Today, the temporal evolution of a landslide is addressed in several ways, encompassing classical and more complex in situ measurements or remotely sensed data acquired from aerial platforms and satellites, with particular focus to new platforms (SAOCOM, Sentinel-1C, LuTan). All these techniques are adopted for the same final scope: measure motion over time, trying to forecast future evolution or, at least, reconstruct its recent past. Real time, near-real time and deferred time strategies can be profitably used for landslide analysis, depending on the type of phenomenon, the selected monitoring tool and the acceptable level of risk.
This session follows the general objectives of the International Consortium on Landslides, namely: (i) promote landslide research for the benefit of society, (ii) integrate geosciences and technology within the cultural and social contexts to evaluate landslide risk, and (iii) combine and coordinate international expertise.
The session is expected to present various topics of innovative applications of remote sensing techniques, as well as case studies in which multi-temporal and multi-platform data are exploited for risk management. The integration and synergic use of different techniques is welcomed, as well as newly developed tools or data analysis approaches, including big data management strategies and Artificial Intelligence-based methods.
Orals: Thu, 7 May, 08:30–10:15 | Room 1.31/32
Landslides represent a crucial issue for Italy due to their impacts on population, environment, cultural heritage, transportation infrastructure, and economic activities. More than 684,000 landslides have been mapped in the Italian Landslide Inventory, and approximately 28% of them are classified as rapid phenomena, such as rock falls and debris flows, often associated with serious consequences in terms of loss of human lives and damage to buildings and infrastructure.
As highlighted in the Kyoto 2020 Commitment for the Global Promotion of Understanding and Reducing Landslide Disaster Risk, effective landslide risk reduction relies on improved understanding of landslide processes, increased public awareness, and the continuous advancement of monitoring technologies. In this framework, landslide in situ monitoring represents a strategic action to assess landslide evolution, support the design of stabilization works, and verify their effectiveness over time, as well as to alert the population through early warning systems (EWS).
A methodology for the prioritization of landslide monitoring sites has been developed and tested at the national scale, in the framework of the RESILIENT research Project “Risk Evaluation and Smart Implementation of Landslide monItoring by citizen Engagement and New Technologies” funded by Fondazione Cariplo's “Safe Territories” initiative. The adopted methodology is the result of a multidisciplinary effort involving geologists, engineers, risk analysts, public authorities, and other stakeholders, ensuring that both scientific robustness and operational needs were addressed.
This prioritization approach takes into account several factors describing landslide hazard (e.g., type of movement, area, velocity) as well as potential impact on human lives and infrastructure (buildings, urban areas, population, road and railway network, service infrastructure, cultural heritage, etc.). Such algorithm may become a valuable tool to support decision-makers in selecting new sites where a landslide monitoring system could provide the greatest benefits for local communities in terms of risk reduction.
To date (January 2026), information on 1,024 in situ monitoring systems across the country is available in the National Register of Landslide in situ Monitoring Systems, designed by ISPRA in 2021, in collaboration with Regions, Provinces, and Regional Environmental Protection Agencies (ARPAs). Active monitoring systems are 545 (53%) while dismantled and under construction systems are 358 (35%) and 121 (12%), respectively. Most of the systems (827; 81%) have a knowledge purpose while 197 systems (19%) are or have been also used as early warning systems. Data acquisition is performed manually (67.4%), automatically (13%) or in both ways (19.6%). The most used instruments are inclinometers and piezometers, followed by topographic instrumentation (e.g., Total Stations, GNSS), crack meters, weather stations, strain gauges, etc. Data is published in the Landslide monitoring section of the IdroGEO national web platform (https://idrogeo.isprambiente.it), accessible from desktops, tablets, and smartphones, that allows viewing monitoring system/stations/sensors location and metadata information, searching and filtering of monitoring systems, and statistics on number, data acquisition and type of instrumentation.
How to cite: Trigila, A., Romeo, S., Iadanza, C., Di Paola, G., Zastrow, L. R., Frattini, P., and Crosta, G. B.: A national-scale methodology for prioritizing landslide monitoring sites, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20581, https://doi.org/10.5194/egusphere-egu26-20581, 2026.
Soil moisture is a key controlling factor in landslide processes, as it directly influences soil strength, cohesion, and pore pressure dynamics. Elevated moisture levels, particularly during prolonged or intense rainfall, reduce frictional resistance and shear strength, thereby increasing slope instability and landslide susceptibility. The state of Lower Austria is especially prone to landslides due to its geological setting, dominated by mechanically weak Flysch and Klippen Zone formations composed of interbedded limestones and deeply weathered materials. These conditions, in combination with hydrological drivers, land-use changes, and anthropogenic influences, result in a high predisposition to slope failures.
Reliable monitoring of soil moisture is therefore essential for improving the understanding of both predisposing and triggering factors of landslides. Recent advances in monitoring technologies, such as Cosmic-Ray Neutron Sensing (CRNS), enable spatially averaged soil moisture measurements that overcome the limitations of conventional point-scale sensors. CRNS provides direct estimates of water content integrated over a footprint with a horizontal radius of several tens of metres and a penetration depth of some tens of centimetres, offering a representative measure of near-surface soil moisture at the hillslope scale.
In this study, CRNS is deployed at the slow-moving Hofermühle landslide in Lower Austria to evaluate its suitability for long-term landslide monitoring over a three-year period. CRNS-derived soil moisture estimates are analysed in conjunction with data from time domain reflectometry (TDR) sensors and piezometers to investigate contrasting hydrological response behaviours during extreme events, including the September 2024 event and the subsequent development of hydrological conditions in the slope. These observations are further related to horizontal displacement rates derived from inclinometer measurements. All datasets are interpreted in the context of local geological and hydrological settings to assess the added value of footprint-scale soil moisture observations for capturing spatially integrated moisture dynamics relevant to slope stability. These findings explore the potential of CRNS to support the development of landslide monitoring strategies by bridging the gap between point-scale and hillslope-scale hydrological observations. Further monitoring is planned to be carried out with the static CRNS as well as mobile rover applications at other study sites in Lower Austria.
How to cite: Marr, P., Jiménez Donato, Y. A., Glade, T., Gazzola, E., Gianessi, S., and Cracco, G.: Monitoring soil moisture with Cosmic Ray Neutron Sensing (CRNS) at a slow-moving landslide using cosmic-ray neutron sensing in Lower Austria, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9277, https://doi.org/10.5194/egusphere-egu26-9277, 2026.
Post-event topography of ancient landslides often provides favorable terrain for settlement in mountainous regions. However, such landslides can undergo long-term creep or even reactivation, posing significant risks to the buildings constructed on them. Joshimath, a town situated on an ancient landslide in the northwest Himalaya, has experienced persistent land subsidence since the early 20th century, with clear indications of accelerated deformation in recent years. Although building damage is widely observed, a regional-scale stability assessment for the roughly 10,000 buildings is still absent. Interferometric Synthetic Aperture Radar (InSAR) enables long-term monitoring of creeping landslides, and its time-series deformation measurements provide a powerful means to evaluate building stability at regional scales due to their millimeter-level sensitivity and extensive spatial coverage. In this study, we integrate the long-term temporal record of Sentinel-1 with the high spatial resolution of TerraSAR-X to assess building stability from both temporal and spatial perspectives. Our results show that Joshimath has undergone continuous subsidence since 2017, with a marked acceleration beginning in 2021. Buildings across the town exhibit widespread settlement, while those located in zones with strong spatial variations in deformation rates are subject to pronounced differential settlement. Based on deformation characteristics, we classify the stability of individual buildings, providing a framework for prioritizing maintenance, reinforcement, and future land-use planning. Given the ongoing deformation in Joshimath, continued monitoring is essential for evaluating both slope stability and structural safety. Overall, our findings highlight the effectiveness of multi-sensor InSAR for assessing building stability in remote, landslide-prone mountain communities.
How to cite: Zhang, B., Motagh, M., Bian, W., and Wang, F.: Spatial-temporal building stability assessment of Himalaya town Joshimath from long-term multi-sensor InSAR analysis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5617, https://doi.org/10.5194/egusphere-egu26-5617, 2026.
The Central Andes are one of the most tectonically and geomorphologically active regions on Earth. The orographic barrier between the eastern foreland and the Central Andean plateau induces a strong East-West climatic gradient, with peak rainfall occurring on the steep eastward-facing slopes. Frequent rainstorms during the South American summer monsoon, coupled with fault-weakened lithologies, drive mass-movement processes including landslides and high-altitude periglacial creep. However, monitoring these instabilities over large areas and long time periods remains computationally expensive using traditional CPU-based methods.
We implemented a high-performance GPU-based workflow using sub-pixel optical image correlation to process 20-year time series using the Landsat 7, 8, and 9 data archive spanning from 22° to 27° S (12 Path/Row combinations) . The workflow consists of two main steps: (1) identification of areas of interest by oversampling the scenes before correlation at a coarse, but sufficiently dense step size, and (2) sub-pixel matching for refined displacement derivation within the detected regions. To ensure data integrity, we employ a pair selection process based on sun-elevation geometry and filters for cloud cover, snow, vegetation changes, and topographic shadows. Additionally, we applied a kinematic filter to exclude displacements inconsistent with hillslope aspect. By stacking multi-decadal imagery, we improved the signal-to-noise ratio and successfully detected velocities ranging from less than 0.5 m/yr to several meters per year. Our results highlight the extent of permafrost processes and the influence of the East-West climatic gradient on hillslope dynamics by capturing the transition from the humid foreland to the arid high-elevation plateaus. The stacking method effectively removed outlier signals caused by transient snow cover at higher elevations.
This 20-year record provides a vital baseline for understanding how Andean hillslope processes respond to a changing climate and how they depend on pre-existing, weakened lithologic conditions related to tectonic stresses. The GPU-accelerated framework enables a transition from localized monitoring to large-scale kinematic analysis in high-relief terrains.
How to cite: Leder, F., Rheinwalt, A., and Bookhagen, B.: GPU-based pixel tracking of hillslope instabilities using multi-decadal optical satellite imagery in the Central Andes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6853, https://doi.org/10.5194/egusphere-egu26-6853, 2026.
Landslides represent a major natural hazard worldwide, particularly in areas characterized by complex geological settings. In many cases, especially where large volumes are involved or where rapid evolution is possible, conventional mitigation measures prove ineffective. Consequently, landslide risk reduction increasingly relies on the development of monitoring-based Early Warning Systems (EWS), capable of detecting precursory deformation phases prior to failure.
This research focuses on the application of distributed fiber-optic sensing technologies for landslide monitoring, with the long-term objective of improving predictive capabilities for both slow-moving and potentially rapid landslide phenomena. In perspective, this monitoring approach shows strong potential for early warning applications in rapid landslides, particularly in pyroclastic soils, where static liquefaction processes may develop and trigger very fast kinematics evolving into destructive mudflows with catastrophic consequences, including loss of life. However, full-scale experimentation on rapid landslides is extremely difficult to pursue, as it would require dedicated pilot sites and the occurrence of rare, rapidly evolving failure events, whose initiation mechanisms are often hard to capture in real time.
For this reason, the technology is currently being tested and evaluated in slow-moving landslide settings, which allow controlled long-term monitoring of deformation processes. In this context, a Smart Extenso-Inclinometer (SEI), based on distributed fiber-optic sensing and stimulated Brillouin scattering technique, has been tested. The system enables continuous soil strain measurements with centimetric spatial resolution, providing both horizontal and vertical strain components and overcoming several limitations of conventional inclinometer techniques.
Field monitoring activities have been carried out in Italy (Centola) and at the Brandstatt landslide observatory (Lower Austria). Although characterized by slow-moving kinematics, the Brandstatt site represents a key test case, as it exhibits higher deformation rates (in the order of cm/year) compared to others slow-moving landslides commonly monitored in Italy. Moreover, it offers a unique opportunity to assess the long-term performance, reliability, and maximum deformation capacity of fiber-optic sensors under conditions where traditional instrumentation has become unserviceable due to excessive deformation.
Preliminary results demonstrate that distributed fiber-optic measurements are consistent with conventional data while providing additional insight into complex deformation mechanisms, including both horizontal and vertical strain components. These findings support the potential of fiber-optic monitoring as a valuable tool for detecting precursory deformation processes and for bridging the gap between slow-moving landslide monitoring and early warning strategies for rapid slope failures.
How to cite: Brunzo, A., Carraro, E., Damiano, E., de Cristofaro, M., Glade, T., Marr, P., Molitierno, E., and olivares, L.: Performance and Reliability of a Fiber-Optic Smart Extenso-Inclinometer for Long-Term Landslide Monitoring, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18343, https://doi.org/10.5194/egusphere-egu26-18343, 2026.
Coastal cliffs are dynamically changing and understanding their evolution is crucial for managing hazards and protecting communities and ecosystems. The UK’s 17,381 km coastline is one of Europe’s fastest-eroding, with both new failures and reactivation of ancient landslides. Coastal instability is influenced by site-specific geology, hydrogeology, and external forcing factors; however, the role of climate-driven changes-such as sea-level rise, increased precipitation, and more frequent storm events-and the contribution of environmental preconditioning to landslide reactivation remain poorly understood. Along the southwest coast of the UK, many large, active landslide complexes occur where low-permeability mudstones, including Gault Clay, are overlain by more permeable sandstones, creating hydrologically sensitive and mechanically unstable slopes.
This study analyses the recent evolution of two such deep-seated landslide complexes in southern England-Stonebarrow Hill (Dorset) and The Landslip (Isle of Wight). The two systems exhibit notably different landslide behaviour and activity patterns despite comparable geological conditions that may have evolved in time with stress building up on the slope to cause failure. We used high-resolution LiDAR digital elevation models (DEM) between 2004 and 2025 alongside optical imagery and field-based geomorphological mapping. This enabled us to estimate mobile erosional and depositional volume, quantify erosional rate along with the assessment of cliff-top and toe evolution, characterise morphological changes and identify areas of instability. Through multi-temporal DEM differencing, we quantified both horizontal and vertical ground displacement and examined how failure mechanisms vary between the two sites.
The results indicate a clear divergence in recent activity. At The Landslip, deformation is extensive, affecting several parts of the complex and including marked upslope retreat, suggesting that the system has undergone a significant phase of renewed movement/reactivation. Stonebarrow Hill, in contrast, is dominated by smaller-scale failures focused along the sea-cliff frontage, accompanied by persistent toe erosion but limited evidence of deeper or inland progression. The spatial configuration of change mapped at The Landslip suggests that earlier movement lower on the slope weakened support and contributed to subsequent instability higher up the slope.
Understanding these patterns helps predict how similar cliffs might respond to changing conditions. The study also provides a foundation for developing predictive models that combine displacement data with climate and environmental factors. Such models can guide targeted management strategies, reduce hazard risks, and support the protection of vulnerable coastal landscapes across the UK. More broadly, the work emphasises the importance of long-term, high-resolution geospatial monitoring for recognising when deep-seated landslide systems are transitioning from background activity to more substantial reactivation, offering insights relevant to other clay-rich coastal settings.
How to cite: Sharan, B., Dini, B., Carey, J., Moore, R., Fitzgerald, R., Stevenson, N., Champagne, A., Fountain, E., and Halwyn, N.: Understanding coastal evolution through multi-temporal LiDAR analysis of deep-seated landslides: Stonebarrow Hill and The Landslip, UK, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1259, https://doi.org/10.5194/egusphere-egu26-1259, 2026.
Selecting reliable negative samples (NS) is a crucial step in enhancing the robustness of landslide susceptibility assessments (LSA). Existing studies frequently utilize InSAR-derived deformation data to identify stable areas, thereby refining the negative samples; however, InSAR is susceptible to residual errors due to decorrelation and geometric distortions, particularly in regions with significant topographic relief and dense vegetation cover. Additionally, the processing workflow for InSAR can be complex and costly.
To address these issues, we examine the Lijiang River Basin in Guangxi, China, as a case study. We propose a novel negative sampling strategy constrained by the temporal stability of two SAR-based indices: Long-term Distillation and Identification (LDI). First, we delineate temporally stable areas (S_SAR) by selecting pixels that exhibit minimal long-term change rates in the Radar Vegetation Index (RVI) and the Radar Forest Degradation Index (RFDI). We then apply Positive-Unlabeled Learning (PU-learning) to refine S_SAR further, resulting in a high-confidence NS set (Nopt). Next, we evaluate stability differences between Nopt and various NS sets generated by conventional sampling strategies, using cumulative deformation and deformation-rate metrics obtained from SBAS-InSAR. Finally, we built Landslide Susceptibility Assessment (LSA) models utilizing Random Forest (RF), Extreme Gradient Boosting (Xgboost), and Categorical Boosting (Catboost). We assess model performance using the Area Under the Curve (AUC) and confusion-matrix-based metrics. Additionally, we analyze spatial patterns in LSA, area proportions across susceptibility classes, and their relationship with the multi-year means and long-term change rates of RVI and RFDI.
The results indicate the following: (1) Deformation values in S_SAR are primarily clustered around 0 mm, confirming the consistency between “stable long-term vegetation change” and “stable ground deformation.” After refining with PU-learning, Nopt shows more minor fluctuations in deformation and exhibits the highest internal consistency. (2) LSA models based on LDI perform the best, with the Xgboost-based LSA achieving the highest AUC (0.843). Additionally, feature contributions quantified by Shapley Additive Explanations (SHAP) are more concentrated and stable, demonstrating that LDI effectively reduces noise. (3) Although various NS sampling strategies result in significant differences in LSA spatial patterns, the Very High Susceptibility (VH) class consistently displays a typical pattern of “higher RFDI and lower RVI, with a weaker RFDI trend and a stronger RVI trend”. This suggests that areas classified as VH have lower vegetation cover, greater inter-annual variability, and weaker disturbance resistance. Overall, LDI provides a cost-effective approach to obtaining reliable NS data in complex terrains, serving as a valuable reference for LSA modeling in the Lijiang River Basin and similar regions.
How to cite: Feng, Z., Yan, L., and Chen, C.: Landslide Negative Sample Construction and Susceptibility Assessment Based on the Temporal Stability of Dual SAR Indices: A Case Study of the Lijiang River Basin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16906, https://doi.org/10.5194/egusphere-egu26-16906, 2026.
Landslides are among the most destructive geological hazards in mountainous regions, and are particularly clustered and persistently active in karst areas due to intense karstification, highly dissected topography, and weak slope materials. In existing landslide susceptibility assessments, negative samples are commonly selected using random or empirical strategies, which can mislabel potentially unstable slopes as stable terrain, introduce label noise, and ultimately degrade both model accuracy and physical consistency. To address this issue, we propose a landslide susceptibility assessment framework for karst regions (InSAR–PU) that tightly integrates deformation constraints from interferometric synthetic aperture radar (InSAR) time series with a Positive–Unlabeled (PU) learning–based negative-sample optimization strategy, and explicitly identifies and constrains label uncertainty in negative samples during sample construction to improve the quality of the negative-sample set and the reliability of susceptibility estimates. A typical karst landscape in Longsheng Various Nationalities Autonomous County, Guilin, Guangxi, China, is selected as the study area. In this area, surface deformation rates from 2019 to 2023 are derived using SBAS-InSAR; low-deformation domains are treated as unlabeled samples, and a Bagging-PU scheme is employed to obtain a high-confidence negative-sample set. Six machine-learning models are used to conduct comparative experiments under three negative-sample strategies: random sampling, buffer-based sampling, and the proposed InSAR–PU approach. The InSAR–PU strategy significantly improves classification performance and stability, with all area under the ROC curve (AUC) values exceeding 0.80; the InSAR–PU-RF model achieves an AUC of 0.867 and an overall accuracy (OA) of 86.7%, representing improvements of 4.5% and 2.2% over random and buffer-based sampling, respectively. Shapley Additive Explanations (SHAP) analysis shows that higher-quality negative samples lead to more stable model responses and clearer contributions of key controlling factors such as rainfall, slope, curvature, and distance to roads. A deformation–susceptibility contingency matrix further indicates higher spatial consistency between InSAR–PU predictions and InSAR-derived deformation patterns, while field investigations confirm that time-series deformation signals in typical areas agree with in situ observations. In summary, InSAR–PU provides a transferable negative-sample optimization strategy for landslide susceptibility mapping in complex karst regions, improving predictive accuracy and enhancing the spatial consistency and physical credibility of the results.
How to cite: Tang, Q., Wang, J., Li, Z., and Jiang, W.: An InSAR Time-Series Constrained PU-Learning Framework for Landslide Susceptibility Mapping in Karst Regions: Negative-Sample Optimization and Enhanced Spatial Consistency, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15567, https://doi.org/10.5194/egusphere-egu26-15567, 2026.
Slow-moving landslides pose prolonged and severe damage to buildings. The assessment of building vulnerability is a critical step in quantifying the risk of slow-moving landslides. Accurately retrieving the real displacement of landslides is essential for developing a more reliable vulnerability model of buildings. However, remote sensing observations acquired from a single orbit are insufficient to capture the three-dimensional (3D) displacement of landslides, which consequently limits the advancement of building vulnerability modeling.
To this aim, this study integrates multi-source displacement monitoring, including synthetic aperture radar (SAR) imagery, optical imagery, and global navigation satellite system (GNSS), to assess the vulnerability of masonry buildings affected by slow-moving landslides. A total of 32 ENVISAT and 193 Sentinel-1A images were collected and processed using the multi-temporal differential interferometric SAR (MT-InSAR) technique to derive the vertical displacement of the landslide. Meanwhile, horizontal displacement was estimated from high-resolution optical imagery through pixel offset tracking. Then the real displacement of landslide can be retrieved by combining GNSS observations. We take 6 slow-moving landslides and masonry buildings on them in the Three Gorges Reservoir Area (TGRA) of China as the research objects. About 50 damaged masonry buildings were found out among near 500 residential buildings on landslides in field survey. These buildings are classified considering crack width on walls. Based on this, we then developed vulnerability curves using Polynomial, Exponential, Logistic, Weibull, and Sigmoid nonlinear regression functions.
The results indicate that building damage responds more acutely to surface deformation in the vertical direction. In terms of fitting functions, the performance of different functions is affected by the choice of intensity parameter. Vulnerability curve derived from cumulative displacement is more suitable for the slow-moving landslides with low temporal heterogeneity in movement. Furthermore, 3D displacement measurements contribute to a comprehensive understanding of landslide movement characteristics and facilitates the development of reliable building vulnerability models. This provides essential guidance for the quantitative assessment of slow-moving landslide risk to buildings.
How to cite: Zhang, X., Chen, L., Yin, K., Chen, Q., and Xia, J.: Vulnerability assessment of masonry buildings on slow-moving landslides through 3D displacement, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6666, https://doi.org/10.5194/egusphere-egu26-6666, 2026.
The predictive capability for rockfall hazards has improved markedly in recent decades; however, integrating complementary observation methods that capture the full range of preparatory and triggering processes remains challenging, particularly at highly sensitive sites such as World Heritage monuments. This study presents a multi-method assessment of rockfall activity at the 3,500-year-old Mortuary Temple of Hatshepsut in Ancient Thebes, Egypt, one of the best-preserved temples of Pharaonic Egypt. The temple is situated directly beneath a ~100 m high, layered cliff of Eocene Thebes Limestone, which is affected by frequent fragmental rockfall. A major historical slope failure in the vicinity previously buried the neighbouring Temple of Thutmose III, highlighting the long-term hazard potential.
Within the framework of the German–Egyptian project High-Energy Rockfall ImpacT Anticipation (HERITAGE), we combine Terrestrial Laser Scanning (TLS), Interferometric Synthetic Aperture Radar (InSAR), and numerical rockfall runout modelling to characterise both recent activity and potential future failure scenarios. TLS and InSAR data acquired between 2022 and 2023 enabled the quantification of volumes associated with small-scale failures and the mapping of potential detachment zones relevant for larger instabilities. The joint application of TLS and InSAR proved essential, as only their combination allows an unambiguous delineation of rockfall-active areas, reducing the uncertainty inherent to individual techniques. Exploratory ambient vibration analyses were applied on selected rock towers to test their applicability for detecting preparatory destabilisation by frequency shifts.
Based on the observed failure inventory, we modelled runout trajectories for single-block failures covering a volume range from 0.01 to 25 m³. In addition, frictional parameters for large-volume (>10³ m³) granular flows resulting from rock tower collapse were constrained using evidence from historical slope failures. These simulations provide first-order estimates of impact areas and energy distributions affecting the temple complex.
Overall, this study demonstrates the value of integrating non-invasive observation and modelling techniques across multiple failure magnitudes within a unified framework. The approach is particularly suited to hyper-arid, geomorphologically complex, and archaeologically sensitive environments. We present the first event-based and impact-oriented analysis of gravitational mass movements at the Temple of Hatshepsut, providing essential baseline data for future hazard and risk assessments at Egyptian World Heritage Sites.
How to cite: Jacobs, B., Ismael, M., Ezzy, M., Keuschnig, M., Mendler, A., Kieser, J., Krautblatter, M., Grosse, C. U., and Helal, H.: From Pre-Failure Deformation to Runout: Integrating TLS, InSAR and Runout Modelling to Quantify Rockfall Hazards at the Temple of Hatshepsut, Egypt., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21236, https://doi.org/10.5194/egusphere-egu26-21236, 2026.
Posters on site: Thu, 7 May, 16:15–18:00 | Hall X3
Slope deformation is one of the most critical precursors to natural hazards such as landslides, embankment failures, and ground subsidence. Reliable early detection and prediction of slope deformations are essential to mitigate disaster risks and support resilient land management. This study reviews recent advances in slope deformation prediction methods using Synthetic Aperture Radar (SAR) and proposes a research method to enhance prediction accuracy and applicability. The review focuses on four main approaches: (i) InSAR time-series analysis for temporal deformation tracking, (ii) conversion of line-of-sight (LOS) displacements into three-dimensional ground motion components, (iii) nonlinear deformation forecasting using machine learning techniques, and (iv) data fusion between high-resolution satellite SAR and ground-based SAR (GB-InSAR) observations.
The analysis of SAR-based studies published over the past five years demonstrates that high-frequency and high-resolution SAR data, when combined with time-series analysis, can quantitatively capture progressive slope deformation and acceleration trends at millimeter-level precision. Integrated models that incorporate climatic, geological, and topographic factors achieved strong predictive performance, with coefficients of determination (R²) exceeding 0.9. Machine learning–based approaches, particularly those employing recurrent neural networks and ensemble algorithms, effectively represented nonlinear and seasonal deformation dynamics. However, prediction accuracy remains constrained by dense vegetation, limited satellite revisit intervals, and the directional sensitivity of LOS measurements, which can introduce uncertainty in estimating the true magnitude and direction of deformation.
This study investigated the strengths, limitations, and practical considerations of current SAR-based deformation prediction methods. The research findings confirm that multi-sensor integration, combining SAR data with meteorological, hydrological, and geotechnical information, can significantly improve the reliability and generalizability of slope deformation forecasts. Moreover, AI-based frameworks offers promising opportunities for interpretable and transferable models applicable to different slope environments. Based on an analysis of current research trends, this study provides a comprehensive overview of the latest SAR-based slope deformation prediction technologies and proposes a research method for developing a real-time monitoring and prediction system for slope deformation under the influences of climate change and anthropogenic factors
ACKNOWLEDGEMENTS
Research for this paper was carried out under the KICT Research Program (project no. 20250285-001, Development of infrastructure disaster prevention technology based on satellites SAR.) funded by the Ministry of Science and ICT
How to cite: Lee, D., Kim, J., Kim, D., and Jung, J.: A Study on the Prediction Methods of Slope Deformation Using SAR Data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14, https://doi.org/10.5194/egusphere-egu26-14, 2026.
Understanding natural hazards in the Himalayan terrain requires an analysis of both tectonic forcing and landscape response, particularly in the Higher Himalaya where steep terrain, active tectonics and heavy rainfall combine to create a high potential for natural hazards. The present study integrates a Multi-Criteria Decision Making (MCDM) and Machine Learning (ML) framework to comprehend tectonic activity and landslide susceptibility in this high mountainous region. The MCDM methods, specifically CRITIC-TOPSIS, provide a consistent assessment of relative tectonic activity and surface deformation patterns. To complement this, multi-year (2020–2025) machine learning methods, Random Forest and XGBoost were applied to generate annual landslide susceptibility maps. These maps revealed a gradual increase in moderate to high susceptibility zones across the years, particularly along fault-controlled slopes and steep valley walls. This indicates an evolving environment that is being actively modified by both human and natural factors. Ultimately, the combined CRITIC–TOPSIS–ML approach provides a powerful, multi-parameter methodology for identifying tectonically active zones and slope instability hotspots, facilitating the early identification of emerging risk zones in rapidly evolving mountainous regions.
How to cite: Tiwari, J., Biswas, M., and Mukherjee, S.: Integrated CRITIC–TOPSIS and Machine-Learning Framework for tectonic activity and landslide hazard assessment in Higher Himalaya, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1076, https://doi.org/10.5194/egusphere-egu26-1076, 2026.
Approximately 70% of South Korea’s land is mountainous, and as expressways and national highways have expanded across this terrain, extensive roadside slopes have been formed. Although various reinforcement and stabilization measures were applied during road construction, failures, such as collapses and settlements, continue to occur due to heavy rainfall, typhoons, and other extreme weather events.
In recent years, South Korea has initiated research efforts to utilize imagery acquired from Synthetic Aperture Radar (SAR) satellites to detect long-term ground displacements along roadside slopes and support preparedness for potential geohazards.
This study assessed the detectability of ground displacements on road slopes by considering the motion direction and incidence angle characteristics of SAR satellites over the Korean Peninsula.
SAR satellites can acquire high-resolution images regardless of daylight or weather conditions. Furthermore, interferometric techniques (InSAR) enable the generation of digital elevation model (DEM) with sub-meter accuracy and ground displacement estimations with millimeter-level precision.
However, as radar satellites observe the Earth at oblique angles relative to their flight direction and employ side-looking geometry perpendicular to the line of sight, geometric distortion may occur in areas with highly irregular terrain. In such conditions, shadowing effects—including layover and radar shadow can arise where radar signals are unable to reach. Steep road slopes are particularly susceptible to these limitations, and accurate observation may be impossible depending on the satellite’s motion direction and incidence angle. Therefore, before applying SAR data to slope-monitoring studies, it is essential to assess the feasibility of observations for the target slopes.
In this study, the motion and observation characteristics of the Sentinel-1B satellite which continuously acquires imagery across the Korean Peninsula at approximately 12 day intervals were analyzed to evaluate the feasibility of observing road slopes nationwide.
To evaluate the SAR observation feasibility for road slopes in South Korea, the evaluation was conducted in three stages: 1. Road-slope information, road networks, and other required datasets were compiled and converted into geospatial (Geographic Information System) data formats. 2. SAR imagery covering the Korean Peninsula was collected, and satellite motion direction and incidence angles were derived from image header files and metadata, then converted into raster datasets. 3. The relative spatial relationship between the road-slope data and satellite-observation data was analyzed, and based on these results, the observation feasibility of each road slope was evaluated considering the effects of the SAR satellite’s motion direction and incidence angle.
As a result, the study evaluated the SAR observation feasibility for approximately 27,000 road slopes across South Korea. Considering the orbit and incidence-angle characteristics of the Sentinel-1B satellite, it was found that about 66% of all slopes were located within the satellite’s effective imaging range.
Acknowledgement
This research is based upon work supported by Korea Institute of Civil Engineering & Building Technology(KICT), Project No.20250285-001
How to cite: Kim, D., Kim, J., Lee, D., and Jung, J.: Evaluation of the Detectability of Road-Slope Displacements Considering SAR Satellite Direction and Incidence-Angle Effects in South Korea, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1365, https://doi.org/10.5194/egusphere-egu26-1365, 2026.
Under a changing climate, landslides pose significant risks to human society and regional sustainability. Advances in remote sensing and Artificial Intelligence (AI) have enabled automated large-scale landslide mapping after events. However, most existing AI-based inventories treat landslides as a single class, overlooking typological differentiation and providing limited assessment of geographic accuracy, which are central concerns in Landslide Inventory Maps (LIMs) research. This oversight obscures geomorphological diversity and spatial heterogeneity, constraining their use in geomorphological investigations and analyses beyond simple detection. As a result, the lack of explicit evaluation of thematic and geographic accuracy in AI-based landslide inventories remains an unresolved scientific problem.
To bridge this gap, this study developed and evaluated AI-based multi-class landslide inventories derived from PlanetScope and SPOT-6 imagery in the northern Noto Peninsula, Japan, and validated them through two-point field surveys (Figure 1). The study area (37.40°–37.49°N, 137.01°–137.19°E) lies along the Sea of Japan coast of central Honshu, at the convergent boundary between the Okhotsk and Amurian plates, where active reverse faults have been repeatedly reactivated. In 2024, the area was affected by two major landslide-triggering events: an Mw 7.6 earthquake on 1st January and an episode of extreme rainfall in September. These events generated widespread but contrasting slope failures and were used to construct two post-event LIMs.
Results indicate that the inventories consistently identify three failure types, falls, slides, and flows, with 1,677 landslides mapped after the earthquake and 2,511 after the rainfall. Landslide areas follow log-normal size distributions, with slides covering the largest total area and flows exhibiting the highest counts. Compared with the earthquake, rainfall triggered more numerous but generally smaller failures.
We further evaluated the thematic and geographic accuracy of the constructed LIMs against established geomorphological understanding. Falls preferentially occur near active faults and on conglomerates, where rock masses are mechanically weakened. Flows predominantly concentrate in natural valleys and headwater channels, and are associated with porous volcanic-ash deposits, favoring material mobilization. Slides mainly develop on sandstone–mudstone interbeds, reflecting contrasting mechanical properties along bedding planes. Slope is the strongest control, especially for falls and flows, with failures concentrated on steep slopes characterized by concave curvature and steep longitudinal profiles. These geomorphologically consistent patterns support both thematic and geographic accuracy.
Finally, suitability for AI applications was assessed using thirteen semantic segmentation models. DRANet and TransUNet achieved the highest accuracy (mIoU > 0.85), providing precise landslide boundaries suitable for geomorphological analysis and modeling. In contrast, SwinUNet and SegFormer offer efficient trade-offs (around 0.80 mIoU with <15 GFLOPs), making them more appropriate for rapid mapping and emergency response under limited computational resources. Overall, strong and stable model performance indicates that the validated LIMs can be effectively used for AI training and operational landslide mapping, providing a foundation for AI-based landslide inventories in both methodology and application.
Figure 1. AI-based landslide inventory maps of the northern Noto Peninsula.
How to cite: Yu, B., Delloye, N., Oguchi, T., Iizuka, K., and Yuan, W.: Construction and Quality Evaluation of AI-based Landslide Inventory Maps for the 2024 Noto Peninsula Events, Japan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2641, https://doi.org/10.5194/egusphere-egu26-2641, 2026.
Slope hazard monitoring systems constitute a fundamental component of slope early warning systems, supporting timely warning issuance and risk mitigation for unstable natural and engineered slopes. However, many operational monitoring networks rely on heterogeneous sensors and data loggers from multiple manufacturers, leading to compatibility issues that undermine long-term reliability and continuity of early warning monitoring. When components require replacement due to durability limitations or product discontinuation, entire monitoring systems are often replaced, resulting in high maintenance costs and prolonged monitoring interruptions that can compromise hazard detection and warning effectiveness.
This study presents an interoperable sensor system and integrated data acquisition device designed to enhance the reliability and sustainability of monitoring components within slope early warning systems. A nationwide survey of 200 slope monitoring sites managed by a Korean government agency identified 5,669 installed sensors and revealed strong dependence on a limited number of manufacturers. Based on these findings, system specifications were established to ensure broad compatibility with dominant commercial products and existing monitoring infrastructures, enabling long-term operation of early warning monitoring networks.
The developed data acquisition system supports multiple sensor types commonly used for slope hazard assessment, including displacement, inclination, rainfall, and groundwater level sensors, and integrates diverse communication protocols (LAN, LTE, Wi-Fi, and LoRa) for real-time data transmission. Key features include bidirectional sensor control with self-diagnostic functions, lightning and overvoltage protection, remote configuration, and intelligent fault detection, ensuring stable monitoring performance under hazardous environmental conditions. Smart compatible sensors were also developed to ensure interoperability, remote monitoring, and enhanced durability compared to conventional slope monitoring sensors.
System performance was verified through certified electromagnetic compatibility testing and field evaluations at laboratory-scale and operational slope monitoring sites. Results demonstrated stable operation, seamless compatibility with existing systems, and measurement performance comparable to commercial products.
By enabling flexible sensor replacement and reducing dependency on specific manufacturers, the proposed approach improves the operational reliability and continuity of monitoring components in slope early warning systems. This contributes to more robust warning chains, proactive hazard management, and disaster risk reduction through resilient and sustainable monitoring infrastructures (Project No. RS-2025-02263904, second year).
How to cite: Park, B., Lee, S., Hwang, S., and Kim, W.: Enhancing Operational Reliability in Slope Hazard Monitoring through an Interoperable Sensor System, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2864, https://doi.org/10.5194/egusphere-egu26-2864, 2026.
The increasing frequency of landslides, driven by intense meteorological events, demands the development of functional, reliable, and cost-effective monitoring strategies. In this context, natural laboratories equipped with multi-sensor infrastructures represent essential facilities for testing the integration of multi-platform and multi-temporal remote sensing data for landslide hazard assessment. This study presents the monitoring framework and its application to a complex landslide sequence that occurred in March 2025 at the Poggio Baldi Natural Laboratory (Northern Apennines, Italy). Triggered by an exceptional rainfall event characterised by 117.8 mm of cumulative precipitation in 24 hours, the sequence involved a two-stage failure process: an initial 30,000 m³ earth flow followed, approximately 48 hours later, by a 35,000 m³ rockslide.
The monitoring infrastructure enabled a multi-scale characterization of the entire rainfall event, documenting the activity of the entire slope and assessing the rock failure activity from the main scarp. At the cliff scale, the permanent ground-based monitoring network — integrating optical and thermal cameras, acoustic sensors, and meteorological stations — captured the kinematic evolution of both failure phases. Digital Image Correlation (DIC) applied to optical and thermal sequences allowed high-frequency quantification of the earth flow displacement field, which reached peak velocities of 100 cm/h. Thermal infrared analysis identified pre-failure anomalies, likely related to localised soil saturation and initial surface deformation during nighttime. For the rockslide, acoustic monitoring enabled a three-phase reconstruction of the collapse dynamics, while motion-triggered optical systems detected a significant increase in rockfall frequency as a clear precursor to the main failure. Post-event characterization was achieved through high-resolution UAV photogrammetry for volumetric quantification and Ground-Based Interferometric Arc-SAR (GB-InSAR) monitoring, which documented the transition from active displacement to slow-moving residual deformation and highlighted the slope's sensitivity to subsequent rainfall events. Satellite imagery from Sentinel-2 and PlanetScope provided detection of the slope response to the meteorological trigger, identifying surface changes in the immediate aftermath of the rainfall, limited to the upper slope.
The continuous monitoring results also highlight its importance in constituting large training datasets suitable for the development of nowcasting and near-forecasting strategies. Furthermore, the multi-year continuous datasets collected from 2021 at Poggio Baldi, combining high-frequency meteorological records with detailed rockfall inventories, are currently being exploited to train deep learning models based on Neural Networks approaches. These models aim to capture the complex, non-linear relationships between meteoclimatic drivers and slope response, with the ultimate goal of developing predictive tools for rockfall occurrence at the cliff scale. The presented monitoring system based on continuous sensors and periodic surveys proved to be a cost-effective framework able to provide robust and scalable solutions for landslide monitoring
How to cite: Molinari, A., Stefanini, C. A., Marmoni, G. M., and Mazzanti, P.: Integrated monitoring of a landslide sequence at the Poggio Baldi Natural Laboratory (Italy) and perspectives for ANN-based learning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6667, https://doi.org/10.5194/egusphere-egu26-6667, 2026.
Landslides represent a well-known and pervasive hazard in the Italian territory, representing a substantial risk to both human safety and critical infrastructure. The Alpine region is particularly susceptible to slope instability due to its complex geomorphology and the heterogeneous nature of glacial and post-glacial deposits that characterize its valleys. In this framework, systematic landslide monitoring and the development of reliable stability models are essential for acquiring accurate and up-to-date data, enabling the assessment of evolving instability conditions and supporting effective risk mitigation strategies.
This study presents the results of an integrated approach of geomorphological mapping, monitoring, and numerical modeling applied to a landslide located in the southeastern Alps, within an area characterized by moraine and colluvial slope deposits, including evidence of a paleo-landslide. The investigation includes a detailed geotechnical characterization of the slope stratigraphy based on borehole logs and inclinometer measurements, as well as a hydrogeological analysis derived from piezometric data collected within the boreholes. Ground deformation has been quantified through displacement measurements obtained using single-frequency GPS receivers, high-precision GNSS sensors, and remote sensing (InSAR) techniques. In addition, slope stability has been evaluated through both two-dimensional and three-dimensional numerical modeling.
The integrated use of multiple monitoring techniques and modeling approaches enables cross-validation of the results and supports a more robust interpretation of the observed displacement patterns. While the preliminary two-dimensional stability analyses are corroborated by three-dimensional modeling outcomes, the incorporation of displacement measurements significantly enhances the reliability of the analytical models, allowing for a detailed reconstruction of the slope deformation mechanisms and their temporal evolution.
How to cite: Tunini, L., Zuliani, D., Di Traglia, F., Borselli, L., De Luca, C., Nolesini, T., and Casu, F.: Landslide monitoring by using GNSS and InSAR observations: the south-eastern Alps case study, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9697, https://doi.org/10.5194/egusphere-egu26-9697, 2026.
Landslides cause numerous fatalities and extensive infrastructure damage every year, resulting in human and economic losses. Climate change and its cascading impacts are increasing both the frequency and magnitude of landslides, rockfalls, and debris flows. Humanitarian organisations, such as Médecins Sans Frontières (MSF), play a crucial role in disaster response, where timely, reliable, and up-to-date information is essential for effective hazard and damage assessments, as well as for coordinating rescue operations and humanitarian aid.
Earth observation (EO) data and technologies have demonstrated strong potential for supporting emergency response and disaster risk management following landslide events. However, despite continuous methodological advances in academic research, EO-based approaches are rarely tested under real operational conditions, such as direct support to humanitarian organisations during ongoing emergencies. In addition, many existing solutions do not adequately address the specific user requirements and information needs that arise at different stages of the disaster cycle. This study therefore aims to generate targeted EO-based landslide information tailored to the operational needs of humanitarian aid. The landslides triggered by Tropical Cyclone Freddy in Malawi in 2023 are used as an illustrative example to demonstrate typical information needs and constraints during real emergency situations.
We test and evaluate a range of landslide mapping tools and methods based on optical and radar satellite data, focusing on the detection, delivery, accuracy, and communication of landslide information for humanitarian applications. The approaches are assessed under typical emergency constraints, including processing time, limited data availability, unstable connectivity, and unsafe field conditions. Suitable methods are identified and customised in close alignment with MSF’s operational requirements. To enhance usability and impact, EO-derived results are combined with principles of risk communication, supporting humanitarian staff in interpreting and applying landslide information during response operations. Thus, this work contributes to bridging the gap between scientific EO research and practical humanitarian applications.
How to cite: Nafieva, E., Arellano, C., Hölbling, D., van Ek, J. J., Henriod, S., Rebois, Y., Schwingshandl, A., Forcieri, S., Dabiri, Z., Heidrich, R., Hörbe, I., and Abad, L.: An Overview of Earth Observation Resources and Services for Landslide Detection in Humanitarian Contexts , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17494, https://doi.org/10.5194/egusphere-egu26-17494, 2026.
Natural disasters - intended as landslides, hurricanes, fires, avalanches, flooding, earthquakes, industrial accidents, terroristic attacks, eruptions, pollution, have been seriously threatening the well-being of the global society. Over the past 50 years, more than 11,000 disasters have been attributed to weather, climate and water-related hazards, involving 2 million deaths. While the average number of deaths recorded for each disaster has fallen by a third during this period, the number of recorded disasters has increased five times, and the economic losses have increased by a factor of seven. Current findings from the United Nations Global Assessment Report on Disaster Risk Reduction (DRR) points out that the economic loss from disasters range from US$250 billion to US$300 billion each year.
In this context Space assets and remotely piloted aircraft (drones) play a crucial role in emergency response and disaster management, especially after the occurrence of landslides. First responders ask for a quick to deploy in-situ solution based on resilient and robust infrastructure to perform accurate mapping and extended surveillance for people and assets localisation.
Accordingly, EUSATfinder is about demonstrating the effectiveness of a synergic use of three main European space programs, namely GOVSATCOM, Copernicus and Galileo in such critical situations. The purpose of the EUSATfinder is to provide an innovative integrated and scalable solution to support first responders in real-life during different operational phases (detection, preparedness, response, recovery and mitigation of emergencies) with particular focus to first responders’ activities in situ for a landslide-disaster management.
The solution is based on a mobile operational centre (MOC) able to join in the proximity of the emergency area and to deploy several assets to support the operations:
- a quick to deploy resilient communication infrastructure;
- a fleet of heterogenous drones for mapping (integrated with Copernicus Emergency Management Service), for extended environmental surveillance and for people and asset localisation;
- innovative equipment for first responders’ health monitoring and localisation (Galileo);
- a distributed platform for First responder operations and citizens alerting management.
The above introduced objectives confer to the EUSATfinder project a worldwide dimension, having European public authorities, industries and research centres with the clear role to bring innovation and know-how to allow an effective crisis area management in emergency situations worldwide.
The EUSATfinder project has received funding from the European Union Agency for the Space Programme (EUSPA), under the European Union’s Horizon Europe research and innovation programme (call HORIZON-EUSPA-2023-SPACE-01-61 and Grant Agreement No 101180157).
How to cite: Raspini, F., Nardini, O., Pazzi, V., Del Soldato, M., and Nisi, M.: EUSATfinder - EUropean Space, Aerial and Terrestrial assets supporting first responders' operations in the context of landslide-induced emergency, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20369, https://doi.org/10.5194/egusphere-egu26-20369, 2026.
Earth Observation (EO)-based methods for automated landslide detection have advanced rapidly in recent years, ranging from simple spectral index approaches to complex deep learning models. Despite these developments, systematic and reproducible benchmarking of such methods remains limited. Existing studies often rely on heterogeneous datasets, inconsistent evaluation metrics, and ad-hoc preprocessing choices, making it difficult to assess detection performance under realistic operational conditions, particularly in near-real-time post-disaster contexts.
This study proposes a model-agnostic benchmarking framework designed to enable transparent and operationally relevant comparison of EO-based landslide detection algorithms. The framework standardizes data preprocessing, scene characterization, evaluation metrics, and reporting. It is implemented using modular, reproducible computational notebooks. Performance is assessed not only globally but also in a stratified manner, accounting for environmental and atmospheric variability such as land cover type, terrain characteristics, and cloud contamination.
The framework is demonstrated using the February 2023 Kahramanmaraş earthquake sequence in Türkiye, which triggered thousands of coseismic landslides across a highly heterogeneous landscape. A high-quality manually mapped landslide inventory serves as ground truth. Two representative detection approaches are used as case studies: (i) an NDVI-based change detection method and (ii) a U-Net deep learning segmentation model, both applied to harmonized Sentinel-2 Level-2A imagery without scene-level cloud filtering to reflect operational constraints.
Benchmarking results will be presented using standardized metrics such as Intersection-over-Union, precision, recall, and false positive/negative rates, complemented by scene-level performance summaries. Rather than ranking models, the emphasis is on demonstrating how structured benchmarking can reveal context-dependent strengths and limitations of different approaches. The proposed framework aims to support reproducibility, informed model selection, and future integration into operational platforms, contributing to more reliable EO-based landslide mapping in disaster response settings.
How to cite: Bonsu, L., Tanyaş, H., and Girgin, S.: Towards a Reproducible Benchmarking Framework for EO–Based Automated Landslide Detection Fueled by Landslides Triggered by the 2023 Türkiye Earthquake Sequence, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20882, https://doi.org/10.5194/egusphere-egu26-20882, 2026.
Landslide is one of the most destructive natural hazard that causes great loss of economy as well as human lives. Landslide is usually monitored by geological surveys, satellite-based monitoring or human based sensor. In this work, Digital Image Correlation (DIC) is applied for landslide monitoring. DIC is a computer vision approach that can be applied on camera images with a random forest-based image segmentation. The study combines near real-time motion detection through Fast Fourier Transform (FFT) combined with vegetation masking to filter any noise induced by vegetation growth. This method can enhance the accuracy of landslide change detection over complex mountainous terrains. Results indicate that DIC along with vegetation masking was able to correctly track the displaced regions, which has significantly improved DIC reliability by filtering vegetation-induced motion artifacts.
How to cite: Das, A., Sajan, A., Bhardwaj, A., Tyagi, A., and Samadhiya, N.: Landslide Detection Using Digital Image Correlation and Vegetation Segmentation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21843, https://doi.org/10.5194/egusphere-egu26-21843, 2026.
Ground subsidence in Joshimath, a geologically fragile town in Uttarakhand's Chamoli district situated on an ancient landslide zone (Mishra Committee Report, 1976), poses severe risks to infrastructure and residents, exacerbated by aquifer disruption, deforestation, heavy development, and a muddy water outburst on 2 January 2023. Major subsidence and cracking occurred between 3–8 January 2023, necessitating precise monitoring methods amid complex Himalayan terrain. While Differential SAR Interferometry (DInSAR) excels in wide-area millimeter-scale Line-of-Sight (LOS) displacement detection using Sentinel-1 SLC data, it struggles with high-gradient deformations; conversely, UAV photogrammetry, which generated high-resolution orthomosaics and DEMs for detailed zone mapping.
This study proposes an integrated DInSAR–UAV approach to leverage complementary strengths, achieving higher accuracy than either method alone, akin to strategies validated in this region. Results delineate subsidence patterns, refine zoning of affected areas, and inform risk mitigation for sustainable urban planning in hazard-prone Himalayan settlements.
How to cite: Pati, R., Dash, R. K., and Kanungo, D. P.: High-Resolution Subsidence Zonation in Joshimath Using Complementary DInSAR and UAV Dem Analysis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16050, https://doi.org/10.5194/egusphere-egu26-16050, 2026.
Posters virtual: Fri, 8 May, 14:00–18:00 | vPoster spot 3
EGU26-13589 | ECS | Posters virtual | VPS14
Co-seismic Landslide Susceptibility Mapping after the 2023 Al Haouz Earthquake (Morocco) Using Machine LearningFri, 08 May, 14:30–14:33 (CEST) vPoster spot 3
Landslides are a global geological phenomenon that constitute serious threats for human lives and engineering infrastructure, making the susceptibility assessment of these landslides a critical step for risk mitigation. The Al Haouz province, which was heavily struck by the Mw 6.8 earthquake of 2023, recorded several slope instabilities caused by seismic motion. In this context, the present study aims to evaluate co-seismic landslides susceptibility using machine learning models to support effective risk mitigations.
Logistic Regression LR and Random Forest RF models were employed to generate the susceptibility maps. The landslide inventory map with 302 landslide points and 600 non-landslide points was utilized with a 70:30 split for training/testing purposes. Sixteen conditioning factors were considered in the modelling process.
The results indicate RF performed better than the LR method, with an accuracy of 97.34% compared 92.92% for LR. The area under the curve (AUC) values ranged between 0.98 for LR and 0.99 for RF. reflecting the high predictive capability of both models. Elevation, Slope, PGA and rainfall had the highest contribution scores amongst the factors identified by both models.
The outcomes indicate the effectiveness of machine learning algorithms, specifically the RF model, for susceptibility mapping related to landslides in a seismic area. Elevation and slope are the most important factors influencing landslides from a geomorphological perspective in Al Haouz province. PGA is the most significant parameter among all factors as landslides are primarily triggered by seismic acceleration associated with earthquake events. Rainfall is a significant parameter that triggers landslides as a result of steep slopes associated with heavy rainfall either continuously or with high intensity.
The co-seismic landslide susceptibility maps produced in this study provide valuable information for identifying vulnerable zones and constitute an effective tool for land-use planning and disaster risk reduction aimed at protecting human lives, infrastructure, and the environment.
Keywors: Landslide susceptibility; Al Haouz earthquake; Machine learning; Morocco
How to cite: Edoudi, A., Cherif, S., Ibouh, H., Ahmadian, N., El Wahidi, F., Chourak, M., Kurtz, R., and Dubovyk, O.: Co-seismic Landslide Susceptibility Mapping after the 2023 Al Haouz Earthquake (Morocco) Using Machine Learning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13589, https://doi.org/10.5194/egusphere-egu26-13589, 2026.
