NH3.8 | Landslide monitoring: recent technologies and new perspectives

NH3.8

EDI

The global increase in damaging landslide events has attracted the attention of governments, practitioners, and scientists to develop functional, reliable and (when possible) low cost monitoring strategies. Numerous case studies have demonstrated how a well-planned monitoring system of landslides is of fundamental importance for long and short-term risk reduction.
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 satellite and aerial platforms. All these techniques are adopted for the same final scope: measure landslide motion over time, trying to forecast future evolution or minimally reconstruct its recent past. Real time, near-real time and deferred time strategies can be profitably used for landslide monitoring, 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.
Considering these key conceptual drivers, this session aims to present successful monitoring experiences worldwide based on both in situ and/or remotely sensed data. 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. The session is expected to present case studies in which multi-temporal and multi-platform monitoring data are exploited for risk management and Civil Protection aims with positive effects in both social and economic terms. Specific relevance is given to the evaluation of the impact of landslides on cultural heritage.

Co-organized by GM4
Convener: Lorenzo SolariECSECS | Co-conveners: Peter Bobrowsky, Mateja Jemec Auflič, Federico Raspini, Veronica Tofani, Simone MineoECSECS, Massimiliano Bordoni
Orals
| Tue, 25 Apr, 14:00–18:00 (CEST)
 
Room 1.34
Posters on site
| Attendance Tue, 25 Apr, 10:45–12:30 (CEST)
 
Hall X4
Orals |
Tue, 14:00
Tue, 10:45

Orals: Tue, 25 Apr | Room 1.34

The oral presentations are given in a hybrid format supported by a Zoom meeting featuring on-site and virtual presentations. The button to access the Zoom meeting appears just before the time block starts.
Chairpersons: Lorenzo Solari, Federico Raspini, Mateja Jemec Auflič
14:00–14:05
14:05–14:10
14:10–14:20
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EGU23-5014
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NH3.8
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Highlight
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On-site presentation
Filippo Vecchiotti and Arben Kociu

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.

14:20–14:30
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EGU23-12618
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NH3.8
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On-site presentation
Maria Prodromou, Christos Theocharidis, Kyriaki Fotiou, Athanasios Argyriou, Thomaida Polydorou, Diofantos Hadjimitsis, and Marios Tzouvaras

Landslides constitute a significant geohazard causing human losses and significantly affecting the economy worldwide. Earth Observation and the exploitation of the freely available Copernicus datasets, such as the Sentinel-1 and Sentinel-2 satellite images, can assist in the systematic monitoring of landslides overcoming the restrictions arising from in situ measurements. This study shows how the Google Earth Engine (GEE) platform can be utilised for the rapid mapping of landslides and effectively integrate both passive and active satellite data to enhance the results’ reliability. The GEE is a cloud computing platform designed to store and process huge datasets for scientific analysis and visualization of geospatial datasets where open-source images are acquired by several satellites. 

For this study, Ground Range Detection (GRD) Sentinel-1 and multispectral Sentinel-2 satellite data were utilised for a time period between 2016 and 2021. Multitemporal SAR change detection was conducted to identify potential landslides using GRD Sentinel-1 satellite images. Moreover, the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Moisture Index (SMI) and Bare Soil Index (BSI) indices were used for the multispectral data. Multi-temporal image composites were created for the two periods. Furthermore, for all image collections, the calculated spectral indices were added as new bands to all images, and the maximum value for each pixel of the vegetation indices was taken. Following, the difference image for each spectral index was created based on two methods, i.e., the first method was based on subtracting the two time periods, and the second one on subtracting each year from the total average for the time period from 2016 until 2021. The possible events were then masked using the thresholding technique based on the trial-and-error procedure where the analyst adjusts manually the thresholds and evaluates the resulting image until satisfied. Based on the results derived from the abovementioned processing, the use of the second method, i.e., subtracting each year from the average, based on the NDVI spectral index provides better results. The proposed methodology was tested in Paphos city in Cyprus because of the occurrence of numerous landslide events in this area, based on the landslide inventory provided by the Geological Survey Department of the Ministry of Agriculture, Rural Development and Environment. The results of this study were validated using high-resolution images from Google Earth in combination with the data from the Geological Survey department. 

Acknowledgements 

The authors acknowledge the 'EXCELSIOR': ERATOSTHENES: Excellence Research Centre for Earth Surveillance and Space-Based Monitoring of the Environment H2020 Widespread Teaming project (www.excelsior2020.eu). The 'EXCELSIOR' project has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 857510, from the Government of the Republic of Cyprus through the Directorate General for the European Programmes, Coordination and Development and the Cyprus University of Technology. The authors would also like to thank the Geological Survey Department of the Ministry of Agriculture, Rural Development and Environment for the provision of the landslide inventory.

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.

14:30–14:40
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EGU23-15086
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NH3.8
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On-site presentation
Pierluigi Confuorto, Nicola Casagli, Francesco Casu, Claudio De Luca, Matteo Del Soldato, Davide Festa, Riccardo Lanari, Giovanni Onorato, and Federico Raspini

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 Vslope 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.

14:40–14:50
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EGU23-8053
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NH3.8
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ECS
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On-site presentation
Silvia Puliero, Sansar Raj Meena, Ascanio Rosi, Filippo Catani, and Mario Floris

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.

14:50–15:00
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EGU23-971
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NH3.8
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On-site presentation
Qingkai Meng, Emanuele Intrieri, Federico Raspini, and Ying Peng

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.

15:00–15:10
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EGU23-11453
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NH3.8
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ECS
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On-site presentation
Camilla Medici, Pierluigi Confuorto, SIlvia Bianchini, Matteo Del Soldato, Ascanio Rosi, Samuele Segoni, and Nicola Casagli

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.

15:10–15:20
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EGU23-16244
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NH3.8
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On-site presentation
Landslides monitoring with UAS photogrammetry and Digital ImageCorrelation
(withdrawn)
Francesco Mugnai, Andrea Masiero, Riccardo Angelini, and Irene Cortesi
15:20–15:25
15:25–15:35
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EGU23-1400
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NH3.8
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ECS
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On-site presentation
Davide Caliò, Simone Mineo, and Giovanna Pappalardo

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.

15:35–15:45
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EGU23-7062
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NH3.8
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ECS
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Highlight
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On-site presentation
Charlotte Groult, Clément Hibert, Jean-Philippe Malet, and Floriane Provost

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.

Coffee break
Chairpersons: Lorenzo Solari, Federico Raspini, Simone Mineo
16:15–16:25
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EGU23-13287
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NH3.8
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ECS
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On-site presentation
Arthur Charléty, Mathieu Le Breton, Eric Larose, and Laurent Baillet

 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.

16:25–16:35
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EGU23-6456
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NH3.8
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On-site presentation
Florian Bourcier, Maurin Vidal, Maxime Bès de Berc, Céleste Broucke, Nicolas Chatelain, Jean-Philippe Malet, Xavier Wanner, Clément Hibert, Jean Letort, Franck Grimaud, Guy Sénéchal, Thomas Lebourg, Lucie Rolland, and Frédéric Masson

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.

16:35–16:45
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EGU23-15842
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NH3.8
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ECS
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On-site presentation
Antonio Cosentino, Giacomo Santicchia, Giandomenico Mastrantoni, Jagadish Kundu, and Paolo Mazzanti

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.

16:45–16:55
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EGU23-7366
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NH3.8
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On-site presentation
Bernhard Groiss, Thomas Gaisecker, and Markus Handl

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.

16:55–17:00
17:00–17:10
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EGU23-11451
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NH3.8
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On-site presentation
Mateja Jemec Auflič, Gerardo Herrera, Rosa María Mateos, Eleftheria Poyiadji, Lídia Quental, Bernardie Severine, Tina Peternel, Laszlo Podolszki, Stefano Calcaterra, Arben Kociu, Bartłomiej Warmuz, Jan Jelének, Kleopas Hadjicharalambous, Gustaf Peterson Becher, Claire Dashwood, Peter Ondrus, Vytautas Minkevičius, Saša Todorović, Jens Jørgen Møller, and Jordi Marturia

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.

17:10–17:20
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EGU23-1757
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NH3.8
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On-site presentation
Renato Macciotta

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.

17:20–17:30
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EGU23-13729
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NH3.8
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On-site presentation
Tina Peternel, Mitja Janža, Ela Šegina, Mateja Jemec Auflič, Jernej Jež, Nejc Bezak, and Matej Maček

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.

17:30–17:40
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EGU23-15218
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NH3.8
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Virtual presentation
Faten Ksantini, Ali Sdiri, Abdelwaheb Aydi, and Ana M Tarquis

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.

17:40–17:50
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EGU23-16952
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NH3.8
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On-site presentation
Susceptibility maps towards shallow landslides in different land use settings: examples from Northern Italian Apennines
(withdrawn)
Claudia Meisina, Valerio Vivaldi, and Massimiliano Bordoni
17:50–18:00
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EGU23-16750
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NH3.8
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ECS
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On-site presentation
Rabieahtul Abu Bakar, Zakaria Mohamad, Tajul Anuar Jamaluddin, and Khamarrul Azahari Razak

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.

Posters on site: Tue, 25 Apr, 10:45–12:30 | Hall X4

The posters scheduled for on-site presentation are only visible in the poster hall in Vienna. If authors uploaded their presentation files, these files are linked from the abstracts below, but only on the day of the poster session.
Chairpersons: Federico Raspini, Simone Mineo, Massimiliano Bordoni
X4.20
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EGU23-331
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NH3.8
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ECS
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Ankur Sharma and Har Amrit Singh Sandhu

The Kalka Shimla Railway was built in the mid-19th century during British rule in India to connect Shimla, then the British summer capital and the headquarters of the British army, with the Indian rail network. Considered the “Crown Jewel” of the Indian National Railways during British times, the rail network features in the Guinness Book of World Records for its steepest rise in altitude, from 656 m at Kalka to 2,076 m at Shimla in a span of 96.57 km. It was granted UNESCO World Heritage Status in 2008 for its profound impact on the social and economic development of the high mountain areas. Despite conservation management plans and regular permanent maintenance, the track faces the vagaries of nature. Slips and landslides, in particular, cause frequent disruptions in its operations. The present study focuses on susceptibility mapping for the Kalka Shimla Railway, often dubbed the “Toy Train”, to determine the degree of its exposure to landslides. Data from 1,484 past landslide locations is used to train a Random Forest classifier with Bayesian hyperparameter optimization to ensure accurate classifications. The trained model is validated using 5-fold cross-validation with an accuracy of 90.6% and an area under the receiver operating characteristic curve (AUROC) value of 0.97. The accuracy and AUROC values during the testing stage for the model are 91.7% and 0.97, respectively. The final susceptibility map is validated using the landslide density method after dividing the posterior probabilities into five classes based on Jenks optimization. The landslide densities of the five susceptibility zones, namely “Very High”, “High”, “Medium”, “Low” and “Very Low” are 17.180, 0.196, 0.036, 0.008, and 0.001 respectively, which reflect the quality of susceptibility zonation mapping because 96.55% of all the landslides lie within only 5.62% of the study area with “Very High” susceptibility. The results of the study show that 36.9% of the total length of the railway is exposed to either “High” or “Very High” landslide susceptibility. The degree of exposure is particularly severe in the Solan district where landslides have interrupted the normal operations of the railway as recently as the last monsoon spell in the region. The results of this study may help policymakers and concerned authorities implement decisive protection measures for the preservation of this heritage site and its outstanding legacy.

How to cite: Sharma, A. and Sandhu, H. A. S.: Kalka Shimla Railways – A UNESCO World Heritage Site Exposed to Landslides, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-331, https://doi.org/10.5194/egusphere-egu23-331, 2023.

X4.21
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EGU23-5091
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NH3.8
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ECS
Lukas Schild, Thomas Scheiber, Paula Snook, Stig Frode Samnøy, Lene Kristensen, Alexander Maschler, and Reza Arghandeh

The unstable rock slope Stampa is located north-east of the touristic town of Flåm, Norway along the Aurlandfjord and displays signs of post-glacial deformation over a large area and a volume of several million m3. Directly below the rock slope lies the European Road E16, a highly frequented connection between Bergen and Oslo. Two high-risk objects have been identified on the instability, which are currently being monitored continuously by the Norwegian Energy and Water Directorate. The Landslide Research Group at Western Norway University of Applied Sciences uses an object on the unstable rock slope, Block 4a, as a field laboratory for sensor networks. The approximately 5,000 m3 Block sits on a highly fractured base of approximately 40,000 m3 and has recently been moving at speeds in excess of 1 cm per day. Different failure scenarios threaten the European Road under the object and potentially the town of Flåm. Data from an on-site sensor network with a range of instruments such as wire-extensometer, inclinometer, temperature loggers and geophones has been collected over a period of three years and combined with remote sensing data from a robotic total station, ground-based InSAR and satellite-based InSAR with the use of a corner reflector as persistent scatterer as well as weather station data from Stampa. Sensor Fusion has been used to merge the data of the different sensors and exploit the different resolutions of the respective sensors. This led to the development of a data set with high spatiotemporal resolution capturing the physical properties of Block 4a, such as displacement direction and velocity. This approach makes use of complementary sensor data to fill gaps in time series of other sensors, which can be caused by sensor faults or are due to sensor down-times during maintenance. Both the sensor fusion approach as well as filtering of outliers requires expert knowledge about the system in question, which sensor fusion research groups often do not integrate into their analysis. We propose thus a holistic analysis approach at the intersection between data science and geology. Preliminary analyses of the augmented data for Block 4a confirm high displacement rates at the end of 2022. This follows a general trend of acceleration that has been observed over the last three years. Furthermore, the displacement accelerations seem to follow a seasonality, with acceleration phases in spring and autumn, while summer and winter coincide with less movement. Based on the sensor fusion analysis we can identify that rain fall periods in autumn as well as snowmelt in spring have an impact on the block displacement. However, we conclude that precipitation alone cannot explain acceleration phases. Instead, we propose a model based on the combined influence of rain and snowmelt paired with air and rock surface temperature on the slope movement. In combination with a refined sensor fusion process, we expect our work to be transferable and relevant for the monitoring of other unstable rock slopes.

How to cite: Schild, L., Scheiber, T., Snook, P., Samnøy, S. F., Kristensen, L., Maschler, A., and Arghandeh, R.: Sensor Fusion for Monitoring Unstable Rock Slopes - A Case Study from the Stampa Instability, Norway, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5091, https://doi.org/10.5194/egusphere-egu23-5091, 2023.

X4.22
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EGU23-6153
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NH3.8
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ECS
Elias Arnar Ninuson, Þorsteinn Sæmundsson, and Benjamin Hennig

In 1968 the Siglufjarðarvegur road was built in north Iceland, serving as the only whole year road connecting the remote town of Siglufjörður with the capital city of Reykjavík. This road cuts through the area of Almenningar where three active slow moving landslides are situated, these are the Tjarnadalir, Þúfnavellir and Hraun landslides. The constant activity of these landslides affect a 5-6 km long stretch of the road that has been a problem both for the road authorities as well as travellers in terms of safety and maintenance. All three landslides have been mapped and studied before and the northernmost area of Tjarnadalir has gained the most attention as rate of movement is considered to be up to 1 m/yr for some periods. This is also the case for the southernmost Hraun landslide (0.83 m/yr) but the Þúfnavellir landslide in the middle is moving much slower with an estimated rate of 0.17 m/yr. The movement rate is constant with shorter periods of increased activity and there is strong evidence that the rate of movement is directly linked with weather patterns as more water in the landslide systems seems to cause increased activity. Ever since the year 1977, the road authorities have conducted GPS displacement measurements on a yearly basis with a limited number of points that are all situated along the road. Little is known about the different movement rates within the landslide bodies themselves but this study extends these displacement measurements through the application of remote sensing. Aerial photogrammetry comparison will be conducted with the feature tracking method in order to estimate the rate of movement throughout the entirity of all three landslide bodies. Available data extends back to the year 1954 to present day, giving an unprecedented insight into the temporal and spatial dynamics of the landslides.

How to cite: Ninuson, E. A., Sæmundsson, Þ., and Hennig, B.: Displacement measurements of three slow moving landslides at Almenningar, north Iceland. A feature tracking application., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6153, https://doi.org/10.5194/egusphere-egu23-6153, 2023.

X4.23
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EGU23-7815
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NH3.8
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ECS
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Rodolfo Rani, Andrea Benini, Andrea Foschi, and Matteo Berti

A common slope instability problem is the presence of hard rock lithotypes (such as limestones or sandstones) overlying weak rocks or soils (such as clays or shales). In this geological setting, hard rocks tend to create steep slopes or cliffs that become unstable because of the low shear strength of the underlying weak material. Landslides can take the form of slow, gradual spreading of rock blocks, or they can occur suddenly, in the form of a quick catastrophic collapse. These catastrophic landslides typically consist of deep rotational failures in the weak rocks induced by the weight of the rock mass above. The presence of groundwater at the contact between the two lithotypes and the generation of tensional cracks within the more brittle rock are additional factors that can influence the triggering of these hazardous landslides.

At the same time, gentle slopes below cliffs are ideal for human settlements. The physical presence of a cliff can act as a natural barrier, protecting the site from external threats and helping to preserve the site. Moreover, natural water springs or water-bearing strata are usually present in these sites due to the presence of permeable rock masses over less permeable materials. For these reasons, many cultural heritage sites are in these peculiar geological conditions. It is therefore important to assess the risk of landslides at cultural heritage sites and take appropriate measures to reduce the risk of damage and ensure visitors' safety.

In this study, we analyze the case of the Balze di Verghereto village located in the Nothern Apennines of Italy (Forlì-Cesena Province). This small historic village is built directly at the foot of a sandstone cliff and sits upon a heterogeneous clay formation. The site was affected by several landslides in the last century, and the main concern is now the collapse of the rock slope due to a deep rotational slide. Slope stability analysis of these phenomena are challenging for many reasons:

  • Geological materials are difficult to characterize, especially in the case of overconsolidated-fissured clays and weakly-cemented rocks.
  • Numerical instability can occur because of the presence of two geological materials characterized by very different mechanical properties (strengths and deformability).
  • The abruptly stepped morphology, produced by the presence of different lithologies, complicates the generation of the grid model used to represent the slope.

To face these problems, stability analyses were conducted using different strategies. In particular, we compared finite-difference modeling performed by FLAC 3.4 2D software adopting different constitutive laws (elastic-plastic and anisotropic ubiquitous-joint), boundary conditions (fixed vs free boundaries), and the presence or absence of interfaces. Numerical simulations were then compared with general limit equilibrium analyses conducted using various potential shapes of slip surfaces (circular, composite, and trapezoidal).

The results show that the collapse of the rock slope is unlikely, but clearly highlight the difficulty of the prediction. Beyond this result, the study provided an understanding of the advantages and disadvantages of different approaches for the analysis of a slope in this peculiar geological setting.

How to cite: Rani, R., Benini, A., Foschi, A., and Berti, M.: Stability of a rock slope overlying a weak clay: the difficult case of Balze di Verghereto (Italy), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7815, https://doi.org/10.5194/egusphere-egu23-7815, 2023.

X4.24
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EGU23-9219
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NH3.8
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ECS
Raniero Beber, Giulia Bossi, Federica Ceccotto, Gianluca Marcato, Alessandro Pasuto, and Matteo Mantovani

The Mediterranean storm “Vaia” hit the north-east of the Italian Alps at the end of October 2018. The strong wind gusts exceeding 200 km/h and the intense precipitation damaged more than 42,000 hectares of forests and caused floods that had a severe impact on the geo-hydrological balance of mountain basins. One of the most affected area, the province of Belluno, still bears the clear signs of the destructive effects of this extreme meteorological event, nevertheless the long-term impact on the slopes stability is yet far to be assessed. This study investigates the “Vaia” storm impact by analyzing time-series of the interferometric data acquired since 2015 by the European Space Agency’s Sentinel-1 mission.  Radar interferometry is, at present, the only technique capable to measure small ground displacements of large areas over long time periods. The rationale of the proposed approach assumes that changes in the response of radar targets, located over slopes, are proxy of change in the style of activity of landslides and in particular of their activation, re-activation and acceleration. The purpose is to evaluate the possibility to detect a statistical relationship between the occurrence of “Vaia” storm and the state of activity of mass movements. This type of analysis could be useful in helping to interpret the impact of extreme meteorological events on the landscape and in developing strategies for mitigating potential risks in the next future. This research is carried out in the framework of  Project VAILAND, a joint research agreement funded by the Veneto Region (Italy).

How to cite: Beber, R., Bossi, G., Ceccotto, F., Marcato, G., Pasuto, A., and Mantovani, M.: Inferring The Impact Of Vaia Storm On Slopes Stability Using Sentinel-1 Data., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9219, https://doi.org/10.5194/egusphere-egu23-9219, 2023.

X4.25
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EGU23-11653
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NH3.8
Giacomo Pepe, Andrea Vigo, Andrea Mandarino, Emanuele Raso, Francesco Marchese, Diego Di Martire, Giacomo Russo, Luigi Guerriero, Patrizio Scarpellini, Marco Firpo, Domenico Calcaterra, and Andrea Cevasco

Dry-stone wall terraces are among the most ancient and widespread agricultural practices on hilly-mountainous landscapes. Their historical, architectural, and environmental value has been recognized worldwide. Recently, “the art of dry-stone walls” was inscribed on the UNESCO Representative List of the Intangible Cultural Heritage of Humanity. These anthropic landscape transformations have important effects on both the slope geomorphology, by reducing steepness, and hydrology, by increasing water infiltration and soil moisture and by mitigating runoff. Under optimal management, dry-stone wall terraces favour farming, providing pedological and ecological advantages. However, following farmland abandonment, dry-stone wall terraces may progressively lose their hydro-geomorphological functions due to lack of maintenance. Over time, these dynamics can be accompanied by the development of severe erosion processes and mass movements which can lead to slope degradation issues and risk scenarios.

A wide variety of factors can influence the hydro-geomorphological response of terraced systems after their abandonment. In this work, the effects of land use and of management practices are explored in a small terraced area (12.5 hectares) surrounding the Manarola hamlet (eastern Liguria Region, north-western Italy), within the UNESCO World Heritage Site of the Cinque Terre. The research purpose is to investigate the hydro-geotechnical behaviour of dry-stone wall terraces in different land use conditions and state of management. The research activities are in the framework of the project Stonewalls4life, a LIFE EU-project aimed at investigating the role of dry-stone walls in increasing the resilience of rural territories and in counteracting the impacts of climate change.

The engineering-geological characterization of the pilot site was developed through a multidisciplinary approach consisting of geological and geomorphological surveys, in situ and laboratory geotechnical soil tests, excavation of shallow test pits and non-invasive geophysical surveys. The stratigraphic and geotechnical modelling of the test site allowed to implement integrated hydro-geotechnical monitoring systems aimed at measuring over time: (i) meteorological data (e.g., rainfall intensity, air humidity, air pressure), (ii) soil hydrological properties (e.g., volumetric water content, matrix suction), and (iii) loads acting on retaining walls (e.g., soil pore pressure). Different monitoring scenarios based on land use conditions (e.g., cultivated and abandoned) and dry-stone wall management practices (e.g., existing not maintained wall and reconstructed wall) were established.

From the whole set of investigations, it is expected to improve the knowledge concerning the hydrological processes occurring in dry-stone wall terraces and to obtain useful information for modelling soil mass movements (e.g., shallow landslides), along with indications for the development of effective land management strategies.

How to cite: Pepe, G., Vigo, A., Mandarino, A., Raso, E., Marchese, F., Di Martire, D., Russo, G., Guerriero, L., Scarpellini, P., Firpo, M., Calcaterra, D., and Cevasco, A.: Hydro-geotechnical monitoring in dry-stone wall terraces for the investigation of rainfall-induced landslides: preliminary results from the UNESCO World Heritage Site of the Cinque Terre, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11653, https://doi.org/10.5194/egusphere-egu23-11653, 2023.

X4.26
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EGU23-13171
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NH3.8
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ECS
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Ariane Mueting and Bodo Bookhagen

Optical image offset tracking is a commonly used technique for mapping surface displacements caused by landslides, glaciers, and earthquakes in planar direction. With its daily temporal and high spatial resolution of 3 m, the PlanetScope cubesat constellation provides an excellent data set for studying dynamic surface changes. However, the limited relative geolocation accuracy among PlanetScope scenes with ~10 m RMSE for the PlanetScope SuperDove constellation poses a problem for the identification of slow-moving targets whose annual displacement rates remain well below this value. In this study, we have used PlanetScope data to measure surface displacement over a slow-moving landslide with velocities between 1 and 6 m/yr in the NW Argentine Andes through image cross-correlation techniques. In this steep, rugged environment, not only the misalignment from scene to scene, but also topography-related artifacts and the changing terrain over time, are sources of error. We present several correction steps to improve coregistration accuracy between PlanetScope scenes that lower the relative geolocation accuracy between selected image pairs into the subpixel range. Through an improved scene-to-scene alignment we can better distinguish displacement signal from noise and thus obtain a better understanding of the dynamics of this slow-moving landslide and its driving factors.

How to cite: Mueting, A. and Bookhagen, B.: Improving image-based tracking of slow-moving landslides with optical PlanetScope data: A case study from the Central Andes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13171, https://doi.org/10.5194/egusphere-egu23-13171, 2023.

X4.27
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EGU23-16099
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NH3.8
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ECS
Hong-Yi Hsiao, Yi-Chung Chen, Chien-Yu Chi, Chia-Shin Chang, and Rou-Fei Chen

In the past, when assessing the stability of roadside slopes in mountainous areas of Taiwan, road maintenance and inspection personnel were often limited by steep terrain and vegetation cover. They could only observe damages on the road surface and sides instead of grasping the key points of landslides and disasters. Among them, Sandimen and Wutai located on Highway 24 in Pingtung repeatedly suffer from natural disasters, such as falling rocks, debris avalanche or subsidence of roadbed, after the strike of Typhoon Morakot whenever a typhoon, torrential rain or earthquake event occurs. The government therefore spent a lot of money on road maintenance works. In this area, we have combined topographic features identified from LiDAR-derived 1-m resolution DEM and large-scale ground surface deformation observed using the multitemporal InSAR technique (MT-InSAR) developed based on ALOS-2 / PALSAR-2 images collected between 2015 and 2022. Then Sections 29.5K and 34.5K are selected as the key study area of this investigation. The cumulative deformation results of these key slopes are: -80 mm at 29.5K from ALOS-2 imagery and -103 mm at 34.5K from ALOS-2. As for the section of 29.5K, from May 8, 2016 to December 4, 2016, the overall slope of the road was affected by several heavy rains and typhoons, among which the maximum rainfall on that day reached 303 mm; and the amount of deformation decreased by 16.7 mm; the average deformation of subarea block A decreased by 33.6 mm, and the average deformation of subarea block B decreased by 14.8 mm. As for the section of 34.5K, the overall roadside slopes were affected by several heavy rains and typhoons, among which the maximum accumulation of rainfall on that day reached 303 mm, from May 8, 2016 to December 4, 2016. The amount of deformation decreased by 13.7 mm. Our primary results demonstrate that the cumulative deformation and rainfall of these two key slopes show a positive correlation.

How to cite: Hsiao, H.-Y., Chen, Y.-C., Chi, C.-Y., Chang, C.-S., and Chen, R.-F.: The Applicability of InSAR and LiDAR Remote Sensing Technologies in the Large-Scale Monitoring of Roadside Slopes and surrounding Structures, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16099, https://doi.org/10.5194/egusphere-egu23-16099, 2023.

X4.28
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EGU23-16543
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NH3.8
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ECS
Floriane Provost, Aline Déprez, Jean-Philippe Malet, and Michael Foumelis

Landslides are an important hazard worldwide in particular in mountainous environment. Monitoring the evolution of the slope motion is hence crucial to detect zones at risk and further understand and control their evolution. Monitoring landslides may be done via the installation of in-situ sensors requiring efforts to maintain the instruments in difficult field conditions. Remote sensing offers the advantage to monitor the Earth at a regular frequency by remote satellite. Among the many processing strategies to monitor landslides using satellite data, InSAR has drastically evolved in the past 30 years and became a widely used technique to monitor ground deformation. Numerous processing chains are now available and there are many examples of its interest for landslide application. However, landslides remain in most cases challenging to monitor with this technique and it is not always easy to understand pros and limitations of the different processing chains available. 

In this work we propose to analyze and compare the output products of four different advanced InSAR processing chains: a) SNAPPING based on the Permanent Scatterer Interferometry (PSI) approach (Foumelis et al, 2022), b) P-SBAS based on Small-Subset Baseline Analysis (SBAS) approach (Casu et al, 2014), c) SqueeSAR based on PS and DS interferometry (Ferretti et al, 2011) and d) the product of the Copernicus European Ground Motion Service (EGMS, Level 2B). We selected three test areas with known landslides in different environnments: Villerville (France), Canton de Vaud (Switzerland) and Tavernola (Italy). The SNAPPING and P-SBAS processing chains are accessible through the Geohazard Exploitation Platform (GEP) and the results were obtained with default parameterization of these services. The SqueeSAR and the EGMS products were processed independently. 

We use different metrics to estimate the similarity of the ground motion time series in space and in time as well as the coverage and the information density of each products. We also analyze the georeferencing of the results by comparing the location of measurement points with man-made structures and known reference points. We also determine the sensitivity of each technique to monitor landslides by inter-comparing the coverage of measurement points in specific landslide targets. The results of this inter-comparison shows that the different products are in general in agreement over large region although their coverage and density may differ significantly. However, significant discrepancies exist in the estimation of the velocity and displacement time series in the studied landslides and this will be discussed.

 

References:

Foumelis, M., Delgado Blasco, J. M., Brito, F., Pacini, F., Papageorgiou, E., Pishehvar, P., & Bally, P. (2022). SNAPPING Services on the Geohazards Exploitation Platform for Copernicus Sentinel-1 Surface Motion Mapping. Remote Sensing, 14(23), 6075.

Casu, F., Elefante, S., Imperatore, P., Zinno, I., Manunta, M., De Luca, C., & Lanari, R. (2014). SBAS-DInSAR parallel processing for deformation time-series computation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(8), 3285-3296.

Ferretti, A., Fumagalli, A., Novali, F., Prati, C., Rocca, F., & Rucci, A. (2011). A new algorithm for processing interferometric data-stacks: SqueeSAR. IEEE transactions on geoscience and remote sensing, 49(9), 3460-3470.

How to cite: Provost, F., Déprez, A., Malet, J.-P., and Foumelis, M.: Sensitivity of advanced InSAR strategies for landslide monitoring, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16543, https://doi.org/10.5194/egusphere-egu23-16543, 2023.