NH3.6

With the advances of ESA’s Sentinel-1 InSAR (Interferometric Synthetic Aperture Radar) mission there are freely available remote sensing ground deformation observations all over the globe that allows continuous monitoring of natural hazards and structural instabilities. The Digital Environment initiative in the UK aims to include these remote sensing data in the effort of forecasting and mitigating hazards across the UK.

However, analyses of low coherence areas (e.g. forested and vegetated areas) with conventional InSAR methodologies are difficult to perform due to the limiting factor of temporal and geometric decorrelation. Even the application of the permanent scatterer (PS) technique may not be successful when there is a low density of stable radar targets. Using artificial reflectors with high radar cross section (RCS) can be a way of overcome this limitation and achieve measurements with a good signal-to-clutter ratio (SCR).

In order to be able to include Sentinel-1 data in the UK’s Digital Environment it is important to understand the advantages and limitations of these observations and interpret them appropriately. The Hollin Hill landslide observatory in North Yorkshire is used by the British Geological Survey in their efforts to understand landslide processes, and to trial new technologies and methodologies for slope stability characterisation and monitoring.

We present InSAR results of the Hollin Hill landslide where a variety of ground-based geophysical measurements (e.g. GPS, Electric resistivity tomography, meteorological observations) are available for comparison with InSAR data. We use Sentinel-1 InSAR data acquired between Oct 2015 and Jan 2021 to study the behaviour of this landslide. We find that the Line of Sight component of the down-slope movement is 2.7 mm/yr in the descending track, and 7.5-7.7 mm/yr in the ascending track. The InSAR measurements also highlight the seasonal behaviour of this landslide.

In July 2019 six corner reflectors were installed to improve the coherence of the InSAR measurements, especially in the ascending acquisition mode. We present comparison with ground-based measurements such as the movement recorded by the GPS measurements of the pegs of the ERT survey or the moisture recorded by the various instruments at the site, and show the improvement introduced by the corner reflectors.

In addition we present results of an experiment that explores the use of smaller corner reflectors for potential urban applications of infrastructure monitoring. A single corner reflector needs to be at least ~67cm wide and tall to be seen by the Sentinel-1 satellites. We show that by placing 4 reflectors with 33cm dimensions in the same pixel coherent signal can be acquired. It is feasible to install small reflectors on bridges, tall buildings, or incorporate “corner-like” features in newly built structures,but care needs to be taken on the precise spacing of the reflectors to avoid destructive interference. Continuous monitoring of infrastructure with remote sensing and machine learning can alert to potential failures where further investigation is needed.

How to cite: Kelevitz, K., Chambers, J. E., Boyd, J., Novellino, A., Jordan, C., Biggs, J., Selvakumaran, S., Hooper, A., and Wright, T. J.: Using corner reflectors for enhancing landslide and infrastructure monitoring in the UK, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5133, https://doi.org/10.5194/egusphere-egu21-5133, 2021.

Based on geomorphological criteria, large-scale slow gravitational deformation affecting entire mountain flank, often being referred as Deep-Seated Gravitational Slope Deformation (DSGSD), have been shown to affect most of the reliefs worldwide. For instance in the European Alps, these deformation patterns were identified in several areas such as the Aosta Valley (Martinotti et al., 2011) or the Mercantour massif (Jomard, 2006). DSGSD inventories based on visual interpretation of scarps and field mapping were then compiled (e.g. Crosta et al., 2013) revealing the widespread occurrence of DSGSD. However, many aspects of these large-scale gravitational processes remain unclear and in particular their present-day activity and temporal evolution remain largely unknown.

The present study aims at characterizing the spatial extent of DSGSD, and their velocity, at the scale of Western Alps through InSAR time series analysis using NSBAS processing chain (Doin et al., 2001). We used the whole SAR Sentinel-1 archive, between 2014 and 2018, with an acquisition every 6 days, on an ascending track. The processing was adapted to fit the specific conditions of the Alps (seasonal snow cover, strong local relief, vegetation and strong atmospheric heterogeneities). In particular we implemented a correction using the ERA 5 weather model and we used snow masks in winter allowing to select long temporal baseline interferograms with as little snow as possible. As we specifically aim to study deformation patterns at the scale of valley flanks, an average high-pass filter on moving subwindows has been applied to the interferograms prior to the implementation of time-serie inversions. This step strongly reduced the impact of residual atmospheric delays.

The resulting velocity map in the line of sight (LOS) of the satellite reveals ubiquitous gravitational deformation patterns over the whole Western Alps, with localized patches of moving slopes showing sharp discontinuities with stable surrounding areas. We used radar geometry and InSAR measurement quality factors as indicators to identify the most trusted areas and to extract an inventory of potential DSGSD with their spatial extent. Doing so, we identified more than two thousands slowly deforming areas characterized by LOS velocities from 4 to 20 mm/year. We then compared the geometries of our “InSAR-detected-deforming-slopes” with previously published DSGSD inventories. Good agreements were found for example in the Aosta valley where most of the deforming areas from our velocity map are falling into the DSGSD outlines of Crosta et al. (2013). Currently, we continue to investigate the potential of this large-scale velocity map for DSGSD understanding and we plan to use artificial intelligence to search for possible generic properties between the detected sites.

How to cite: André, P., Doin, M.-P., Mathey, M., Zerathe, S., Vassallo, R., and Baize, S.: Four years of InSAR time series analysis reveals an unprecedent inventory of active DSGSD in the Western Alps, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12693, https://doi.org/10.5194/egusphere-egu21-12693, 2021.

In this work, the A-DInSAR techniques are applied in a mountainous area located in the Central South of Asturias (N Spain), where there are significant landslide and subsidence phenomena. The main aim of this study is detecting and analysing ground deformations associated to slope instabilities and subsidence processes. For this, 113 SAR images, provided by Sentinel-1A/B between January 2018 and February 2020, were acquired and processed by means of PSIG software (developed by the Geomatics Division of the CTTC). The results show a velocity range between -18.4 and 10.0 mm/year, and minimum and maximum accumulated ground displacements of -35.0 and 17.5 mm. This study has made possible to differentiate local sectors with recent deformation related to landslide incidence, urban/mining subsidence, and land recuperation due to aquifer recharge. This work corroborates the reliability and usefulness of the A-DInSAR processing as a powerful tool in the study and analysis of geological hazards on regional and local scales using Sentinel-1 data collection, showing also the high difficulty of processing mountainous areas with few urban sectors.

How to cite: Cuervas-Mons, J., Domínguez-Cuesta, M. J., Mateos-Redondo, F., Monserrat, O., and Barra, A.: Ground motion detection in Central South Asturias (N Spain) by using Sentinel-1 SAR data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-28, https://doi.org/10.5194/egusphere-egu21-28, 2021.

Landslides and floods are the two most important geohazards in Sweden. Due to the climate change effects, it is believed that the risk of occurring these geohazards will increase in Sweden causing for example the land to become more prone to landslides. Additionally, due to the isostatic uplift caused by the retreating of the ice sheet, approximately 10,000 years ago, marine sediments involving marine clays have become exposed above sea level in Scandinavia. Infiltration of fresh water has (and is) leached the salt from the pores within the marine clays leading to the formation a special kind of clay known as the quick clay in the northern countries. These glacial clays and postglacial silts cause more ground surface instability and become slops more prone to trigger landslides, which is the case for concentration of the most landslides in the southwest of Sweden. Hence, quick-clay landslides are common geohazards in Nordic countries, which potentially could cause a considerable economical and live cost. The most recent Gjerdurm landslide in Norway was of this kind quick-clay related.

In recent years, an area close to the Göta River of southeast of Sweden has been the subject of numerous surface and airborne geophysical surveys for detailed subsurface mapping and delineation of the quick-clay and sediments hosting them including the very undulating the crystalline bedrock. These existing studies including access to borehole observations and geotechnical studies motivated us to study also long-term surface deformation in order to study climate effects, erosion, precipitation and underlying quick-clay presence in this area and neighboring regions. We employed radar data with Syntenic Aperture Radar (SAR) interferometry techniques. To this end, Sentinel-1 data from 2015 to 2019 were processed with the Small BAsline Subset (SBAS) technique to estimate time-series displacements and to generate deformation map for that region. The initial results show that the heterogenous deformation observed in the study area with maximum subsidence rate of -22 mm/yr. The deforming areas appear to be located on regions with the thickest column of the clay near the river where we anticipate also thicker quick-clay layers present. The quick-clays in this region overlie a thick (ca. 20 m) coarse-grained layer interpreted from the surface geophysical measurements to be associated with the formation and triggering of quick-clays in the area. With such a large surface deformation and the underling geology, we observe two phenomena in the study. A possible sudden risk of quick-clay landslide but also a long-term creeping of clays and destabilizing effect that may accelerate erosion at the river bank causing more landslides in the future. The cause of the large deformation is still unclear and will be investigated together with hydrogeological and geophysical data available in the study. This study however provides compelling evidence of major surface deformation that should be considered for long-term risk mitigation and planning.  

How to cite: Malehmir, A., Darvishi, M., and Nilfouroushan, F.: Integration of InSAR and ground-based geophysical measurements to study an area prone to quick-clay landslide in Sweden, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15677, https://doi.org/10.5194/egusphere-egu21-15677, 2021.

Monitoring ground surface motion is a key information to locate active landslides and possibly detect failure onsets but also to better understand their mechanical behavior in relation with environmental forcing. In-situ and remote technologies are available to provide measures of the ground displacement with different advantages and limitations (in terms of spatial coverage, sampling frequency, etc.). Image matching techniques have been commonly used to detect and measure landslide acceleration but this is often limited to a small amount of images. In the recent years, the number of optical satellite constellations have significantly increased providing global coverage with a frequent revisit time at medium to high spatial resolution and an open access policy (e.g. Sentinel 2, Landsat 7/8). These datasets present new perspectives for the monitoring of slow (cm/day) to moderate (m/month) landslide motion and poses challenges to discriminate between the different spatio-temporal sources (e.g. rainfall correlated signal, noise, seasonal signal, etc.) present in the time -series. 

We investigate the use of spatiotemporal ICA/PCA decomposition on optical displacement stacks of landslide areas. The main goal aims at testing 1) the capability of ICA/PCA analysis to detect relevant deformation deformation sources in the case of landslide monitoring and 2) the possibility to improve the time-series inversion of landslide motion by removing spatiotemporal sources that can result from seasonal sun exposition or geometric inaccuracies. We use the MPIC-OPT-Slide service of the GeoHazards Exploitation Platform (GEP) to compute several correlograms and displacement fields (>500 per site) from Sentinel-2 acquisitions on the slow-moving La Valette landslide (Alpes-de-Haute-Provence, France) and the moderately-moving Aiguilles-Pas de l’Ours landslide (Hautes-Alpes, France). We show that in case of steady-state deformation, the noise can be significantly removed around the active parts of the slope. In the case of more complex deformation evolution, pertinent sources can be manually isolated but the choice of the number of sources and their automatic selection remain challenging. 

How to cite: Provost, F. and Malet, J.-P.: Spatiotemporal ICA/PCA decomposition of optical displacement field stacks: perspective for landslide time series inversion, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15756, https://doi.org/10.5194/egusphere-egu21-15756, 2021.

Slow-moving landslides exhibit persistent but non-uniform motion at low rates which makes them exceptional natural laboratories to study the mechanisms that control the dynamics of unstable hillslopes. Here we leverage 4.5+ years of satellite-based radar and optical remote sensing data to quantify the kinematics of a slow-moving landslide in the tropical rural environment of the Kivu Rift, with unprecedented high spatial and temporal resolution. We measure landslide motion using sub-pixel image correlation methods and invert these data into dense time series that capture weekly to multi-year changes in landslide kinematics. We cross-validate and compare our satellite-based results with very-high-resolution Unoccupied Aircraft System topographic datasets, and explore how rainfall, simulated pore-water pressure, and nearby earthquakes control the overall landslide behaviour. The landslide exhibited seasonal and multi-year velocity variations that varied across the landslide kinematic units. While rainfall-induced changes in pore-water pressure exerts a primary control on the landslide motion, these alone cannot explain the observed variability in landslide behaviour. We suggest instead that the observed landslide kinematics result from internal landslide dynamics, such as extension, compression, material redistribution, and interactions within and between kinematic units. Our study provides, a rare, detailed overview of the deformation pattern of a landslide located in a tropical environment. In addition, our work highlights the viability of sub-pixel image correlation with long time series of radar-amplitude satellite data to quantify surface deformation in tropical environments where optical data is limited by persistent cloud cover and emphasize the importance of exploiting synergies between multiple types of data to capture the complex kinematic pattern of landslides.

How to cite: Dille, A., Kervyn, F., Handwerger, A., d’Oreye, N., Derauw, D., Mugaruka Bibentyo, T., Samsonov, S., Malet, J.-P., Kervyn, M., and Dewitte, O.: When image correlation is needed: combining very dense radar-amplitude and optical times series for unravelling the complex dynamics of a not so slow slow-moving landslide in the tropics, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10627, https://doi.org/10.5194/egusphere-egu21-10627, 2021.

Analysis of the stability conditions of rock masses starts from detailed geo-structural surveys based on a systematic and quantitative description of the systems of discontinuities. Traditionally, these surveys are performed by implementing the classical geomechanical systems, available in the scientific literature since several decades, through the use of simple tools such as the geological compass to measure dip and dip direction directly on the discontinuity systems, and to fully describe their more significant physical characteristics (length, spacing, roughness, persistence, aperture, filling, termination, etc.). In several cases, this can be difficult because the discontinuities, or even the rock face, cannot be easily accessible. To have a complete survey, very often the involvement of geologists climbers is required, but in many situations this work is not easy to carry out, and in any case it does not cover the whole rock front.

Today, to solve these problems, traditional geomechanical surveying is implemented by innovative remote techniques using, individually or in combination, instruments such as terrestrial laser scanners and unmanned aerial vehicles to build a point cloud.

This latter permits to extract very accurate data on discontinuities for stability analyses, based on areal and non-point observations. In addition, the point cloud allows to map sub-vertical walls in their entirety in much shorter times than traditional surveying.

At this regard, two rock slopes were detected in the Sorrento Peninsula (Campania, southern Italy) with techniques that include traditional mapping, dictated by the guidelines of the International Society for Rock Mechanics, and the remote survey, through laser scanning and drone photogrammetry. The data obtained were processed automatically and manually through the Dips, CloudCompare and Discontinuity Set Extractor softwares.

In the present contribution we highlight the limits and advantages of the main data collection and the processing techniques, and provide an evaluation of the software packages currently available for the analysis and evaluation of discontinuities, in order to obtain a better characterization of the rock mass.

How to cite: Caccia, A., Palma, B., and Parise, M.: Comparing traditional geomechanical and remote sensing techniques for rock mass characterization, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10147, https://doi.org/10.5194/egusphere-egu21-10147, 2021.

The use of high resolution 3D point clouds and digital terrain models (DTM) from laserscanning or photogrammetry becomes more and more state of the art in landslide studies. Based on a multi-temporal terrestrial laserscanning (TLS) dataset of the deep-seated compound rockslide Laatsch, South Tyrol, we present a multi-method approach to characterize processes such as sliding, falling, toppling, and flows. Sliding is the predominant process of the Laatsch rockslide, accompanied by secondary processes such as rockfall, debris flows and erosion. The presented methods are applicable to all kind of 3D point clouds and not limited to TLS data. For remote sensing-based landslide analyses a distinction between two classes of surface processes is necessary: i) processes where the original surface is destroyed and no correlations between the shape and texture of the pre- and post-failure surfaces can be found (falls, rapid flows, rapid slides) and ii) processes where the surface is displaced without major surface changes (slow slides, slow flows and toppling). For processes where the original surface is destroyed, the distance between the pre- and post-failure terrain surface is measured with the aim to delineate the scarp and depositional area, and to quantify the failure volume as well as the scarp thickness. With DTMs of differences (DoD), the distance is measured along the plumb line. DoDs can be used to quickly and reliably assess the volume and extent of fall processes on flat to moderate slopes. For steep or even overhanging terrain, a 3D distance measurement approach must be used, where the distance is measured along the local surface normal. After 3D distance measurement, the volume of steep scarp areas can be calculated by first rotating, the point cloud into the horizontal plane (by making use of the average surface normal) and by interpolating the rotated 3D distance measurement values into a grid. Summing up the distances and multiplying with the cell area of the grid yields the scrap rupture volume. Remote sensing-based analyses of sliding and toppling processes are more complex compared to fall processes because the displaced surface patch must be detected in both surveys. Displacement analyses based on image correlation of ambient occlusion shaded relief images, together with DTMs of both epochs, are used to analyse the displacement of the entire rockslide area. The result is a map with displacement vectors. Disadvantages of image correlation are the coarse spatial resolution and the inability, as it is a 2.5D approach, to deal with steep slope parts. To analyse the displacement and toppling of steep rock walls a combination of the 3D distance measurement approach and an iterative closest point (ICP) based approach is applied. The 3D distance measurement values are clustered and used for a segmentation of the point cloud. In a next step, the ICP is applied on each of the resulting segments. This approach can deal with 3D displacements. The results are still sensitive towards the geometric contrast within the segments and not fully automated yet.

How to cite: Fey, C., Voit, K., Wichmann, V., Zangerl, C., and Mair, V.: Multi-method approach for high resolution 3D data based process analyses of compound rock slides, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1385, https://doi.org/10.5194/egusphere-egu21-1385, 2021.

Monitoring landslides is essential to understand their dynamics and to reduce the risk of human losses by raising warnings before a failure. A decade ago, a decrease of apparent seismic velocity was detected several days before the failure of a clayey landslide, that was monitored with the ambient noise correlation method. It revealed its potential to detect precursor signals before a landslide failure, which could improve early warning systems. To date, nine landslides have been monitored with this method, and its ability to reveal precursors before failure seems confirmed on clayey landslides. However three challenges remain for operational early-warning applications: to detect velocity changes both rapidly and with confidence, to account for seasonal and daily environmental influences, and to check for potential instabilities in measurements. The ability to detect a precursory velocity change requires to adapt the processing workflow to each landslide: the key factors are the filtering frequency, the correlation time window, and the choice of temporal resolution. The velocity also fluctuates seasonally, by 1 to 6% on the reviewed landslide studies, due to environmental influences, with a linear trend between the amplitude of seasonal fluctuations and the filtering frequency over the 0.1–20 Hz range, encompassing both landslide and non-landslide studies. The environmental velocity fluctuations are caused mostly by groundwater levels and soil freezing/thawing, but could also be affected by snow height, air temperature and tide depending on the site. Daily fluctuations should also occur on landslides, and can be an issue when seeking to obtain a sub-daily resolution useful for early-warning systems. Finally, spurious fluctuations of apparent velocity—unrelated to the material dynamics—should be verified for. They can be caused by changes in noise sources (location or spectral content), in site response (change of scatterers, attenuation, or resonance frequency due to geometrical factors), or in inter-sensor distance. As a perspective, the observation of seismic velocity changes could contribute in assessing a landslide stability across time, both during the different creeping stages occurring before a potential failure, and during its reconsolidation after a failure.

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Main references :

• Le Breton M., Bontemps N., Guillemont A., Baillet L., Larose E., 2021. Landslide Monitoring Using Seismic Ambient Noise Correlation: Challenges and Applications, Earth Science Reviews, In press
• Larose, E., Carrière, S., Voisin, C., Bottelin, P., Baillet, L., Guéguen, P., Walter, F., Jongmans, D., Guillier, B., Garambois, S., Gimbert, F., Massey, C., 2015. Environmental seismology: What can we learn on earth surface processes with ambient noise? Journal of Applied Geophysics 116, 62–74. https://doi.org/10.1016/j.jappgeo.2015.02.001
• Mainsant, G., Larose, E., Brönnimann, C., Jongmans, D., Michoud, C., Jaboyedoff, M., 2012. Ambient seismic noise monitoring of a clay landslide: Toward failure prediction. J. Geophys. Res. 117, F01030. https://doi.org/10.1029/2011JF002159

How to cite: Larose, E., Le Breton, M., Bontemps, N., Guillemont, A., and Baillet, L.: Updates on ambient noise correlation for landslide monitoring, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16532, https://doi.org/10.5194/egusphere-egu21-16532, 2021.

Seismic sensors installed in areas prone to rockfalls provide a continuous record of the phenomenon, allowing real-time detection and characterization. Detection of small scale rockfalls (< 0.001 m³), that might be precursors of larger events, can be worthwhile for early warning systems of rockfalls. However, seismic signals are closely dependent on the characteristics of the event and on the geotechnical characteristics of the ground, making the detection of small rockfalls complex and requiring detailed in-situ analyzes. For this reason, an experiment was carried out on the UB experimental site (Puigcercós Cliff, Catalonia, NE Spain) on 6^th-7^th of June 2013, where 21 rocks with volumes ranging from 0.0015 m³ to 0.0004 m³ were thrown from the top of the cliff (200 m long and 27 m high) and the seismic signals were registered with three 3D short period seismic sensors located at different distances from the rock wall: 57 m, 67 m, and 107 m.

The recorded seismic signals have a frequency content between 10-30 Hz, and the duration of the peak amplitudes varied between 0.3 and 0.6 s. Based on these characteristics, different phases of the dynamics of the rockfalls were identified, including main impacts, rebounds, flights, rolling and final stop of the events. The furthest station recorded the lowest frequency and amplitude values, limiting our ability to detect those blocks smaller than 0.0015 m³. Comparing the results with the nearest station, seismic attenuation phenomena is detectable even at distances of 50 m.

After the experiment, a permanent seismic station was installed in the area, at 107 m from the cliff. Using LiDAR and 2D imagery monitoring, two naturally triggered rockfalls were identified on 30^th and 31^st August 2017 (0.28 m³ and 0.25 m³ respectively). Based on the results from the experiment and an automatic detection system, these main events and prior minor events have been found in the continuous seismic records of this permanent station. The characteristics of these natural detachments differ partially from the artificially triggered rockfalls during the experiment, since the geometry of the seismic signals is different. The observed shapes of the natural detachments are similar to that of granular flows, much more continuous than the sharp shapes that were observed in the isolated blocks of the experiment. This shows the possibility of incorporating seismic stations for the automatic detection and initial characterization of rockfalls and its effectiveness in detecting frequencies of occurrence.

In order to evaluate the possibility of estimating rockfall volumes, diverse energy ratios (E_s/E_p) were calculated. However, precise volume estimation is not possible. Nevertheless, the combination of seismic data with LiDAR and photographic techniques allows accurate new volume calculations of rockfalls to be incorporated progressively into the study of rockfalls.

ACKNOWLEDGMENTS: The authors would like to acknowledge the financial support from CHARMA (CGL2013-40828-R) and PROMONTEC (CGL2017-84720-R AEI/FEDER, UE) projects, Spanish MINEICO. We are also thankful to Origens UNESCO Global Geopark.

How to cite: Telletxea, B., Tapia, M., Guinau, M., Royán, M. J., Roig Lafon, P., Blanch, X., Khazaradze, G., Suriñach, E., Furdada, G., Garcia-Sellés, D., Abellán, A., and Vilaplana, J. M.: Identification and characterization of rockfalls using seismic signals, LiDAR, and imagery. Advances on real-time detection, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13149, https://doi.org/10.5194/egusphere-egu21-13149, 2021.

On 12 May 2008, the mountainous area of Longmenshan, which separates the Tibetan Plateau from the Sichuan Basin, was hit by the 8.0 Ms Wenchuan earthquake which triggered about 200,000 landslides, some of which caused river damming with the formation of temporary lakes. Failures of the landslide dams can induce severe flooding downstream, therefore, it is important to study their structure and mechanical properties in order to evaluate their stability conditions.

The present study investigates the landslide dam deposits of a rock avalanche triggered in Yang Jia Gou, in Sichuan Province, using single-station three component recordings of ambient noise, with the aim of obtaining information about thickness and mechanical properties of the deposits from their resonance properties. Three noise measurement campaigns and two ERT surveys were conducted to support data interpretation. The data were analyzed using the traditional Nakamura’s technique, HVNR, and the innovative technique HVIP, both based on the calculation of ratios between horizontal and vertical amplitude of ground motion. Both methods revealed the presence  of resonance peaks, a major one at lower frequency, and a minor one at higher frequencies, representative of the deposit layering. HVNR showed a considerable instability in terms of amplitude of H/V, likely because this technique analyzes the entire noise wave field recorded, so to be subject to a large variability related to a variable composition of the noise field. This problem does not affect the HVIP method, which is based on the analysis of the ellipticity of Rayleigh waves, isolated from the recording.

Rayleigh wave ellipticity curves were used as targets in the inversion phase to obtain the velocity profile of the site. The subsoil model was  constrained by the data derived from the resistivity profiles. The results revealed:  different velocity layers inside the deposit; lateral variations in thickness, in accordance with the higher frequency peak, and in mechanical properties, with an increase of stiffness, probably due to a major portion of rocky blocks; an increase in thickness of the entire deposit, probably because of the irregularities of the substrate.

Further investigations are in progress through other kinds of noise analysis exploiting the synchronization of simultaneous recordings. This can provide additional constraints (to be derived from the dispersion of group velocity of Rayleigh waves) and aid resolving interpretation ambiguities.

How to cite: Capone, P., Del Gaudio, V., Wasowski, J., Hu, W., Venisti, N., and Li, Y.: Integration of ambient noise and ERT data to investigate the structure of the Yang Jia Gou rock avalanche deposits (Sichuan - China), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4578, https://doi.org/10.5194/egusphere-egu21-4578, 2021.

How to cite: Nava, L., Catani, F., and Monserrat, O.: Rapid mapping of landslides by Deep-Learning of combined optical and SAR data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13181, https://doi.org/10.5194/egusphere-egu21-13181, 2021.

Landslides are common natural disasters around the globe. Understanding the accurate spatial distribution of landslides is essential for landslide analysis, prediction, and hazard mitigation. So far, many techniques have been used for landslide mapping to establish landslide inventories. However, these techniques either have a low automation level (e.g., visual interpretation-based methods) or a low generalization ability (e.g., pixel-based or object-based approaches); and improvements are required for landslide mapping. Therefore, we have developed an interactive, user-friendly web portal for landslide labeling. The web portal takes multi-temporal satellite images as inputs. A deep learning model will first detect landslide-suspicious areas in the image and present results to users for validation. Users can then review and annotate these machine-labeled landslides through a user-friendly interface. Users’ editions on landslide annotation will further improve the accuracy of the deep learning model. Two landslide-affected regions in Washington were selected to test the capability of our web portal for landslide mapping. The detected landslides were validated by expert labelers. The results indicated that our annotation tool was able to produce landslide maps with high precision, a high rate of annotation, and reduced human efforts.

How to cite: Pei, T., Nagendra, S., Banagere Manjunatha, S., He, G., Kifer, D., Qiu, T., and Shen, C.: Utilizing an interactive AI-empowered web portal for landslide labeling for establishing a landslide database in Washington state, USA, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13974, https://doi.org/10.5194/egusphere-egu21-13974, 2021.

InfraRed Thermography (IRT) spread quickly during the second half of the 20^th century in the military, industrial and medical fields. This technique is at present widely used in the building sector to detect structural defects and energy losses. Being a non-destructive diagnostic technique, IRT was also introduced in the Earth Sciences, especially in the volcanology and environmental fields, yet its application for geostructural surveys is of recent development. Indeed, the acquisition of thermal images on rock masses could be an efficient tool for identifying fractures and voids, thus detecting signs of potential failures.
Further tests of thermal cameras on rock masses could help to evaluate the applicability, advantages and limits of the IRT technology for characterizing rock masses in different geological settings.
We present some results of IRT surveys carried out in the coastal area of Polignano a Mare (southern Italy), and their correlation with other remote sensing techniques (i.e. Terrestrial Laser Scanning and Structure from Motion). The case study (Lama Monachile) is represented by a 20 m-high cliff made up of Plio-Pleistocene calcarenites overlying Cretaceous limestones. Conjugate fracture systems, karst features, folds and faults, were detected in the rock mass during field surveys. In addition, dense vegetation and anthropogenic elements, which at places modified the natural setting of the rock mass, represent relevant disturbances for the characterization of the rock mass. In this context, IRT surveys were added to the other techniques, aimed at detecting the major discontinuities and fractured zones, based on potential thermal anomalies. 
IRT surveys were carried out in December 2020 on the east side of the rock mass at Lama Monachile site. Thermal images were acquired every 20 minutes for 24 hours by means of a FLIR T-660 thermal imager mounted on a fixed tripod. Ambient air temperature and relative humidity were measured during the acquisition with a pocketsize thermo-hydrometer. A reflective paper was placed at the base of the cliff to measure the reflected apparent temperature. In addition, three thermocouple sensors were fixed to the different lithologic units of the rock face. These parameters, together with the distance between the FLIR T-660 and the rock face, were used in order to calibrate the thermal imager and correct the apparent temperatures recorded by the device, during the post-processing phase. Successively, vertical profiles showing the temperature of the rock face over time were extracted from the thermograms. Thermal anomalies were correlated with stratigraphic and Geological Strength Index profiles, obtained by means of field surveys and Structure from Motion techniques. The presence of fracture and voids in the rock mass was also investigated.

How to cite: Loiotine, L., La Salandra, M., Andriani, G. F., Apicella, E., Jaboyedoff, M., Parise, M., and Derron, M.-H.: Implementation of InfraRed Thermographic surveys in complex coastal areas: the case study of Polignano a Mare (southern Italy), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3264, https://doi.org/10.5194/egusphere-egu21-3264, 2021.

The project “Geo-information Services for Landslides in the Alps (eo4alps-landslide)” has the main objective of exploiting the potential of new satellite data coupled to advanced modelling for gravitational hazards assessment in the Alpine region. The two-year project starts in early 2021 and consists of a broad consortium of universities, geological consultancies, ICT companies and geological services. More than 20 authorities and other stakeholders responsible for landslide disaster risk management are actively involved in all project phases. “eo4alps-landslide” aims at ensuring that satellite-based Earth Observation (EO) products are increasingly and more efficiently used in practice for science and operational DRM procedures. “eo4alps-landslide” produces harmonised and advanced landslide inventories and susceptibility/hazard maps for the Alps based on InSAR and optical ground motion services and landslide-specific models embedded in the Geohazards Exploitation Platform (GEP). These EO-based services and products can be complemented by local datasets and terrain data from the end users directly in GEP. Planned products of “eo4alps-landslide” includes 1) automatic landslide detection using satellite optical and InSAR-based services, 2) harmonised and advanced landslide catalogues resulting from the satellite based detection and local inventories, 3) susceptibility/hazard maps consisting of possible landslide source areas and landslide type-specific runout modelling. Further fields of application and products will be adapted to the needs of end users. The methods will be generic in order to be used at several spatial scales from Tier 1 (region) to Tier 2 (municipality) and Tier 3 (local slope). Furthermore, the products of “eo4alps-landslide” will be compatible with products and services of other “eo4alps” initiatives, as well as with the European Ground Motion Service (EGMS).

How to cite: Malet, J.-P., Michoud, C., Oppikofer, T., Crosta, G. B., Frattini, P., Pacini, F., Garcia Robles, J., and Foumelis, M.: Landslide Geo-Information Services for the Alps, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3358, https://doi.org/10.5194/egusphere-egu21-3358, 2021.

Heavy rainfall can trigger thousands of landslides, which have a significant effect on the landscape and can pose a hazard to people and infrastructure. Inventories of rainfall triggered landslides are used to improve our understanding of the physical mechanisms that cause the event, in assessing the impact of the event and in the development of hazard mitigation strategies. Inventories of rainfall-triggered landslides are most commonly generated using optical or multispectral satellite imagery, but such imagery is often obscured by cloud-cover associated with the rainfall event. Cloud-free optical satellite images may not be available until several weeks following an event. In the case where rain falls over a long period of time, for example during the monsoon season or successive typhoon events, the timing of the triggered landslides is usually poorly constrained. This lack of information on landslide timing limits both hazard mitigation strategies and our ability to model the physical processes behind the triggered landsliding.

Satellite radar has emerged recently as an alternative source of information on landslides. The removal of vegetation and movement of material due to a landslide alters the scattering properties of the Earth’s surface, thus giving landslides a signal in satellite radar imagery. Satellite radar data can be acquired in all weather conditions, and the regular and frequent acquisitions of the Sentinel-1 constellation, could allow landslide timing to be constrained to within a few days.  Satellite radar data has been successfully used in detecting the spatial distribution of landslides whose timing is known a-priori (for example those triggered by earthquakes). Here we demonstrate that time series of Sentinel-1 satellite radar images can also be used to achieve the opposite: the identification of landslide timing for an event whose spatial extent is known.

We analyse radar coherence and amplitude times series to identify changes in the time series associated with landslide occurrence. We compare pixels within each landslide with nearby pixels outside each landslide that have been identified to be similar in pre-rainfall Sentinel-1 and Sentinel-2 imagery. We test our methods on rainfall-triggered landslides in Nepal and Japan, both of which are mountainous countries that experience regular heavy rainfall events that are often obscured by cloud cover in optical satellite imagery.

How to cite: Burrows, K., Marc, O., and Remy, D.: Establishing the timings of rainfall-triggered landslides using Sentinel-1 satellite radar data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11028, https://doi.org/10.5194/egusphere-egu21-11028, 2021.