Landslide Monitoring Research Articles

Synergistic use of SAR satellites with deep learning model interpolation for investigating of active landslides in Cuenca, Ecuador

Among the most intense geological disasters, landslides frequently occur throughout the world. These phenomena have been studied using space geodetic techniques, including Global Navigation Satellite Systems (GNSS) and Multi-Temporal Interferometric Synthetic Aperture Radars (MT-InSAR). Nevertheless, complete mapping and analysis of landslides’ surface deformation in most areas can be complicated due to a large diversity in kinematics, such as periods of quiescence and acceleration in the toe and crown. One of these landslides is the Cuenca landslides in Ecuador, where the geological investigation revealed that the toe of the landslides was located in urban areas, with more noticeable deformation effects. In contrast, its crown was located mainly in a rural and green land area. In this study, we show the potential of a synergistic use of COSMO-SkyMed (CSK) and Sentinel-1A (S1A) synthetic aperture radar (SAR) data for comprehensively monitoring the Cuenca landslides. To this aim, we have used Long-Short Term Memory (LSTM) and Convolutional Neural Networks (CNN) as two different Deep Learning Algorithms (DLAs) to integrate results in the temporal and spatial domain, respectively. A cross-comparison of the results was made with the nine GPS-derived deformations and the visual effects (i.e. crack width and pattern) on the field. This validation against GPS observation reveals that the RMSEs of the final MT-InSAR-derived velocity after applying the synergic double band SAR dataset decrease at more than 73% of nine GPS stations. Highlights Synergic MT-InSAR approach for studying landslide deformation in diverse kinematic areas. Utilized DLAs (LSTM and CNNs) for effective temporal and spatial interpolation of InSAR results. Findings emphasize the potential of multi-sensor SAR and DLAs for landslide monitoring regarding improving the RMSE at nine stations with an average of 73%.

Previously hidden landslide processes revealed using distributed acoustic sensing with nanostrain-rate sensitivity

Landslides sometimes creep for decades before undergoing runaway acceleration and catastrophic failure. Observing and monitoring the evolution of strain in time and space is crucial to understand landslide processes, including the transition from slow to fast movement. However, the limited spatial or temporal resolution of existing landslide monitoring instrumentation limits the study of these processes. We employ distributed acoustic sensing strain data below 1 Hertz frequency during a three-day rainfall at the Hollin Hill landslide and quantify strain-rate changes at meter and sub-minute scales. We observe near-surface strain onset at the head scarp, strain acceleration at a developing rupture zone, retrogression towards the scarp, and flow-lobe activity. These processes with displacements of less than 0.5 mm are undetected using other methods. However, the millimeter processes over three days agree with previously observed seasonal landslide patterns. Here, we show landslide processes occurring with nanostrain-rate sensitivity at spatiotemporal resolution previously not possible.

Research Trends and ‘Space-Sky-Ground-Underground’ Monitoring Technology Analysis of Landslide Hazard

Landslide is a typical geological disaster distributed in most countries worldwide. Due to long-term natural weathering and human engineering disturbances, the instability of landslides is prone to occur. Once monitoring and disposal methods are implemented inappropriately, they can lead to landslide hazards, seriously threatening the safety of people’s lives and property. For a long time, extensive research on landslide monitoring has been conducted from various countries, providing crucial technical support for reducing the incidence and severity of landslide hazards. However, considering the complex geological conditions of actual landslides and the direct impact of internal and external factors such as rainfall, storms, and earthquakes, the early warning accuracy of landslide hazards is still relatively low. Therefore, based on advanced research achievements, it is significant to carry out research on the current status and development trends of landslide monitoring technology. Based on the Web of Science core database, this study quantitatively analyzes the advanced research achievements in global landslide monitoring in the past decade using bibliometric analysis. A systematic analysis of landslide monitoring technology development is conducted according to each study’s publication time, keywords, and countries. On this basis, a multi-dimensional monitoring system for landslides was proposed, which utilizes the complementary advantages of multi-dimensional monitoring technology to achieve all-round, high-precision, and real-time monitoring of landslides. Finally, taking the Xinpu landslide in the Three Gorges Region of China as an example, a multi-source and multi-field-monitoring experiment was conducted. The application of landslide multi-field-monitoring technology provides an essential reference for monitoring, early warning, as well as the scientific prevention and control of landslide hazard.

A Priori Estimation of Radar Satellite Interferometry’s Sensitivity for Landslide Monitoring in the Italian Emilia-Romagna Region

The InSAR technique is known to be a powerful tool for precise monitoring of wide areas in terms of displacements. It is conceivable to also use this technique to monitor landslide areas, but geometrical distortions due to ground morphology and land cover could make InSAR processing ineffective for such applications. Because of the computational burden of InSAR processing, it is important to have preliminary knowledge about the possible suitability of the technique for the inspected area before acquiring and processing the data. This paper aims to perform a preliminary analysis of the InSAR sensitivity for the specific case of landslide monitoring. A new approach is proposed considering aspects specific to landslide displacements, which are basically tangent to the slope direction. Pre-processed coherence maps were used to account for the impact of land cover. The whole analysis can be carried out without acquiring cumbersome SAR datasets and can be used as a preliminary step. The Italian Emilia-Romagna region has been considered as the study area, with landslide areas accounting for more than 12% of its territory. The outcomes show that the inspected area has favourable morphological conditions, mainly thanks to its mild slopes and the limited number of landslides facing north, but the land cover has a strong negative impact on the InSAR sensitivity. Nevertheless, 7.5% of the landslide areas have promising conditions for monitoring using radar interferometry.

Stability monitoring and analysis of submerged landslides in the Three Gorges Reservoir area based on time-series InSAR data and GNSS data

ABSTRACT Precise landslide monitoring is crucial for understanding and mitigating their impact. The Three Gorges Dam has exacerbated landslides due to steep slopes and rising water levels. This study used ALOS-2 PALSAR-2 (2016–2017) and Sentinel-1A (2016–2018) data to monitor the Daping slope. Findings showed deformation rates of −86.3 mm/y (ALOS-2) and −62 mm/y (Sentinel-1A), confirmed by GNSS data. Analysis indicated that rainfall and water level fluctuations drive the Daping landslide, highlighting key factors influencing landslides in the Three Gorges region and their threats to life and property.

Innovative landslide disaster monitoring: unmanned aerial vehicle-deployed GNSS technology

Real-time monitoring technologies of surface deformation represented by the global navigation satellite system (GNSS) are essential for landslides early warning. Monitoring methods such as GNSS require on-site manual installation. Therefore, it becomes nearly impossible to deploy surface monitoring equipment when landslides are located in high mountain valleys that are challenging for personnel to access, and/or in hazardous situations. We propose an intelligent real-time monitoring and early-warning technology that employs an unmanned aerial vehicle (UAV) as a carrier to deploy GNSS equipment. A system was initially designed with five components: an adaptive sampling GNSS receiver, an intelligent cooperative network transmission module, UAV-dropped GNSS equipment, special delivery UAV, and an intelligent monitoring and early warning cloud platform. This new technology was applied to a real landslide in Gansu Province, China, in 2020. The mean absolute error of 30 days in the east, north, and upward directions were 1.2, 1.3, and 2.9 mm/d, respectively, comparing the deformation velocity between the traditional monitoring station and the UAV-dropped monitoring station. This observation facilitated the successful prediction of a landslide hazard on 27 January 2021, allowing for a timely alert to be issued. Collectively, the results of this study established that UAV-deployed GNSS technology can accomplish unmanned deployment of GNSS equipment for landslide monitoring to ensure early warning issuance in inaccessible and/or high-risk areas.

Image compression–based DS-InSAR method for landslide identification and monitoring of alpine canyon region: a case study of Ahai Reservoir area in Jinsha River Basin

Image compression–based DS-InSAR method for landslide identification and monitoring of alpine canyon region: a case study of Ahai Reservoir area in Jinsha River Basin

Landslide displacement prediction model based on multisource monitoring data fusion

This study presents a displacement prediction model that integrating various monitoring data sources to comprehensive landslide monitoring information utilization. The research focuses on the landslide in Lingwan Village, utilizing Ridge Regression (RR) to integrate diverse monitoring data. The fused displacement data undergo decomposition into trend and periodic terms using the Smoothness Priors Approach (SPA). The Autoregressive Integrated Moving Average (ARIMA) model predicts trend and periodic term displacements separately, which are then combined to forecast the overall landslide surface displacement accurately. The RR model demonstrates a strong correlation coefficient of 0.998, indicating high predictive ability. Trend term displacement prediction outperformed periodic term prediction, especially during the rainy season. The RR-SPA-ARIMA model has an average absolute error of 1.73 mm, a mean bias error of −0.08 mm, and a root mean square error of 1.87 mm. The model demonstrates high prediction accuracy.

Instrumental Monitoring of a Slow-Moving Landslide in Piedmont (Northwest Italy) for the Definition of Rainfall Thresholds.

The prediction and prevention of landslide hazard is a challenging topic involving the assessment and quantitative evaluation of several elements: geological and geomorphological setting, rainfalls, and ground motion. This paper presents the multi-approach investigation of the Nevissano landslide (Asti Province, Piedmont, NW Italy). It shows a continuous and slow movement, alongside few paroxysmal events, the last recorded in 2016. The geological and geomorphological models were defined through a field survey. An inventory of the landslide's movements and rainfall records in the period 2000-2016 was performed, respectively, through archive investigations and the application of "Moving Sum of Daily Rainfall" method, allowing for the definition of rain thresholds for the landslide activation (105 mm and 193 mm, respectively, in 3 and 30 days prior to the event). The displacements over the last 8 years (2016-2023) were monitored through an innovative in-continuum monitoring inclinometric system and Earth Observation (EO) data (i.e., relying on Interferometric Synthetic Aperture Radar, or InSAR data): it gave the opportunity to validate the rainfall thresholds previously defined. This study aims to provide information to public authorities for the appropriate management of the site. Moreover, the proposed workflow could be adopted as a guideline for investigating similar situations.

Joint InSAR and discrete element numerical simulation method for landslide identification and monitoring: a case study of the Gongjue landslide, Jinsha River, China

Joint InSAR and discrete element numerical simulation method for landslide identification and monitoring: a case study of the Gongjue landslide, Jinsha River, China

Potential Landslide Identification in Baihetan Reservoir Area Based on C-/L-Band Synthetic Aperture Radar Data and Applicability Analysis

The Baihetan reservoir region is characterized by complex geomorphology, significant altitude differences, and rugged terrain. Geological hazards in such areas are often characterized by high concealment, wide distribution, and difficulty in field investigation. Traditional identification techniques are unable to detect and monitor geological hazards on a large scale with high efficiency and accuracy. In recent decades, interferometric synthetic aperture radar (InSAR) techniques, such as small baseline subset InSAR (SBAS-InSAR), have been widely applied to landslide identification. However, due to factors such as vegetation and the degree of landslide deformation, single-band synthetic aperture radar (SAR) still has certain limitations in detecting landslides. In this study, SBAS-InSAR was conducted based on ALOS-2 and Sentinel-1 ascending-descending images covering the Baihetan reservoir region. Deformation identification results were utilized to conduct a statistical analysis of the SAR detection performance and landslide characteristics, and the effect of vegetation on the detection effectiveness of different SAR bands was discussed. The study revealed that when surface vegetation coverage reaches a high degree, the percentage of areas with coverage greater than 0.6 is greater than 95%, the SAR coherence is mainly affected by vegetation thickness; the comparison of the difference change in the average coherence of the C/L bands among the four vegetation types shows that the ratio of the average coherence of the L-bands to the C-bands increases by a factor of three with the increase in thickness and the transition from crops to shrubs and trees. The results showed that the L-band has better detectability than the C-band in alpine-canyon terrain with vegetation coverage and complex vegetation composition. However, considering the high temporal resolution and accessibility of Sentinel-1 SAR data, it is still the main data choice for wide-area identification of landslides in the reservoir area, while other satellite-borne SAR data with different wavelengths and resolutions, such as ALOS, can be used to assist in the identification and monitoring of landslide hazards with significant magnitude of deformations and dense vegetation coverage. Therefore, the combined utilization of multi-band SAR data has the potential to enhance the dependability of landslide identification and monitoring, resulting in more accurate detection results.

Landslide monitoring and prediction system using geosensors and wireless sensor network

Landslides in hilly regions are a frequently occurring natural phenomenon which takes a heavy toll of human lives and causes damage to different properties. Hence, prediction of landslide is essential for averting its deleterious effects by providing early warning to neighboring residents about the impending hazard of landslide. A landslide monitoring system has been designed using geosensors and high-range wireless network for on-line monitoring and prediction of landslides with the application of multivariate statistical analysis of prevailing site parameters. The system consists of various geosensors, wireless sensor network, server, and landslide monitoring and forecasting software. Crackmeter, in-place inclinometer, raingauge, tiltmeter, piezometer and other sensors are set up in the selected hill slope prone to landslide for continuous monitoring of influencing parameters. The system measures real-time landslide parameters using the said geosensors connected with wireless nodes and establishing a dynamic wireless network to overcome redundancy issue using wireless nodes of around 1200 m communication range using a high performance low power microcontroller, integrated solar panel and additional external omni-directional antenna for monitoring landslide in a large and hazardous hilly region from a distant safer location. The application software consists of different modules, namely data monitoring, analysis, storing, viewing, prediction, and generation of audio-visual, SMS and email alerts for 3 levels of landslides situations. The paper enumerates the system architecture and the application software details.Graphical

Research and application of a flexible measuring array for deep displacement of landslides

Abstract. The multidimensional and multi-sliding surface measurement of deep-seated displacement on landslides poses a significant technical challenge in landslide monitoring and early warning. The fixed-borehole inclinometer serves as an important measurement method based on drilling for this purpose. In this study, a novel flexible measurement array for deep-seated landslide displacement and its installation and measurement processes were developed, enabling higher accuracy in full-hole multidimensional deformation measurement. The measurement array consists of individual measurement probes as basic units, connected in series through coaxial cables and high-pressure rubber hoses, forming a flexible measurement array. Each probe is equipped with acceleration and magnetic field sensors, allowing for the measurement of borehole inclination and azimuth angles and providing a more comprehensive understanding of the deformation of deep-seated landslides. This flexible measurement array resolves the limitations of traditional fixed inclinometers, such as limited probe quantity or inaccurate installation positions that fail to reflect the deformation trend of the landslide body. Moreover, it eliminates the need for auxiliary installation accessories like pulleys and inclinometer pipes, simplifying the mechanical structure and installation process, which represents an advancement in methodology and an improvement in measurement techniques. This array provides a more comprehensive and improved monitoring tool for disaster prevention and mitigation, thereby enhancing the level of geological hazard monitoring and early warning technology.

PERFORMANCE EVALUATION OF TEMPORAL MODEL AND SPATIAL CORRELATION METHODS FOR PERSISTENT SCATTERER DETERMINATION IN LANDSLIDE PRONE AREAS

Abstract. The Philippines is no stranger to natural hazards. Aside from flooding, landslides emerge as a primary contributor to damage dealt by natural hazards. Traditional methods for landslide monitoring require on-site field measurements, which makes them resource-intensive and time-consuming. Moreover, the frequency of updating of existing landslide hazard maps is limited by resources and manpower. This research introduces the application of remote sensing technology, specifically Persistent Scatter Interferometry (PSI), as a complementary tool for updating hazard maps. PSI enables the identification of stable ground points, eliminating the need for labor-intensive fieldwork and facilitating assessments of potential slope instability. Two distinct PSI processing methods are evaluated in the study; the Temporal Model approach and the Spatial Correlation model approach. Findings reveal that the Temporal Model approach successfully detected 11,647 Persistent Scatterers (PS) points, while the Spatial Correlation approach identified 272,614 PS points. Furthermore, the Spatial Correlation approach demonstrated its capability to detect PS points within high landslide susceptibility areas. Consequently, the results highlight the suitability of the Spatial Correlation approach for detecting PS points, particularly in landslide-prone regions, offering support to local landslide hazard monitoring systems. This research supports the initiative to a more efficient and cost-effective approach to maintaining up-to-date landslide susceptibility maps in the Philippines in enhancing disaster preparedness and mitigation efforts.

Towards the development of a landslide activity map in Slovenia

To create the landslide activity map, we implemented and tested the procedure to fully utilise the 6-day repeatability of the Sentinel-1 constellation in three pilot areas in Slovenia for the observation period from 2017 to 2021. The interferometric processing of the Sentinel-1 images was carried out with ENVI SARScape, while the interpretation of the persistent scatterers InSAR data was done in three steps. In the first step, a preliminary interpretation of the landslide areas was performed by integrating the PS InSAR data into a GIS environment with information that could be relevant to explain the movement patterns of the PS InSAR points. In the second step, a field validation was performed to check the PS InSAR in the field and record the potential damage to the objects indicating the slope mass movements. In the third step, the deformations were identified, and areas of significant movement were determined, consisting of clusters of at least 3 persistent scatterers (PS) with a maximum spacing of 10 m. The landslide activity map was created based on the landslide areas categorised into four classes based on the geotechnical analyses, yearly velocity data obtained by PS InSAR, and validation of annual velocity data obtained by in-situ and GNSS monitoring and field observation. A total of 21 polygons with different landslide activities were identified in three study areas. The overall methodology will help stakeholders in the early mapping and monitoring of landslides to increase the urban resilience.

Practical considerations for using petrophysics and geoelectrical methods on clay rich landslides

Understanding the geological and hydrological conditions present within an unstable slope is crucial for assessing the likelihood of failure. Recently, geoelectrical characterization and monitoring of landslides has become increasingly prevalent in this context, due to the spatial sensitivity of electrical methods to critical hydro-mechanical parameters. We explore a situational relationship between resistivity and matric potential (or negative pore pressure), which is a key parameter in estimating the resistance to shear in geological materials, and gravimetric moisture content (GMC). We have chosen a well-characterized active landslide instrumented with geoelectrical monitoring technology, the Hollin Hill Landslide Observatory, situated in Lias rocks in the southern Howardian Hills, United Kingdom. We report on petrophysical relationships between porosity, GMC, electrical resistivity, and matric potential. We trial the application of these petrophysical relationships to inverted resistivity images. Ground model development is achieved through a mixture of clustering resistivity distributions and analysis of surface movements. Our findings show the shrink swell properties of clay result in a variable porosity, which is problematic for applying classic petrophysical relationships documented in the literature. Moreover, directly translating resistivity distributions into matric potential has additional challenges. Nonetheless, volumetric imaging of resistivity suggest that low shear strengths are concentrated downslope of a rotational backscarp. We infer that an accumulation of moisture drives the development of a slip surface at depth, which subsequently manifests in failure at the ground surface. We conclude that the time-lapse resistivity images alone could not be used to infer the pore pressure conditions present within the slope without development of the petrophysical relationships shown here. Therefore, we suggest that the results have practical implications for landslide monitoring with geophysical methods.

Indicating landslide hazard from tree rings – Ecosystem service provided by an alder forest in the hengduan Mts, Sichuan, China

Landslides are destructive geomorphological processes that cause economic and social losses. This stimulates the development of new tools related to landslide hazard. Recently, trees, their growth rings and dendrochronology have become widely used in landslide studies. Thus, this study aims to explore the potential of trees in providing landslide-monitoring ecosystem services through dendrochronology. In our opinion, establishing such an idea would help promote empirical evidence on the efficiency of tree-ring-based tools to decision-makers. We use the example of a landslide in the Moxi basin, Sichuan, China and present results of dendrochronological analysis of growth eccentricity in 48 Nepalese alder (Alnus nepalensis) trees. This analysis provided data on event timing and magnitudes, average frequency and recurrence interval for reactivation of the study landslide, as well as spatial variability of landslide active. Based on dendrochronological data we were also able to determine the current slope balance and general hazard of landslide reactivation on the study slope. Our study shows that trees and dendrochronology can provide data on the activity of landslides that can complement and improve the results of standard engineering methods. Moreover, dendrochronology itself can provide the full information needed for landslide hazard assessment, monitoring and prediction.

Application of non-contact video quantitative measurement method in reservoir bank landslide monitoring

The geological structure of the reservoir bank landslide is complex and intricate. After deformation and damage, it causes river blockage, surges, and loss of people’s lives and property, posing a huge threat. At present, in academia and engineering applications, a large number of techniques such as inclinometers, rain gauges, and surface GNSS deformation monitoring are still used for monitoring landslides on reservoir banks. This type of monitoring method has the problem of “point to surface” which can easily lead to missed detection and reporting in some areas, some disaster points are close to water and steep, difficult to reach, and equipment installation is difficult. This work designs and implements a non-contact video quantitative monitoring system for surface deformation of geological disaster. By constructing a deep learning neural network, deformation area recognition and displacement quantitative calculation are achieved; By obtaining continuous images for a long time, draw the surface displacement-time curve, and output the surface deformation data and landform changes of the disaster. Meanwhile, this work explores the impact of different lighting conditions on the recognition results of target areas. This work can provide non-contact monitoring methods and dynamic warning support for large-scale monitoring of geological disasters.

Coupling effect of impoundment and irrigation on landslide movement in Maoergai Reservoir area revealed by multi-platform InSAR observations

The mountainous areas of Southwest China are crucial regions for hydropower resource development. The unique geological environment and numerous constructed hydropower stations make landslides the primary geological hazard for the region. Therefore, the deformation monitoring and mechanism analysis of reservoir landslides have attracted extensive attention from the academic community. The Maoergai Reservoir (MEGR) is located in the middle reaches of the Heishui River in Sichuan Province, China. Following impoundment, numerous active slopes have arisen in the reservoir area, with the Xierguazi-Mawo landslide complex (XMLC) potentially being the most threatening. In this paper, we first acquired the surface displacement of the MEGR area, before and after impoundment, using time series Synthetic Aperture Radar Interferometry (InSAR) analysis of the ALOS-1/PALSAR-1, ALOS-2/PALSAR-2 and Sentinel-1 datasets. Then, the three-dimensional (3D) deformation field was inverted by combining multi-track InSAR observations and a topography-constrained model. Results show the presence of 47 active landslides within the 675 km2 reservoir area, a significant increase compared to the seven landslides identified prior to impoundment. Based on the derived 3D velocity vectors, we found that the motion of the XMLC exhibits heterogeneity in terms of spatial and temporal patterns. Further, the displacement time series of all detected active landslides suggests that irrigation and water level fluctuations are two major factors influencing the kinematic behaviors of landslides. Irrigation accelerates landslide movement and thus permanently alters the deformation trend, while water level fluctuations cause seasonal oscillations in the displacement time series with a time lag of approximately 100 days. The above results can contribute to landslide management and prevention in the MEGR area and provide a representation for numerous water reservoirs in southwest China.

Introduction and testing of a cost-effective GNSS system for landslide monitoring

Introduction and testing of a cost-effective GNSS system for landslide monitoring