Landslide Monitoring Research Articles

GNSS Surface Occlusion Model Analysis Based on Different Fitting Functions

Accuracy and reliability are determining factors when selecting where to place a global navigation satellite system (GNSS) receiver for landslide monitoring and early warning in real time. Previous studies have revealed that surface occlusion models (SOMs) are beneficial to reduce the multipath error of satellite observation when the satellite line of sight is near the ground and improve the accuracy and reliability of GNSS monitoring. This paper investigates fitting functions to determine appropriate SOMs for GNSS stations located in different spatial environments. Based on their similarity to terrain patterns, Gaussian, Fourier, and sum of sine functions were chosen to build SOMs for the rover sites with different occlusions; the performance of fitting accuracy and application on the positioning were analyzed. The results indicate that the three functions present a similar performance considering the goodness of fit, but the Fourier and sum of sine functions can significantly improve the efficacy of SOMs and enhance performance in positioning for landslide monitoring. Therefore, these two functions can effectively support accurate and reliable GNSS landslide monitoring in real time.

Mechanism Analysis and Process Inversion of the “7.26” Landslide in the West Open-Pit Mine of Fushun, China

Mine landslides are geological disasters with the highest frequency and cause the greatest harm worldwide. This typically causes significant casualties and damage to property. The study of the formation mechanisms and kinematic processes of mine landslides is important for the prevention and control of mine geological disasters and mine production safety. This study examined the “7.26” landslide, which occurred in the West Open-pit Mine of Fushun, China, in 2016, based on detailed investigations, interferometric synthetic aperture radar (InSAR) monitoring, and numerical simulations. The mechanism of landslide formation was explored, its kinematic process was inverted, and its disaster evolution process was summarized. The results indicate that: (1) For the formation mechanism of the “7.26” landslide, in July 2015, the old sliding mass was reactivated and deformed along the dominant joints in the shale. The following year, owing to continuous rainfall during the rainy season, the sliding surface accelerated its connection. Finally, a rainstorm on 25–26 July 2016, triggered slope instability. (2) The process of continued movement after landslide instability was approximately 250 s. It can be divided into the landslide initiation (0–10 s), collision scraping (10–150 s), and accumulation stages (150–250 s). (3) The entire process of landslide disasters includes four stages. During the weak-deformation stage, the maximum deformation of the sliding mass monitored by InSAR was approximately 50 mm. During the strong deformation stage, the tensile cracks at the rear edge of the landslide continued to expand, and shear outlets at the front edge had already formed. During the instability and failure stages, rainstorms trigger slope instability, leading to landslides. During the sliding accumulation stage, the landslide mass transformed into debris flow along the slope surface and accumulated at the bottom of the pit. This study provides a theoretical basis for the subsequent evaluation, treatment, monitoring, and warning of landslides in the Fushun West Open-pit Mine and other deep excavation open-pit mines.

FIM-based DSInSAR method for mapping and monitoring of reservoir bank landslides: an application along the Lancang River in China

FIM-based DSInSAR method for mapping and monitoring of reservoir bank landslides: an application along the Lancang River in China

IPSO-ELM intelligent prediction of landslide displacement in complex and unstable area of karst landform

In southern China, the karst landform areas possess a complex geological and topographic environment, a fragile ecosystem, poor surface stability, and frequent occurrences of landslides and other geological disasters. To effectively monitor and predict such events, it is crucial to process landslide monitoring data and establish reliable prediction models. This paper presents an IPSO-ELM displacement prediction model that integrates the improved particle swarm optimization algorithm (IPSO) and extreme learning machine (ELM). The proposed coupling model predicts decomposed displacement subsequences individually, which are then reconstructed to obtain the total displacement prediction value. In this study, displacement monitoring data from a typical landslide in the karst landform area between 2007 and 2012 were selected. Various prediction and verification scenarios were established to validate the accuracy and stability of the prediction model. The MAPE of the IPSO-ELM model is 0.18%, which outperforms the ELM and BPNN models with MAPEs of 0.56% and 0.65%, respectively, in predicting landslide displacement in karst landform areas. This study provides a solid theoretical foundation and practical value for landslide displacement prediction.

Spatio–temporal attention based real-time environmental monitoring systems for landslide monitoring and prediction

Spatio–temporal attention based real-time environmental monitoring systems for landslide monitoring and prediction

Underground Displacement Measurement Method Based on Double Mutual Inductance Voltage Contour Model of Optimized Dual Kriging

Landslide is a common geological hazard. Existing displacement monitoring sensors for landslides are classified into surface displacement and underground displacement. However, current underground displacement sensors cannot effectively monitor the vertical displacement caused by soil accumulation of landslides, which cannot meet the needs of landslide monitoring. Therefore, this study has designed a three-dimensional underground displacement measurement sensor based on the double mutual inductance voltage contour method. Since the double mutual inductance voltage measurement model has low measurement accuracy due to low interpolation accuracy and complex location calculation method when solving the spatial position, the improved Dual-Kriging interpolation method based on quantum pigeon inspired algorithm is adopted to improve the original interpolation accuracy. The experimental results show that this method avoids the complex problem of Kriging interpolation calculation and improves the calculation speed. At the same time, the overall accuracy is significantly improved, reducing its fitting error and actual value to a maximum of three ADC12 resolutions. The landslide experiment on the landslide simulation experiment platform shows that the sensor can effectively reflect the displacement of underground soil, and has important application value for slope displacement monitoring.

Fifteen-year professional monitoring and deformation mechanism analysis of a large ancient landslide in the Three Gorges Reservoir Area, China

Fifteen-year professional monitoring and deformation mechanism analysis of a large ancient landslide in the Three Gorges Reservoir Area, China

High-resolution monitoring of landslides with UAS photogrammetry and digital image correlation

ABSTRACT Periodically monitoring landslides is a key factor for supporting the realisation of hazard warning systems and risk reduction in the corresponding neighbourhood areas. Although satellite remote sensing solutions can be considered for low spatial resolution monitoring, this approach is still inappropriate for high spatial resolution investigations. Ground-based Radar Interferometry is also a widely used technique that allows for working at a proper spatial resolution, but it can often be an overbudget solution for most applications. Instead, photogrammetric surveys based on Unmanned Aerial System (UAS) imagery appear as a very interesting approach in terms of both spatial resolution and flexibility in temporally repeating the survey. Motivated by this observation, this work investigates the use of multi-temporal UAS surveys for landslide monitoring. To be more precise, Digital Image Correlation (DIC) has been applied to orthomosaics generated from different UAS photogrammetry surveys to compute the area’s deformation map. Compared with a reference GNSS survey, the results obtained using NHAZCA IRIS software and an in-house DIC approach show a deformation estimation accuracy of approximately 0.1 m, a reasonable accuracy for landslides moving at moderate velocity.

Landslide Monitoring and Detection

The project titled "Landslide Monitoring and Detection" aims to detect and monitor potential landslides using a combination of sensors and machine learning algorithms. The sensors used in this project are MQ, rainfall, vibration, soil moisture, DHT, and water flow sensors. These sensors collect data that is sent to an Arduino Uno, which then sends it to a Node MCU. The Node MCU sends the data to a Java server using NetBeans IDE, which stores the data in a text file. The machine learning algorithms used in this project are KNN, random forest, XGBoost, and decision tree. These algorithms are applied to the data in the text file, with a given threshold value based on training data, to determine which algorithm provides the best accuracy. The algorithm with the highest accuracy is then used as the result for landslide detection and monitoring. The use of multiple sensors and machine learning algorithms improves the accuracy of landslide detection and monitoring. The sensors provide real-time data that is continuously monitored and analyzed by machine learning algorithms. The results of this project can aid in preventing and mitigating the effects of landslides, which can cause significant damage to infrastructure and loss of life. This project has the potential to be implemented in areas prone to landslides, providing early warning systems and improving disaster management strategies.

UAV, GNSS, and InSAR Data Analyses for Landslide Monitoring in a Mountainous Village in Western Greece

Areas in Western Greece are particularly prone to landslides. Usually triggered by earthquakes or intense rainfalls, they cause damage to infrastructure (roads, bridges, etc.) and human properties. Hence, there is an urgent need for the implementation of monitoring and landslide prevention methodologies. In the last years, Unmanned Aerial Vehicles (UAVs), Global Navigation Satellite Systems (GNSS), and Interferometric SAR (InSAR) techniques have been applied for landslide mapping and monitoring. The current study focuses on the systematic and long-term analysis of a landslide that occurred in Ano Kerassovo village, within the region of Western Greece. To precisely measure the current evolution of the landslide, we performed repetitive UAV campaigns in conjunction with corresponding GNSS surveys, covering a time period between February 2021 and April 2023. The identification of surface modification was based on a change detection approach between the generated point clouds. The results are validated through GNSS measurements and field observations. Added to this, we collected archived Persistent Scatterer Interferometry (PSI) measurements derived from the European Ground Motion Service (EGMS) to extend the observation period and gain a more complete understanding of the phenomenon. It is proven that archived PSI measurements can be used as an indicator of possible landslide initialization points and for small-scale large coverage investigations, while UAVs and GNSS data can precisely identify the microscale deformations (centimeter scale).

Prefabricated acoustic emission array system for landslide monitoring

The quality of landslide monitoring data is crucial to the reliability of early warning systems. One effective approach to improving data quality is through innovation in monitoring techniques. In the case of existing acoustic emission (AE) monitoring methods, the achievable deformation quantification accuracy is limited by the variability of internal and external factors that affect the generated AE signal. To overcome these limitations and further improve monitoring effectiveness, this study developed a novel monitoring equipment consisting of an AE array, which facilitates the standardisation of equipment manufacturing and improves the consistency of data interpretation, thus providing a system that is simple and practical for field application. The developed AE monitoring system includes modules for on-site monitoring, data transmission, information management, and early warning analysis. The AE monitoring system was installed on different soil landslides in China and is continuously operating. Because the AE array provides a standardised and simple tool for landslide subsurface deformation monitoring, it is well-suited for low-income mountainous areas with a high risk of landslides.

A research on excavation compensation theory for large deformation disaster control and a review on the multiphysical–multiscale responses of salt rock for underground gas storage

A research on excavation compensation theory for large deformation disaster control and a review on the multiphysical–multiscale responses of salt rock for underground gas storage

Real-time high-precision landslide displacement monitoring based on a GNSS CORS network

GNSS technology is an effective all-weather, continuous and real-time method of detecting three-dimensional landslide displacement. However, current positioning methods such as the network real-time kinematic (RTK) technique based on virtual reference station (VRS) technology have some limitations, particularly a sensitivity to atmospheric modeling error. In this study, taking advantage of existing GNSS continuously operating reference stations (CORS), we propose a network RTK algorithm (virtual atmosphere constraint (VAC)) for high-precision landslide monitoring. Network modeling atmosphere delays were used as virtual observations for the terminal position solution, by which their accuracy information can be considered. Additionally, a noise reduction method that combines complementary ensemble empirical mode decomposition and median filtering was proposed to reduce GNSS signal residual errors in the original RTK coordinate time series. To validate the effectiveness of the algorithm, we processed one week of data from two monitoring receivers on the Heifangtai landslide and four surrounding CORS using the VAC and VRS methods. Results show that the original displacement time series processed with VRS was prone to jitter, whereas the VAC method exhibited better accuracy, stability, and ambiguity fixing rates. Horizontal and vertical RMS errors at a stable monitoring point were 1.7 cm and 3.9 cm for VRS-RTK and 0.9 cm and 2.3 cm for VAC-RTK; thus, VAC reduced the error by 40.1% and 20.5%, respectively. After noise reduction, the RMS values of the VAC method at the stable and the other collapsed monitoring point were both reduced to an mm-level accuracy.

Thermo-hydro-poro-mechanical responses of a reservoir-induced landslide tracked by high-resolution fiber optic sensing nerves

Thermo-poro-mechanical responses along sliding zone/surface have been extensively studied. However, it has not been recognized that the potential contribution of other crucial engineering geological interfaces beyond the slip surface to progressive failure. Here, we aim to investigate the subsurface multi-physics of reservoir landslides under two extreme hydrologic conditions (i.e. wet and dry), particularly within sliding masses. Based on ultra-weak fiber Bragg grating (UWFBG) technology, we employ special-purpose fiber optic sensing cables that can be implanted into boreholes as “nerves of the Earth” to collect data on soil temperature, water content, pore water pressure, and strain. The Xinpu landslide in the middle reach of the Three Gorges Reservoir Area in China was selected as a case study to establish a paradigm for in situ thermo-hydro-poro-mechanical monitoring. These UWFBG-based sensing cables were vertically buried in a 31 m-deep borehole at the foot of the landslide, with a resolution of 1 m except for the pressure sensor. We reported field measurements covering the period 2021 and 2022 and produced the spatiotemporal profiles throughout the borehole. Results show that wet years are more likely to motivate landslide motions than dry years. The annual thermally active layer of the landslide has a critical depth of roughly 9 m and might move downward in warmer years. The dynamic groundwater table is located at depths of 9–15 m, where the peaked strain undergoes a periodical response of leap and withdrawal to annual hydrometeorological cycles. These interface behaviors may support the interpretation of the contribution of reservoir regulation to slope stability, allowing us to correlate them to local damage events and potential global destabilization. This paper also offers a natural framework for interpreting thermo-hydro-poro-mechanical signatures from creeping reservoir bank slopes, which may form the basis for a landslide monitoring and early warning system.

Evaluation of a Tilt-Based Monitoring System for Rainfall-Induced Landslides: Development and Physical Modelling

Landslides in northern India are a frequently occurring risk during the rainy season resulting in human, animal, and property losses as well as obstructing transportation facilities. Usually, numerical and analytical approaches are applied to predicting and monitoring landslides, but the unpredictable nature of rainfall-induced landslides limits these methods. Sensor-based monitoring is an accurate and reliable method, and it also collects accurate and site-specific required data for further investigation with a numerical and analytical approach. This study developed a low-cost tilt-based rainfall-induced landslide monitoring system using the economical and precise MEMS sensor to record displacement and volumetric water content. A self-developed direct shear-based testing setup was used to check the system’s operational performance. A physical slope model was also prepared to test the monitoring system in real scenarios. A debris failure occurred at Kotrupi village in the Mandi district of Himachal Pradesh, India, which was chosen for the modelling to investigate the failure mechanism. A rainfall generator was developed to simulate the rainfall, equipped with a flow sensor for better simulation and data recording. The tilt angle records the deviation in terms of angle with a least count of 0.01 degrees, and the moisture content was recorded in terms of percentage with a least count of 1. The results show that the developed system is working properly and is very effective in monitoring the rainfall-induced landslide as it monitors the gradual and sudden movement effectively. This study explains the mechanism behind the landslide, and it can be helpful in monitoring the slope to enable the implementation of preventative actions that will mitigate its impact.

Quantitative long-term (1922 to 2021) monitoring of a large-scale landslide of a LIA lateral moraine due to glacial debuttressing

Quantitative long-term (1922 to 2021) monitoring of a large-scale landslide of a LIA lateral moraine due to glacial debuttressing

A Cost-Effective GNSS Solution for Continuous Monitoring of Landslides

The development of low-cost dual-frequency global navigation satellite system (GNSS) receivers in recent years has enabled the use of these devices in numerous applications. In the monitoring of natural hazards, such as landslides, these devices can be considered suitable sensors. In this work, dual-frequency GNSS receivers and antennas were used for setting up near-real-time continuous low-cost GNSS monitoring systems (LGMSs) under field conditions. The SimpleRTK2B board, which integrates the u-blox ZED-F9P dual-frequency GNSS chip and the survey-calibrated GNSS antenna are the main components of the GNSS system. The LGMS was installed and tested for six months in the Laze landslide located in the northwestern part of Slovenia. A total of four GNSS systems were deployed, three of which were located in pillars in the landslide itself and one in a stable area. Open-source software was used to postprocess the acquired data, providing daily coordinates in static relative and precise point positioning (PPP) positioning modes. The results of six months of near-real-time monitoring showed that the Laze landslide was stable during this period, with only minor changes in the vertical component. The trend of decreasing ellipsoid height was evident at all stations, although it was in the range of a few millimeters. To validate the results in static relative positioning mode, the coordinate differences between low-cost and high-end geodetic GNSS instruments were estimated and found to be in the range of 5 mm or less, while the difference between horizontal and spatial positions was less than 7 mm for all stations. The same data were processed in PPP, vertical displacements were not detected as in the static relative positioning mode due to the lower accuracy of the method itself. Considering the six-month performance of a low-cost GNSS system under field conditions, it can be emphasized that these devices are capable of performing near real-time continuous monitoring of slow movements with high accuracy and decreased costs. In addition, an experimental test was performed to identify the size of detected displacements in real-time kinematic (RTK). Based on the achieved results, it was concluded that 20 mm spatial displacements are detectable with LGMSs in RTK considering only 15 s of observations.

Time Series Prediction Model of Landslide Displacement Using Mean-Based Low-Rank Autoregressive Tensor Completion

Landslide displacement prediction is a challenging research task that can help to reduce the occurrence of landslide disasters. The frequent occurrence of extreme weather increases the probability of landslides, and the subsequent increase in the superimposed economic development level exacerbates disaster losses, emphasizing the importance of landslide prediction. The collection of landslide monitoring data is the foundation of landslide displacement prediction, but the lack of various data severely limits the effectiveness of the landslide monitoring system. To address the issue of missing data during the landslide monitoring process, this paper proposes a time series prediction model of landslide displacement using mean-based low-rank autoregressive tensor completion (MLATC). Firstly, the reasons for the missing data of landslide displacement are analyzed, and the corresponding dataset of missing data is designed. Then, according to the characteristics and internal correlation of landslide displacement monitoring data, the establishment process of mean-based low-rank tensor completion prediction model is introduced. Finally, the proposed method is used to complete and predict the missing data for the random missing and non-random missing landslide displacement. The results show that the data completion and prediction results of the model are essentially consistent with the original displacement monitoring data of the landslide, and the accuracy and precision are relatively high. It shows that the model has good landslide displacement completion and prediction effects, which can provide a certain reference value for the missing data processing and landslide displacement prediction.

Landslide Identification in Human-Modified Alpine and Canyon Area of the Niulan River Basin Based on SBAS-InSAR and Optical Images

Landslide identification in alpine and canyon areas is difficult due to the terrain limitations. The main objective of this research was to explore the method of combining small baseline subset interferometric synthetic aperture radar (SBAS-InSAR), multi-temporal optical images and field surveys to identify potential landslides in the human-modified alpine and canyon area of the Niulan River in southwestern China based on terrain visibility analysis. The visibility of the terrain is analyzed using the different incident and heading angles of the Sentinel satellite’s ascending and descending orbits. Based on the SAR image data of Sentinel-1A satellites from 2016 to 2019, the SBAS-InSAR method was used to identify landslides, and then multi-temporal optical images were used to facilitate landslide identification. Field surveys were carried out to verify the identification accuracy. A total of 28 landslides were identified, including 13 indicated by SBAS-InSAR, 8 by optical imaging and 7 by field investigation. Many landslides were induced by the impoundment and fluctuation of reservoir water. The comparison and verification of typical landslide monitoring data and reservoir water fluctuations revealed that a sudden drop of reservoir water had a great influence on landslide stability. These research results can facilitate a comprehensive understanding of landslide distribution in the reservoir area and guide the follow-up landslide risk management.

Landslide surface horizontal displacement monitoring based on image recognition technology and computer vision

The deformation and displacement of landslides has always been the priority of landslide monitoring and early warning. With the development of image processing technology in recent years, the analysis of landslide surface deformation through image data has become a popular method and different methods of image analysis are rapidly increasing. However, the current method of analyzing landslide deformation and displacement using a single image processing method tends to analyze the displacement of the local deformation area of the landslide, which lacks overall monitoring of the slope and is difficult to use for pre-disaster monitoring of landslide hazards, easily leading to missed detection of landslide potential hazard points. Therefore, this paper combines image recognition technology and computer vision (IR-CV) to propose a method for monitoring and identifying the deformation displacement of landslide surfaces, which has been validated by indoor landslide model tests and unmanned aerial vehicle field simulation displacement recognition tests. In the model tests, the IR-CV method successfully identified the displacements generated during the deformation and damage of the landslide model while using the model deformation time series image data recorded by the camera to analyze the overall deformation of the surface of the landslide model. Further, in this paper, a field simulation displacement identification test was conducted using an unmanned aerial vehicle (UAV), and the orthophoto data obtained by the UAV verified that the IR-CV method can be used for overall deformation displacement identification of landslides in the field. The results of indoor model tests and field UAV simulation tests show that the monitoring method of IR-CV can effectively identify the deformation and displacement of landslide surfaces, which has good application prospects in pre-disaster monitoring and displacement identification of landslides, thus providing a cost-effective and feasible solution for monitoring the deformation and displacement of landslide surfaces.