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Article

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

by
Gang Cheng
1,2,3,
Haoyu Zhang
1,
Ye Wang
1,*,
Bin Shi
2,
Lei Zhang
4,*,
Jinghong Wu
5,
Qinliang You
1,
Youcai Li
6 and
Peiwei Shi
1
1
School of Computer Science, North China Institute of Science and Technology, Beijing 101601, China
2
School of Earth Sciences and Engineering, Nanjing University, Nanjing 210023, China
3
Engineering Research Center of Zero-Carbon and Negative-Carbon Technology in Depth of Mining Areas, Ministry of Education, China University of Mining and Technology, Xuzhou 221116, China
4
School of Water Resources and Environment, China University of Geosciences, Beijing 100084, China
5
School of Civil Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
6
School of Engineering, North China Institute of Science and Technology, Beijing 101601, China
*
Authors to whom correspondence should be addressed.
Water 2024, 16(14), 2005; https://doi.org/10.3390/w16142005
Submission received: 31 May 2024 / Revised: 2 July 2024 / Accepted: 12 July 2024 / Published: 15 July 2024
(This article belongs to the Special Issue Recent Research on Reservoir Landslide Stability)
Browse Figures
Versions Notes

Abstract

:
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.

1. Introduction

A slope refers to the rock and soil mass with a particular inclination angle formed by natural factors or human interference. When a weak part of it deforms relative to the main body of the slope due to gravity and other external factors, a landslide forms [1,2,3]. In addition, factors such as rainfall, earthquakes, and engineering disturbances can easily disrupt a slope’s equilibrium state, leading to landslide hazards. Landslides are sudden geological disasters that are often accompanied by secondary disasters such as mudslides and floods [4,5,6]. They have strong destructive power and cause a large number of casualties and property losses worldwide every year (Figure 1). Long-term and large-scale landslide movements can even cause changes in terrain and landforms.
Many countries have realized the severe harm of landslide hazards in recent years. Taking China, which experiences frequent landslides, as a case study, the application of technologies such as 3S (Remote Sensing, Geography Information Systems, and Global Positioning Systems) in landslide monitoring has become increasingly widespread [7,8]. Moreover, with the in-depth integration of various optical and electrical technologies and artificial intelligence algorithms, a large number of new technologies, such as DFOS (Distributed Fiber Optical Sensing) and InSAR (Interferometric Synthetic Aperture Radar) technology, have been widely applied in the field of landslide monitoring [9,10], which have achieved automated, high-precision, and real-time monitoring of landslides. Early warning and prevention of landslide hazard has significantly improved, and the overall number of disasters has shown a downward trend [11] (Figure 2). However, the success rate of early warning is still unstable. Despite numerous studies on landslide susceptibility and critical thresholds for triggering landslides, it is still difficult to achieve comprehensive and precise control of landslides on a global scale [12]. Therefore, it is of great significance to achieve real-time monitoring of landslides in all dimensions by researching advanced landslide monitoring technologies.
In this paper, bibliometric analysis is used to visually analyze essential indicators (keywords, authors, years, and advanced monitoring technologies, etc.) related to global landslide monitoring from 2010 to 2023. The development trends of various landslide monitoring technologies and the correlation among the leading monitoring technologies are discussed. A ‘Space-Sky-Ground-Underground’ monitoring system for landslides is further proposed. Finally, taking a reservoir landslide in the Three Gorges Region of China as an example, on-site monitoring research on landslides is carried out. The research results provide an essential reference for the rapid development of landslide monitoring technology.

2. Research on Visualization of Landslide Monitoring Literature

Bibliometric analysis, first demonstrated by Derek J. Solla Price in 1965, is one of the mainstream analysis methods in academic research and subject evaluation. The advantages of low cost, high efficiency, reliable data, and no time and space constraints provide essential references for academic research hotspots and disciplinary development trends [13,14]. The primary process of this method is to firstly quantify and statistically analyze relevant information such as keywords, authors, and publication times. Then, the statistical dataset is clustered and analyzed, and based on this, the data characteristics and changes in the discipline or profession are extracted to grasp hot issues and development trends accurately.
Based on the Web of Science core database, the literature related to landslide monitoring technologies was searched, screened, and cluster-analyzed using bibliometric analysis. Firstly, (TS = (landslide)) AND (TS = (monitor)) AND (ALL = (geology) OR ALL = (geological engineering) OR ALL = (geotechnics) OR ALL = (engineering)) were set as search criteria to retrieve relevant research from 1 January 2010 to 31 December 2023. Secondly, irrelevant studies were excluded by analyzing the title, abstract, and full text. In total, 2470 papers, including 2389 articles and 60 reviews, were selected for subsequent analysis, accounting for 96.721% and 2.429% of the total, respectively. The specific retrieval process is shown in Figure 3. Based on the above method, visual analysis was conducted using the studies’ publication time, keywords, and authors’ countries to obtain the current status and development trends of landslide hazard monitoring.

2.1. Analysis of Publication Time

Based on the search criteria, a statistical analysis was conducted on the number of publications related to landslide monitoring technology from 1 January 2010 to 31 December 2023. The number of papers increases year by year, and the overall trend is increasing (Figure 4). It can be concluded that with the rapid development of the world economy, coupled with the implementation of critical projects such as the Sichuan-Xizang Railway Project, and the Three Gorges Project, the frequency of landslides has increased significantly. At the same time, the increasingly active Earth plate and crustal movements have led to an increasing incidence of landslides under earthquake action in recent years, which resulted in massive casualties and economic losses. Governments and scholars worldwide pay significant attention to preventing and controlling landslide hazards, thereby promoting the rapid development of research in landslide monitoring technology towards multi-dimensional, multi-parameters, and intelligent directions.

2.2. Keyword Analysis

By analyzing keywords of the literature, the hotspots of landslide monitoring in current research can be displayed clearly. In the quantitative analysis of keywords, the minimum frequency of keyword occurrence was set to 20, and 155 eligible keywords were obtained. The top 10 keywords with the highest degree of association are shown in Table 1. Among them, total link strength refers to the total number of co-occurrence times of a keyword with other keywords, including the number of repeated co-occurrence times.
Presently, VOSviewer 1.6.19 (https://www.vosviewer.com/download (accessed on 14 July 2024)) is a widely used bibliometric analysis software in the study of development trends in various fields [15,16,17], that enables visual analysis of a vast array of studies by utilizing keywords, titles, publication years, and other relevant information. By using VOSviewer, the high-frequency keywords in 2470 articles were visualized, and a visual correlation map (Figure 5) was obtained. It clearly shows the correlation between the various influencing factors of landslides. Through the high-frequency keyword analysis, key factors affecting the occurrence of landslide hazards can be concluded. Monitoring, early warning, and preventing landslide hazards have received widespread attention from society. The occurrence of landslides is often influenced by multi-field coupling effects such as rainfall seepage, slope deformation under earthquake action, and stress imbalance under engineering disturbance. In addition, there is a strong correlation between multi-dimensional landslide monitoring technologies in space, sky, ground, and underground, indicating that multi-dimensional monitoring technologies will play an increasingly important role in the early warning and prevention of landslide hazards in the future.
To further analyze the hotspots of landslide monitoring methods, the monitoring technologies involved in the above 2470 articles were analyzed and the top 5 landslide monitoring technologies with the highest frequency of occurrence as well as their annual changes are presented in Figure 6.
It can be concluded that the most frequent monitoring technology for landslides is GNSS (Global Navigation Satellite System), which consists of GPS (Global Positioning System), GLONASS (Global Navigation Satellite System), BDS (Beidou Navigation Satellite System), and Galileo (Galileo Satellite Navigation System). GNSS can conduct all-weather real-time monitoring of the entire landslide and makes it easy to grasp the overall deformation status of the slope from a macro perspective, thus achieving precise control of the overall condition of the landslide [18]. In addition, as an emerging technology, the InSAR technology included in the 3S technology has the advantage of large-scale and all-weather monitoring, which is especially prominent in landslide deformation monitoring and has become one of the most practical and effective technologies in the field of landslide monitoring [19]. The third most frequently occurring monitoring technology is GIS (Geographic Information System). By accurately controlling the geometric shape, geological and hydrological conditions, as well as the characteristics of large-scale landslide hazards, early warning and prevention of landslide hazards can be achieved. Due to the advantages of convenient implementation, reliable accuracy, and intuitive results, GIS plays an important role in landslide monitoring. In addition, it can be seen that UAV technology and DFOS technology, as emerging technologies, have unique advantages in the field of landslide monitoring. They play an increasingly important role in this field [20,21,22]. However, in actual landslide monitoring projects, it is necessary to combine the advantages and adaptability of various technologies considering the different monitoring environments and conditions. It is worth noting that about 10.93% of the above-mentioned indexed studies involve the Three Gorges Region in China.

2.3. Statistical Publications from Various Countries

By analyzing the publication numbers of multiple countries under the abovementioned retrieval, it can be seen that unique geological conditions and comprehensive economic strength are important factors that influence the progress of landslide hazard monitoring in various countries. The top 10 countries in terms of publication numbers were identified, as shown in Figure 7. Among them, 1337 papers were published by Chinese scholars, ranking the first in the world with 21,581 citations. A total of 343 papers were published by Italian scholars, ranking second in the world with 10,091 citations. In addition, 230 papers were published by American scholars, ranking third in the world with 6497 citations. According to statistics from the United Nations and the International Monetary Fund (IMF), the top 5 countries account for about 24.199% of the world’s total population, and account for about 50.262% of the world’s gross domestic product [23].
Visual analysis was conducted on the number of publications in the field of landslide monitoring in various countries, and the analysis results clearly showed the number of publications and cooperation relationships among countries (Figure 8). Considering the geological conditions and economic development, China has a complex geological structure and a high population density, making it one of the countries with the most severe geological disasters in the world. Moreover, China is highly concerned about landslide monitoring and has strong cooperation with other countries. The publication number of China ranks first, which is about 3.90 times that of Italy, in second place. Italy carried out landslide monitoring very early, as 80% of its land area is mountainous and hilly. In the 1860s, Italy first established a civil engineering force to conduct landslide monitoring research, pioneering modern landslide monitoring studies [24]. The research results have great reference significance for the modern monitoring of global landslides. The United States rank third in publication volume and the country has a high total link strength. Leading advanced technology in high-tech fields such as computer science and information science has effectively promoted the high-level development of its landslide monitoring technology.
However, due to the differences in terrain and landforms among different countries, as well as the landslide formation and evolution feature under different geological conditions, there are significant differences in the applicability and application effects of various landslide monitoring technologies under different terrain and landforms. For example, InSAR technology can play a good role in the United States with a wide geographical area as a whole, but its applicability is poor in western China, where there is a large amount of vegetation cover. In addition, one landslide may be composed of different geological structures, and actual conditions need to be comprehensively considered. Therefore, there is an urgent need to research a comprehensive landslide monitoring system based on ‘Space-Sky-Ground-Underground’ monitoring technology to compensate for the shortcomings of single monitoring technology, such as data dispersion, low monitoring accuracy, difficulty in real-time, and poor robustness, and to further achieve integrated landslide monitoring with full distribution, high precision, full real-time monitoring, and high robustness.

3. A Comprehensive Monitoring System for Landslides in ‘Space-Sky-Ground-Underground’

With the rapid development of various high-tech technologies such as computer science and information technology, advanced monitoring technologies such as InSAR, and DFOS have been widely applied in geological disaster monitoring as well as engineering construction and maintenance. Especially in the field of landslide monitoring, various contact and non-contact monitoring technologies have emerged, providing essential evaluation criteria for scientifically studying the stability of landslides [25,26]. Specifically, a comprehensive monitoring system based on ‘Space-Sky-Ground-Underground’ can achieve comprehensive and refined monitoring as well as real-time and continuous acquisition of landslides. Among them, space-based and sky-based monitoring focus on the overall deformation of the landslide surface, ground-based monitoring focuses on the deformation and failure process of the surface and shallow layers of the landslide, and underground-based monitoring focuses on the interaction between various rock layers in the deep part of the landslide [27,28,29]. Based on multiple advanced monitoring technologies in various monitoring dimensions, a comprehensive and integrated monitoring system of ‘Space-Sky-Ground-Underground’ was constructed (Figure 9) to obtain the evolution process of surface and internal state characteristics information of landslides from different dimensions. Thereby, the crucial guarantees for the accurate warning, the emergency response, and the scientific prevention and control of landslide hazards were provided.

3.1. Space-Based Monitoring

Space-based monitoring technology mainly monitors macro factors such as surface deformation and failure mode. The main sliding direction and sliding trend of landslides are monitored by using satellite remote sensing technology to achieve precise control of the overall condition of landslides. GNSS (Global Navigation Satellite System), mainly composed of GPS, GLONASS, BDS, and Galileo four subsystems, is most widely used in space-based landslide monitoring. The real-time monitoring and transmission of landslide status are achieved through satellite constellations, ground monitoring systems, and user receivers [30,31,32,33]. This method is particularly suitable for full coverage and all-weather monitoring of large-scale landslides. Figure 10 shows the development process of global satellite navigation systems.
InSAR technology has been widely applied in the field of landslide monitoring for a long time. However, there is a certain degree of uncertainty in the results of a single monitoring, and so the monitoring results have certain uncertainties. Based on this, Yao et al. [34] proposed a method combining SBAS-InSAR (small baseline subset InSAR) and PS-InSAR (permanent scatterer InSAR) technology for landslide deformation measurement (Figure 11). The feasibility of this method was verified through drone images, multi-resource remote sensing data, and field surveys, providing an important reference for the wide application of InSAR technology in the field of landslide monitoring.

3.2. Sky-Based Monitoring

Sky-based monitoring carries out aerial photography and surveying of the test area, mainly by aircraft, UAVs, airships, and other aircraft equipped with high-speed cameras and aerial survey instruments. Among them, the applications of UAVs in landslide monitoring are constantly expanding with the continuous improvement in various types of UAV technologies. This technology primarily utilizes UAVs equipped with high-resolution cameras and Lidar to capture high-definition images of landslides to further compare and analyze the changes in landslides at different times. Based on this, the direction and rate of landslide sliding are analyzed. Then, the prediction of landslide instability and judgment are carried out combined with other landslide monitoring technologies. With the continuous development of space-based monitoring technology, various types of UAVs suitable for landslide monitoring in different environments have emerged, with measurement accuracy reaching the centimeter level. The commonly used UAV can be divided into fixed-wing UAVs, rotary-wing UAVs, uncrewed airships, and flapping-wing UAVs based on the configuration of the flight platform (Table 2), which provides diversified equipment options for landslide monitoring in different types and geological environments [35,36]. Currently, with the advancement of artificial intelligence technology, UAV technology can be integrated with techniques such as artificial intelligence and machine learning to analyze and process monitoring data. In actual landslide monitoring, the selection of UAV is often based on the terrain, topography, and meteorological conditions of the landslide area. On this basis, technicians can combine actual monitoring objectives with the situation to achieve the best monitoring effect.
Gupta et al. [37] collected high-resolution images of landslides by DJI Phantom 3 Advanced UAV and generated the 3D model to realize visual analysis of landslides (Figure 12). The model parameters were compared and verified with the data obtained from the total station. The results show that the difference between uncrewed aerial vehicle monitoring data and total station monitoring values is slight, indicating that this technology has good applicability in estimating the size of landslides. The established three-dimensional model of landslides has excellent application prospects for early warning and prevention of landslide hazards.

3.3. Ground-Based Monitoring

Ground-based monitoring mainly focuses on the surface of landslides. This method primarily uses measuring robots, 3D laser scanners, digital cameras, and other instruments to conduct real-time monitoring of landmarks and observation stations on landslide surfaces in an unmanned manner around the clock. Furthermore, the status of and variation in external environmental parameters, such as the influence of environmental factors and meteorological data on the landslide’s surface deformation and slope surface, can be obtained [38,39]. The landslide surface’s deformation status and evolution process are grasped accurately, which provides crucial shallow deformation data support for landslide monitoring and early warning. Figure 13 shows the instruments and features commonly used in foundation monitoring.
Based on 3D laser scanning technology, the entire landslide can be scanned and analyzed, enabling precise inspection of the characteristics of any specific point. Dong et al. [40] explored the application of 3D laser scanning technology in landslide monitoring. Through analyzing multiple landslide monitoring cases, it can be seen that this technology has great advantages in the case of landslides with a high risk of sliding and less vegetation coverage (Figure 14). By analyzing the 3D monitoring data of the two periods before and after the landslide occurrence, the boundary range and volume of the landslide can be determined. It was verified that this technology has the advantage of accurately grasping the state of the landslide while ensuring the personal safety of the investigators.

3.4. Underground-Based Monitoring

Various acoustic, optical, electrical, and other sensors deployed inside the landslide are used in underground-based monitoring technology to obtain multi-field information such as deformation, seepage, temperature, and geoelectricity of the underground rock and soil mass of the landslide. Based on this, it is easy to grasp the changes between the various rock layers and their interfaces in the deep part of the landslide. Commonly used sensors include inclinometers, osmometers, thermometers, and electrodes. Especially of note, DFOS technology has developed rapidly in recent years, and has been well applied in various landslide hazard and slope monitoring due to its unique advantages of distribution, anti-interference, high accuracy, and long distance. DFOS uses light waves as a carrier and optical fibers as a medium to sense the changing processes of multiple physical parameters such as stress, vibration, temperature, and seepage inside landslides (Figure 15), and critical, innovative results have been achieved [41,42,43]. Based on this, scholars have researched the mechanism of landslide hazards, which provides essential technical support for promoting deep monitoring of landslides. In landslide monitoring, selecting the particular sensing optical cables based on different geological conditions and monitoring environments is necessary to achieve a comprehensive and accurate perception of the state information of deep rock and soil in landslides.
Zhang et al. [44] monitored multiple field factors such as deformation, temperature, stress, and seepage of the Majiagou Reservoir landslide in the Three Gorges area by using DFOS. Figure 16 shows the temperature profile monitored by optical fiber inside the landslides. At the same time, they conducted comparative verification through the indoor physical model experiments. The landslide deformation mode and key deformation positions were determined through large-scale and fully distributed fiber optic monitoring. Subsequently, combined with quasi-distributed fiber optic monitoring, accurate real-time information on multiple fields at key landslide locations were obtained. The research results indicate that rainfall can significantly reduce the effective stress inside the soil, thereby putting the landslide in a state of easy instability. In addition, considering the particularity of the influence of reservoir water level on the reservoir bank landslide, the rapid rise and fall of the reservoir water level will also lead to a significant decrease in the stability coefficient of the landslide. The above research results provide important references for underground-based monitoring of reservoir bank landslides.
In summary, monitoring technology based on various dimensions of space, sky, ground, and underground is significant for landslide monitoring, early warning, and prevention. However, limited by the monitoring scope, equipment performance, and monitoring system robustness, for actual landslide monitoring under complex geological conditions and environments, it is often difficult to fully complete the monitoring task with a single monitoring technology. For example, InSAR technology in space-based monitoring is susceptible to the influence of atmospheric and vegetation cover, resulting in certain uncertainties in monitoring results. UAV technology in sky-based monitoring is generally challenging to operate in harsh weather conditions such as heavy rainfall; so, it is difficult to apply for landslide monitoring under rainfall conditions. Three-dimensional laser scanning technology in ground-monitoring is prone to errors in landslide monitoring with complex terrain structures, resulting in a significant decrease in the accuracy of test results. Although DFOS technology in underground-based monitoring can achieve large-scale and real-time monitoring, there is often a problem of sensor damage when landslides undergo a large deformation. Therefore, researching the integrated monitoring system of ‘Space-Sky-Ground-Underground’ in all dimensions complements the advantages of multi-dimensional monitoring technology, and the limitations of a single monitoring technology in various landslide monitoring situations. In this way, achieving comprehensive, high-precision, and continuous monitoring of landslides is natural. On this basis, the advanced machine learning algorithms could be combined to improve the processing speed and utilization rate of massive monitoring data. After that, landslide hazard development trend prediction could be conducted to provide a reference basis for landslide hazard monitoring, early warning, and scientific control.

4. Monitoring of the Xinpu Landslide in the Three Gorges Based on the ‘Space-Sky-Ground-Underground’ Monitoring Technology

As the vital waterway for China’s water transportation and the barrier to ecological protection, the Three Gorges Region of the Yangtze River plays a crucial role in promoting China’s economic development and regulating regional environmental balance. However, as it is located on the northern edge of the Yangtze Plate, connected to the Qinling Mountains, Sichuan Basin, North China Plate, and South China Fold Belt, it has complex geological structures and frequent tectonic activities, resulting in various geological disasters such as landslides, collapses, and mudslides. Since the completion and impoundment of the Three Gorges Dam in 2003, the frequency of earthquakes in the reservoir has increased to more than ten times that before the impoundment. At the same time, it has also caused specific changes in regional temperature. Multiple influencing factors such as climate change, engineering disturbance, the rise and fall of the reservoir water level, and their combined effects have exacerbated the further deterioration of the regional geological environment, leading to the frequent occurrence of various large-scale and destructive landslide hazards. To ensure the transportation of the Yangtze River Three Gorges waterway and the safety of local people’s lives and property, a large number of on-site experiments have been conducted. The applicability and advantages of various multi-dimensional monitoring technologies in the field of landslide hazard prevention and control are verified.
The Xinpu landslide is in Anping Town, Fengjie County, Chongqing, China (Figure 17). It is a super large landslide composed of multiple deformation zones (with a land-slide area of 0.86 km2, and a volume of 5.4 × 107 m3). The slope is of low-to-medium mountain valley landform. The elevation of the slope’s top is 809 m, and the water level of the Three Gorges Reservoir at the front edge fluctuates between 145 m and 175 m, with a relative height difference of 634 m to 664 m. The slope aspect is 350°, with an average slope inclination of 14°, and the overall shape is a single-sided mountain with low north and high south. The slope structure is a steep south and a gentle north. The attitude of the exposed rock strata on the slope is 320–350°∠16–33°, and the dip angle of the rock strata gradually decreases from south to north. The slope structure is in a “reclining” shape. The main strata exposed on the slope are Quaternary landslide deposits (Q4del), the upper section of the Lower Jurassic Zhenzhuchong Formation (J1zh2), the lower section of the Lower Jurassic Zhenzhuchong Formation (J1zh1), and the Upper Triassic Xujiahe Formation (T3xj). [45,46,47]. Due to the periodic fluctuation in the water level and the abundant rainfall of the Three Gorges Reservoir area, the landslide is in a periodic sliding state. In addition, the surface layer of the slope is composed of landslide deposits (Q4del), with a thickness that gradually increases from the upper part of the slope to the slope’s bottom. The deposits in the middle of the slope are thicker, while those on both sides near the ridge are thinner. The main material composition is gravel-bearing crushed stone silt, grayish brown to yellowish brown, slightly dense to dense, and slightly wet, with plastic silt. The mother rock of the crushed stone consists of siltstone, sandstone, and carbonaceous shale. The landslide has multiple sliding surfaces. The upper sliding surface is the contact zone between the Quaternary overburden layer and the fragmented rock mass of the bedrock, with a buried depth of 20 to 30 m and a sliding zone thickness of 1 to 1.5 m. The lower sliding surface is the contact zone between the fragmented rock mass and the bedrock (sandstone), with a buried depth of 10 to 30 m as well as a sliding zone thickness of generally 1 to 1.5 m, reaching a maximum of 3.1 m. It poses a serious threat to both the safety of the Three Gorges waterway and the lives and property of residents along the route [48,49,50]. Therefore, the Xinpu landslide in the Three Gorges Region of China was selected as the on-site monitoring point in this paper and multi-field data monitoring experiments were conducted on the underground rock and soil mass of the landslide so as to provide essential references for landslide hazard prevention and ecological environment governance in the region.
Based on the unique geographical location and geological conditions of the Xinpu landslide, numerous landslide monitoring studies have been conducted based on the ‘Space-Sky-Ground-Underground’ monitoring technology, which provides significant references for the stability evaluation of the Xinpu landslide (Figure 18). To enhance the monitoring performance of InSAR technology in areas with dense vegetation (such as Xinpu), Zheng et al. introduced a framework using interferometric synthetic aperture radar (InSAR). This framework utilizes ICA-assisted intermittent MT-InSAR methods to achieve time-series deformation measurement in areas with seasonal vegetation changes. Compared to conventional methods, this method can achieve an average RMS improvement of up to 46.2% [51], which effectively improves the monitoring accuracy of InSAR technology in vegetation-covered areas. Thereby, real-time monitoring of landslides can be achieved. Chen et al. conducted space-based monitoring of the Xinpu landslide using the UAV LiDAR topographic survey. The evolution process of the Xinpu landslide was divided into four stages based on the UAV monitoring data, including the shallow creep-crack extension stage, overall sliding-front edge bending deformation stage, sliding bending-front edge shearing stage and ancient landslide sliding-revival stage [52]. This research provides an important reference for the genetic mechanism and early warning prevention of single landslides on reservoir banks such as the Xinpu landslides. To reduce the impact of atmospheric changes on ground-based interferometric synthetic aperture radar (GB InSAR) deformation monitoring, Bai et al. modelled the atmospheric phase based on the actual geographical environment of the Xinpu landslide. They proposed a two-stage semi-empirical model to estimate the atmospheric phase of each layer to eliminate atmospheric phase changes in the spatial domain. It was proven to reduce the atmospheric phase error by up to 2 mm compared to conventional methods, providing an important reference for the foundation monitoring of the Xinpu landslide [53].
In addition, the author’s research group conducted a large number of experimental studies on the underground monitoring of the Xinpu landslide, especially on strain and temperature monitoring. Landslides are constantly influenced by the surrounding air temperature, water temperature, and ground surface temperature, which, in turn, cause changes in their internal temperature. Usually, the shallow layer of landslides is greatly affected by the temperature, while the deep layer is relatively less affected. The temperature field can affect the permeability coefficient and strength of the slope soil, which can induce slope instability. Habibagahi [54] found that as the temperature increases, the viscosity of pore water in the soil decreases, and the permeability coefficient of the soil increases with the increase in temperature. Wang et al. [55] held the belief that the primary determinants of water movement in soil are the saturation water conductivity and the matrix potential at the wetting front. Furthermore, the authors posited that as temperature rises, the soil water conductivity also exhibits an upward trend. Moritz et al. [56] conducted extensive field tests to simulate the effects of seasonal cycling and heating on clay properties over an extended period. Their findings reveal several key insights: Firstly, prolonged exposure to high temperatures causes soil to undergo hardening, altering its physical characteristics. Secondly, as temperatures rise, the soil’s moisture content diminishes, leading to an enhancement in its strength properties. Lastly, the study highlights an intriguing trend in the shear strength of clay, which initially decreases with temperature elevation before reversing course and increasing. These observations offer valuable insights into the dynamic behaviour of clay under varying thermal conditions. In short, the temperature will lead to changes in the physical parameters inside the landslide such as the lithology of rock and soil mass, the coupling state of rock and soil mass, and the seepage coefficient. In addition, it will also affect the monitoring accuracy of sensors buried in the landslide. To verify the applicability and accuracy of multi-field monitoring sensors for landslides, experimental research on underground temperature and strain monitoring of landslides was carried out according to the terrain and topography of the Xinpu landslide. Three in-situ boreholes were drilled in the experiment. PT100 temperature sensors and FBG sensors were used as supplementary means of temperature and strain monitoring. The actual on-site layout plan is shown in Figure 19.
The temperature field changes from 0:00 on 2 February 2021, to 23:00 on 10 February 2022, obtained in the experiment are shown in Figure 20. The temperature changes at 0.2 m, 0.4 m, 0.6 m, 0.8 m, 1.0 m, 1.2 m, 1.4 m, 1.6 m, 1.8 m, and 2.0 m underground of landslides over time were obtained. It can be concluded that external climate change significantly impacts the temperature field of the underground rock and soil of landslides, especially for the shallow soil of landslides. The effect of sunshine will cause a significant change in its temperature. With the increase in temperature, the viscosity of pore water in the soil decreases, and the permeability coefficient of the soil will increase with the temperature rise. Thus, the strength of the shallow soil of landslides will be reduced. The permeability of soil is influenced by lithology and burial conditions. Based on the previous research of the research group, the groundwater level in this area is about 13 m, and the permeability coefficient of the silty clay containing detritus in the shallow layer (<1 m) is 1.2107–1.377 × 105 m/s; the permeability coefficient of sandy soil and sandstone below the groundwater table is 1.2107–5.817 × 105 m/s [57]. In addition, data analysis shows that the rock and soil below 1.0 m of the landslide surface are less affected by climate, with a smaller annual temperature change amplitude than that of rock and soil at 0–1 m depth. Moreover, the further away from the surface, the more minor the temperature fluctuation. The maximum annual temperature difference between the rock and soil at 0.2 m of the surface exceeds 30 °C, while it is lower than 15 °C between the rock and soil at 2.0 m of the surface. Frequent rainfall events occurred in this area from July to September 2021, which led to an increase in pore water pressure. Meanwhile, it is important to consider the influence of temperature rise on the viscosity of pore water. At this time, the landslide rock and soil mass were in a state of poor stability. Therefore, the variation pattern of shallow temperature in landslides can also reflect, to some extent, the changes in climate such as sunshine and rainfall.
The deformation field of landslides is the main controlling factor affecting the evolution state of slopes, which is a coupled field generated by the joint action of temperature, seepage, and other multiple fields. Figure 21 clearly shows the strain changes in the deformation field inside the Xinpu landslide. Two sets of densely distributed grating strings with a spacing of 2 m were selected for the monitoring of strain changes at different depths (0–16 m and 16–32 m from the surface). According to the analysis of the cloud chart, the optical cable used for shallow strain monitoring of landslides broke at a depth of 7 m on October 7, resulting in a lack of subsequent monitoring data for rocks and soil between 7 and 16 m from the ground surface. It is inferred that the breaking of the fiber optic is a consequence of the landslide deformation rather than incorrect deployment. This assertion is strongly supported by three key observations: (1) The FBG in-situ inclinometer array suggests that the potential sliding surface is likely situated at depths of 5 m and 10 m. However, due to the limitation of the 5m spatial sampling interval of the inclinometer sensor, this array can only provide an approximate location of the sliding surface. (2) Highly detailed deformation (strain) data are obtained by FBG strain sensing cables with a 2 m spatial resolution in Figure 21. The potential sliding surface position identified through this strain profile aligns closely with the inclinometer findings, pinpointing it to approximately 7 m and 12 m depths. (3) Additionally, the stratigraphic material composition revealed by our drill core samples serves as further evidence to validate the location of the potential sliding surface [57]. The frequent rainfall from July to September 2021 led to an increase in pore water pressure and resulted in a decrease in the effective stress of the soil, which weakened its ability to resist deformation. In addition, the Three Gorges Reservoir Area begins to store water at the end of every September, and the rise in the reservoir water level also increases the buoyancy of the landslide slope angle, which significantly reduces the overall stability of the landslide. As shown in Figure 21, the stress borne by the landslide soil at a depth of 6 m from the ground surface on the day of the optical cable fracture reached over 3800 με, indicating greater stress at the 7 m depth where the fracture occurred. In summary, we infer that the soil at a depth of 12 m from the ground surface became viscous due to rainfall and accumulated water, forming a sliding zone. The area at a depth of 7 m from the ground surface contains rubble and soil, and the shear force exerted by the sliding zone on the soil caused irreversible mechanical damage to the monitoring optical cable above, leading to the breaking of the optical cable. Therefore, it is necessary to consider the particular climate conditions of the subsequent landslide monitoring in the Three Gorges Region. By combining conventional techniques with drilling data, InSAR technology and conventional sensors can estimate the deformation value of landslides to grasp the balance point between sensor sensitivity and tensile capacity. In this way, the strength of sensors can be increased and small deformations inside the landslide can be accurately captured to ensure real-time acquisition of monitoring data from multiple underground landslide sites.

5. Conclusions

(1) Based on the bibliometric analysis, a statistical analysis was conducted on the theme, keywords, publication time, authors, and other factors of papers in landslide monitoring technology. It was found that comprehensive economic strength is an important factor that affects the progress of landslide hazard monitoring research in various countries. With the continuous strengthening of disaster prevention and reduction awareness, research in landslide monitoring technology has been increasingly valued by scholars from different countries in the past decade. According to the keyword analysis of the literature retrieved from the Web of Science core database, the deformation monitoring of landslides is a core element in the research of landslide hazards. Based on the high-frequency keywords of landslide monitoring technology from the Web of Science database, it can be concluded that the currently most widely used technologies in landslide monitoring are GNSS, InSAR, and GIS. Among them, the application frequency of GNSS and InSAR is more than twice that of GIS, indicating that 3S is still one of the most critical technologies in the field of landslide monitoring. In addition, due to the unique advantages of high precision, large scale, and full real-time monitoring, DFOS and InSAR will play an increasingly important role in landslide monitoring. It is necessary to accelerate future research and improvement in various advanced landslide monitoring technologies. After that, the impact of crucial control factors such as deformation, rainfall, and vibration on landslides can be accurately controlled so as to provide a vital identification basis for the early warning and prevention of landslide hazards.
(2) The ‘Space-Sky-Ground-Underground’ comprehensive monitoring system of landslide proposed in this study comprehensively utilizes the advantages of different dimensional monitoring technologies. Through this comprehensive monitoring system, it is possible to overcome the limitations of a single monitoring technology in landslide hazard monitoring to further achieve comprehensive, high-precision, and continuous monitoring of landslides. To be specific, InSAR in space-based monitoring is easily affected by atmospheric visibility and vegetation cover; a UAV in sky-based monitoring is generally challenging to operate in harsh climates such as heavy rainfall and typhoons; three-dimensional laser scanning in ground-based monitoring is prone to errors in landslide monitoring with complex terrain structures; and DFOS in underground-based monitoring often has the problem of sensors exceeding the range and being easily damaged during large deformation of landslides. On this basis, advanced machine learning algorithms such as artificial neural networks, support vector machines, and random forests are combined to perform denoising, clustering, and other processes of analysis to improve the utilization rate of the massive monitoring data. Therefore, these can provide a reference basis for predicting the development trend of landslide hazards.
(3) According to the on-site measurement results of the Xinpu landslide, it can be concluded that the temperature of the shallow soil is significantly affected by the external climate, and will change accordingly with seasonal changes. The deeper the rock and soil inside the landslide, the less affected it is by sunlight. When it reaches a certain depth, the rock and soil are in a relatively constant temperature state. At this time, if a significant temperature anomaly is found in the area during monitoring, it can be determined preliminarily that there is a certain degree of relative sliding of the rock and soil nearby. Therefore, more attention should be paid to this area during subsequent monitoring. In addition, during the rainy season from July to September each year in the monitoring area, the rainfall not only increases the water content of the rock and soil but also imposes significant loads on the shallow layers of landslides. It leads to a substantial decrease in the overall stability coefficient of the landslide distribution area and makes the landslide hazard enter a prone period. Therefore, during this period, the frequency of landslide monitoring should be increased, and exceptional optical cables with strong tensile and shear capabilities should be selected for combined monitoring to ensure the real-time acquisition of monitoring data from multiple underground sites of landslides. This ensures that immediate warnings can be issued before landslides occur to minimize the impact of landslide hazards. In addition, to improve the monitoring accuracy and reliability of large-scale landslides, it is necessary to comprehensively utilize the advantages of various mechanical, optical, and electrical monitoring technologies to build a comprehensive system of landslide monitoring and ensure the overall stability of the landslide system. However, considering the impact of extreme weather conditions such as typhoons and short-term heavy rainfall on monitoring systems, it is necessary to develop distributed, robust, durable, and stable landslide sensing monitoring technologies vigorously in the future. In particular, it is essential to strengthen the research and development of installation for deep landslide sensors to meet the survival rate of sensors under extreme conditions. Meanwhile, in actual monitoring, the reliability of monitoring data should be ensured by combining wavelet denoising and clustering analysis. Specifically, in monitoring landslides in the reservoir area, the time delay effect of landslide deformation caused by periodic changes in factors such as reservoir water level and rainfall needs to be considered. In the next step of reservoir bank landslide monitoring, the periodic displacement and trend displacement of the landslide should be comprehensively regarded. A disaster warning model based on full-cycle data machine learning should be established based on this. Through long-term data accumulation, the probability and degree of landslide hazard should be evaluated, and the potential instability of landslides in the region should be evaluated in the future. Based on this, landslide hazard monitoring and warning accuracy should be maximized to provide crucial technical support and a reference basis for disaster prevention and control.

Author Contributions

G.C. and L.Z. designed the overall framework and conceived the idea of this paper; H.Z., Q.Y., Y.L. and P.S. conducted tests and organized test information; G.C., H.Z. and J.W. completed the analysis and summary of the test progress and result; B.S. provided some suggestions on the structure of the paper; and G.C., H.Z. and Y.W. wrote the paper. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (42377200); the Central Government Guided Local Science and Technology Development Fund (226Z5404G); the Natural Science Foundation of Hebei Province, China (D2022508002); the Fundamental Research Funds for the Central Universities (3142019011); the Hebei IoT Monitoring Engineering Technology Innovation Center (21567693H); the Engineering Research Center of Zero-carbon and Negative-carbon Technology in Depth of Mining Areas, Ministry of Education (China University of Mining and Technology) (2023-0014); and the Opening fund of State Key Laboratory of Geohazard Prevention and Geoenvironment Protection (SKLGP2023K021).

Data Availability Statement

The data are available from the corresponding author on reasonable request.

Acknowledgments

The authors would like to thank Honghu Zhu and Xiao Ye for their assistance in processing data and images, and thank Long Chen for providing more detailed geological data and drone monitoring results image of the monitoring area.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Typical landslide hazards in various countries around the world.
Figure 1. Typical landslide hazards in various countries around the world.
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Figure 2. Statistics on landslide hazard in China from 2012 to 2022.
Figure 2. Statistics on landslide hazard in China from 2012 to 2022.
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Figure 3. Analysis of the steps for bibliometric screening of landslide literature.
Figure 3. Analysis of the steps for bibliometric screening of landslide literature.
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Figure 4. Annual publication statistics of landslide monitoring technology.
Figure 4. Annual publication statistics of landslide monitoring technology.
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Figure 5. Keyword association analysis graph for landslide monitoring technology.
Figure 5. Keyword association analysis graph for landslide monitoring technology.
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Figure 6. Analysis of popular technologies for landslide monitoring.
Figure 6. Analysis of popular technologies for landslide monitoring.
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Figure 7. Statistics on the volume and relevance of publications in various countries.
Figure 7. Statistics on the volume and relevance of publications in various countries.
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Figure 8. Atlas of association analysis of posts by various countries.
Figure 8. Atlas of association analysis of posts by various countries.
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Figure 9. Monitoring system for landslides in ‘Space-Sky-Ground-Underground’.
Figure 9. Monitoring system for landslides in ‘Space-Sky-Ground-Underground’.
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Figure 10. The development history of global satellite navigation systems.
Figure 10. The development history of global satellite navigation systems.
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Figure 11. Landslide monitoring based on SBAS-InSAR and PS-InSAR [34]: (a) spatial position of landslides; (b–e) time-series points of two large landslides; (f,g) histogram of deformation rate, the normal density function is fit by red dotted lines.
Figure 11. Landslide monitoring based on SBAS-InSAR and PS-InSAR [34]: (a) spatial position of landslides; (b–e) time-series points of two large landslides; (f,g) histogram of deformation rate, the normal density function is fit by red dotted lines.
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Figure 12. Establishment of a 3D landslide model based on UAV technology [37].
Figure 12. Establishment of a 3D landslide model based on UAV technology [37].
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Figure 13. Technologies and instruments related to ground monitoring.
Figure 13. Technologies and instruments related to ground monitoring.
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Figure 14. Analysis of landslide scale based on 3D laser scanning technology [40].
Figure 14. Analysis of landslide scale based on 3D laser scanning technology [40].
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Figure 15. Deep optical fiber monitoring profile of landslides.
Figure 15. Deep optical fiber monitoring profile of landslides.
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Figure 16. Deep optical fiber monitoring profile of landslides [44].
Figure 16. Deep optical fiber monitoring profile of landslides [44].
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Figure 17. The geographical location of the Xinpu landslide.
Figure 17. The geographical location of the Xinpu landslide.
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Figure 18. Monitoring of the Xinpu landslide based on the ‘Space-Sky-Ground-Underground’ monitoring technology.
Figure 18. Monitoring of the Xinpu landslide based on the ‘Space-Sky-Ground-Underground’ monitoring technology.
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Figure 19. On-site layout of landslide monitoring system.
Figure 19. On-site layout of landslide monitoring system.
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Figure 20. Landslide-temperature-monitoring data.
Figure 20. Landslide-temperature-monitoring data.
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Figure 21. Landslide-deformation-monitoring layout and cloud map.
Figure 21. Landslide-deformation-monitoring layout and cloud map.
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Table 1. High-frequency keyword statistics.
Table 1. High-frequency keyword statistics.
NumberKeywordOccurrencesTotal Link Strength
1Landslide7153259
2Deformation2741563
3Model2731450
4Rainfall2341332
5Failure1981132
6Monitoring2021111
7InSAR1691076
8Stability164882
9Prediction124702
10Interferometry124676
Table 2. Characteristics and parameters of various landslide monitoring UAVs.
Table 2. Characteristics and parameters of various landslide monitoring UAVs.
Model of UAVFeaturesAdvantageDisadvantageParameter
Fixed-wing UAVWater 16 02005 i001The wings remain fixed and rely on the wind passing through them to provide lift. UAVs need to run up during takeoff and glide down during landing.Long battery life and high load capacityHigh requirements for takeoff and landing site conditionsRange:
30–80 h
Scale:
light, small, medium
Multirotor UAVWater 16 02005 i002A UAV that generates upward lift from its propeller and drives the entire UAV to fly. This type of UAV is divided into multi-rotor UAVs, helicopters, autorotation rotor UAVs, etc.Easy to operate and low site requirementsShortest battery life and low load capacityRange:
0.3–1 h
Scale:
light, small, medium
AirshipWater 16 02005 i003The airship is an aircraft lighter than air, similar to hot air balloons, but with propulsion and control devices for flight status. It is mainly used in fields such as air surveillance, patrol, relay communication, etc.Low cost, low energy consumption, and long battery lifePoor mobility and load-bearing capacityRange:
120–720 h
Scale:
large
Flapping UAVWater 16 02005 i004The flight patterns of birds and insects inspire this aircraft type. The fuselage has deformable tiny wings and utilizes the aerodynamic principles of unstable airflow and biomimetic actuators to complete agile flight monitoring tasks efficiently.Lightweight, flexible, and easy to carryLimited arm lengthRange:
About 1.5 h
Scale:
miniature
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MDPI and ACS Style

Cheng, G.; Zhang, H.; Wang, Y.; Shi, B.; Zhang, L.; Wu, J.; You, Q.; Li, Y.; Shi, P. Research Trends and ‘Space-Sky-Ground-Underground’ Monitoring Technology Analysis of Landslide Hazard. Water 2024, 16, 2005. https://doi.org/10.3390/w16142005

AMA Style

Cheng G, Zhang H, Wang Y, Shi B, Zhang L, Wu J, You Q, Li Y, Shi P. Research Trends and ‘Space-Sky-Ground-Underground’ Monitoring Technology Analysis of Landslide Hazard. Water. 2024; 16(14):2005. https://doi.org/10.3390/w16142005

Chicago/Turabian Style

Cheng, Gang, Haoyu Zhang, Ye Wang, Bin Shi, Lei Zhang, Jinghong Wu, Qinliang You, Youcai Li, and Peiwei Shi. 2024. "Research Trends and ‘Space-Sky-Ground-Underground’ Monitoring Technology Analysis of Landslide Hazard" Water 16, no. 14: 2005. https://doi.org/10.3390/w16142005

APA Style

Cheng, G., Zhang, H., Wang, Y., Shi, B., Zhang, L., Wu, J., You, Q., Li, Y., & Shi, P. (2024). Research Trends and ‘Space-Sky-Ground-Underground’ Monitoring Technology Analysis of Landslide Hazard. Water, 16(14), 2005. https://doi.org/10.3390/w16142005

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