Landslide Monitoring Research Articles

Landslide assessment through integrated geoelectrical and seismic methods: A case study in Thungsong site, southern Thailand

Many landslides can cause significant damage to infrastructure, property, and human life. To study landslide structure and processes, geophysical techniques are most productive when employed in combination with other survey and monitoring tools, such as intrusive sampling. Here, the integration of electrical resistivity tomography (ERT) and seismic refraction tomography (SRT) methods is used to assess landslides in Thungsong district, Nakhon Si Thammarat, the south of Thailand, where is a hilly and seasons of prolonged rainfall region. The 2D cross-plot analysis of P-wave velocity and resistivity values obtained by these two methods is introduced to identify potential landslide-prone zones in this region. The results of the 2D cross-plot model reveal detailed image of the subsurface conditions, highlighting areas of low P-wave velocity (lower than 600 m/s) and low resistivity (lower than 600 Ωm). These areas are indicative of weak zone and are potential to be sliding materials. Moreover, an intrusive sampling data from boreholes is also used for the calibration and validation geophysical data with geological data. This can improve the accuracy of landslide assessment and develop effective mitigation strategies to reduce the risk of landslides in this area. In addition of the 2D cross-plot, the volume of sliding material is also determined from the difference of the surface and slipping plane elevations. The volume calculation of sliding material is roughly 33447.76 m3. This approach provides a preliminary tool for landslide studies and monitoring landslides in this region, thus enabling an improved understanding of slope failure processes in this context, and the basis of a landslide mitigation strategy in the future.

Ground investigation: a tool for landslide risk mitigation of infrastructures

Recent monitoring experiences for bridges in Central and South Italy have shown that it is not common for bridges to be directly damaged by landslides phenomena in their vicinities, either because their siting was very carefully selected at the origin or because appropriate protection measures have been taken to reduce geological risk to acceptable levels. In the first case, periodic visual inspections of the manufacts may suffice to monitor possible consequences of ground instability phenomena, but in the second case monitoring of the infrastructure and of the ground is the only possible tool to evaluate the existence of a landslide-structure interaction which can influence its performance and safety. Ground investigation is a fundamental activity for landslide monitoring and analysis, and this is specifically true when a landslide-infrastructure interaction problem needs to be investigated and assessed. By discussing the results from case studies recently inspected, this paper shows how the knowledge of the physical processes and the monitoring of selected parameters are the first irreplaceable tools to mitigate risks associated with instability phenomena interacting with sensitive infrastructures.

The role of intra-arc strike-slip fault systems on landslide generation at Curarrehue (Central Southern Chilean Andes)

Climate change has increased extreme rainfall events in the Central Southern Andes, favouring slope instabilities and triggering landslides. However, other predisposing factors that control landslides in this region are still poorly constrained. We systematically study the link between landslides and active faults systems thought detailed mapping and classification, highligthing mainly rock avalanches. Our analysis was complemented with hand-specimen and optical microscope host rock description, clay classification via x-ray diffraction and fluorescence, and an exhaustive fault kinematics assessment to determine the controlling the strain in the zone. Our results show that 90.5% of the landslides are linked with active strike-slip faults, characterised by phyllosilicate-bearing hydrothermally altered host rock. In this regard, the presence of clay minerals in fault-slip planes contributes to the generation of landslides in mountain ranges. This phenomenon could be enhanced due the ability to clays induce pressure variations by swelling triggering by extreme rainfalls. This interaction between faults and landslides is important for geological hazard assessment and landslide monitoring as being a recurrent configuration in this section of the Central Southern Chilean Andes.

Detection and analysis of potential landslides based on SBAS-InSAR technology in alpine canyon region.

The Lancang River flows through the alpine canyon region of southwest China, an area that has experienced frequent geological disasters over the years. Early monitoring of geological hazards is essential for disaster prevention and mitigation. However, traditional ground monitoring techniques are limited by the complex terrain conditions in high-altitude valley regions. In contrast, interferometric synthetic aperture radar (InSAR) technology can provide a high-precision, wide-range monitoring of slow rock-slope deformation, making it an effective tool for studying geological hazards. Within the study area, multiple synthetic aperture radar (SAR) images from the Sentinel-1A satellite were collected, and surface deformation was obtained using the small baseline subset InSAR (SBAS-InSAR). The results demonstrate that combining ascending and descending orbit images can be successfully applied to landslide monitoring in complex mountainous areas. Over 30 potential landslides were identified by combining InSAR results with optical images. The Line-Of-Sight (LOS) direction deformation features and their relationship with precipitation were analyzed based on two typical landslides, and two-dimensional/three-dimensional (2D/3D) deformation decomposition was carried out to reveal its motion characteristics. It was found that the cumulative deformation fluctuation amplitude was higher during the rainy season, and the main movement direction of the landslide was east-west. In addition, based on the spatial distribution and statistical analysis of deformation points along with meteorological data, geological elements, human activities, and topographic conditions, it is inferred that factors such as low vegetation coverage, tectonic movements, human activities, and high-altitude glacier thawing may contribute to the occurrence of disasters. And it was found that areas with high vegetation cover, high rainfall, and snow cover exhibit lower coherence coefficients. This study offers valuable insights for investigating large-scale geological in alpine canyon regions.

Analysis of the Spatial Distribution and Deformation Types of Active Landslides in the Upper Jinsha River, China, Using Integrated Remote Sensing Technologies

The Upper Jinsha River (UJSR) has great water resource potential, but large-scale active landslides hinder water resource development and utilization. It is necessary to understand the spatial distribution and deformation trend of active landslides in the UJSR. In areas of high elevations, steep terrain or otherwise inaccessible to humans, extensive landslide studies remain challenging using traditional geological surveys and monitoring equipment. Stacking interferometry synthetic aperture radar (stacking-InSAR) technology, optical satellite images and unmanned aerial vehicle (UAV) photography are applied to landslide identification. Small baseline subset interferometry synthetic aperture radar (SBAS-InSAR) was used to obtain time-series deformation curves of samples to reveal the deformation types of active landslides. A total of 246 active landslides were identified within the study area, of which 207 were concentrated in three zones (zones I, II and III). Among the 31 landslides chosen as research samples, six were linear-type landslides, three were upward concave-type landslides, 10 were downward concave-type landslides, and 12 were step-type landslides based on the curve morphology. The results can aid in monitoring and early-warning systems for active landslides within the UJSR and provide insights for future studies on active landslides within the basin.

A Simulation Framework of Unmanned Aerial Vehicles Route Planning Design and Validation for Landslide Monitoring

Unmanned aerial vehicles (UAVs) have emerged as a highly efficient means of monitoring landslide-prone regions, given the growing concern for urban safety and the increasing occurrence of landslides. Designing optimal UAV flight routes is crucial for effective landslide monitoring. However, in real-world scenarios, the testing and validating of flight path planning algorithms incur high cost and safety concerns, making overall flight operations challenging. Therefore, this paper proposes the use of the Unreal Engine simulation framework to design UAV flight path planning specifically for landslide monitoring. It aims to validate the authenticity of the simulated flight paths and the correctness of the algorithms. Under the proposed simulation framework, we then test a novel flight path planning algorithm. The simulation results demonstrate that the model reconstruction obtained using the novel flight path algorithm exhibits more detailed textures, with a 3D model simulation accuracy ranging from 10 to 14 cm. Among them, the RMSE value of the novel flight route algorithm falls within the range of 10 to 11 cm, exhibiting a 2 to 3 cm improvement in accuracy compared to the traditional flight path algorithm. Additionally, it effectively reduces the flight duration by 9.3% under the same flight path compared to conventional methods. The results confirm that the simulation framework developed in this paper meets the requirements for landslide damage monitoring and validates the feasibility and correctness of the UAV flight path planning algorithm.

APPLICATION OF LUCAS-KANADE DENSE FLOW FOR TERRAIN MOTION IN LANDSLIDE MONITORING APPLICATION

Abstract. Landslides are natural hazards that can cause severe damage and loss of life. Optical cameras are a low-cost and high-resolution alternative among many monitoring systems, as their size and capabilities can vary, allowing for flexible implementation and location. Computer vision is a branch of artificial intelligence that can analyze and understand optical images, using techniques such as optical flow, image correlation and machine learning. The application of such techniques can estimate the motion vectors, displacement fields, providing valuable information for landslide detection, monitoring and prediction. However, computer vision also faces some challenges such as illumination changes, occlusions, image quality, and computational complexity. In this work, a computer vision approach based on Lucas-Kanade optical dense flow was applied to estimate the motion vectors between consecutive images obtained during landslide simulations in a laboratory environment. The approach is applied to two experiments that vary in their illumination and setup parameters to test its applicability. We also discuss the application of this methodology to images from Sentinel-2 satellite optical sensors for landslide monitoring in real-world scenarios.

Efficient 3D Frequency Semi-Airborne Electromagnetic Modeling Based on Domain Decomposition

Landslides are common geological hazards that often result in significant casualties and economic losses. Due to their occurrence in complex terrain areas, conventional geophysical techniques face challenges in early detection and warning of landslides. Semi-airborne electromagnetic (SAEM) technology, utilizing unmanned aerial platforms for rapid unmanned remote sensing, can overcome the influence of complex terrain and serve as an effective approach for landslide detection and monitoring. In response to the low computational efficiency of conventional semi-airborne EM 3D forward modeling, this study proposes an efficient forward modeling method. To handle arbitrarily complex topography and geologic structures, the unstructured tetrahedron mesh is adopted to discretize the earth. Based on the vector finite element formula, the Dual–Primal Finite Element Tearing and Interconnecting (FETI-DP) method is further applied to enhance modeling efficiency. It obtains a reduced order subsystem and avoids directly solving huge overall linear equations by converting the entirety problem into the interface problem. We check our algorithm by comparing it with 1D semi-analytical solutions and the conventional finite element method. The numerical experiments reveal that the FETI-DP method utilities less memory and exhibits higher computation efficiency than the conventional methods. Additionally, we compare the electromagnetic responses with the topography model and flat earth model. The comparison results indicate that the effect of topography cannot be ignored. Further, we analyze the characteristic of electromagnetic responses when the thickness of the aquifer changes in a landslide area. We demonstrate the effectiveness of the 3D SAEM method for landslide detection and monitoring.

Systematic Quantification and Assessment of Digital Image Correlation Performance for Landslide Monitoring

Accurate and reliable analyses of high-alpine landslide displacement magnitudes and rates are key requirements for current and future alpine early warnings. It has been proved that high spatiotemporal-resolution remote sensing data combined with digital image correlation (DIC) algorithms can accurately monitor ground displacements. DIC algorithms still rely on significant amounts of expert input; there is neither a general mathematical description of type and spatiotemporal resolution of input data nor DIC parameters required for successful landslide detection, accurate characterisation of displacement magnitude and rate, and overall error estimation. This work provides generic formulas estimating appropriate DIC input parameters, drastically reducing the time required for manual input parameter optimisation. We employed the open-source code DIC-FFT using optical remote sensing data acquired between 2014 and 2020 for two landslides in Switzerland to qualitatively and quantitatively show which spatial resolution is required to recognise slope displacements, from satellite images to aerial orthophotos, and how the spatial resolution affects the accuracy of the calculated displacement magnitude and rate. We verified our results by manually tracing geomorphic markers in orthophotos. Here, we show a first generic approach for designing and optimising future remote sensing-based landslide monitoring campaigns to support time-critical applications like early warning systems.

Advanced technologies for landslide monitoring

The article reviews modern landslide monitoring methods. The purpose of the observation method in the design and control of the behavior of geotechnical structures and its role in creating chances for cost savings or pre-emptively averting construction risks is a driving force in the context of landslides. This approach is established within Eurocode 7 as a fundamental tenet of geotechnical design, serving as a valid validation method for limit states. Monitoring of large areas is common in everyday engineering practice which makes the installation of transducers in multiple points very difficult. Due to this reason the use of so-called contactless approaches is widely accepted. A distinguishable representor of that method is DInSAR – technique for successful displacements measurement by means of SAR data which is economically feasible and capable of detecting classic failure modes. The article presents and compares methods such as: LiDAR, Infrared sensors, Fiber oprics, Shape array accelerometers, In-place inclinometers, and others. Despite the development of technologies, the application of monitoring in Bulgaria is not enough used.

Optical and Thermal Image Processing for Monitoring Rainfall Triggered Shallow Landslides: Insights from Analogue Laboratory Experiments

This study explores the innovative use of digital image processing (DIP) techniques, also named PhotoMonitoring, for analysing the triggering conditions of shallow landslides. The approach, based on the combination of optical and infrared thermographic imaging (IRT), was applied to a laboratory-scale slope, reproduced in a flume test apparatus. Three experiments were conducted to replicate rainfall-induced shallow landslides, applying change detection and digital image correlation analysis to both optical and thermal images. The method combines IRT’s ability to measure ground surface temperature changes with DIP’s capacity to track movement and displacement. Results showed the high reliability of the displacement time-series obtained through IRT-DIP with respect to the reference optical-DIP. The IRT-DIP technique also detects anomaly signals two minutes before landslide occurrence that can be regarded as a possible failure precursor. This study testifies to the potential of image analysis as a remote sensing technique, demonstrating the ability of DIP to capture the dynamics of shallow landslides, as well as the advantages of optical–IRT combinations to follow slope deformation processes during night-time. This approach, if properly adapted to real-scale scenarios, may contribute to a better understanding of landslide behaviour, improve landslide monitoring strategies, and promote more effective early warning systems (EWS).

Application of digital technologies in landslide prediction, mapping, and monitoring

This paper presents a scoping review on the use of digital technologies for predicting, mapping, or continuously monitoring landslides on natural slopes. Articles and reviews published between 2001 and 2023 indexed by Scopus (Elsevier) were selected. The results showed that the number of publications involving this theme has been growing every year, with two periods of prominence: 2008-2010 and 2015-2021. China, Italy, India, USA and Taiwan are the five countries that published the most on the subject during the studied period. It was also found that remote sensing tools were the most used and showed strong stability, accompanied by artificial intelligence tools. Digital sensors have been widely used in Early Warning Systems, composing Wireless Sensors Network, monitoring terrain or climate variables. There is no doubt that digital technologies are extremely advantageous in relation to traditional technologies and that they already present themselves as a solution and confirm their trend of future consolidation.

Dynamic prediction of landslide life expectancy using ensemble system incorporating classical prediction models and machine learning

With the development of landslide monitoring system, many attempts have been made to predict landslide failure-time utilizing monitoring data of displacements. Classical models (e.g., Verhulst, GM (1,1), and Saito models) that consider the characteristics of landslide displacement to determine the failure-time have been investigated extensively. In practice, monitoring is continuously implemented with monitoring data-set updated, meaning that the predicted landslide life expectancy (i.e., the lag between the predicted failure-time and time node at each instant of conducting the prediction) should be re-evaluated with time. This manner is termed “dynamic prediction”. However, the performances of the classical models have not been discussed in the context of the dynamic prediction yet. In this study, such performances are investigated firstly, and disadvantages of the classical models are then reported, incorporating the monitoring data from four real landslides. Subsequently, a more qualified ensemble model is proposed, where the individual classical models are integrated by machine learning (ML)-based meta-model. To evaluate the quality of the models under the dynamic prediction, a novel indicator termed “discredit index (β)” is proposed, and a higher value of β indicates lower prediction quality. It is found that Verhulst and Saito models would produce predicted results with significantly higher β, while GM (1,1) model would indicate results with the highest mean absolute error. Meanwhile, the ensemble models are found to be more accurate and qualified than the classical models. Here, the performance of decision tree regression-based ensemble model is the best among the various ML-based ensemble models.

The Utility of LiCSBAS to Carry out InSAR Time Series to Analyze Surface Deformation: An Overview

Surface deformation analysis plays a crucial role in understanding geodetic phenomena and their implications for hazard assessment, infrastructure management, and environmental studies. In recent years, the incorporation of synthetic aperture radar (InSAR) and advanced processing techniques have been used to monitor surface deformation. One such technique, LiCSBAS, has shown great promise in analyzing InSAR time series data to accurately detect and quantify surface displacements. It has been successfully employed in various geodetic monitoring scenarios such as landslide monitoring, infrastructure stability assessment, and volcanic deformation analysis. In this study, studies were reviewed in which performance evaluation and comparison of LiCSBAS with other technologies were discussed as well as future developments and challenges in this field. The results of a review of previous studies show the effectiveness of LiCSBAS in detecting and analyzing surface deformation, allowing for a better understanding of geodetic processes. This overview will provide a set of ideas that will assist stakeholders involved in surface deformation analysis and geodetic monitoring and contribute to decision-making to achieve sustainable development.

An ensemble learning-based experimental framework for smart landslide detection, monitoring, prediction, and warning in IoT-cloud environment.

Landslides occur every year during the monsoon season in hilly areas. This natural disaster annually leads to several fatalities, injuries, and property destruction. Monitoring landslides and promptly alerting people to looming disasters in light of these injuries and fatalities are crucial. To date, no efficient technique is in practice to predict landslides. The tools that are now available monitor landslides at a very high cost and do not offer early warning or forecasts of soil movement. An innovative, low-cost Internet of Things (IoT)-based system for landslip warning, monitoring, and prediction is the major objective of this research. Its assessment, implementation, and development are described in detail. This study proposes an IoT-based smart landslide detection, warning, prediction, and monitoring system. The pre and post-measures use sensors and other hardware to deal with landslide disasters. It uses real-time environment monitoring (landslide site) for any changes and provides appropriate output by comparing the threshold values. The proposed system is tested on a prototype model, which performed well in our tests. The database was updated 2.5 s after the landslide thanks to a steady Internet connection. In less than 5 s after the event, the Thingspeak channel can display a graphical depiction of the data and its position. Multiple readings showed an 80-85% system accuracy rate. Further, the proposed ensemble learning-based risk prediction model is applied to static and dynamic data to predict the landslide for future reference. The ensemble classifier model has 98.67% recall, 96.56% accuracy, 97.35% F1-value, and 96.07% precision. The alert SMS is also sent to concerned authorities for medical emergency/PWD department/district administration.

Velocity distribution and movement of multiple slow-moving landslides characterized by an optimized MTInSAR workflow

Slow-moving landslides can be found worldwide, exhibiting gradual movement over years to decades, with velocities ranging from millimeters to meters per year. However, slow-moving landslides can undergo transformation into rapid and catastrophic events, leading to severe damage to human society. Given the presence of >2500 pre-existing slow-moving landslides identified and cataloged using LiDAR bare ground data in Taiwan, it is crucial to efficiently assess their activity prior to the typhoon and heavy rainfall seasons. Therefore, a key research objective is to enhance the ability to detect landslide velocities and movements over large areas spanning tens of thousands of square kilometers. This study aims to establish a workflow that employs the multitemporal interferometric synthetic aperture radar (MTInSAR) technique for the detection and monitoring of multiple slow-moving landslides across wide regions. Specifically, we propose an optimized analysis package called “multi-snap2stamps,” based on the existing snap2stamps method, to generate interferograms for multiple regions simultaneously. Focusing on the analysis of nine pre-existing slow-moving landslides, with velocities <1.6 m/year (classified as very slow or extremely slow landslides according to established criteria), distributed across Taiwan with diverse terrain and geological conditions, this study investigates the characteristics of these deep-seated landslides using the proposed MTInSAR analysis workflow in both spatial and temporal domains. The results reveal that two slow-moving landslide sites exhibit significant seasonal signals in the time series, while seven sites do not display such signals. Additionally, the MTInSAR time series effectively captures accelerated movement in the Shiding-T001 landslide, which corresponds well with in situ inclinometer monitoring data. This study demonstrates the effective analysis of velocity distribution and movement characteristics of slow-moving landslides using the proposed MTInSAR workflow. Furthermore, it highlights the potential of this workflow for improving landslide motion detection over vast areas of interest in the future.

Stability analysis of reference station and compensation for monitoring stations in GNSS landslide monitoring

The Real-Time Kinematic (RTK) positioning method of the Global Navigation Satellite System (GNSS) has been widely used for landslide monitoring. The stability of its reference station is crucial to obtain accurate and reliable monitoring results. Unstable reference stations due to the geological environment and human activities are difficult to detect and in practical applications often ignored. As a result, it affects the positioning solutions and subsequently the interpretation and detection of landslide motions, which must be addressed in GNSS landslide monitoring. To solve this problem, we propose using the Precise Point Positioning (PPP) technique to analyze the stability of the reference station by verifying its position. The deformations of the monitoring stations are then compensated. First, the reference station coordinates are obtained by the PPP technique and tectonic motion is considered in data processing. The change or breakout of the reference station position is then determined using a cumulative sum control chart method. Finally, each monitoring station’s displacements are compensated according to the displacements of the reference station. According to the results of the Tengqing landslide experiment, the PPP technique can be used in GNSS landslide monitoring to analyze the stability of reference stations. With PPP, millimeter-level accuracy for the coordinates of reference stations is achieved. Compared to the traditional deformation series, the compensated displacement series more reliably reflects the landslide motions. This study will increase the reliability of monitoring results and contribute to implementing GNSS in monitoring landslides.

Adaptability Analysis of Sentinel−1A and ALOS/PALSAR−2 in Landslide Detection in the Qinling-Daba Mountains

Due to the complex terrain and intense tectonic activity, and harsh climate in the Qinling-Daba Mountains, many landslides occur in the area. Most of these landslides are extremely active, posing a serious threat to the safety and property of local residents. As a mature deformation-monitoring technology, InSAR has been widely used in landslide detection, but the steep terrain and dense vegetation in the Qinling-Daba Mountains make detection challenging. Hence, it is important to choose suitable data sources and methods for landslide detection via InSAR in this area. This study was the first to collect ALOS/PALSAR−2 and Sentinel−1A images to detect landslides in the Qinling-Daba Mountains, applying a method combining IPTA and SBAS. In total, 88 landslides were detected and validated. The results show that the deformation-detection error rate of Sentinel−1A is 2% higher than that of ALOS/PALSAR−2 and that its landslide-recognition rate is 47.7% lower than that of ALOS/PALSAR−2. Upon comparing and analyzing the visibility, coherence, closed−loop residuals, and typical time series of landslide deformation from the two kinds of data, it was found that the extremely low quality of available Sentinel−1 A summer data is a major factor influencing that system’s performance. ALOS/PALSAR−2 is more likely to detect landslides in areas with high vegetation coverage, meeting more than 90% of the monitoring needs. It is thus highly suitable for landslide detection in the Qinling–Daba Mountains, where seasonality is significant. In this paper, for the first time, multiple data sources are compared in detail with regard to their utility in landslide detection in the Qinling–Daba Mountains. A large number of accuracy metrics are applied, and the results are analyzed. The study provides important scientific support for the selection of data sources for future landslide monitoring in the Qinling–Daba Mountain area and similar areas and for the selection of methods to evaluate the accuracy of InSAR monitoring.

Development and application of self-powered landslide monitoring sensor

Development and application of self-powered landslide monitoring sensor

Deformation Monitoring and Dynamic Analysis of Long-Runout Bedding Landslide Based on InSAR and Particle Flow Code

Long-runout landslides occur frequently in the sandstone and mudstone mountainous areas in southwestern China under heavy rainfall conditions. This has been a key issue in the field of disaster prevention and reduction. Considering the Niuerwan landslide in Wulong, Chongqing, on 13 July 2020, as an example, we employed technical methodologies, including unmanned aerial vehicle (UAV) images, field investigation, geological condition analysis (including geomorphology and topography, stratigraphic structure and formation lithology, etc.), interferometric synthetic aperture radar (InSAR) monitoring and Particle Flow Code 3D (PFC3D) simulations to study failure mechanism and a long-runout motion model of flow-like landslides induced by the heavy rainfall. The results showed that (1) the large differences between the upper and lower strata are the root cause of the instability and long-runout fluidization movement; (2) heavy rainfall is the key driving factor of slope instability and deep-seated landslides, leading to long-distance movement of the upper saturated residual soil; (3) the long-runout fluidization model of bedding landslides is mainly divided into the overall sliding in the lower layer, the mixing of coarse and fine particles in the middle layer, and saturation fluidization in the upper layer; and (4) the long-runout fluidization process of bedding landslides is composed of three stages: overall instability, mixed acceleration, and fluidization accumulation. In view of these findings, in the risk evaluation and prediction of long-runout fluidization landslides in sandstone and mudstone mountainous areas, this particular disaster model can be used to provide quantitative references for disaster prevention and mitigation.