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Landslide monitoring: recent technologies and new perspectives

The global increase in damaging landslide events is raising the attention of governments, practitioners and scientists to develop functional, reliable and (when possible) low cost monitoring strategies. Several 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 its future evolution or at least to 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.
The 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, we aim 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 welcome, as well as newly developed tools or data analysis approaches (focusing on big data management). We expect case studies in which multi-temporal and multi-platform monitoring data are exploited for risk management and Civil Protection aims with positive effects in social and economic terms.

Co-organized by GM3
Convener: Lorenzo SolariECSECS | Co-conveners: Peter Bobrowsky, Mateja Jemec Auflič, Federico Raspini, Veronica Tofani

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Mon, 26 Apr, 09:00–10:30

Chairpersons: Lorenzo Solari, Veronica Tofani, Federico Raspini

5-minute convener introduction

Mateja Jemec Auflič et al.

Landsliding is the downslope movement of surface material under the force of gravity, initiated when gravitational and other types of shear stresses within the slope exceed the shear strength of the material that forms the slope. Often, landslides pose a physical and environmental threat to communities living in landslide-prone areas. While much landslide research focuses on monitoring techniques to define the background of the landslide (extent, volume, velocity, magnitude) one of the main goals of the Geological Surveys (GS) are to support and understand the regional and local geology to identify areas susceptible to landslides. With this perspective, a questionnaire on landslides monitoring techniques was distributed among GS of Europe to define which techniques are most widly used at GS and to distinguish those that can be considered as powerful tool for landslide mapping, monitoring, hazard analysis, and early warning, according to the type of geological settings. The initial results of the questionnaire showed that the most commonly used monitoring techniques are geotehnical and mapping, followed by remote sensing and hydrological techniques. Among the 849,543 landslide records evidenced by the Geological Surveys of Europe in the paper of Herrera et al. (2017), we found only 47 landslides that have been monitored. However, only landslides that directly threatning the population and infrastructure or landslides with a volume greater than 10,000 m3 have been monitored. Compared to other research (Hague et al., 2016; Froude and Petley, 2018) the questionnaire showed that the fundamental basis for any geologically-related study is geological field mapping. The results of this traditional method are commonly compiled and interpreted together with boreholes, other advanced geodetic (UAV photogrammetry, TLS, GNSS, GBInSAR), and geophysical techniques (electrical resistivity, seismic refraction, GPR). One of the critical survey findings shows on starting landslide monitoring after the failure, only 3% of observed landslides have been monitored before the occurrence. Considering these results, we evaluate the landslide-monitoring techniques and reveal different monitoring strategies between the GS of Europe.


Froude, M.J. and Petley, D. (2018) Global fatal landslide occurrence from 2004 to 2016. Natural Hazards and Earth System Sciences, 18. pp. 2161-2181

Haque U, Blum P, da Silva PF, Andersen P, Pilz J, Chalov SR, Malet J-P, Auflič MJ, Andres N, Poyiadji E, Lamas PC, Zhang W, Peshevski I, Pétursson HG, Kurt T, Dobrev N, García-Davalillo JC, Halkia M, Ferri S, Gaprindashvili G, Engström J, Keellings D (2016) Fatal landslides in Europe. Landslides 13:1–10

Herrera, G., Mateos, R. M., García-Davalillo, J. C., Grandjean, G., Poyiadji, E., Maftei, R., Filipciuc, T.C., Jemec Auflič, M., Jež, J., Podolszki, L., Trigila, A., Iadanza, C., Raetzo, H., Kociu, A., Przyłucka, M., 446 Kułak, M., Sheehy, M., Pellicer, X. M., McKeown, C., Ryan, G., Kopačková, V., Frei, M., Kuhn, D., 447 Hermanns, R. L., Koulermou, N., Smith, C. A., Engdahl, M., Buxó, P., Gonzalez, M., Dashwood, C., 448 Reeves, H., Cigna, F., Liščák, P., Pauditš, P., Mikulėnas, V., Demir, V., Raha, M., Quental, L., Sandić, C., and Jensen, O. A. (2018) Landslide databases in the Geological Surveys of Europe, Landslides, 15, 450: 359-379.

How to cite: Jemec Auflič, M., Herrera, G., María Mateos, R., Poyiadji, E., Quental, L., Severine, B., Peternel, T., Podolszki, L., Iadanza, C., Kociu, A., Warmuz, B., Jelének, J., Hadjicharalambous, K., Peterson Becher, G., Dashwood, C., Liščák, P., Minkevičius, V., Todorović, S., and Jørgen Møller, J.: Landslides monitoring techniques review in the Geological Surveys of Europe, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8536,, 2021.

Kaushik Ramanathan and Nirmala Vasudevan

Are we justified in referring to all landslides as natural hazards? With the effects of climate change, landslide incidences are increasing all over the world, and many of them accompany floods and occur due to extreme weather events. It has been unequivocally established that humans are responsible for global climate change. Further, landslides also occur in deforested areas. Even if one were to discount the effects of deforestation on climate change and the subsequent occurrence of landslides, one cannot ignore the fact that deforestation leads to slope instabilities in multiple ways. It decreases the effective retaining strength of the slope materials and also exposes more slope material to weathering and consequent leaching. Thus, deforestation and climate change, caused directly or indirectly by human beings, have a significant bearing on landslide occurrence. Furthermore, several catastrophic landslides in recent times have occurred due to indiscriminate human activity, such as constructing dams and other structures on fragile slopes, blasting slopes for road construction without providing adequate toe support, excessive mining, constructing faulty retaining structures on unstable slope material, etc. Over the years, such human activity has resulted in landslides of all types and at various scales. Whether a landslide is natural, caused due to anthropogenic factors, or a combination of the two, the investigation approach and monitored parameters remain the same; we still need to identify the various causative factors and quantify their rates of change over time in the run up to the landslide event. However, we need a paradigm shift in our perspective and treatment of landslides. We need to accept that human activity is, or can be, responsible for landslide occurrence. With this change in perspective, we would monitor slopes with an increased awareness that human actions could negatively impact slope stability. This, in turn, would entail monitoring at every stage to ensure that no human activity adversely impacts the natural balance, thus paving the way for truly sustainable development. We would be doing great disservice to the investigation and monitoring of landslides by such preconceived notions as all landslides are natural hazards. It is high time that we accept our part in compounding the problem of landslide occurrences and come up with solutions to monitor the impact of human activity on the environment to prevent landslides.

How to cite: Ramanathan, K. and Vasudevan, N.: Anthropogenic causes of landslides and their implications for monitoring, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6933,, 2021.

Qingkai Meng et al.

InSAR is an advanced earth observation (EO) technique for retrieving past, subtle (millimetre-level) and continuous surface movements over a long period, which has been widely applied in landslide deformation monitoring and detecting precursory signals of deformation. However, limited by the maximum detected deformation gradient from two consecutive scenarios, singular InSAR has hampered the recognition application for high-speed slides or earth flows, leading to a misleading understanding of slope evolution. Being a high-resolution photogrammetry technology, UAV represents a suitable tool to detect meter-level displacement rates and estimate ground detachment. Thus, InSAR and UAV's synergic analysis can detect the kinematic variation of geographical and geomorphological features, corresponding surface displacements to cross-validation. In the present work, two representative cases illustrated how the combination of InSAR and UAV could be applied in loess landslide deformation monitoring. One case, located in Hongheyan, Gansu Province, China, was selected to reconstruct landslide morphology, identify deformation evolution behaviour and produce dynamic deformation zonation maps using 85 Sentinel-1A SAR images and three UAV fight surveys from pre-sliding to post-sliding. The integrated deformation results illustrate the slide of theHongheyan slope was triggered by heavy rainfall, became suspended owing to the topography effect after the occurrence, and reactivated recently. Another case, located in Qinghai-Gansu province, calculated two-dimensional displacements (vertical-horizontal) by decomposing the ascending and descending Sentinel-1 images to reclassify the regional slope failure type into the translational slide, rotational slide and loess flow based on deformation characteristic. Overall, multi-source information fusion is a new approach for landslide monitoring from regional-scale failure classification to specific-scale slope deformation evolution, giving the comprehensive understanding for local government or Civil Protection to take sufficient precautions for risk mitigation.

How to cite: Meng, Q., Raspini, F., Confuorto, P., Peng, Y., and Liu, H.: Deformation monitoring of typical loess landslide case studies through combining InSAR and UAV, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-462,, 2021.

Davide Festa et al.

The launch of the Sentinel-1 constellation by the European Copernicus Program, primarily devoted to scientific community research, has allowed acquiring huge volumes of radar images with worldwide coverage and a short temporal sampling (12 days reduced to 6 days thanks to their position at 180° in the same orbit). The systematic collection of imagery and the repeated processing of each new pair of images acquired opened the unprecedent possibility of conducting a continuous monitoring of Earth surface deformations, such as subsidence and slope instabilities over different geomorphological settings. At present, Tuscany, Veneto and Valle d’Aosta regions (Italy) are benefiting from systematical Sentinel-1-based monitoring of geological and geomorphological hazards. The promising outcomes so far obtained constitute a decisive step towards near-real-time monitoring and therefore a valid support for geohazard risk management activities. Retracing the pattern set by the encouraging results from the three Italian Regions, an operating workflow chain is proposed in the framework of an operational monitoring service, from the collection of satellite images to the possibility of conducting field surveys. The procedure is based on 4 different steps: i) continuous collection of Sentinel-1 images, constant data processing through an MT-InSAR (Multi-Temporal Interferometric Synthetic Aperture Radar) technique and exploitation of a data-mining algorithm able to retain only meaningful Measurement Points (MP) in terms of abrupt change of displacement rate; ii) radar-interpretation of the filtered MP for the detection of the possible causes of the anomalies through the use of ancillary informative layers or pre-existing databases; iii) dissemination of the relevant radar-interpreted information to hydrological risk managing actors by a direct alert or periodic bulletins; iv) field investigation, preliminary risk assessment and possible remedial works design. The procedure was successfully applied gathering evidence of its usefulness in practical terms. The cases of the Bosmatto landslide (Valle d’Aosta Region) and the case of the Zeri Landslide (Tuscany Region) which belong to two alpine and apennine environments, respectively, are reported. In the first example, in response to a relevant acceleration interpreted from the MP available on the area of interest, an alert was sent to the regional officers who increased their awareness related to the risk posed by the Bosmatto Landslide. In the second example, a monitoring bulletin which is periodically delivered for the Tuscany Region pointed out the meaningfulness and persistency of anomalies identified in the Zeri municipality. This led the regional authorities to conduct a site investigation oriented to the assessment of preliminary risks. The presented results highlight the effective benefits-cost ratio, the high precision and the short amount of time required to complete the procedure representing a best practice for the early detection of ground deformation events.

How to cite: Festa, D., Confuorto, P., Del Soldato, M., Bianchini, S., and Casagli, N.: From Sentinel-1 data processing to field survey: an operating workflow for the continuous monitoring of the Earth surface deformations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9629,, 2021.

Andre C. Kalia

Landslide activity is an important information for landslide hazard assessment. However, an information gap regarding up to date landslide activity is often present. Advanced differential interferometric SAR processing techniques (A-DInSAR), e.g. Persistent Scatterer Interferometry (PSI) and Small Baseline Subset (SBAS) are able to measure surface displacements with high precision, large spatial coverage and high spatial sampling density. Although the huge amount of measurement points is clearly an improvement, the practical usage is mainly based on visual interpretation. This is time-consuming, subjective and error prone due to e.g. outliers. The motivation of this work is to increase the automatization with respect to the information extraction regarding landslide activity.

This study focuses on the spatial density of multiple PSI/SBAS results and a post-processing workflow to semi-automatically detect active landslides. The proposed detection of active landslides is based on the detection of Active Deformation Areas (ADA) and a subsequent classification of the time series. The detection of ADA consists of a filtering of the A-DInSAR data, a velocity threshold and a spatial clustering algorithm (Barra et al., 2017). The classification of the A-DInSAR time series uses a conditional sequence of statistical tests to classify the time series into a-priori defined deformation patterns (Berti et al., 2013). Field investigations and thematic data verify the plausibility of the results. Subsequently the classification results are combined to provide a layer consisting of ADA including information regarding the deformation pattern through time.

How to cite: Kalia, A. C.: Classification of landslide activity based on spaceborne interferometric SAR at the Moselle Valley, Germany, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11873,, 2021.

Veronica Pazzi et al.

Interferometric Synthetic Aperture Radar (InSAR) enables detailed investigation of surface landslide movements but lacks information about subsurface recognition/identification. It can be obtained by means of direct measurements (e.g., geotechnical data) and geophysical techniques. InSAR observations, seismic noise measurements, and geotechnical data were integrated to assess the deformation on the ground surface and to determine the depth of the failure surface of the Villa de Independencia landslide, Cochabamba (Bolivia) affecting the village. It is a compound slow-moving landslide (total area approximatively 3.8·106 m2) composed by three sub-blocks slide exhibiting diverse geometries, multiple failure surfaces, and magnitudes.

For investigating the spatiotemporal characteristics of the landslide motion, Sentinel-1 time series from October 2014 to December 2019 were analysed. A new geometric inversion method was also proposed to determine the best-fit sliding direction and inclination of the landslide. Results of the Sentinel-1 time series show two substantial accelerations in early 2018 and 2019, coinciding with an increment of precipitations in the late rainy season. It allows supposing the rainy as the most likely triggers of the identified accelerations.

The seismic noise measurements (more than one hundred spreaded over the whole landslide), analysed according to the Vertical to Horizontal Spectral Ratio technique (H/V), were calibrated and validated by means of the geotechnical data derived by three boreholes and 13 between rock and soil samples. H/V data allowed identifying the different dynamic characteristics of the three sub-blocks: movements are possibly due to the different properties of shallow and deep slip surfaces. The landslides caused damage on the edifices, probably mainly caused by the shallow slip interface (located at a mean depth of 5 m) since the foundation depth of the buildings is at most 2 m. In the town centre a deeper failure surfaces, approximatively with depth between 15 and 75 m, can be identified which may be responsible for its different direction and acceleration magnitude of sliding (inferred by InSAR) compared to the other parts of the landslides. Finally, the determination of the slip surface depths allowed to estimate the overall landslide volume assessed approximatively 9.18·107 m3.

The study shows the great potential for landslide motion characterization and mechanism investigation by combing InSAR, seismic noise and geotechnical measurements.

How to cite: Pazzi, V., Del Soldato, M., Song, C., Yu, C., Li, Z., Cruz, A., and Utili, S.: InSAR, seismic noise, and geotechnical data to assess landslide activity and geometry: the Villa de Independencia (Cochabamba, Bolivia) case study, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12300,, 2021.

Susanne Wahlen et al.

Transportation corridors in mountain regions are often situated at the bottom of narrow valleys. Changing slope stability conditions can put these routes at critical risk. Slope stabilization works (e.g., rock scaling, blasting) or structural protection measures (e.g., rock sheds, reinforced embankments, tunnels) are not always feasible or may not be cost-effective due to low average daily traffic or the expected event size. Route SP29 is the main connection road to Santa Catarina, a popular tourist resort in the Frodolfo River Valley, Lombardy, Italy. The Ruinon landslide is a major slope instability involving approximately 30 million m3 of rock and debris and causes repeated rockfalls that can reach as far as the road. 

We present a Doppler radar system for real-time rockfall detection and immediate road closure in case of an event. The rockfall radar permanently monitors the landslide area from the opposite side of the slope with a range of more than 1 km to the upper scarp. Radar technology works reliably regardless of visibility, i.e. in rain, fog or snowfall as well as at night. After an initial calibration period in summer 2020, we activated automatic road closure and reopening in case of a rockfall event; upon detection of rockfall in a defined region of interest, the radar system automatically switches the traffic lights to red. If the rock fall event reaches a defined zone near the road or the road itself, it remains closed and requires manual reset after site inspection with webcams at the radar site and the traffic lights. If the rockfall event remains above the road, then the radar system automatically releases the road again after 90 seconds. Automatic notifications about the status are sent to authorized user via email and SMS. In addition to the deployment of the alarm system using Doppler radar, the embankment along the endangered road section was reinforced and raised. These combined measures of protection structures and alarm system aim at maximising the opening hours of the street while providing the highest possible level of protection. Between July (installation) and December 2020, 60 rockfall events caused a road closure, with the road being automatically reopened by the system in approximately 85% of cases.

How to cite: Wahlen, S., Stähly, S., Schmid, L., Meier, L., Carlà, T., and Casagli, N.: Real-time Rockfall Detection System with Automatic Road Closure and Reopening using Doppler Radar Technology at the Ruinon Landslide, Italy, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14818,, 2021.

Cristiano Gygax et al.

The search for low-cost equipment solutions in geomatics and other domains is a theme that is increasingly addressed by a growing number of researchers. Today, the open-source resources and the availability of cheap electronic equipment and easy to program microcontrollers to manage them (e.g. Arduino) make this type of research accessible to everyone.

The goal of this project is to assemble, program, test and evaluate a low-cost short-range terrestrial LiDAR scanner, i.e. a device that can scan a surface with a laser and represent it in 3D as a point-cloud. An initial prototype was assembled and programmed from low-cost electronics and mechanical components partly ordered and partly 3D printed, at a total cost of around USD 340. Conceptually, the operation of the device is simple: two stepper motors drive a laser sensor on two axes (horizontal and vertical), and a distance measurement for each of the motors positions is taken. These components are controlled by an Arduino Mega 2560, a powerful microcontroller known for its simplicity and versatility, which also receives the measurements and stores them on a SD card. A smartphone application was also developed to send scanning parameters to the LiDAR via Bluetooth. This first prototype detects on average 150 points/second at a maximum distance of about 40 m with an average error of 2 cm and a maximum resolution of less than 0.012° (1 point every 2.9 mm at a distance of 15 m).

Initial tests of the device in the laboratory and in the field are encouraging. In order to obtain a better-performing device, some mechanical components will be improved (to make the device more robust and reduce vibrations), a better-performing laser sensor installed (less error and higher maximum distance of at least 100 m) and a small solar panel coupled , so that the device can be tested in the field on several consecutive days.

This device will have two main applications: 1) it will be used for continuous monitoring in areas where the probability of destruction is too high to put a commercial device thousands of time more expensive; 2)  it is planned to develop a DIY kit to be used by students in geosciences to understand the principles of laser scanning.

How to cite: Gygax, C., Derron, M.-H., and Jaboyedoff, M.: Arduino based low-cost short-range terrestrial LiDAR Scanner, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7579,, 2021.

Luca Mauri et al.

The presence of roads is closely linked with the activation of land degradative phenomena such as landslides. Factors such as ineffective road management and design, local rainfall regimes and specific geomorphological elements actively influence landslides occurrence. In this context, recent developments in digital photogrammetry (e.g. Structure from Motion; SfM) paired with Remotely Piloted Aircraft Systems (RPAS) increase our possibilities to realize low-cost and recurrent topographic surveys. This allows the realization of multi-temporal (hereafter 4D) and high-resolution Digital Elevation Models (DEMs), fundamental to analyse geomorphological features and quantify processes at the fine spatial and temporal resolutions at which they occur. In this research is presented a 4D comparison of geomorphological indicators describing a landslide-prone agricultural system, so as to detect the noticed high-steep slope failures. The possibility to analyse the evolution of landslide geomorphic features in steep agricultural systems through high-resolution and 4D comparison of such indicators is still a challenge to be investigated. In this research, we considered a case study located in the central Italian Alps, where two shallow landslides (L1, L2) were activated below a rural road within a terraced vineyard. The dynamics of the landslides were monitored through the comparison of repeated DEMs (DEM of Difference, i.e. DoD), that reported erosion values of above 20 m3 and 10 m3 for the two landslides zones and deposition values of more than 15 m3 and 9 m3 respectively. The elaboration of Relative Path Impact Index (RPII) highlighted the role played by the road in the alteration of surface water flow directions. Altered water flows were expressed by values between 2σ and 4σ of RPII close to the collapsed surfaces. The increasing of profile curvature and roughness index described landslides evolution over time. Finally, the multi-temporal comparison of features extraction underlined the geomorphological changes affecting the study area. The computation of the quality index underlined the accuracy of features extraction. This index is expressed in a range between 0 (low accuracy) and 1 (high accuracy) and resulted equal to 0.22 m, regarding the landslide observed during the first RPAS survey (L1-pre); 0.63 m, concerning the same landslide detected during the second RPAS survey (L1-post); 0.69 m for L2. Results prove the usefulness of high-resolution and 4D RPAS-based SfM surveys for the investigation of landslides triggering due to the presence of roads at hillslope scale in agricultural systems. This work could be a useful starting point for further studies of landslide-susceptible zones at a wider scale, to preserve the quality and the productivity of affected agricultural areas.

How to cite: Mauri, L., Straffelini, E., Cucchiaro, S., and Tarolli, P.: RPAS-SfM 4D mapping of shallow landslides activated in a steep terraced vineyard, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2368,, 2021.

Nafsika-Ioanna Spyrou et al.

This project aims at the use of Unmanned Aircraft Systems (UAS) applications for mapping. Geomorphological mapping of features and changes with the use of UAS, in cases of floods, landslides, stream flows, etc. has been growing rapidly in recent years. It is combined with traditional mapping methods as well as modern technologies such as Geographic Information System (GIS). Our work concerns landslide hazard in the study area of Chios, in particular along the Chios - Kardamila road in the Mersinidi - Miliga region with a record of landslides and particular geological interest. During the field survey a) three-dimensional model of the slope was made across the road using UAS and the apropriate software, b) point cloud, c) a mosaic orthophotomap and d) a Digital Surface Model (DSM). After the data collection components material we followed detailed geological and tectonic mapping with enormous accuracy because the innovative technologies provided us multiple data compared to older methodologies. The exploitation of the Structure from Motion provided us with information of the inaccessible parts of the study area.


How to cite: Spyrou, N.-I., Stanota, E. S., Andreadakis, E., Skourtsos, E., Lozios, S., and Lekkas, E.: Mappıng wıth the use of uas. Project plannıng and adjustment: the case of Mersınıdı landslıdes (Chıos Island, NE Aegean), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14380,, 2021.

Danilo Godone et al.

In landslide monitoring, the attention is mainly focused on rapidly evolving phenomena. However, slow and very slow landslides are equally significant as they often involve settlements and infrastructures. Additionally, they are characterized by remarkable extension and depth. Due to their low displacement rate, often they are underestimated as impacting events; but in a longer timespan, their continuous and slow activity may lead to damages to buildings and roads thus worsening the living conditions of the involved area. In order to assure a peaceful coexistence between phenomena and inhabitants, a multi-source monitoring network is recommended, by integrating surface data with subsoil ones in order to better understand the whole and real kinematic. Moreover, the data acquisition rate should be high enough to detect early any increases in displacements rate. Surface monitoring approaches are extremely wide (GNSS, remote sensing, InSAR…); on the contrary subsoil measurement systems, are few and limited to in-place inclinometers. Concerning them, the Geohazard Monitoring Group (IRPI-CNR) has developed and manufactured a robotic measuring system for the acquisition of deep-seated ground deformations and, particularly, deep horizontal displacements. The instrumentation combines the advantages of the traditional measurement technique (double readings 0/180˚) with a robotized approach improving the results in terms of revisit time, repeatability and accuracy. The robotized device also called “Automated Inclinometer System” (AIS) allows the automatic check of all the length of the borehole (up to 120m tube length) with just one inclinometer probe. The traditional cable (including probe signal and power supply) is replaced with a thin polyethylene cable (φ 2mm) for sustaining and moving the probe up/down into the standard inclinometer borehole. AIS is completely automatized, but can be also controlled by a remote web interface and, with the same mean, transmits measurement results and system diagnostic messages, such as alerts, warnings, etc. The described system is, currently and extensively, employed in landslide monitoring networks in European mountain ranges obtaining interesting results. In fact, thanks to the described features it is able to rapidly define the deep and surface kinematics of the observed phenomena and, consequently, evaluate the displacements accelerations. Furthermore, due to its high-frequency measurement, it is possible to find a relationship between rainfalls/snow melting and piezometric water levels measured by nearby stations. AIS represents a trustworthy option to realize a more complete integrated network for landslide interpretation and monitoring.

How to cite: Godone, D., Allasia, P., Guenzi, D., Notti, D., and Baldo, M.: Monitoring of slow-moving landslides. The importance of integration between surface and depth measurements, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8231,, 2021.

Giuseppe Ruzza et al.

The stationary or in-place inclinometer is the main high-performance solution in landslide monitoring applications due to its capability of tracking real time displacement at different depth and supporting early warning. Despite that and the general need of data for understanding landslide behaviour, the high cost of in-place inclinometers, in most cases, limit or prevent their use. On this basis, we started developing a low-cost and open source, modular MEMS-based inclinometer that uses multiple Arduino boards as processing units. Although MEMS accelerometers have many advantages in comparison with traditional high-precision electromechanical sensors, they are very sensible to temperature variation (i.e. thermal drifting).

In order to compensating thermal drifting a specific thermal analysis and an associated simple compensation strategy were used. After the mitigation of thermal bias, the electronic devices were designed, built and assembled.

The developed inclinometer system is composed of two main electronic systems: 1) a multiple electronic device (i.e. a MEMS accelerometer, the IMU reading interface and a communication board) installed within each measuring module; 2) an external master control unit, based on the Arduino platform coupled with a dedicated developed interface board. The master unit reads tilt value from each measuring module through a communication interface. This unit was developed to allow interfacing of additional digital or analog sensors (e.g. water content, rain gauge, etc..), and control additional parameters.

A steel casing for measuring components was designed and built. For each measuring unit, a squared-section case, consisting of a 30 cm long tube equipped with 4 elements that allow the installation the instrument within a standard inclinometric tubes, was prepared and assembled.

After system assembling, displacement of the inclinometric column was first simulated by a laboratory test. In particular, we used a supporting frame that allowed to vertically align the modules. The auxiliary frame was specifically designed to drive displacement along a selected axis and to register the maximum displacement at the head of the inclinometric column. In this way, the lower module is kept fixed. This test permitted to obtain a number of different synthetic deformation curves that form a basis for checking the accuracy of the instrumentation measurement. Result obtained highlight the potential use of our system for real monitoring application. The next step will be to install the instrumentation on site to check its operation in real field conditions.

How to cite: Ruzza, G., Revellino, P., and Guadagno, F. M.: Laboratory test results of a new developed low-cost and open-source inclinometer based on MEMS technology, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15309,, 2021.

Moritz Gamperl et al.

Worldwide, cities in mountainous areas struggle with increasing landslide risk as consequence of global warming and population growth, especially in low-income informal settlements. For these situations, current monitoring systems are often too expensive and too difficult to maintain. Therefore, innovative monitoring systems are needed in order to facilitate low-cost landslide early warning systems (LEWS) which can be applied easily.

Based on technologies such as micro-electro-mechanical systems (MEMS) sensors and the LoRa (Long Range) communication standard, we are currently developing a cost-effective IoT (Internet of Things) geosensor network. It is specifically designed for local scale LEWS in informal settlements.

The system, which is open source and can be replicated without restrictions, consists of versatile LoRa sensor nodes which have a set of MEMS sensors (e.g. tilt sensor) on board and to which various additional sensors can be connected. The nodes are autonomous and can operate on standard batteries or solar panels. The sensor nodes can be installed on critical infrastructure such as house walls or foundations. Two of the possible additions are the Subsurface Sensor Node and the Low-Cost Inclinometer. Both are installed underground and offer tilt- and groundwater-measurements of the subsurface.

Complemented with further innovative measurement systems such as the Continuous Shear Monitor (CSM) and a flexible data management and analysis system, the newly developed monitoring system offers a great cost to benefit ratio and easy application for similar sites and LEWS, especially in urbanized areas in developing countries.

This work is being developed as part of the project Inform@Risk, where the monitoring system will be installed as part of an early warning system in Medellín, Colombia. It is funded by the German Ministry of Education and Research (BMBF).

How to cite: Gamperl, M., Singer, J., and Thuro, K.: A new IoT geosensor network for cost-effective landslide early warning systems, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8447,, 2021.

Mathieu Le Breton et al.

Billions of passive Radiofrequency tags are produced by the Radio-Frequency Identification (RFID) industry every year to identify goods remotely. Enhanced RFID adds the capacity for localisation and sensing that can be used in earth science for long-term and spatially dense monitoring with low-cost tags. Localisation has been used already to monitor displacements of coarse sediment and landslides over several metres. Sensing capabilities have been developed in laboratories, but never implemented on real fields. This work presents the first RFID sensing application in earth science, using the simplest 1-bit sensor to detect millimetric motion of unstable rocks. The application required designing custom RFID tags adapted for outdoor usage at long range, adapting the data acquisition of an existing tag microcircuit, and designing a sensor that triggers when displacement exceeds a few millimetres, which threshold displacement can be adapted for each sensor. In complement, the system embeds displacement sensing to measure larger displacements> 5 mm, using the phase-based measurement already deployed on landslides. The presentation display results from laboratory tests and from an implementation in a real site in Eastern France. The advantages and drawbacks of the method are discussed, as well as the future potential RFID sensors that could monitor unstable terrains.

Author’s published work on the topic:

Le Breton, M., Baillet, L., Larose, E., Rey, E., Benech, P., Jongmans, D., Guyoton, F., 2017. Outdoor UHF RFID: Phase Stabilization for Real-World Applications. IEEE Journal of Radio Frequency Identification 1, 279–290.

Le Breton, M., Baillet, L., Larose, E., Rey, E., Benech, P., Jongmans, D., Guyoton, F., Jaboyedoff, M., 2019. Passive radio-frequency identification ranging, a dense and weather-robust technique for landslide displacement monitoring. Engineering Geology 250, 1–10.

Le Breton, M., 2019. Suivi temporel d’un glissement de terrain à l’aide d’étiquettes RFID passives, couplé à l’observation de pluviométrie et de bruit sismique ambiant (PhD Thesis). Université Grenoble Alpes, ISTerre, Grenoble, France.

Le Breton, M., Baillet, L., Larose, É., Rey, E., Jongmans, D., Guyoton, F., Benech, P., 2020. Passive RFID, a new technology for dense and long-term monitoring of unstable structures: review and prospective. (No. EGU2020-19726). Presented at the EGU2020, Copernicus Meetings.

Le Breton M., 2020, Suivi de terrains instables à l'aide d'un réseau dense de capteurs RFID: Émergence de nouvelles applications, presented at Journées Nationales de Géotechnique et de Géologie de l'ingénieur (JNGG), Jean Goguel Award public session, 2021.

How to cite: Le Breton, M., Grunbaum, N., Baillet, L., and Larose, É.: Monitoring rock displacement threshold with 1-bit sensing passive RFID tag, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15305,, 2021.

Balaji Hariharan and Ramesh Guntha

With the Landslide Tracker mobile app's launch to track landslides through a crowdsourcing model during the monsoon season of 2020, we learned several important lessons that may help us improve the data quality, volunteer participation, and participation from institutions. The 'Landslide Tracker' mobile application allows tracking the landslides and details such as GPS location, date & time of occurrence, images, type, material, size, impact, area, geology, geomorphology, and comments. This app is available on Google Play Store for free, and at, with software conceived and developed by Amrita University in the context of the UK NERC/FCDO funded LANDSLIP research project ( The Landslide tracker app was released during the 2020 monsoon season, and more than 250 landslides were recorded through the app across India and the world.

Due to the nature of crowdsourcing, we have seen test entries, duplicate entries, entries with apparent mistakes such as the wrong location. In many cases, these entries were deleted by the administrator through proactive verification. To sustain the removal of invalid entries with continued usage, we can allow users to mark a landslide for verification. The administrator can remove invalid entries or approach the original contributor to update the data with minimum effort. Currently it takes under three minutes to record a landslide. To reduce the time further, it is requested to make a single page form to record date, location, images and few questions. To improve volunteer participation for contributing and validating landslide entries, we can implement digital rewards such as points, badges, titles, leader boards, etc. Additionally, allow users to like, comment, and share the landslide entries to improve the engagement. To improve the participation of universities, disaster management authorities, district authorities, and other governmental and non-governmental agencies for contributing and using landslide information, we can implement the institutional management functionality. It allows the institution to configure the staff and manager user. The manager can review, update, delete entries from the team, get reports on the contribution of the staff, and download and share the landslides contributed by the whole institution.

How to cite: Hariharan, B. and Guntha, R.: Crowdsourced Landslide Tracking – Lessons from Field Experiences of Landslide Tracker Mobile App, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12711,, 2021.

Ramesh Guntha and Maneesha Vinodini Ramesh

Substantially complete landslide inventories aid the accurate landslide modelling of a region’s susceptibility and landslide forecasting. Recording of landslides soon after they have occurred is important as their presence can be quickly erased (e.g., the landslide removed by people or through erosion/vegetation). In this paper, we present the technical software considerations that went into building a Landslide Tracker app to aid in the collection of landslide information by non-technical local citizens, trained volunteers, and experts to create more complete inventories on a real-time basis through the model of crowdsourcing. The tracked landslide information is available for anyone across the world to view. This app is available on Google Play Store for free, and at, with software conceived and developed by Amrita University in the context of the UK NERC/FCDO funded LANDSLIP research project (

The three technical themes we discuss in this paper are the following: (i) security, (ii) performance, and (iii) network resilience. (i) Security considerations include authentication, authorization, and client/server-side enforcement. Authentication allows only the registered users to record and view the landslides, whereas authorization protects the data from illegal access. For example, landslides created by one user are not editable by others, and no user should be able to delete landslides. This validation is enforced at the client-side (mobile and web apps) and also at the server-side software to prevent unintentional and intentional illegal access. (ii) Performance considerations include designing high-performance data structures, mobile databases, client-side caching, server-side caching, cache synchronization, and push-notifications. The database is designed to ensure the best performance without sacrificing data integrity. Then the read-heavy data is cached in memory to get this data with very low latency. Similarly, the data, once fetched, is cached in memory on the app so that it can be re-used without making repeated calls to the server every time when the user visits a screen.  The data persists in the mobile database so the app can load faster while reopening. A cache-synchronization mechanism is implemented to prevent the caches' data from becoming stale as new data comes into the database. The synchronization mechanism consists of push-notifications and incremental data pulls. (iii) Network resiliency considerations are achieved with the help of local storage on the app. This allows recording the landslides even when there is no internet connection. The app automatically pushes the updates to the server as soon as the connectivity resumes. We have observed over 300% reduction in time taken to load 2000 landslides, between the no-cache mode to cache mode during the performance testing. 

The Landslide tracker app was released during the 2020 monsoon season and more than 250 landslides were recorded through the app across India and the world.

How to cite: Guntha, R. and Vinodini Ramesh, M.: Technical Considerations for Building a Landslide Tracker Mobile App, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6019,, 2021.

Maneesha Vinodini Ramesh et al.

Monsoons are characterised by the widespread occurrence of  landslides. Tracking each landslide event, developing early warning thresholds, understanding triggers, and initiating disaster rescue and relief efforts are complex for researchers and administration. The ever increasing landslides demand real-time data collection of events to enhance disaster management. In this work we designed and developed a dedicated crowd sourced mobile application, for systematic way of collection, validation, summarization, and dissemination of landslide data in real-time. This unique design of mobile app uses a scalable real-time data collection methodology for tracking landslide events through citizen science, and is available on Google Play Store for free, and at, with software conceived and developed by Amrita University in the context of the UK NERC/FCDO funded LANDSLIP research project ( This work implemented a structured database that integrates heterogeneous data such as text, numerical, GPS location, landmarks, and images. This methodology enables real-time tracking of landslides utilizing the details such as GPS location, date & time of occurrence, images, type, material, size, impact, area, geology, geomorphology, and comments in real-time. The mobile application has been uniquely designed to avoid missing landslide events and to handle the tradeoff between real-time spatial data collection without compromising the reliability of the data.  To achieve this a multi level user account was created based on their expert levels such as Tracker, Investigator, Expert.  A basic tracking form is presented for the Tracker level, and an extensive form is presented to the Expert level. The reliability of landslide data enhances as the user level increases from Tracker to Expert. Unique UI designs have been utilized to capture, and track the events. The tracking interface is divided into multiple screens; the main screen captures the landslide location through GPS enabled map interface and captures the date/time of the occurrence. Three additional screens capture images, additional details and comments. The 40 questions for landslide event collection used by the Geological Survey of India has been adapted through the collaborative effort of LANDSLIP partners to collect the additional details. The submitted landslides are immediately available for all users to view. The User can view entered landslides through the landslide image listing, Google maps interface, or tabular listing. The landslides can be filtered by date/time and other parameters. The mobile app is designed to be intuitive and fast, and aims to increase awareness about landslide risk through the integrated short documents, and videos. It has guidelines for safety, capturing images, mapping, and choosing the data from the multiple options. The uniqueness of the proposed methodology is that it enhances community participation, integrates event data collection, event data organizing, spatial and temporal summarization, and validation of landslide events and the impact. It pinpoints, maps and alerts real-time landslide events to initiate right disaster management activities to reduce the risk level. The Landslide tracker app was released during the 2020 monsoon season, and more than 250 landslides were recorded through the app.

How to cite: Vinodini Ramesh, M., Guntha, R., Arnhardt, C., Singh, G., Kr, V., Rao, P., Halan, G., and Malamud, B.: Spatial Temporal Tracking of Landslide Events: A Crowdsourced Mobile App, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16104,, 2021.

George Suciu et al.

The MEWS project describes all the stages of a system that monitors and predicts avalanches. In the project we use and present some of the latest technologies for avalanche detection and prediction. In this article we present a state of the art of the existing technologies when predicting avalanches and the technical and functional requirements of the MEWS project. The state of the art is presented because the MEWS project is based on existing technologies and we need to present what technologies are of interest for our project. The technical and functional requirements for the MEWS project describes what we want to do in the project. We can say that the MEWS project is one very important at european level, is build on modern technologies and is endorsed by the european community.

How to cite: Suciu, G., Iordache, G., Trufin, D., Segarceanu, S., and Petrescu, G.: MEWS – a project for detecting and predicting avalanches, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8062,, 2021.

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