The advent of the EGMS service offers chances and opportunities to EU Member States practitioners and researchers into the field of landslide monitoring. As member of the EGMS validation team, under the lead of SIXENSE, the Geosphere Austria carried out the in situ validation activity for five test sites spread over Europe. The focus of this paper is the inter-comparison of an automatic geodetic monitoring system installed at two landslide locations in Tyrol, Austria against the main products offered by the EGMS:

• level 2a
• level 2b
• level 3

The comparison was performed in a Jupiter hub environment created ad hoc for the validation project by our partner Terrasigna. The workflow was developed in R language and validates error, precision and accuracy of the (in-situ) velocities and time series (TS) against the correspondent MT-InSAR values of the EGMS.

The workflow is made of several highly customisable modules:

• reads and visualises the two datasets;
• performs a series of analysis such as smoothing (simplification), outliers search and trends for both time series;
• inter-compares all the combinations of derived TS datasets and calculates for each couple RMSE, Coefficient of Determination (R²) and index of agreement;
• plots the TS and bar diagrams of the best scores in terms of minimum errors, maximum accuracy and maximum precision;
• delivers a Quality Index (QI) between 0-1 for each EGMS product;

The results of the in-situ validation activity will be presented and explained. In fact, considering the type of natural hazard (deep-seated gravitational slope deformation) and his location (vegetated and high relief alpine morphology), this validation set the perfect example to discuss strength and weakness of the EGMS if compared to state-of-the art in-situ monitoring systems installed in such extreme and remote areas.

How to cite: Vecchiotti, F. and Kociu, A.: The Copernicus European Ground Motion Service (EGMS) validation: a landslide monitoring prospective., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5014, https://doi.org/10.5194/egusphere-egu23-5014, 2023.

How to cite: Prodromou, M., Theocharidis, C., Fotiou, K., Argyriou, A., Polydorou, T., Hadjimitsis, D., and Tzouvaras, M.: Fusion of Sentinel-1 and Sentinel-2 satellite imagery to rapidly detect landslides through Google Earth Engine, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12618, https://doi.org/10.5194/egusphere-egu23-12618, 2023.

Landslide inventory maps are fundamental tools for territorial planning, recording the location, the state of activity and the type of mass movement that affects an area (Guzzetti et al., 2012). In the last decades, the satellite Remote Sensing has represented one of the most useful techniques for studying landslides with its capability to detect large portions (km-scale) of the Earth surface: in this sense, DInSAR (Differential Interferometry Synthetic Aperture Radar) data are capable of retrieving surface displacements with centimeter to millimeter accuracy. The launch of Sentinel-1 (S1) satellites and the flourishing of fully automatic processing chains has encouraged the development of national scale monitoring service for the study of natural and anthropogenic hazards. Accordingly, the Parallel Small Baseline Subset (P-SBAS) processing chain, in the framework of an Operative Agreement with the Italian Ministry of Economic Development (MiSE) aimed at generating the displacement time-series and corresponding velocity maps of the entire Italian territory, has significantly boosted the systematic update of the landslide state of activity.

In this work, the Italian national database of landslides (IFFI landslide inventory) has been updated up to 2018 by exploiting national scale P-SBAS S1 analysis. In particular, the past landslide state of activity, which was obtained by exploiting the Envisat data (2003-2010 temporal range), has been compared with the one retrieved with P-SBAS S1 results (2014-2018 temporal range). With this comparative analysis, more than 56,000 landslides have been identified. The 74% of the studied landslides has been classified as dormant, having annual average velocity (projected along the slope direction) <7 mm/year (considering a value of two times the standard deviation) while the 26% has been considered as active (mean velocity >7 mm/year). In addition, a landslide reliability matrix was introduced to assess the quality of the new updated inventory, by using the point density and the standard deviation of the mean V_slope value of each considered landslide. Finally, the 2D horizontal (along the E-W direction) and vertical components of the MPs have been computed, aiming at the characterization of each landslide's movement direction and magnitude. The obtained results show the heterogeneity and the complexity of the Italian territory, with major differences among each region and between the Alpine and Apennine sectors. The work demonstrates that nation-wide monitoring service Sentinel-1 DInSAR data, such as those generated by the P-SBAS method, can be very useful to systematically update landslide inventories, providing significant support to risk reduction practices.

How to cite: Confuorto, P., Casagli, N., Casu, F., De Luca, C., Del Soldato, M., Festa, D., Lanari, R., Onorato, G., and Raspini, F.: Update of the Italian National Landslide Inventory Map by exploiting Sentinel-1 P-SBAS data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15086, https://doi.org/10.5194/egusphere-egu23-15086, 2023.

Following extreme climate events, a timely and detailed landslide mapping is necessary to determine which areas have been most affected and to support civil protection in rescue operations. Moreover, the monitoring of slope instabilities can lead to an appropriate hazard and risk assessment of the affected areas and to an effective design of remediation works. The integration of optical and SAR remote sensing data acquired by spaceborne sensors plays a key role in these types of evaluations. Optical sensors perform better in terms of spatial and temporal resolution; SAR sensors have the advantage of acquiring images in all weather and light conditions. In this case study, an unstable slope located on the left side of the Boite river in the municipality of Valle di Cadore (northeastern Italian Alps) was investigated after windstorm Vaia event that occurred in October 2018. Medium and high-resolution optical imagery acquired by Sentinel-2 and PlanetScope missions, respectively, have been exploited to calculate NDVI values before and after the event as well as to identify and delineate the most damaged areas using the change detection technique. Then, the processing of Sentinel-1 SAR data through the Small BAseline Subset (SBAS) multi-temporal algorithm allowed for monitoring the evolution of the slope during the post-event. The results show the benefits of combining optical and SAR data to map and monitor the evolution of a slope that was affected by an extreme event such as the windstorm Vaia. In particular, the optical data show the sectors affected by slope instabilities and the time series derived by the SBAS analysis quantifies the displacement rate, emphasizing that the slope is still active. In conclusion, the analysis carried out reveals how these techniques can now become a concrete part of the design of systems to mitigate geological risks derived from hydrometeorological phenomena, whose frequency appears to be increasing due to climate change.

How to cite: Puliero, S., Meena, S. R., Rosi, A., Catani, F., and Floris, M.: Combining optical and SAR remote sensing data for landslide detection and monitoring after extreme climate events: a case study in the northeastern Italian Alps., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8053, https://doi.org/10.5194/egusphere-egu23-8053, 2023.

The headwater regions of the Yangtze, Yellow, and Mekong rivers are called Sanjiangyuan in Qinghai-Tibet Plateau. During the last decades, glaciers and permafrost are suffering from rising temperatures and precipitation, thus exacerbating surface instability and fostering landslides consequently. We utilized satellite-based interferometric monitoring to detect instability precursors and reconstruct deformation scenarios with 106 descending Sentinel-1 SAR images acquired from February 2016 to July 2020 in Yushu, Sanjiangyuan region where a typical earthflow occurred. Considering freezing and thawing would induce a large bias from linear deformation, the newly developed model was proposed by integrating in-situ soil temperature and moisture to separate the gravity-driven displacement and seasonal deformation. Four potential landslide prone slopes were identified in a less steep and shady landform with a maximum creep speed up to 45 mm at the regional scale. For the Yushu slope case, slow creep and accelerating creep behaviors were retrieved as precursory with the displacement rate varying from 11 mm/yr to 21 mm/yr before the failure. A seasonal oscillation pattern without gravity displacement was detected at the post-failure stage. In addition, we found that complex piecewise deformation patterns can be characterized by fast uplift (with the maximum deformation up to 20 mm in less than 30 days) in the early winter, and relatively slow subsidence in summer thawing (with the maximum value estimated by 10 mm in more than 37 days). The magnitude and duration of seasonal displacement were highly correlated with the internal hydro-thermal regime, especially soil moisture. Our result highlighted that a deformation separation model is necessary for identifying potential solifluction, evaluating the deformation state, and even forecasting risk in the periglacial regions.

How to cite: Meng, Q., Intrieri, E., Raspini, F., and Peng, Y.: Identification of earthflow-prone slopes (solifluction) in permafrost regions by a combination of satellite-based interferometry and in-situ investigations - a case study in Yushu, Sanjiangyuan, Qinghai-Tibet Plateau, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-971, https://doi.org/10.5194/egusphere-egu23-971, 2023.

Satellite ground deformation monitoring is now a well-established reality and the MTInSAR (Multi-Temporal Interferometry Synthetic Aperture Radar) techniques have widely demonstrated their feasibility for detecting a wide range of slow-moving phenomena, e.g., landslides and subsidence at different scales. The launch of the ESA’s Sentinel-1 constellation has allowed acquiring massive quantities of radar images with a worldwide coverage and a short revisiting time. These characteristics, combined with the increasing computation capabilities and advanced processing techniques, have opened the opportunity of implementing a continuous monitoring service of ground surface deformations at regional scale. Tuscany, Veneto, and Valle d’Aosta regions (Italy) have benefited from this service exploiting ground deformation maps, periodically updated, and identifying the so-called anomalies of movement of radar targets, i.e., trend changes (e.g., accelerations) in the time series of displacement. However, the continuous monitoring system only has the ability to detect the anomalies of movement without being able to assess the propensity of a territory to be affected by them. Therefore, an approach for assessing the spatial probability of trend changes of InSAR-based ground deformations occurrence has been proposed. The occurrence probability of anomalies is determined by a Machine Learning (ML) algorithm, Random Forest, and the data used for the application of the model are the anomalies database and the predisposing factors (PF). The selected PFs can be split into two groups, indeed, in addition to the classical morphological and geological features, even five variables related to the radar system have been integrated. The latter parameters are two radar visibility indexes (C-index and R-index), the horizontal, along East-West direction, and vertical component of the velocity of displacement and the standard deviation of the satellite line of sight (LOS) velocity. These two groups of PFs can be considered a synthesis of the main factors that lead to the generation of anomalies. The procedure has been tested on the Tuscany region for assessing the spatial probability of anomalies occurrence related to landslides and subsidence. The outcomes of the procedure are two maps of the spatial probability of occurrence of landslides and subsidence anomalies. A cross-validation procedure has also been performed to verify the reliability of the final maps by exploiting anomalies collected in a different timespan from the input data and the official landslide and subsidence inventories. The resulting information, periodically updated, can represent a useful instrument for the regional authorities to identify the main driving forces leading to ground deformation anomalies and the areas where site investigations are to be carried out to assess the preliminary risk.

How to cite: Medici, C., Confuorto, P., Bianchini, S., Del Soldato, M., Rosi, A., Segoni, S., and Casagli, N.: Machine learning model to assess the spatial probability of Sentinel-1 based deformation trend changes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11453, https://doi.org/10.5194/egusphere-egu23-11453, 2023.

The analysis of a three-dimensional digital model, derived from aerophotogrammetric data, is presented herein as an alternative and homogeneously improved tool for the study of rock masses in restricted areas, such as nature reserves, which are often protected by dedicated management strategies. Airborne photogrammetric and infrared thermography techniques were applied for the geostructural and morphological characterization of the tourist path at Lachea Island, belonging to the nature reserve archipelago "Lachea Islet and Cyclop Rocks" in eastern Sicily (Italy). Geologically, it is considered one of the earliest evolutionary stages of the volcano Etna that occurred about half a million years ago, which has been on the UNESCO World Heritage List since 2013 due to its exceptional level of volcanic activity. It is a world-renowned tourist destination that suffers from limited enjoyment due to the instability of the rock masses. This methodological approach provided quantitative and qualitative data on both the spatial orientation of discontinuities and the location of major structural features, as well as the volume of protruding blocks and the identification of areas of block detachment. The digitally derived spatial data were used to perform a kinematic analysis of the rock masses, highlighting the most recurrent unstable failure patterns. Infrared thermography allowed also defining the most relevant discontinuities. Through the detailed analysis of the 3D model, it was also possible to recognize potential source areas of future rockfalls, which were modelled through trajectory simulations. The results showed that rockfall threat is a crucial issue affecting the nature reserve and that the methodological approach carried out allows a quick, reliable rock mass characterization for practical purposes. Digital data were validated by a field surveying campaign, which returned a satis-factory match proving the usefulness and suitability of the approach, allowing quick and reliable rock mass characterization in the frame of practical use and risk management purposes.

How to cite: Caliò, D., Mineo, S., and Pappalardo, G.: Digital rock mass characterization for landslide risk mitigation in a nature reserve, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1400, https://doi.org/10.5194/egusphere-egu23-1400, 2023.

Recent large landslides in many parts of the World (Nuugaatsiaq, Greenland; Taan-Tyndall, US; Culluchaca, Peru) as well as the increase in the frequency of mass movements in the European Alps (e.g. collapse of the Drus, Mont Blanc Massif, France; Piz Cengalo, Switzerland) revealed the threat of such events to human activity. Seismology provides continuous recordings of landslide activity at long distances. The objective of this work is to present a method to identify and construct instrumental landslide catalogs from massive seismological data. The method is developed and applied for the period 2000-2022 at the scale of the European Alps (~ 900 x 300 km). 

The detection method applied to the seismological observations consists of computing the energy of the signal between 2 and 10 Hz. Then, a supervised Random Forest classifier is trained to identify the source of the event (earthquakes or landslides). To implement  the seismological detection and identification methods, we compiled a database of 65 landslides and 4515 earthquakes (of MLv > 0.1). The dataset is composed of 2221 seismological traces of landslides and 17353 traces of earthquakes. Tests of the Random Forest identification method gave us a rate of good identification of around 100% for landslides and 96% for earthquakes. Tests on continuous data of the 65 days of the reference landslide events allow finding 235 new landslides including 61 over 65 reference events.

The trained model is then applied on continuous seismic data (~ 400 stations) acquired over the European Alps since 2000. To reject as many noise detections as possible, a first sorting of all detections is performed by looking at SNR ratio, number of stations involved in the detection in a small area and probability scores given by the Random Forest. The instrumental catalog is composed of ~ 183.000 possible landslides. In order to review the catalog, reject possible false detections and interpret the inventory, we developed a localization method. A first order of the localization is given by the spatial clusters of seismological stations that have detected the landslide signals. Then, to refine localizations, we compute travel times from seismological stations to all points of the area with a fast marching method and we perform the inversion by using NonLinLoc software (Lomax et al. 2000). The final landslide instrumental catalog will be presented and discussed.

How to cite: Groult, C., Hibert, C., Malet, J.-P., and Provost, F.: Identifying landslides from massive seismic data and machine learning: the case of the European Alps, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7062, https://doi.org/10.5194/egusphere-egu23-7062, 2023.

 Radio-Frequency Identification (RFID) shows great potential for earth-sciences applications [1], notably in landslide surface monitoring at high spatio-temporal resolution [2] with meteorological robustness [3]. Ten 865MHz RFID tags were deployed on part of a landslide (Harmalière) and continuously monitored for 12 months by a station composed of 4 reader antennas. 2D relative localization was performed using a Phase-of-Arrival approach [4,5], and compared with optical reference measurements.

    The spatio-temporal accuracy of the method allowed for a thorough exploration of the landslides mechanisms during a 6-months period of activity. Laplacian clustering was applied to the RFID data and groups of tags with coherent behavior were identified, allowing a fine description of the kinematic motion of the landslide blocks and various mass transfer mechanisms. Each identified block can be monitored individually. 
    
    Different deformation zones were highlighted on the monitored zone. The surface movement was initiated by the topmost blocks, transferring after several weeks to the bottom of the monitored zone. This opens the way to building a landslide mechanical model in order to interpret the acquired data.

    RFID landslide monitoring allows dense observation of ground surface movements at a centimeter scale and with sub-hourly time precision, and new results bring a finer understanding the the landslides inner mechanisms.

References :

[1] M. Le Breton, F. Liébault, L. Baillet, A. Charléty, E. Larose, and S. Tedjini,
“Dense and long-term monitoring of earth surface processes with passive
rfid—a review,” Earth-Science Reviews, p. 104225, 2022.

[2] M. Le Breton, L. Baillet, E. Larose, E. Rey, P. Benech, D. Jongmans, F. Guy-
oton, and M. Jaboyedoff, “Passive radio-frequency identification ranging, a
dense and weather-robust technique for landslide displacement monitoring,”
Engineering geology, vol. 250, pp. 1–10, 2019.

[3] M. Le Breton, L. Baillet, E. Larose, E. Rey, P. Benech, D. Jongmans, and
F. Guyoton, “Outdoor uhf rfid: Phase stabilization for real-world appli-
cations,” IEEE Journal of Radio Frequency Identification, vol. 1, no. 4,
pp. 279–290, 2017

[4] A. Charléty, M. Le Breton, E. Larose, and L. Baillet, “2d phase-based rfid lo-
calization for on-site landslide monitoring,” Remote Sensing, vol. 14, no. 15,
p. 3577, 2022. 

[5] P. V. Nikitin, R. Martinez, S. Ramamurthy, H. Leland, G. Spiess, and
K. Rao, “Phase based spatial identification of uhf rfid tags,” in 2010 IEEE
International Conference on RFID (IEEE RFID 2010), pp. 102–109, IEEE,
2010

How to cite: Charléty, A., Le Breton, M., Larose, E., and Baillet, L.: Landslide mechanisms unraveled by RFID monitoring with a Machine Learning approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13287, https://doi.org/10.5194/egusphere-egu23-13287, 2023.

Envirosciences is developing an integrated multi-parameter low-cost monitoring station encompassing the geohazard and geophysical community needs. It consists in integrating co-located sensors ((meteorology, seismology, GNSS) on the same data acquisition card with modular configurations compatible with the EPOS - European Plate Observing System- specifications (sensor type, data sampling, noise level, data and metadata format). On-line data dissemination sand on-demand processing services are further being developed in order to propose advanced products such as GNSS position time series, advanced hydro-meteorological variables and seismic/micro-seismic catalogues.

A dense network of 45 stations is currently being implemented in the Western and Central Pyrenees (South France). The network consists of a seismological, meteorological and geodetic (GNSS) antennas. The measurement network is semi-permanent with at least ten years of observation. It will allow to create catalogues of hydro-geomorphological and tectonic events, to document Pyrenean tectonic uplift, and to better constrain local micro-meteorology from the valley bottoms to the summit ridges (by combining co-localised measurements of classic meteorological parameters - wind, temperature, pressure, humidity, precipitation - and tomography of vertical water vapor profiles from GNSS delays).

The objective of the presentation is to present the technological development of the station which combines several types of sensors (2 Hz seismometers, dual-frequency GNSS receivers and meteorological stations), a high-frequency geophysical digitization module, a communication module (WiFi or4G) and a power supply module (by solar energy or 220V). Softwares to control the station have been created, as well as software to supervise the database and codes to interpret the measurements.

We will further present the processing worklows and the time series of data acquired since November 2022 on 8 measurement stations already deployed in the Pyrenees. By the end of 2023, the full network of 45 autonomous real-time stations will be deployed with inter-station distances of around 5 km.

How to cite: Bourcier, F., Vidal, M., Bès de Berc, M., Broucke, C., Chatelain, N., Malet, J.-P., Wanner, X., Hibert, C., Letort, J., Grimaud, F., Sénéchal, G., Lebourg, T., Rolland, L., and Masson, F.: Envirosciences - a multi-parametric low-cost and modular station for documenting geohazards: station specifications and time series products, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6456, https://doi.org/10.5194/egusphere-egu23-6456, 2023.

In the last decades, technologies such as LiDAR, terrestrial and satellite SAR interferometry (InSAR) and photogrammetry demonstrated a great potential for rock slope assessment. However, studies and applications are still limited for ArcSAR Interferometry, Gigapixel imaging, Acoustic sensing and PhotoMonitoring. With an aim to explore deeper potentials of all above mentioned techniques in monitoring rockfalls and the related debris talus, a permanent natural monitoring site was founded in Poggio Baldi landslide (Central Italy) with various remote monitoring instruments. In detail, the annual volume lost from the cliff is about 3x103 m3 due to frequent rockfalls (up to 84 in three days). Officially inaugurated in October 2021, the permanent Natural Laboratory of Poggio Baldi is completely energy independent and remotely controlled, thus allowing a continuous and efficient monitoring of the rock slope. It is equipped with optical tools (multi resolution cameras), 3D modelling tools (LiDAR and drone photogrammetry), radar tools (linear and arc GB-InSAR, and doppler radar), acoustic tools, seismic tools (sound level meter and geophone) and a weather station. Thanks to the Department of Earth Sciences of the Sapienza University of Rome and NHAZCA SRL for the foundation, contribution, and continuous management of the site. The Poggio Baldi natural laboratory is now continuously monitoring the mass movement activities in a failed slope in Poggio Baldi. The goal is to understand the relationship between rockfalls, predisposing and triggering factors such as thermal, seismic, and meteorological stress that can provide critical information for setting up early warning systems. The acquired data are frequently analysed to assess and improve the prevailing facilities. Additionally, various tools, techniques and methodologies are being developed and implemented at the site to further enhance the capabilities of the monitoring activity. The laboratory is open to host third-party companies and research agencies for testing experimental instruments related to rock and slope deformation and associated risks.

How to cite: Cosentino, A., Santicchia, G., Mastrantoni, G., Kundu, J., and Mazzanti, P.: The Poggio Baldi Natural Laboratory: an experimental and permanent monitoring site for the assessment of rockfall phenomena, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15842, https://doi.org/10.5194/egusphere-egu23-15842, 2023.

RIEGL Laser Measurement Systems GmbH produces different laser scanners for a very wide range of applications. The technology is based on the time-of-flight principle and thus allows surfaces to be measured over a large distance in a very short time.

These devices are designed for use under difficult external conditions. Therefore terrestrial laser scanners have been used for many years for monitoring purposes e.g. landslides, erosion, avalanches etc. The effort to process the data and make it usable for further steps was left to the individual user.

We at RIEGL have taken on the topic and developed a solution how to achieve the mentioned results quickly and above all reliably.
In combination with increasingly efficient processors and communication technologies, it is possible to make the results of measurements, differences to previous measurements, available almost in real time for further interpretation via the Internet.

The current terrestrial laser scanners allow apps to be run directly on board. With the existing interfaces, the sensor can also be connected with the RIEGL V-Line CB23, a communication box, which ensures smooth 24/7 operation with SMS notification in case of a system failure and full remote operation of the system via LTE mobile network. The complete package, represents a very efficient monitoring solution for measuring surfaces, even at long distances and under demanding environmental conditions.

How to cite: Groiss, B., Gaisecker, T., and Handl, M.: Permanent Monitoring Solution based on 3D Terrestrial Laser Scanner, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7366, https://doi.org/10.5194/egusphere-egu23-7366, 2023.

This work was developed by the Earth Observation and Geohazards Expert Group from EGS and provides an overview of landslide monitoring techniques from 2005 to 2021. Based on the questionnaire, the following objectives were set: (1) to identify the type of monitored landslides, (2) to identify the landslide monitoring techniques, (3) to identify the spatial resolution, temporal resolution, and status of the technique (operational, non-operational), time of using (before the event, during the event, after the event), and applicability of the technique to the early warning system. The main contribution of this paper is to show the involvement of EGS in landslide monitoring and discuss the importance of geological data, which is often underestimated because of the use of relatively traditional, time-consuming methods. The collaborative work of 17 Geological Survey members of the Earth Observation and Geohazards Expert Group (EOEG) provided the landslide monitoring information and made this review possible. This review builds on landslide monitoring techniques at Geological Surveys, not only providing the review of the most often used techniques but also serving to highlight the importance of geological data in landslide monitoring. In addition, it provides new insights into the role of Geological Surveys in landslide monitoring.

Reference: Jemec Auflič, M., Herrera, G., Mateos, R.M. et al. Landslide monitoring techniques in the Geological Surveys of Europe. Landslides (2023). https://doi.org/10.1007/s10346-022-02007-1

How to cite: Jemec Auflič, M., Herrera, G., Mateos, R. M., Poyiadji, E., Quental, L., Severine, B., Peternel, T., Podolszki, L., Calcaterra, S., Kociu, A., Warmuz, B., Jelének, J., Hadjicharalambous, K., Peterson Becher, G., Dashwood, C., Ondrus, P., Minkevičius, V., Todorović, S., Møller, J. J., and Marturia, J.: Role of Geological Surveys of Europe in landslide monitoring, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11451, https://doi.org/10.5194/egusphere-egu23-11451, 2023.

The adoption of quantitative risk assessments (QRA) for land-slide management decision-making has increased over the last few decades, particularly when projects threaten sensitive built environments and heritage sites. The QRA process provides a quantitative estimate of the level of risk that can then be evaluated against adopted criteria for decision-making purposes regarding the need for prevention and mitigation. Although the QRA process provides for considerations of uncertainty in landslide hazard (occurrence probability, volumes, velocities, runout distances, etc.) and consequence (e.g. quantity and vulnerability of exposed population and infrastructure); The uncertainty associated with quantification in the QRA process is seldom understood or quantified.  

This presentation shares the outcome of a research project where the uncertainties associated with the QRA process were quantified in order to gain an understanding of the reliability in landslide QRA. The results are evaluated in terms of typical ranges within common risk tolerability criteria. The knowledge gained on this project was used to develop a simplified approach to consider uncertainty in QRA for practical purposes, which is illustrated for a section of highway exposed to rock fall hazards in Canmore, Alberta, Canada. The QRA was selected to inform decision-making for the selection of rock fall protection strategies at a location where environmental concerns, tourism activities, and economic activities are of significant value for the public. This significantly increased the complexity of the decision-making process, and therefore required a robust, clear approach for balancing public socio-economic expectations and safety. In the QRA process, uncertainty was associated with hazard and consequence quantification. The work elicited the plausible ranges for the input variables for risk calculation. The expected and the range in risk were calculated for the current conditions and considering the implementation of the mitigation option. The individual risk to highway users was considered low because of the limited exposure of any particular individual. The calculated current total risk (probability of fatality) was 2.9 × 10⁻⁴ with a plausible range between 2.0 × 10⁻⁵ and 5.5 × 10⁻³. The residual total risk considering implementation of the slope protection was calculated between 9.0 × 10⁻⁴ and 2.9 × 10⁻⁶, with an expected value of 4.5 × 10⁻⁵.The risk levels considering implementation of the mitigation options were evaluated against criteria previously used in Canada. These were considered an adequate balance between project costs, public safety, environmental concerns, tourism, and economic activities.

How to cite: Macciotta, R.: Considering uncertainty in the Quantitative Risk Analysis process to inform decision-making for landslide risk mitigation strategies, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1757, https://doi.org/10.5194/egusphere-egu23-1757, 2023.

The study deals with the composed landslide Urbas, located in the hinterland of the densely populated settlement in northwestern Slovenia at the foothills of the Karavanke mountain ridge. The Urbas landslide was recognized as the largest landslide among five other landslides that pose a direct danger to the underlying settlement of Koroška Bela. The Urbas landslide has a length of 500 m and a width of about 440 m. The landslide covers an area of 177,000 m2. The formation of the Urbas landslide is related to complex geological and tectonic conditions. It is defined as a rotational landslide and was formed at the tectonic contact between the Triassic carbonate and the Carboniferous clastic rocks, mainly composed of siltstone and claystone. To determine the characteristics and mechanism of the Urbas landslide, several investigations and monitoring projects have been carried out using data from the Global Navigation Satellite System (GNSS), a wire extensometer, unmanned aerial vehicle (UAV) photogrammetry and hydrometeorological sensing (groundwater table, precipitation). The results of this study show that the dynamics of the Urbas landslide exhibited different kinematic trends associated with different triggering mechanisms, depending on local geological and hydrogeological conditions. Consequently, certain parts of the landslide are at different evolutionary states and respond differently to the same external triggers.

Reference: Peternel, T.; Janža, M.; Šegina, E.; Bezak, N.; Maček, M. Recognition of Landslide Triggering Mechanisms and Dynamics Using GNSS, UAV Photogrammetry and In Situ Monitoring Data. Remote Sens. 2022, 14, 3277. https://doi.org/10.3390/rs14143277

Acknowledgments: This research was funded by Slovenian Research Agency through grants Z1-2638, P1- 0419, P2-0180 and J6-4628. Additional financial support was provided by the Ministry of Environment and Spatial Planning, and the Municipality of Jesenice.   

How to cite: Peternel, T., Janža, M., Šegina, E., Jemec Auflič, M., Jež, J., Bezak, N., and Maček, M.: Landslide monitoring and triggering mechanism detection in case of composed landslide in northwestern Slovenia, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13729, https://doi.org/10.5194/egusphere-egu23-13729, 2023.

Landslide is a common natural geological hazard that causes most damaging effects on natural features worldwide. Around the world, landslides have caused casualties, property damages and other deleterious effects in geological, ecological, environmental and infrastructures. It delineates a significant limitation for the development of urban and industrial planning. Thus, it is fundamentally important to create a landslide susceptibility map that would contribute to a realistic assessment of the threatening natural hazard for a subsequent efficient management and prediction of those potential disasters.

In this context, the present study attempted to apply an integrated multicriteria analysis with focus on Analytic Hierarchy Process (AHP) and Fuzzy-AHP (F-AHP) for the assessment of landslide vulnerability in Jabbeus area (Southwestern, Tunisia), which is potential candidate for future excavation as an open pit phosphate mine. The thematic layers and the landslide-causing factors were collected from various geospatial data sources. The main identified causative factors included lithology (LI), slope (S), distance to faults (F), distance to drainage lines (D) and the topographic wetness index (TWI). In addition Synergetic Fuzzy Analytic Hierarchy Process (SF-AHP) was applied as an innovative methodology to quantify the synergetic effect of LI-S and LI-F using fuzzy functions to enhance the interactions of these factors.

Finally, Landslide Susceptibility Index (LSI) values were computed according to the weighted linear combination (WLC) based on which zonation maps using the three methods were generated. These zones were classified in four categories, from non vulnerable to highly vulnerable, to landsliding events. The formulated SF-AHP showed significant improvement in vulnerability assessment accuracy compared to other conventional approaches.

Keywords: Landslide, assessment, analytical hierarchy process, multi-criteria approach

ACKNOWLEDGEMENTS

The first author acknowledges the financial support by the Minister of Higher Education

and Scientific Research,University of Carthage,Tunisia. Project No PID2021-

122711NB-C21, from Spanish Ministerio de Ciencia e Innovación, partially funded this

work.

How to cite: Ksantini, F., Sdiri, A., Aydi, A., and Tarquis, A. M.: Synergetic Fuzzy Analytic Hierarchy Process for a realistic assessment of landslide vulnerability, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15218, https://doi.org/10.5194/egusphere-egu23-15218, 2023.

Natural dam built up in mountainous regions is hazardous definatively a potential disaster. Kundasang is located on the highest mount in Malaysia and was hit by the 2015 earthquake. There are numerous kinds of dams that form by natural processes, dams formed from landslides on a mountainous landscape present one of the potential threat to people and property. Landslide dams form in a wide range of physiographic settings. The most common types of mass movements that form landslide dams are rock and debris avalanches; rock and soil slumps and slides; and mud, debris, and earth flows. The most common initiation mechanisms for dam-forming landslides are excessive rainfall and earthquakes. Natural dams may cause upstream flooding as the lake rises and downstream flooding as a result of failure of the dam.

Many landslide dams fail and mostly caused by over-topping as the most common cause of failure. The timing of failure and the magnitude of the resulting floods are controlled by dam size and geometry; material characteristics of the blockage; rate of inflow to the impoundment; size and depth of the impoundment; bedrock control of flow; and engineering controls such as artificial spill-ways, diversions, tunnels, and planned breaching by blasting or conventional excavation. One of the rare creation of landslide dams are when a single landslide sends multiple tongues of debris into a valley and forms two or more landslide dams in the same reach of river.

These dams pose hazards because back in 2015 there was an earthquake that shock the mount and destabilised the soil. Then, many trees were uprooted and fall. Thus, these phenomenon has shown in 2023 young vegetation has not stabilized mount Kinabalu slopes. There are many dam faces are steeper than the angle of repose, these dams and lakes are immediately downslope from steep crevassed glaciers and near-vertical rock slopes, and downstream from these dams are steep slopes with easily erodible materials that can be incorporated in the flow and increase flood peaks. The most recent reported failure mechanism is overtopping and breaching progressive rainfall.

How to cite: Abu Bakar, R., Mohamad, Z., Jamaluddin, T. A., and Razak, K. A.: Natural Dam Hazard in Kundasang, Sabah Mountainous Region, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16750, https://doi.org/10.5194/egusphere-egu23-16750, 2023.