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.

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.

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.