIRAFFE, an AI-based tool utilizing the LightGlue matching algorithm for precise feature registration.
Our methodology was validated against GNSS and TS surveys, demonstrating its ability to deliver spatially comprehensive and temporally detailed velocity data. The results revealed previously unattainable spatio-temporal patterns of landform activity, highlighting the suitability of this approach for monitoring rapid and localized changes. By leveraging existing time-lapse imagery, the methodology provides a low-cost alternative to traditional techniques, with potential applications in less-developed regions where resources for monitoring are limited.
This research underscores the potential of integrating time-lapse images, AI, and geomorphometric analysis to enhance the understanding of landslide behaviour and related hazards. The proposed approach not only advances the capabilities of landslide monitoring but also provides actionable data for long- and short-term risk reduction. Its versatility and cost-effectiveness make it a valuable tool for addressing landslide risks worldwide, contributing to more effective hazard assessment, climate change adaptation, and infrastructure safety planning.
Zheng, Y., Harley, A. W., Shen, B., Wetzstein, G., & Guibas, L. J. (2023). Pointodyssey: A large-scale synthetic dataset for long-term point tracking. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 19855-19865).
Hendrickx, H., Elias, M., Blanch, X., Delaloye, R., and Eltner, A.: AI-Based Tracking of Fast-Moving Alpine Landforms Using High Frequency Monoscopic Time-Lapse Imagery, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-2570, 2024.
How to cite: Hendrickx, H., Elias, M., Blanch, X., Delaloye, R., and Eltner, A.: AI-Driven Approaches applied on Time-Lapse Imagery to Monitor Landform Kinematics, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9364, https://doi.org/10.5194/egusphere-egu25-9364, 2025.
Landslides are a serious hazard globally and the exposed areas require a strong effort for their surveillance and protection of populations and infrastructures at risk. When the volumes involved in complex gravity-driven processes are enormous, it would be difficult or impossible to implement active works for landslide hazard mitigation. Therefore, a better understanding of the underlying processes is necessary. A possible way to go forward is to implement a monitoring system in an affected area which allows to observe possible accelerations of the movement in order to implement appropriate mitigation strategies. In active slow landslides, inclinometer monitoring is a valuable resource despite its limitations, such as low spatial resolution, time-consuming activities, unserviceability in case of high deformation of the inclinometer casing.
To overcome these challenges, a new Smart Extenso-Inclinometer (SEI) has been developed. This instrument is realized by disposing of four patented NSHT (New Smart Hybrid Transducers) transducers, based on fiber-optic sensing technology, on the outer surface of an inclinometer casing, enabling traditional measurements to be conducted simultaneously. The adopted sensing technique is based on the stimulated Brillouin scattering phenomena which allows detection of strain and temperature changes along the NSHT with a spatial resolution up to 20cm.
To test the effectiveness of the new device in different contexts and conduct an in-depth investigation of the landslide mechanics, some SEIs were installed at the study area of Centola (Italy) and at the Brandstatt landslide observatory in Lower Austria (NE Austria). In the first site, an active landslide system involves a layer of landslide debris and a conglomeratic formation which extensively outcrops above the marl-clayey Mesozoic formation. Here, n.2 SEIs have been installed to couple manual inclinometric measures. The Austrian study area represents a good example of a potentially deep-seated, complex slow-moving earth slides system that involves clay-rich lithological formations and deeply weathered materials. This slope exhibits surface geomormological features often indicative of continuous, slow landslide activity, which is also shown by traditional inclinometer measurements in selected locations across the slope. Here, n.1 SEI has been installed in an inclinometer casing in the most active sector of the slope instability.
The first monitoring results show that the strain profiles obtained with the innovative instrument are consistent with the inclinometer data in revealing the main characteristics of both monitored slope movements. Moreover, the use of SEI added information not recognizable with the conventional inclinometer, as it revealed not only the horizontal but also the vertical component of soil strain, so acting like a distributed extenso-inclinometer. This is particularly important in scenarios such as the one of Centola , where the displacement components in horizontal and vertical directions are of the same order of magnitude.
The ongoing research activity demonstrates the effectiveness of the SEIs, highlighting the advantages of distributed soil strain detection compared to traditional displacement measurement techniques, for accurate and long-term monitoring of complex landslides.
How to cite: Molitierno, E., Brunzo, A., Carraro, E., Damiano, E., de Cristofaro, M., Glade, T., Marr, P., and Olivares, L.: New Smart Extenso-Inclinometer for monitoring slow moving landslides, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5731, https://doi.org/10.5194/egusphere-egu25-5731, 2025.
Geo-hydrological hazards, particularly rapid flow-like landslides, present a critical challenge for transportation infrastructures globally. These phenomena pose severe risks due to their ability to propagate rapidly and cause extensive damage to railway tracks, vehicles, and human life. Climate change exacerbates these risks by intensifying precipitation patterns, further increasing landslide frequency and impact.
This study introduces an innovative methodology for assessing the exposure of railway infrastructure to rapid flow-like landslides on a national scale [1]. Applying this methodology to Italy's extensive railway network, we integrate statistical and conceptual models, utilizing digital elevation models (DEMs) and landslide inventories to identify landslide source areas, simulate runout paths, and evaluate exposure. The results yield susceptibility and exposure maps that highlight vulnerable railway segments and provide a foundation for risk mitigation and resource allocation.
The methodology involves distinguishing between hillslope and channelized landslides, each with unique source area characteristics and propagation behaviors. Channelized landslides, often occurring within confined channels, exhibit longer runout distances and lower reach angles compared to hillslope phenomena, which are more dispersed and occur on open slopes. This distinction allows for tailored modeling approaches to improve the accuracy of predictions. Validation using an independent landslide dataset confirmed the model's robustness, achieving Area Under the Receiver Operating Characteristic (AUROC) curve values between 0.7 and 0.95 in most regions, demonstrating its effectiveness for large-scale assessments. However, in areas where model performance was lower, biases in the validation dataset, such as inconsistent landslide classifications or incomplete coverage, were often identified as contributing factors.
Key findings indicate that approximately 20.1% of the Italian railway network exhibits exposure values exceeding 0.5, with 13.4% classified as highly exposed (exposure >0.75) to rapid flow-like landslides. Regions such as Trentino-Alto Adige, Campania, and Sicily are particularly affected due to their geomorphological and climatic conditions. This highlights the urgent need for targeted interventions to safeguard critical infrastructure and minimize disruptions to transportation services.
The study emphasizes the utility of high-quality landslide inventories and DEMs in developing predictive models applicable at national scales. The outputs enable stakeholders to prioritize interventions, such as reinforcing vulnerable railway segments, implementing early warning systems, and optimizing maintenance schedules. These measures not only mitigate immediate risks but also contribute to long-term infrastructure resilience. Furthermore, the methodology’s adaptability makes it applicable to other linear infrastructures and regions facing similar hazards, showcasing its potential for broader implementation.
Marchesini et al., Eng. Geol. 332 (2024) https://doi.org/10.1016/j.enggeo.2024.107474
How to cite: Marchesini, I., Althuwaynee, O., Santangelo, M., Alvioli, M., Cardinali, M., Mergili, M., Reichenbach, P., Peruccacci, S., Balducci, V., Agostino, I., Esposito, R., and Rossi, M.: Assessing Railway Exposure to Rapid Flow-Like Landslides: A National-Scale Methodology, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18503, https://doi.org/10.5194/egusphere-egu25-18503, 2025.
Posters on site: Mon, 28 Apr, 10:45–12:30 | Hall X3
In Taiwan, due to the sparse population in mountainous areas, the 4G and 5G coverage is incomplete and the power system in the landslide area is not well-connected. Therefore, how to transmit the monitoring data back to the server and upload it to the cloud in real time during the monitoring of the slopes has always been a crucial issue in disaster prevention technology. Long-Range Low Power Wide Area Network (LoRa LPWAN) is a relatively new communication technology in recent years. According to the related literature and the project report made by our team, the temperature and humidity of the LoRa transmission system have a great influence on its Received Signal Strength Indicator (RSSI) under indoor experiments, and the higher the temperature, the lower the RSSI, and the higher the temperature, the higher the humidity will amplify the effect of RSSI, and the higher humidity, the higher RSSI, the higher humidity, the higher RSSI. The RSSI is higher in high humidity, and the transmission range is wider in urban areas and smaller in forested areas, and the transmission range is much smaller than that in urban areas. In this study, the effects of common climatic conditions in Taiwan and the changes in transmission distance on RSSI in forested areas were simulated and the effects of RSSI strength on the data leakage rate were collected. In addition, this study has also placed the LoRa system into the existing landslide sites for testing, and the results found that during rainfall, although there is no change in RSSI, the data leakage rate will be increased, and whether or not the communication sites are visible or not will produce a great change in RSSI and data leakage rate. In the number one site, the distance between the test stations is 766m and can be viewed, and the average RSSI is -88.0 dB. In the number two site, the distance between the test stations is 713m away and cannot be viewed, the RSSI is -112.1dB on average and the data leakage rate is high. Comparing overall factors, the terrain is the most influenceable factor in the performance of LoRa.
How to cite: Kuo, C., Tsai, P., Tseng, X., and Liu, M.: A Study on Infucencible Factors to LoRa LPWAN for monitoring System in Slopes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7, https://doi.org/10.5194/egusphere-egu25-7, 2025.
The rapid and cost-effective identification of slip surface geometry is essential for efficient landslide investigations and mitigations. Conventional methods for analyzing slip surfaces were typically determined by observations from boring surveys. However, when these surveys are prolonged, they can impose significant economic burdens. In this study, we proposed a novel method for estimating slip surfaces using high-density surface displacement vectors derived from multi-temporal topographic data collected with laser-equipped UAVs. The study focused on landslides in the Neogene formations of the Tohoku region, Japan, where boring data were available for validation (c.f. Ogita et al., 2024). This method was employed to estimate the geometrical dimensions of two-dimensional (2D) and three-dimensional (3D) slip surfaces, achieving maximum agreement rates of 90% and 84%, respectively. These results validate the proposed approach as sufficiently accurate for planning future landslide mitigation measures.
Reference：
OGITA, S., HAYASHI, K., ABE, S., TSOU, C.-Y. (2024): Estimation of slip surface geometry from vectors of ground surface displacement using airborne laser data : case studies of the Jimba and Tozawa landslides in Akita Prefecture, Journal of the Japan Landslide Society, 61(4) 123-129 (In Japanese with English Abstract).
How to cite: Ogita, S., Tsou, C.-Y., Hayashi, K., and Abe, S.: Advancing Landslide Investigations: High-Resolution Slip Surface Estimation Using UAV Technology, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2255, https://doi.org/10.5194/egusphere-egu25-2255, 2025.
Landslides pose a significant threat to human lives and infrastructure in various regions worldwide. To mitigate the risks associated with these geological hazards, the deployment of monitoring systems is crucial. This study presents a comparative analysis of monitoring systems, specifically tilt-meters, piezometric sensors, and GPS, UAV employed in landslide-prone area. The objective is to assess their effectiveness in detecting and monitoring landslide event in Libani str. (Tbilisi city, Georgia).
The deployment of monitoring systems, such as tilt-meters, piezometric sensors, and GPS, UAV plays a pivotal role in landslide risk management. Tilt-meters provide crucial information about slope stability by measuring changes in ground tilt, while piezometric sensors offer insights into groundwater levels and pore pressure variations. GPS and UAV technology enables precise monitoring of ground displacements and deformation patterns. However, the comparative effectiveness of these systems in diverse geological settings remains a subject of exploration.
Tbilisi city is characterized by a diverse range of geological conditions, including variations in soil types, morphology, tectonic, hydrogeological and climate characteristics. Real-time data collected from the monitoring system will be analyzed to detect precursory signs of landslides and assess the performance of the systems in capturing critical events. Landslide in Libani str. is situated in the capital city, a public school and a multi-storey building are under the landslide risk zone.
The findings of this study are anticipated to provide valuable insights into the strengths and limitations of each monitoring system in landslide detection. Furthermore, the research underscores the importance of integrating multiple monitoring systems to enhance the accuracy and reliability of landslide monitoring networks. The outcomes will guide decision-makers, geotechnical engineers, and researchers in selecting appropriate monitoring systems for effective landslide risk management strategies.
How to cite: Gaprindashvili, M., Gaprindashvili, G., Rikadze, Z., Kurtsikidze, O., Koberidze, R., and Kumladze, R.: Local scale Landslide Monitoring in Tbilisi city (Georgia), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5015, https://doi.org/10.5194/egusphere-egu25-5015, 2025.
On April 3, 2024, coseismic landslides (CL) triggered by the Hualien earthquake with local magnitude of 7.2 caused significant economic losses and casualties. To better understand the occurrence patterns and factors influencing CL, this study built a multivariate logistic regression model using the CL consists of a total number of 3,191 samples, which can provide the probability of landslide occurrence in a given area. To ensure accurate sampling of non-coseismic landslides (NCL), all polygon areas where CL occurrence existed in slope units were removed, and 3,191 random slope areas were mapped as NCL samples. Causative factors used in analysis include gradient, aspect, elevation and curvature of slope, distances to the earthquake and a fault, the angle between slope aspect and earthquake-to-slope azimuth, lithology. Seismic shacking factors including peak ground acceleration (PGA) and peak ground velocity (PGV) are used as the triggering factors. The CL and NCL samples are assigned the class label values of 1 and 0, respectively. The dataset was split into training (70%) and testing (30%) subsets, with each sample containing 29 features and 1 target class label. To balance complexity and accuracy, stepwise regression based on Akaike Information Criterion (AIC) and multicollinearity control (VIF < 5) were used to select key variables. The model was then developed to predict landslide probabilities in the test set. To determine the optimal classification threshold, the Youden index was calculated from the Receiver Operating Characteristic (ROC) curve. Model performance was evaluated using confusion matrices, with metrics such as accuracy, recall, and F1 score to assess overall effectiveness. Additionally, SHapley's Additive Interpretation (SHAP) was applied to quantify the contributions of individual variables. Model 1 (landslide threshold: 0.4569) demonstrated strong performance, achieving 96.26% accuracy, 97.30% precision, 95.16% recall, and a 96.22% F1-score on the training set, and 97.18% accuracy, 97.38% precision, 96.97% recall, and a 97.18% F1-score on the test set. To enhance interpretability, Model 2 (threshold: 0.4939) excluded variables like area, minimum slope, and slope range. By focusing on key variables, Model 2 reduced overfitting risks and improved prediction reliability, offering more consistent results in new regions or emergency scenarios. Despite a slight drop in performance, Model 2 maintained high accuracy (95.28% training, 95.82% test) and reliable metrics across precision, recall, and F1-scores. Partial correlation plots (PDP) and boxplots confirmed its enhanced reliability in predicted probabilities, showing improved consistency compared to Model 1. To further enhance disaster response, the study incorporated an early warning system using the peak displacement of initial P-wave (Pd). When the on-site vertical displacement exceeds 0.12 cm, a CL alarm is issued, providing a lead time of 6 to 14 seconds before peak ground shaking. This integrated approach bridges early warning and post-disaster assessment, enhancing resilience and preparedness in seismically active regions.
This study proposes a framework that integrates early warning and post-disaster assessment for coseismic landslides (CL). Combining logistic regression with P-wave information, the system enables timely alerts and effective damage evaluation, bridging hazard detection and recovery planning to enhance disaster resilience.
How to cite: Chou, C.-H., Chao, W.-A., and Yang, C.-M.: Rapidly assessing coseismic landslide occurrence using logistic regression model and initial P-wave amplitude, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10535, https://doi.org/10.5194/egusphere-egu25-10535, 2025.
Glacial Lake Outburst Floods (GLOFs) represent a threat to communities living downstream of rapidly expanding glacial lakes, the hazard from which is exacerbated by ongoing climatic warming and global glacier mass loss. Glacial retreat also exposes unstable and unconsolidated moraine slopes that border glacial lakes, which can trigger GLOFs through mass-movements into the lake. However, few studies investigate the detailed links between multi-year moraine destabilisation mechanisms and eventual failure in these environments. In this study, we explore the pre-collapse deformation of the frozen lateral moraine of South Lhonak Lake, Sikkim, India, that collapsed into the lake and triggered the October 2023 GLOF.
We investigate the deformation using feature tracking of Sentinel-2 optical satellite imagery – a methodology better adapted for monitoring very rapid moraine deformation (>metres per year) than more commonly-used InSAR, particularly for N-S oriented displacements. The results confirm the presence of a dynamic frozen moraine complex in and around the 2023 collapse zone. Two zones of movement are identified, a fast-moving (~10m yr-1), western Zone ‘A’ and - from 2020 onwards - an emergent eastern Zone ‘B’ (~5m yr-1). Coupling of these two zones of moraine movement drives dynamic reorganisation of the entire deforming zone of the moraine complex, triggering a two-year acceleration and reorientation of flow direction in Zone A, followed by an abrupt slowdown in 2022. Our results indicate that emergent zones of landslide motion can alter the wider deformation pattern of adjacent moraine slopes, potentially driven by a reduction in slope shear strength following removal of lateral support. The co-occurrence of this movement and the eventual failure zone lead us to interpret that the observed movements are the precursory motion of the October 2023 permafrost landslide, although the results cannot forecast the exact timing or geometry of the collapse. Whilst glacier retreat undoubtedly facilitated the GLOF through growth of the lake and exposure of the unstable moraine, we find no instantaneous acceleration of the landslide velocities following glacial debuttressing. We highlight the possibility of using open-access remote-sensing data to assess mass-movement trajectories around glacial lakes to better inform GLOF hazard assessment and mitigation efforts.
How to cite: Bell, L. E., Van Wyk de Vries, M., Dell, R., and Novellino, A.: Pre-Glacial Lake Outburst Flood moraine deformation at South Lhonak Lake, Sikkim, from optical satellite feature-tracking, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11100, https://doi.org/10.5194/egusphere-egu25-11100, 2025.
Alpine environments are shaped by slow-moving mass movements that can accelerate suddenly, potentially leading to catastrophic failures that threaten human life, infrastructure, and ecosystems. Recent events which occurred without recognized prior warning signs highlight the need for systematic regional-scale monitoring, aimed at improving our understanding of landslide dynamics and associated risks. Spaceborne interferometric synthetic aperture radar (InSAR) provides high-resolution surface displacement data, making it a powerful tool for observing slope activity at different spatial and temporal scales. However, the interpretation of InSAR data remains time-intensive and subjective, limiting its utility for large-scale, continuous assessment. Artificial Intelligence (AI) may offer a solution to these challenges, by enabling automated analysis of InSAR data. Deep learning models, such as convolutional neural networks (CNNs), can be exploited to extract information on the location and activity status of mass movements from interferograms. Moreover, such an approach would reduce subjectivity of expert interpretation while increasing scalability and maximizing spatial coverage.
This work combines AI-driven surface displacement detection with geomorphological assessments to identify correlations between mass movement behaviour and driving factors across different types of mass movements in space and time. Mass movements in the canton of Valais, Switzerland, were manually mapped on Sentinel-1 wrapped interferograms acquired from two ascending and two descending tracks, spanning 12- to 18-day baselines. Classification was performed by considering an internationally established landslide classification scheme – with the addition of the rock glacier class. A specifically developed U-Net model trained on this dataset is applied and evaluated against expert mapping on previously unseen imagery. Performance, assessed via Intersection over Union metric, indicates that AI results are comparable to expert manual mapping. Future iterations aim to incorporate activity status detection and then also automated process classification using optical imagery and digital elevation models. This will allow us to focus on uncovering the underlying mechanisms of landslides through extensive spatio-temporal analyses that integrate geomorphological factors such as geological conditions, topography, and climate variables.
How to cite: Dasser, G., Maissen, A., Aaron, J., and Manconi, A.: Improving the Understanding of Alpine Mass Movements by leveraging AI on Spaceborne InSAR Data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4094, https://doi.org/10.5194/egusphere-egu25-4094, 2025.
The FORMATION project aims at fostering the implementation of new approaches for the description of geomorphological processes and representation of landforms, whose spatial distribution represents the most immediate tool to detect areas affected by geological risks, such as landslides.
The FORMATION project aims to fill this gap, integrating emerging remote sensing techniques into the new Italian guidelines for the geomorphological mapping provided by ISPRA (Istituto Superiore per la Protezione e la Ricerca Ambientale in Italian, Italian Institute for the environmental protection and research). The main driver of the FORMATION project is the design of new paradigms for geomorphological mapping, where outcomes of traditional geomorphological survey and land degradation models, coupled with multi-band satellite analysis and multi-platform LiDAR and UAV data are convoyed within GIS (Geographic Information System) environment for the classification of landforms and the creation of a multi-scale, digital geomorphological map.
Databases, models, tools and methods has been implemented at pilot Italian cases in the Alps and Apennines, which share common pressing challenges on the environment, such as gravitational and running water-based processes causing several damages with a direct implication on human life and millions of euros spent in environmental remediation. Target basins have been selected to cover different geological, geomorphological and climatic settings and to demonstrate the effectiveness and replicability of the proposed methodology.
Here we present results for the Val d’Orcia, an area in Central Tuscany (Italy) with a long history of landslides and erosive processes. We exploited outputs provided by interferometric processing of Sentinel-1 data to create ground deformation maps used to scan wide areas, flag unstable zones and support the definition of priorities starting from the situations deemed to be most urgent. A database of active moving areas has been created to support further activities of the project, including field surveys, further investigation with landscape investigations and modeling.
Activities performed has been funded by MUR (Ministry of University in Italy) within the PRIN 2022 call Directive Decree n. 104 del 02/02/2022, Codice Progetto MUR 2022C2XPK7, “Full cOveRage, Multi-scAle and multi-sensor geomorphological map: a practical tool for TerrItOrial plaNning - FORMATION”- CUP B53D23007000006, that is included in within the activities funded by European Union (Next Generation EU).
How to cite: Raspini, F., Confuorto, P., Barbadori, F., Nardini, O., and Pelacani, S.: FORMATION - Full cOveRage, Multi-scAle and multi-sensor geomorphological map: a practical tool for TerrItOrial planning and landslide analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12346, https://doi.org/10.5194/egusphere-egu25-12346, 2025.
ABSTRACT
Landslides are a natural phenomenon that has been extensively studied and frequently leads to substantial financial losses and fatalities. The prevalence of non-contact methods for obtaining high-resolution terrain data is increasing due to the rapid advancement of scanning technology. Non-contact remote sensing techniques, including terrestrial laser scanning (TLS) and aerial photography by drones, are becoming increasingly popular for the purpose of monitoring landslides in high and precipitous mountainous regions. Nevertheless, discrepancies in data accuracy may result from the complex terrain with dense vegetation, the use of ground control points (GCPs), and the diversity of UAV varieties, which can restrict their practical application. The Kedarnath and Sonprayag regions in Uttarakhand, India, are significant examples of regions where landslides frequently imperil infrastructure and communities. Consequently, these regions are crucial for the examination of the feasibility of these technologies. The objective of this mission is to enhance landslide monitoring in this geologically sensitive region by addressing accessibility and accuracy issues using unmanned aerial vehicles (UAVs) and terrestrial laser scanning (TLS). Initially, this mission will employ laser scanning to augment the quantity and distribution of ground control points (GCP) for unmanned aerial vehicles (UAV). (TLS). Next, the UAV model is reconstructed using the identified control points (ACP) to estimate the deviations in areas that are not readily visible. The Newton coordinate model is employed to ascertain the discrepancy between the actual displacement (RD) and the coordinate displacement (CD). This method has facilitated the effective monitoring of landslides in locations with restricted access and unseen areas when researchers analyze real-world landslide scenarios. The implication is that the proposed technique enhances the precision of landslide surveillance by incorporating less precise ground control points, surpassing the inherent accuracy of ground control points (GCP). Improved landslide monitoring by fusion analysis of TLS and UAV photogrammetry Techniques. This approach has been implemented to supervise landslides in the villages of Sonprayag and Kshetrapal in Uttarakhand, India, and has been corroborated by data from other sources.
Keywords: Data Fusion, Landslide Monitoring, Terrestrial Laser Scanning, Unmanned Aerial Vehicle, Sonprayag and Kshetrapal landslide.
How to cite: Anand, A. and Bhardwaj, A.: Fusion Analysis of the Sonprayag and Kshetrapal Landslides in Uttarakhand Improved Landslide Monitoring through the Application of TLS and UAV Photogrammetry Techniques, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12440, https://doi.org/10.5194/egusphere-egu25-12440, 2025.
The Rhineland coalfields, a major lignite mining hub in Germany, are vital for national energy production and economic stability. However, the region faces persistent challenges from subsidence driven by natural and anthropogenic factors, resulting in structural damage such as cracks in walls and differential settlement. Historical leveling data since the 1990s reveal vertical deformations of up to 4 meters in mining-impacted areas, highlighting the interplay of mining activities, geological features, fault lines, and groundwater dynamics that influence ground stability.
This study investigates subsidence susceptibility and its potential risks to infrastructure by integrating ground motion data from the European Ground Motion Service (EGMS) with historical leveling datasets. Machine learning techniques, including Random Forest and Light Gradient Boosting Machine (LightGBM), were employed to develop a robust model for identifying areas at high risk of subsidence. Geological, lithological, groundwater, and elevation data were utilized to create susceptibility maps, pinpointing regions of significant concern.
High-risk areas identified in the mapping were further analyzed for their impact on infrastructure. Using EGMS data, angular distortion and horizontal strain were evaluated to understand structural vulnerabilities. Results indicated angular distortion (β) of 1/150 and horizontal strain (ε) reaching 0.01% along fault zones, presenting critical threats to structural integrity.
The findings underscore the value of susceptibility mapping and risk analysis for managing subsidence in mining regions. By offering insights into deformation patterns and classifying risk zones, the study provides policymakers with essential tools to implement mitigation strategies and promote sustainable development. These approaches are critical for balancing energy production with environmental and infrastructure protection in regions facing geological instability.
How to cite: Ritushree, D. K., Baes, M., Liu, M., and Motagh, M.: Mapping Subsidence Susceptibility and Risks in the Rhineland Coalfields: Leveraging EGMS Data and Machine Learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13592, https://doi.org/10.5194/egusphere-egu25-13592, 2025.
Landslide mapping is essential for hazard assessment and disaster response, and methods based on Earth observation (EO) enable the mapping of large areas impacted by major disasters. These methods, however, often rely on cloud-free optical images, which are rarely available in high-rainfall areas prone to landslides, delaying timely detection. Furthermore, mosaicking multiple consecutive images to eliminate cloud cover discards valuable temporal information, such as the actual timing of landslide occurrences and the progression of their extents over time.
To address these challenges, we introduce a novel method that processes successive partially cloudy images to detect visible landslide extents and automatically aggregates this information for rapid first detection and accurate spatiotemporal mapping of landslides. The model-agnostic method supports various EO-based landslide detection models from the literature. It uses binary model outputs (landslide / no landslide), associated uncertainty levels (if available), and cloud mask data together with cloud uncertainty to classify individual image cells into four states: landslide, background, unknown (e.g., cloud covered or other unusable data), and anomaly (e.g., identified as landslide despite cloud cover). A confidence level is also calculated for each cell state. The method continuously refines cell states by analyzing time series data from successive images, reducing unknowns and anomalies to improve landslide detection accuracy. Alternating labels are considered as an indication of uncertainty, whereas cells without a clear pattern are classified as unknown. The method generates robust, time-aware landslide maps by integrating spatial classification from model outputs and cloud masks with temporal consistency checks.
We present an overview of the developed method and demonstrate its practical application through a case study conducted in Adıyaman, Türkiye. The study focuses on landslides triggered by the February 2023 Türkiye earthquake sequence and a subsequent rainfall event in March 2023. Using various landslide detection methods (e.g., an NDVI-based approach and a deep learning model) and optical EO data with different ground resolutions (e.g., Sentinel-2, Planet SuperDove), the case study showcases the method’s ability to enhance temporal insights into landslide occurrence and progression. These results underline its potential as a valuable tool for rapid hazard monitoring and disaster response.
How to cite: Ozbakir, A. D., Girgin, S., and Tanyas, H.: Mapping of landslides by using partially cloudy optical Earth observation imagery: a case study of 2023 Türkiye Earthquakes , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17549, https://doi.org/10.5194/egusphere-egu25-17549, 2025.
Copper mining activities in the Chilean Andes are located at elevations ranging from 2,500 to 5,000 meters above sea level (masl). These industrial operations significantly increase exposure to risks in these inhospitable areas, which are highly vulnerable to natural hazards. The CODELCO ANDINA mining facilities cover approximately 350 km² and encompassing several glaciers, mountain valleys and rivers. The most intense mineral extraction activities take place above 4,300 masl and continue uninterrupted during the winter (rainy season).
Traditionally, the primary geomorphologic hazards identified in these areas have been rockfalls, debris flows, and snow avalanches. However, in the past decade, new torrential hazards, such as debris floods and debris flows, have emerged. These new hazards are driven by climate change, particularly its relation to liquid precipitation. The 0°C isotherm, which marks the boundary between areas of liquid and solid precipitation, plays a critical role in these changes. As the isotherm rises, it expands the area of liquid precipitation, increasing runoff surfaces and, consequently, drainage network discharges. Extreme event analysis now requires careful monitoring of the correlation between rainfall intensity and the isotherm's location.
Additionally, CODELCO ANDINA is situated on the edge of permafrost regions. With the retreat of permafrost, large areas of cold-climate weathered material—ranging from silt to boulder-sized debris—are becoming erodible. The geomorphology of the landscape, traditionally classified as periglacial, is rapidly transitioning to fluvial due to climate change. This creates an "explosive cocktail" of high-mountain geomorphology, increased sediment availability, and increased water discharge.
Over the past 15 years, risk management plans have been developed and implemented. However, climate change necessitates a reformulation of these plans. In 2023, two extraordinary events occurred, one of which involved over 300 mm of liquid precipitation within 48 hours—an event entirely unexpected for this region.
These new conditions require updated risk management strategies. This study introduces new hazard assessments obtained by integrating observational meteorological data (1964–2023) with climate models (ERA5-Land reanalysis and CMIP5/CMIP6 ) to identify trends in temperature, precipitation, and extreme events. A combined modeling methodology was applied to characterize fluvial and torrential processes.
How to cite: Riba, S., Medina, V., Vera, C., Barros, M., Oorthuis, R., and Hürlimann, M.: Climate Change impacts in Andean Mining risk management regarding torrential processes. Case Study at CODELCO ANDINA Division, Chile., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19164, https://doi.org/10.5194/egusphere-egu25-19164, 2025.
Intense rainfall events are the primary cause of landslides and heavy torrential flows, two of the most hazardous processes that can occur along hillslopes. When a rainstorm event causes many highly-mobile landslides at the same time in a large area, the considerable input of the moved material that these events rapidly throw into the hydraulic network can start a chain reaction of further dangers. These can be combined with the phenomena of forced erosion by the surface water drainage. For this reason, it is of fundamental importance to study the sediment productivity of the river basin, taking into consideration the transport connectivity between the slopes and the involved riverbeds. A remarkable weather event occurred in the Marche region on September 15–16, 2022, with localized rainfall of 419 mm in twelve hours, a record intensity over the previous decades. A self-regenerating storm system produced this enormous amount of precipitation causing the watercourses overflow and extensive flooding. Peak rainfall intensities reached 90 mm/h. The research focuses on the study of a small torrential basin, the Tenetra Creek, which has minimal anthropogenic influences. The rainfall event triggered several highly mobile landslides, most of them represented by debris flows, that in some cases reached the river network, contributing to the increase in river solid transport and causing considerable morphological changes. The methodology of this work started with a detailed basin-scale analysis of regional landslides databases (Italian Landslide Inventory, ISPRA; Hydrogeological Planning, Authority Basin) and a comparison of the mapped elements with the mass movements during the 2022 event in order to determine the source areas of material and the availability of material on the slopes. Then, the sediment connectivity index, based on the tool developed by Crema and Cavalli (2018), was used to investigate the mobilization of material according to topographic laws and to quantify the topographic control on sediment connectivity. The index expresses the potential connection between different parts of the catchment area; in particular, it describes the probability that sediments eroded from hillsides will reach the drainage network defined as a target. Since the sediment balance in a basin system is modelled also by the input of sediment bank collapse within the high water levels, we effectively evaluated the lateral changes in banks in relation to the 2022 event, by utilizing hydraulic sections within GIS environmental models. Geomorphic Change Detection software allowed for precise calculations of volumetric changes in storage, underscoring the significance of monitoring such alterations for future flood management strategies. The intricate relationship between flooding and geomorphological landscapes reveals the profound impact that natural occurrences can have on our environment. Understanding all the above-mentioned changes to which it is subject is essential for effective flood management, which necessitates continuous research and adaptive strategies to respond to evolving conditions. Implementing integrated methodologies allows for a comprehensive assessment of sedimentary supply in non-man-made river systems, providing crucial insights into the dynamic processes at play.
How to cite: Ahmed, M., Pappafico, G. F., and Guidi, E.: Quantification of sediment supply and availability from hillslope to channel network: the case study of the Tenetra Creek (Marche, Italy), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19581, https://doi.org/10.5194/egusphere-egu25-19581, 2025.
Abstract:
A landslide occurred on January 22, 2024 in Zhenxiong county, China, resulting in 44 fatalities and the collapse of around 400 structures and buildings. Timely understanding of landslide formation mechanism is crucial for guiding emergency relief, disaster prevention and reduction, and post disaster reconstruction.
This study evaluates the potential of China’s L-band SAR satellites (LuTan-1A/Lutan-1B) for slope stability analysis in Zhenxiong County based on R-index and sensitivity evaluation. Using stacking methodology and differential interferometry, the displacement velocity field is obtained. The results show that, by combination of LT-1 ascending and descending, the proportion of shaded areas in SAR imaging can be almost overcome, and the proportion of well imaged areas in SAR imaging for slope instability analysis is increased to 88.9%. The descending orbit data have poor visibility of Zhenxiong Landslide and weak sensitivity to the deformation measurement due to imaging distortions. The mean deformation obtained by stacking and cumulative displacement both indicate an instability zone at the top of the slope , where cumulative displacement reaches to around 200mm in 3 months before the failure. This shift in trend of background deformation was larger than other parts of the slope, suggesting that the landslide was initiated by instability in the steep cliff area. The research findings are discussed which provide important insight for understanding the mechanisms of catastrophic failure in this part of China
Key words: InSAR, Lutan-1, landslide, SAR sensitivity
How to cite: Lyu, S., Li, T., Motagh, M., Tang, X., Yang, C., Zhang, X., Zhang, X., Lu, J., and Liu, Z.: The 22 January Zhenxiong Landslide: Slope stability analysis using Lutan-1 SAR Data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19956, https://doi.org/10.5194/egusphere-egu25-19956, 2025.
Extreme weather events, increasingly frequent in the Mediterranean due to climate change, pose significant risks by triggering hydrogeomorphic processes such as slope failures. These phenomena, particularly prevalent in tectonically active and steeply sloped coastal areas, present challenges for monitoring due to their spatial and temporal dynamics.
Unmanned aerial systems (UAS) and advanced photogrammetric techniques, including structure-from-motion (SfM) and multi-view stereo (MVS), have emerged as transformative tools for capturing high-resolution terrain data. This study employs UAS-aided photogrammetry alongside change detection methods, such as digital elevation models of differences (DoD) and cloud-to-cloud distance (C2C), to analyze geomorphic changes induced by extreme storms in highly visited and geologically dynamic coastal areas in Greece.
The findings reveal the utility of UAS in providing detailed morphometric measurements, delineating areas of erosion and deposition, and identifying high-risk zones. These capabilities facilitate a deeper understanding of geomorphic processes, enabling informed risk assessment and management strategies. The study underscores the potential of integrating UAS and photogrammetry for continuous monitoring in regions with high socioeconomic and environmental value. This approach not only supports sustainable development by minimizing disruptions but also enhances safety standards in vulnerable, high-exposure coastal areas. Through this methodological framework, the research contributes to addressing the pressing need for resilient hazard management in the context of evolving climatic conditions.
How to cite: Kotsi, E., Vassilakis, E., Diakakis, M., Mavroulis, S., Konsolaki, A., Filis, C., Lozios, S., and Lekkas, E.: Using UAS to Monitor and Quantify the Geomorphic Effects of extreme storms in tectonically active coastal areas: Evidence from Greece , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20080, https://doi.org/10.5194/egusphere-egu25-20080, 2025.
Accurate 3D surface deformation analysis is essential for understanding geodynamic processes and mitigating related hazards. We present a methodology that fuses GNSS and InSAR time series to achieve robust deformation estimates. Our case study focuses on the Groningen region in the Netherlands, an area undergoing significant subsidence and seismicity due to decades of gas extraction. In addition, Groningen benefits from a dense GNSS network spanning approximately 50 × 50 km, offering an ideal testbed for integrated deformation analyses.
The proposed workflow involves preparing GNSS time series from Nevada Geodetic Laboratory by removing common-mode errors and detrending for plate motion, then referencing all stations to a central GNSS antenna. A moving average filter further refines the GNSS time-series. In parallel, we refine the “Basic” EGMS InSAR products by applying smoothed calibration trends derived from the “Calibrated” products. Subsequently, the daily average deformation of InSAR Line-of-Sight (LOS) points near the reference GNSS station is subtracted from all persistent scatterers, ensuring consistent reference frames across both datasets.
To combine InSAR LOS deformation with GNSS 3D data, we identify persistent scatterers within a 100-meter radius of each GNSS antenna and synchronize the reference epochs between both datasets. We then rotate the GNSS East-North-Up coordinates so that one axis aligns with the InSAR LOS, apply an error-weighted least-squares solution to fuse the measurements, and finally reintroduce the out-of-LOS components derived from the pre-processed GNSS data. The resulting full 3D deformation field is then converted back to the ENU coordinate system.
Preliminary analyses suggest that integrating GNSS and InSAR improves reliability in all three components, with particularly notable benefits in the north component. Moving forward, this fusion strategy can be extended to smaller-scale monitoring projects (e.g., dams or bridges), offering a versatile approach to detecting and characterizing localized deformation anomalies.
How to cite: Aponte, O., Gatti, A., and Realini, E.: Reconstruction of 3D Deformation from GNSS and InSAR Data: A Case Study in Groningen Using EGMS Data., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11795, https://doi.org/10.5194/egusphere-egu25-11795, 2025.
Climate change is severely altering rainfall patterns and increasing the frequency and severity of wildfires, resulting in significant changes in the physical characteristics of catchments. These changes, particularly in hydrological and stability characteristics, contribute to an increased occurrence of hillslope hydrological hazards, including landslides, debris flows, flash floods, and hillslope erosion. The dynamic interplay between climate-induced changes and catchment characteristics drives complex multi-hazard interactions—such as cascading, compounding, conditional, and concurrent events— that amplify the magnitude and impact of these hazards on communities and infrastructure.
Central Chile is particularly vulnerable to climate change, especially to El Niño-Southern Oscillation (ENSO) variability, which affects rainfall patterns, and to prolonged droughts, which climate projections indicate will increase wildfires. This study examines the Marga-Marga catchment, a highly urbanised coastal area in central Chile, which has experienced large-scale wildfires in recent years that have removed significant vegetation cover, leaving hillslopes more prone to hillslope hydrological hazards during rainfall events.
This study uses advanced multi-hazard modelling and climate scenario analysis to investigate the response of the Marga-Marga catchment to evolving climate conditions. By integrating high-resolution geospatial data and physically-based modelling, and scenario simulations, it explores how climate change-driven alterations in catchment characteristics intensify multi-hazard dynamics and interactions. Preliminary results show that key catchment characteristics - such as soil infiltration capacity, moisture content and slope stability are significantly affected by vegetation loss and soil degradation due to wildfires and urbanisation. These characteristics respond differently to rainfall, thereby increasing the susceptibility of the catchment to hillslope hydrological hazards interactions.
The results provide valuable insights into the mechanisms driving multi-hazard interactions and illustrate how these processes amplify risks to natural and urban systems. This research highlights the urgent need for adaptive urban planning and disaster risk reduction strategies to mitigate these impacts. By addressing critical gaps in the understanding of multi-hazard dynamics under climate change, this study provides actionable recommendations for improving resilience in Mediterranean coastal catchments.
How to cite: López Filun, P., Martinez Reyes, C., and Gironás León, J.: Unravelling Multi-Hazard Events in a Coastal Catchment: Implications of Climate Change for Central Chile, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13855, https://doi.org/10.5194/egusphere-egu25-13855, 2025.
Due to the geographical characteristics, the intense rainfall brought by typhoons frequently triggers landslides and debris flows in Taiwan, posing significant risks to lives and properties. The deep-seated landslide in the Lantai area, northern Taiwan, is adopted in this study. Rainfall records from the Central Weather Bureau of three significant typhoon events from 2019 to 2022 were analyzed, and the total effective cumulative rainfall records were derived (Lee, 2006). The seepage analysis was then performed to obtain the groundwater level time variations caused by the rainfall. We conducted numerical simulation of the three events using a commercially available program Geostudio. The numerical analysis starts by simulating variation of groundwater level caused by rainfall, and the time variation of groundwater level was implemented in the slope stability analysis adopting limit equilibrium method. Results of seepage analysis indicate a strong correlation between the total effective cumulative rainfall and groundwater level variations. The time variation in the factor of safety reduction was deduced by accounting for groundwater response delays. The results were validated against on-site monitoring data, and the sliding surfaces were compared to the borehole logging and geological profile. The threshold groundwater levels for the Lantai area deep-seated landslide can then be estimated to range between 20.22m and 20.04m below ground surface, which can be used for issuing a landslide early warning.
How to cite: Lin, M.-L. and Peng, K.-T.: Numerical simulation of rainfall-induced deep-seated landslide in Lantai area, Taiwan, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14446, https://doi.org/10.5194/egusphere-egu25-14446, 2025.
A recent article using data from the European Ground Motion Service (EGMS) to assess the vulnerability of the Spanish coastline to ground movements was published in October 2024 (López-Vinielles et al., 2024). The study, funded by the “Plan de Recuperación, Transformación y Resiliencia - Financiado por la Unión Europea - Next Generation EU” programme and conducted within the framework of the RISKCOAST project (Ref. SOE3/P4/E0868), the EGMS RASTOOL project (Grant Agreement No. 101048474), and the SARAI project (PID2020-116540RB-C22), examines the coastline's exposure to ground movements and their potential impacts on roads, buildings, and populations.
Utilizing a suite of post-processing tools including ADAfinder, 9,010 Active Deformation Areas (ADAs) across 805 coastal municipalities were identified, with 1,916 affecting roadways and 2,596 affecting buildings. Most ADAs exhibited vertical movement due to land subsidence, while horizontal movements, mainly linked to landslides, were also significant. The majority of ADAs showed moderate to low displacement rates (<25 mm/yr). The potential economic impact was estimated at €19,428.4 million, with €1,716.4 million attributed to roads and €17,712.0 million to buildings. Additionally, 134,236 people were identified as potentially vulnerable.
The study highlights a higher exposure of Spain's Mediterranean coast compared to the Atlantic coast, and a higher exposure of the Canary archipelago compared to the Balearic Islands. Andalusia and Murcia are identified as the most vulnerable regions. The higher exposure of the Mediterranean coast is particularly evident in the southern Mediterranean, where rapid tourist expansion and extensive urban and infrastructure development increase the incidence of ground motion processes affecting built-up areas. Specifically, climatic conditions and intense water demand along this stretch of coast have led to aquifer overexploitation, contributing to widespread land subsidence. Additionally, landslides pose a significant concern along this region, particularly in the Alpine mountain ranges running parallel to the coast.
The research underscores the potential of the EGMS for conducting both preliminary population exposure analyses and preventive risk assessments to mitigate road and building damage. While the study provides a static overview of the potential socio-economic impact of ground motion on the Spanish coast, the EGMS offers significant potential for ground movement mapping across Europe, making it an invaluable tool for risk management, particularly in regions experiencing rapid urban and infrastructure expansion. In this context, the work represents a first step towards developing new EGMS-based applications for impact assessment.
Reference
López-Vinielles J., Ezquerro P., Béjar-Pizarro M., Sarro R., Cuevas-González M., Barra A., Mateos R.M. (2024). Potential socio-economic impacts of ground movements in the coastal municipalities of Spain: Insights from the supra-regional implementation of the European Ground Motion Service. Ocean and Coastal Management, 259, art. no. 107452. https://doi.org/10.1016/j.ocecoaman.2024.107452
How to cite: López-Vinielles, J., Ezquerro, P., Béjar-Pizarro, M., Sarro, R., Cuevas-González, M., Barra, A., Bru, G., Martínez-Corbella, M., Rivera-Rivera, J. S., Miranda-García, P. V., Monserrat, O., Guardiola-Albert, C., Herrera, G., and Mateos, R. M.: Applying an EGMS-based approach to assess potential ground movement impacts on Spain's coastal municipalities, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9349, https://doi.org/10.5194/egusphere-egu25-9349, 2025.
The Ili River basin is situated at the intersection of China and Central Asia. Due to the Tien Shan’s complex terrain and geological structures, frequent and widespread landslides occur in this region, accounting for nearly 60% of all geological hazards in Xinjiang Province. Although satellite-based interferometric monitoring (InSAR) is an effective approach for identifying potential landslides, challenges remain regarding the interpretability of observed deformation signals. In this study, wide-area InSAR processing was employed to detect the distribution of potential landslides. An explainable artificial intelligence (XAI) model—LSTM-SHAP—was then proposed to analyze deformation mechanisms and elucidate landslide types. Notably, the SHAP map provided a quantitative and detailed explanation of landslide attributions, revealing how controlling factors vary during deformation evolution. By training on historical deformation patterns, future scenarios can be generated for more accurate deformation prediction and landslide risk assessment. Our research is expected to provides a new technical reference for landslide monitoring. Moreover, these findings suggest that XAI-based methods can offer civil protection agencies a data-driven perspective for understanding deformation evolution and implementing precautionary measures.
How to cite: Meng, Q., Dai, Y., Chen, S., Wu, H., Peng, Y., and Li, Q.: Identification, Analysis, and Prediction of Landslide Deformation Based on InSAR and an Explainable Neural Network Model: A Case Study in the Ili River Basin, Xinjiang, China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10690, https://doi.org/10.5194/egusphere-egu25-10690, 2025.
Posters virtual: Mon, 28 Apr, 14:00–15:45 | vPoster spot 3
EGU25-7494 | Posters virtual | VPS12
Risk evaluation of rainfall-triggered landslides on multiple scales of JapanMon, 28 Apr, 14:00–15:45 (CEST) | vP3.7
Landslide risk is the product of landslide hazards, exposure, and vulnerability. Spatial and temporal variations in risk and its three components of rainfall-triggered landslides were examined on multiple scales in Japan. Landslide fatalities in Japan decreased between the 1940s and the 1990s. The factors affecting the decrease changed the decrease in household members, increase in people evacuated, and change in the structure of houses to the increase in forest maturity and implementation of structural measures. Similar trends were also found in Kure City with three destructive landslide events in 1945, 1967, and 2018. However, the timing of the main contributions was different from that in Japan overall. In Japan, landslide frequency (i.e., landslide hazards) also decreased with time. Based on a model estimating landslide frequency from the forest age components and rainfall, a larger contribution of the increase in forest maturity to landslide frequency than rainfall was demonstrated on the national scale. Factors determining the number of landslide disasters were examined using generalized linear models on prefectural scales. The factor differed among the three landslide types (i.e., steep-slope failure, deep-seated landslide, and debris flow). For all types, rainfall and the number of landslide-prone areas were selected with positive coefficients: the accretionary complexes geological type with negative coefficients. In addition, forests and land for buildings were selected for steep-slope failures with negative and positive coefficients, respectively, which were not selected for deep-seated landslides and debris flows. The historical and future populations in landslide-affected areas (i.e., landslide exposure) were examined in all municipalities of Japan. The population in the landslide-affected areas continuously decreased during the analysis period. The decrease was gentler than those in landslide risk, hazards, and vulnerability, suggesting that the effects of landslide exposure on temporal changes in landslide risk were less than those of landslide hazards and vulnerability, on the national scale. Finally, the mortality rate in collapsed-houses by landslides was examined from 2014 to 2027. The database for victims and survivors in collapsed houses was developed mainly based on newspapers. The floor number, gender, and type of trigger affected the mortality of landslides. These evaluations can be used to develop strategies for the mitigation of landslide disasters.
How to cite: Shinohara, Y.: Risk evaluation of rainfall-triggered landslides on multiple scales of Japan, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7494, https://doi.org/10.5194/egusphere-egu25-7494, 2025.
EGU25-654 | ECS | Posters virtual | VPS12
Spatiotemporal quantification and trajectory modelling of land displacements in Western Greece using recent InSAR and GNSS observationsMon, 28 Apr, 14:00–15:45 (CEST) | vP3.28
Detecting and monitoring ongoing surface deformation with satellite geodesy is fundamental for the analysis of geophysical processes and geohazards. Here, we focused on the area of Western Greece, due to its complex geophysical setting, characterized by numerous faults and high seismicity, and we quantified the spatiotemporal patterns of land displacements in the area from 2018 to 2022. We analysed Sentinel-1 Synthetic Aperture Radar (SAR) data with Multi-temporal Interferometric SAR (MT-InSAR) techniques and calibrated the derived estimates using velocity time series from multiple permanent Global Navigation Satellite System (GNSS) stations available in the area. The derived displacement time series were also compared with openly available data from the European Ground Motion Service (EGMS) and, jointly, were used to map possible active fault areas. In addition, trajectory modelling was performed in both MT-InSAR and GNSS velocity time series through the Greedy Automatic Signal Decomposition (GrAtSiD) algorithm, in order to identify seasonal loading and therefore improve detection of accelerations in tectonic or anthropogenic motion. Overall, the study explores recent geodetic observations with state-of-the-art data analysis techniques, and, building upon previous literature, offers a comprehensive spatiotemporal assessment of land displacements in Western Greece, with implications for scientific and engineering applications.
How to cite: Fasoulis, K., Bedford, J., Garcia, C., Hadjidoukas, P., and Pappas, C.: Spatiotemporal quantification and trajectory modelling of land displacements in Western Greece using recent InSAR and GNSS observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-654, https://doi.org/10.5194/egusphere-egu25-654, 2025.
