Landslide Monitoring Research Articles

A semantic information-driven stepwise landslide displacement prediction model.

Landslide prediction is critical for the early warning of a landslide occurrence. Existing stepwise landslide displacement prediction methods are mostly data-driven approaches. However, these models are vulnerable to overfitting, and the low-dimensional numerical features with high numerical volatility prevent them from precisely quantifying the rapid increase in daily displacement in the acceleration phase. Therefore, we propose a semantic information-driven stepwise landslide displacement prediction model comprising an identifier in the displacement phase and a predictor in the acceleration phase. First, the raw landslide monitoring data are converted into text-based semantic information and the semantic features are fused. Subsequently, based on the daily displacement and velocity, we propose a sliding window phase division algorithm to divide the stepwise landslide phase into stationary and acceleration phases. Finally, the landslide displacement phase is identified, and the displacement during the acceleration phase is predicted. The experimental results of the model on the Xinpu and Qingshi landslides in Chongqing, China, show that the proposed model exploits the derived semantic information to identify the landslide acceleration phase qualitatively, and predict the daily displacement of the acceleration phase quantitatively. The proposed model provides a valuable reference for the early warning of stepwise landslides.

Timely and Low-Cost Remote Sensing Practices for the Assessment of Landslide Activity in the Service of Hazard Management

Landslides are among the most dangerous and catastrophic events in the world. The increasing progress in remote sensing technology made landslide observations timely, systematic and less costly. In this context, we collected multi-dated data obtained by Unmanned Aerial Vehicle (UAV) campaigns and Terrestrial Laser Scanning (TLS) surveys for the accurate and immediate monitoring of a landslide located in a steep and v-shaped valley, in order to provide operational information concerning the stability of the area to the local authorities. The derived data were processed appropriately, and UAV-based as well as TLS point clouds were generated. The monitoring and assessment of the evolution of the landslide were based on the identification of instability phenomena between the multi-dated UAV and TLS point clouds using the direct cloud-to-cloud comparison and the estimation of the deviation between surface sections. The overall evaluation of the results revealed that the landslide remains active for three years but is progressing particularly slowly. Moreover, point clouds arising from a UAV or a TLS sensor can be effectively utilized for landslide monitoring with comparable accuracies. Nevertheless, TLS point clouds proved to be denser and more appropriate in terms of enhancing the accuracy of the monitoring process. The outcomes were validated using measurements, acquired by the Global Navigation Satellite System (GNSS).

Landslide Detection and Mapping Based on SBAS-InSAR and PS-InSAR: A Case Study in Gongjue County, Tibet, China

The rock mass along the Jinsha River is relatively broken under complex geological action. Many ancient landslides were distributed along the Jinsha River in Gongjue County, which is very dangerous under the action of gravity, tectonic stress and river erosion. Efficient and accurate identification and monitoring of landslides is important for disaster monitoring and early warning. Interferometric synthetic aperture radar (InSAR) technology has been proved to be an effective technology for landslide hazard identification and mapping. However, great uncertainty inevitably exists due to the single deformation observation method, resulting in wrong judgment during the process of landslide detection. Therefore, to address the uncertainties arising from single observations, a cross-comparison method is put forward using SBAS-InSAR (small baseline subset InSAR) and PS-InSAR (permanent scatterers InSAR) technology. Comparative analysis of the spatial complementarity of interference points and temporal deformation refined the deformation characteristics and verified the reliability of the InSAR results, aiding in the comprehensive identification and further mapping of landslides. Landslides along the Jinsha River in Gongjue County were studied in this paper. Firstly, 14 landslides with a total area of 20 km2 were identified by using two time-series InSAR methods. Then, the deformation characteristics of these landslides were validated by UAV (unmanned aerial vehicle) images, multiresource remote sensing data and field investigation. Further, the precipitation data were introduced to analyze the temporal deformation characteristics of two large landslides. Lastly, the influence of fault activity on landslide formation is further discussed. Our results demonstrate that the cross-comparison of the time-series InSAR method can effectively verify the accuracy of landslide identification.

Comprehensive Remote Sensing Technology for Monitoring Landslide Hazards and Disaster Chain in the Xishan Mining Area of Beijing

The Xishan coal mine area in Beijing, China has a long history of mining. Many landslide hazards, in addition to collapses and ground fractures, have occurred in this area. This study used multi-temporal satellite images to extract this region’s deformation information, identify landslides and analyze the deformation evolution process of these landslides. Taking the Anzigou ditch as an example, we investigate the “Quarry–Landslide–Mudslide” disaster chain model. We found that the landslide evolution process is closely related to the geological conditions, and usually goes through four stages: initial deformation, slope front swelling and collapsing, rear part connecting and rupturing, and landslide creeping. The surface deformation can be identified and tracked by high-resolution optical images and InSAR monitoring. Under the combined effects of rainfall and topographic conditions, medium and large landslides may occur and trigger a “Quarry–Landslide–Mudflow” disaster chain. The identification and analysis of these landslide hazards and the disaster chain help with geological disaster prevention, and provide reference for early identification and research of similar disasters.

Monitoring of rainfall-induced landslides at Songmao and Lushan, Taiwan, using IoT and big data-based monitoring system

Monitoring of rainfall-induced landslides at Songmao and Lushan, Taiwan, using IoT and big data-based monitoring system

A low-cost active reflector and a passive corner reflector network for assisting landslide monitoring using multi-temporal InSAR

ABSTRACT A C-band Low-cost Active Reflector (AR) has been tested in a real experimental campaign aimed at monitoring through multi-temporal InSAR an area threatened by a landslide that occurred in 2019. To monitor and characterize the movement, a network of eight Passive Corner Reflectors and one Active Reflector were installed along a forested slope. A set of 285 interferograms obtained combining 60 Sentinel-1 SAR images were processed to evaluate the stability of the area. The AR, installed in a stable location close to the landslide, was used to provide a reference point in this low coherence area. Despite the high sensitivity of the phase response of such devices to temperature changes, the device operates with a stability of ±2 mm in deformation retrieval, a value acceptable for monitoring purposes, with a moderate range of temperature values.

Assessment of the ASAR and PALSAR Sensors Applicability for Detecting and Monitoring of Landslides in the Zagros Area (Iran) Through Differential Synthetic Aperture Radar Interferometry (DInSAR)

Assessment of the ASAR and PALSAR Sensors Applicability for Detecting and Monitoring of Landslides in the Zagros Area (Iran) Through Differential Synthetic Aperture Radar Interferometry (DInSAR)

LoRa-Based Wireless Sensors Network for Rockfall and Landslide Monitoring: A Case Study in Pantelleria Island with Portable LoRaWAN Access

Rockfalls and landslides are hazards triggered from geomorphological and climatic factors other than human interaction. The economic and social impacts are not negligible, therefore the topic has become an important field in the application of remote monitoring. Wireless sensor networks (WSNs) are particularly suited for the deployment of such systems, thanks to the different technologies and topologies that are evolving nowadays. Among these, LoRa modulation technique represents a fitting technical solution for nodes communication in a WSN. In this paper, a smart autonomous LoRa-based rockfall and landslide monitoring system is presented. The structure has been operating in Pantelleria Island, Sicily, Italy. The sensing elements are disposed in sensor nodes arranged in a star topology. Network access to the LoRaWAN and the Internet is provided through gateways using a portable, solar powered device assembly. A system overview concerning both hardware and functionality of the nodes and gateways devices, then a power analysis is reported, and a monthly recorded result is presented, with related discussion.

Evaluating and Analyzing the Potential of the Gaofen-3 SAR Satellite for Landslide Monitoring

Gaofen-3 is the first Chinese spaceborne C-band SAR satellite with multiple polarizations. The Gaofen-3 satellite’s data has few applications for monitoring landslides at present, and its potential for use requires further investigation. Consequently, we must evaluate and analyze the landslide interference quality and displacement monitoring derived from the Gaofen-3 SAR satellite’s data, particularly in high and steep, mountainous regions. Based on the nine Gaofen-3 SAR datasets gathered in 2020–2021, this study used DInSAR technology to track landslide displacement in Mao County, Sichuan Province, utilizing data from Gaofen-3. Our findings were compared to SENTINEL-1 and ALOS-2 data for the same region. This study revealed that due to its large spatial baseline, Gaofen-3’s SAR data have a smaller interference effect and weaker coherence than the SENTINEL-1 and ALOS-2 SAR data. In addition, the displacement sensitivity of the Gaofen-3 and SENTINEL-1 data (C-band) is higher than that of the ALOS-2 data (L-band). Further, we conducted a study of observation applicability based on the geometric distortion distribution of the three forms of SAR data. Gaofen-3’s SAR data are very simple to make layover and have fewer shadow areas in hilly regions, and it theoretically has more suitable observation areas (71.3%). For its practical application in mountainous areas, we introduced the passive geometric distortion analysis method. Due to its short incidence angle (i.e., 25.8°), which is less than the other two satellites’ SAR data, only 39.6% of the Gaofen-3 SAR data in the study area is acceptable for suitable observation areas. This study evaluated and analyzed the ability of using Gaofen-3’s data to monitor landslides in mountainous regions based on the interference effect and observation applicability analysis, thereby providing a significant reference for the future use and design of Gaofen-3’s data for landslide monitoring.

Remote Sensing Precursors Analysis for Giant Landslides

Monitoring and early warning systems for landslides are urgently needed worldwide to effectively reduce the losses of life and property caused by these natural disasters. Detecting the precursors of giant landslides constitutes the premise of landslide monitoring and early warning, and remote sensing is a powerful means to achieve this goal. In this work, we aim to summarize the basic types and evolutionary principles of giant landslide precursors, describe the remote sensing methods capable of identifying those precursors, and present typical cases of related sliding. Based on a review of the literature and an analysis of remote sensing imagery, the three main types of remote sensing techniques for capturing the geomorphological, geotechnical, and geoenvironmental precursors of giant landslides are optical, synthetic aperture radar (SAR), and thermal infrared methods, respectively. Time-series optical remote sensing data from medium-resolution satellites can be used to obtain abundant information on geomorphological changes, such as the extension of cracks and erosion ditches, and band algebraic analysis, image enhancement, and segmentation techniques are valuable for focusing on the locations of geomorphological landslide precursors. SAR sensors have the ability to monitor the slight slope deformation caused by unfavorable geological structures and can provide precursor information on imminent failure several days before a landslide; furthermore, persistent scatterer interferometric SAR has significant advantages in large-scale surface displacement monitoring. Thermal infrared imagery can identify landslide precursors by monitoring geoenvironmental information, especially in permafrost regions where glaciers are widely distributed; the reason may be that freeze–thaw cycles and snowmelt caused by increased temperatures affect the stability of the surface. Optical, SAR, and thermal remote sensing all exhibit unique advantages and play an essential role in the identification of giant landslide precursors. The combined application of these three remote sensing technologies to obtain the synthetic geomorphological, geotechnical, and geoenvironmental precursors of giant landslides would greatly promote the development of landslide early warning systems.

An improved method of GB-SAR phase unwrapping for landslide monitoring

The ground-based Synthetic Aperture Radar (GB-SAR) technique can be applied to the safety monitoring and early warning of geo-hazards, especially for monitoring displacement of various types of landslide masses. One of the key techniques of processing GB-SAR monitoring data is phase unwrapping, which can be dramatically affected by atmospheric humidity, atmospheric pressure, sampling interval, etc. In high mountains and valleys where environmental change is drastic, GB-SAR monitoring data is vulnerable to incoherence both spatially and temporally. Therefore, an improved phase unwrapping method of GB-SAR data for landslide monitoring is proposed in this paper, which can realize three-dimensional phase unwrapping in time and space. The method adopts the idea of sparse data processing and realizes phase unwrapping of monitoring data in two steps. Firstly, taking full advantage of the high temporal resolution of GB-SAR monitoring data, the one-dimensional phase unwrapping method in the spatial domain is applied to the time domain, and the interference phases of PS points are unwrapped in the time domain. Then, the PS network is constructed based on the time-phase unwrapping results, the double-difference phases of the network baselines are obtained and the spatial consistency adjustment is applied to them, and the spatial phase unwrapping can be realized by the indirect least square adjustment method. This method successfully solves the problem of path dependence, island isolation and large computation of the common method of space-borne SAR phase unwrapping, which can process GB-SAR data with high accuracy and efficiency.

Accurate IoT Based Slope Instability Sensing System for Landslide Detection

Landslides are one of the prominent natural disasters in India that account for an estimated property loss of 66 million USD and the fatality of 200 lives in the Himalayan region and the Western Ghats every year. The present systems for monitoring, detecting and early warning of these landslides are based upon radar satellite interferometry, laser scanning or LIDAR, high-resolution imaging via satellites and wireless geophysical sensor networks. All the above systems are either too expensive for large area deployment or less accurate or lack real-time characteristics required for landslide detection. Above all different types of landslides like rainfall induced landslides, earthquake induced landslides, creep landslides, shallow landslides and deep seated landslides pose several challenges in monitoring and detection such as (a) time duration for event detection (swift to long duration) (b) localizing the slip surface depth for event detection (c) recurrence of the precursor triggering events (d) extent of durability of sensor system (e) uncertainty in spatial coverage of the event. To address the above challenges, this paper proposes the design and development of a wireless integrated landslide monitoring system. To enhance the reliability of this system, a low-cost slope instability sensing sensor was also integrated to this Landslide Early Warning System. This sensor’s design, development, testing and calibration is the primary focus of this paper. Further,the paper also discusses about the real-world deployment of this system to monitor one of the thickly populated landslide-prone area in Sikkim, North Eastern Himalayas.

Detecting slow-moving landslides using InSAR phase-gradient stacking and deep-learning network

Landslides are a major geohazard that endangers human lives and properties. Recently, efforts have been made to use Synthetic Aperture Radar Interferometry (InSAR) for landslide monitoring. However, it is still difficult to effectively and automatically identify slow-moving landslides distributed over a large area due to phase unwrapping errors, decorrelation, troposphere turbulence and computational requirements. In this study, we develop a new approach combining phase-gradient stacking and a deep-learning network based on YOLOv3 to automatically detect slow-moving landslides from large-scale interferograms. Using Sentinel-1 SAR images acquired from 2014 to 2020, we developed a burst-based, phase-gradient stacking algorithm to sum up phase gradients in short-temporal-baseline interferograms along the azimuth and range directions. The stacked phase gradients clearly reveal the characteristics of localized surface deformation that is mainly caused by slow-moving landslides and avoids the errors due to phase unwrapping in partially decorrelated areas and atmospheric effects. Then, we trained the improved Attention-YOLOv3 network with stacked phase-gradient maps of manually labeled landslides to achieve quick and automatic detection. We applied our method in an ∼180,000 km2 area of southwestern China and identified 3,366 slow-moving landslides. By comparing the results with optical imagery and previously published landslides in this region, the proposed method can achieve automatic detection over a large area precisely and efficiently. From the derived landslide density map, we determined that most landslides are distributed along the three large rivers and their branches. In addition to some counties with known high-density landslides, approximately 10 more counties with high landslide density were exposed, which should attract more attention to their risks for geohazards. This application demonstrates the potential value of our newly developed method for slow-moving landslide detection over a nation-wide area, which can be employed before applying more time-consuming time-series InSAR analysis.

Removing InSAR Topography-Dependent Atmospheric Effect Based on Deep Learning

Atmospheric effects are among the primary error sources affecting the accuracy of interferometric synthetic aperture radar (InSAR). The topography-dependent atmospheric effect is particularly noteworthy in reservoir areas for landslide monitoring utilizing InSAR, which must be effectively corrected to complete the InSAR high-accuracy measurement. This paper proposed a topography-dependent atmospheric correction method based on the Multi-Layer Perceptron (MLP) neural network model combined with topography and spatial data information. We used this proposed approach for the atmospheric correction of the interferometric pairs of Sentinel-1 images in the Baihetan dam. We contrasted the outcomes with those obtained using the generic atmospheric correction online service for InSAR (GACOS) correction and the traditional linear model correction. The results indicated that the MLP neural network model correction reduced the phase standard deviation of the Sentinel-1 interferogram by an average of 64% and nearly eliminated the phase-elevation correlation. Both comparisons outperformed the GACOS correction and the linear model correction. Through two real-world examples, we demonstrated how slopes with displacements, which were previously obscured by a significant topography-dependent atmospheric delay, could be successfully and clearly identified in the interferograms following the correction by the MLP neural network. The topography-dependent atmosphere can be better corrected using the MLP neural network model suggested in this paper. Unlike the previous model, this proposed approach could be adjusted to fit each interferogram, regardless of how much of the topography-dependent atmosphere was present. In order to improve the effectiveness of DInSAR and time-series InSAR solutions, it can be applied immediately to the interferogram to retrieve the effective displacement information that cannot be identified before the correction.

A Spatio-Temporal Monitoring Method Based on Multi-Source Remote Sensing Data Applied to the Case of the Temi Landslide

It is challenging to monitor landslides due to their heavy concealment and the extreme destructiveness during the long development of landslides. Many landslide monitoring tools are somewhat onefold. In this paper, a comprehensive landslide monitoring method involving multiple factors from time-series multi-data sources is proposed. We focus on the changes in three aspects consisting of the vegetation condition, the surface deformation information and the landslide susceptibility. Firstly, the fractional vegetation cover of the landslide is extracted from optical remote sensing Gaofen-1 (GF-1) images using the dimidiate pixel model. Next, the surface deformation information of the landslide is derived from SAR remote sensing Sentinel-1A images applying the SBAS-InSAR method. Then, the landslide susceptibility based on GF-1, Sentinel-1A images and DEM data is computed using the analytic hierarchy process method. Finally, the spatio-temporal correlations of the vegetation condition, the surface deformation information and the landslide susceptibility are compared and interpreted. The Temi landslide is located along the Jinsha River and poses a high risk of blocking the river. Taking the Temi landslide as the study area, it is indicated from the results that the fractional vegetation cover, surface deformation information and landslide susceptibility reveal a consistency in the patterns of changes in spatial and temporal terms. As the surface deformation information improves, the status of the landslide vegetation also deteriorates and the landslide susceptibility becomes high, which indicates an increased probability of the creep and even the occurrence of landslides. In contrast, when the surface deformation information drops, the vegetation condition of the landslide becomes superior and the landslide becomes less susceptible, which means the likelihood of sliding declines. This study provides a new idea for a landslide monitoring method and potential way for natural disaster prevention and mitigation.

Algorithms to enhance detection of landslide acceleration moment and time-to-failure forecast using time-series displacements

Landslide monitoring data are characterized by scatter that can make the velocity and acceleration of a landslide unclear. The presence of scatter can therefore influence the reliability of an early warning system (EWS) too. Data filters such as simple moving average (SMA) are commonly used to reduce scatter in the data and enhance the reliability of EWSs. Therefore, evaluating the adequacy of these filters to reproduce displacement characteristics representative of the landslide is important. Gaussian-weighted moving average (GWMA) and Savitzky-Golay (SG) filters are examined here against SMA. To this aim, a comprehensive numerical analysis of a synthetic database was carried out on accelerating scenarios to quantify the reliability of each filter to detect the onset of acceleration and forecast the failure time using the inverse velocity method. GWMA and SG applications in the synthetic scenarios reached reliability thresholds of 90% at 30% and 4% of the corresponding time by SMA, respectively, and provided a timelier capture of moment patterns. Specifically, these synthetic cases show the application of GMWA and SG improves failure time forecasting by 60 to 80% and 90 to 100%, respectively, compared to SMA depending on the amount of data used by the filter and the remaining time to failure. Additionally, nine failed cases (17 datasets) from the literature were examined after employing these three filters. Results of these cases show using alternatives to SMA would increase the accuracy of failure time forecasts by 60%.

Research on SSA-LSTM-Based Slope Monitoring and Early Warning Model

For geological disasters such as landslides, active prevention and early avoidance are the main measures to avoid major losses. Therefore, landslide early warning is an effective means to prevent the occurrence of landslide disasters. In this paper, based on geological survey and monitoring data, a landslide monitoring and early warning model based on SSA-LSTM is established for the landslide in Yaoshan Village, Xiping Town, Anxi County, Fujian Province, China. In the early warning model, the hyper parameters of the LSTM neural network are optimized using the SSA algorithm in order to achieve high-accuracy displacement prediction of the LSTM displacement prediction model, and are compared with the unoptimized LSTM, and the results show that the prediction effect of the optimized SSA-LSTM model is significantly improved. Since landslide monitoring and early warning is a long-term work, the model trained by the traditional offline learning method will inevitably have distortion of the prediction effect as the monitoring time becomes longer, so the online migration learning method is used to update the displacement prediction model and combine with the tangent angle model to quantify the warning level. The monitoring and early warning model put forth in this research can be used as a guide for landslide disaster early warning.

Use of low-cost accelerometers for landslides monitoring: results from a flume experiment

Early Warning Systems (EWS) are non-structural measures for landslides disaster prevention. They are based on the detection of impending failure signals. The results of a landslide simulation experiment where accelerometers were used to identify pre-failure signals are presented in this paper. Landslide was simulated in a tilting flume filled with sandy soil. During the experiment, the flume was fixed at 30° inclination and water percolated through the soil until it slid. Accelerometers were embedded into the soil and recorded acceleration data from the beginning of the experiment until failure. Acceleration data were analyzed in time domain aiming at estimating translational velocity of the movement. Angular variation was also estimated from acceleration data. The experiment was recorded with a camera and pictures were used for Particle Image Velocimetry (PIV) analysis, in order to validate the estimated translational velocity. Results showed that accelerometers can identify prefailure signals before any macroscopic movement could indicate impending failure in fast to very fast landslides, showing their potential to be used in EWS. Validation of estimated velocities was not always possible due to PIV setup constraints and the velocity of the mass movement simulated. In fact, the estimated translational velocities seem to be unreliable. On the other hand, the results suggest that acceleration data and angular position variation trend and rate can be incorporated into EWS.

Multi-GNSS positioning for landslide monitoring: a case study at the Recica landslide

Global Navigation Satellite System (GNSS) positioning has characteristics of simple operation, high efficiency and high precision technique for landslide surface monitoring. In recent years, finalization of modern GNSS systems Galileo and BeiDou has brought a possibility of multi-GNSS positioning. The paper focuses on evaluation of possible benefits of multi-GNSS constellations in landslide monitoring. While simulating observational conditions of selected Recica landslide in the Czech Republic, one-month data from well-established permanent GNSS reference stations were processed. Besides various constellation combinations, differential and Precise Point Positioning techniques, observation data lengths and observation sampling intervals were evaluated. Based on the results, using a combination of GPS and GLONASS, or GPS, GLONASS and Galileo systems can be recommended, together with a static differential technique and observation periods for data collection exceeding eight hours. In the last step, data from GNSS repetitive campaigns realized at the Recica landslide during two years were processed with optimal setup and obtained displacement results were compared to standard geotechnical measurements.

2D Phase-Based RFID Localization for On-Site Landslide Monitoring

Passive radio-frequency identification (RFID) was recently used to monitor landslide displacement at a high spatio-temporal resolution but only measured 1D displacement. This study demonstrates the tracking of 2D displacements, using an array of antennas connected to an RFID interrogator. Ten tags were deployed on a landslide for 12 months and 2D relative localization was performed using a phase-of-arrival approach. A period of landslide activity was monitored through RFID and displacements were confirmed by reference measurements. The tags showed displacements of up to 1.2 m over the monitored period. The centimeter-scale accuracy of the technique was confirmed experimentally and theoretically for horizontal localization by developing a measurement model that included antenna and tag positions, as well as multipath interference. This study confirms that 2D landslide displacement tracking with RFID is feasible at relatively low instrumental and maintenance cost.