Next Article in Journal
Developments in Scope and Availability of HF Radar Wave Measurements and Robust Evaluation of Their Accuracy
Next Article in Special Issue
Rapid Mapping of Landslides Induced by Heavy Rainfall in the Emilia-Romagna (Italy) Region in May 2023
Previous Article in Journal
CCRANet: A Two-Stage Local Attention Network for Single-Frame Low-Resolution Infrared Small Target Detection
Previous Article in Special Issue
Deep Learning and Machine Learning Models for Landslide Susceptibility Mapping with Remote Sensing Data
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Remote Sensing
Volume 15
Issue 23
10.3390/rs15235538
remotesensing-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Deformation Behavior and Reactivation Mechanism of the Dandu Ancient Landslide Triggered by Seasonal Rainfall: A Case Study from the East Tibetan Plateau, China

by
Sanshao Ren
1,2,
Yongshuang Zhang
1,2,*,
Jinqiu Li
1,2,
Zhenkai Zhou
3,
Xiaoyi Liu
4 and
Changxu Tao
1
1
School of Engineering and Technology, China University of Geosciences, Beijing 100083, China
2
Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Sciences, Shijiazhuang 050061, China
3
Institute of Geomechanics, Chinese Academy of Geological Sciences, Beijing 100081, China
4
China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2023, 15(23), 5538; https://doi.org/10.3390/rs15235538
Submission received: 27 October 2023 / Revised: 24 November 2023 / Accepted: 26 November 2023 / Published: 28 November 2023
(This article belongs to the Special Issue Remote Sensing Techniques for Landslides Studies and Their Hazards Assessment)
Browse Figures
Versions Notes

Abstract

:
In recent years, numerous ancient landslides initially triggered by historic earthquakes on the eastern Tibetan Plateau have been reactivated by fault activity and heavy rainfall, causing severe human and economic losses. Previous studies have indicated that short-term heavy rainfall plays a crucial role in the reactivation of ancient landslides. However, the deformation behavior and reactivation mechanisms of seasonal rainfall-induced ancient landslides remain poorly understood. In this paper, taking the Dandu ancient landslide as an example, field investigations, ring shear experiments, and interferometric synthetic aperture radar (InSAR) deformation monitoring were performed. The cracks in the landslide, formed by fault creeping and seismic activity, provide pathways for rainwater infiltration, ultimately reducing the shear resistance of the slip zone and causing reactivation and deformation of the Dandu landslide. The deformation behavior of landslides is very responsive to seasonal rainfall, with sliding movements beginning to accelerate sharply during the rainy season and decelerating during the dry season. However, this response generally lags by several weeks, indicating that rainfall takes time to infiltrate into the slip zone. These research results could help us better understand the reactivation mechanism of ancient landslides triggered by seasonal rainfall. Furthermore, these findings explain why many slope failures take place in the dry season, which typically occurs approximately a month after the rainy season, rather than in the rainy season itself.

1. Introduction

There have been numerous giant ancient landslides triggered by paleo-earthquakes on the eastern Tibetan Plateau [1,2,3], and the platforms of these landslides are important living sites for people in high mountain canyon areas. However, in recent decades, many ancient landslides have been reactivated by fault activity and heavy rainfall (Figure 1), causing severe human and economic losses [4,5]. In 2018, the Jiangdingya ancient landslide in Zhouqu County, Gansu Province, was reactivated by heavy rainfall, blocking the Bailong River, flooding the upstream villages and towns, and destroying roads [6]. In 2018, influenced by the continuous cumulative rainfall in the previous 14 days, a giant ancient landslide in Boli Village, Yanyuan County, was reactivated, damaging 186 houses and causing significant economic losses [7]. In 2021, heavy rainfall led to the reactivation of the Moli landslide in Guoye Township, Zhouqu County, Gansu Province, causing the deformation of a large number of houses and threatening the lives of more than 1000 people [8]. In 2021, the Aniangzhai ancient landslide in Danba County, Sichuan Province, China, induced by heavy rainfall, was reactivated, and the Dadu River was dammed [9].
Currently, there are still numerous ancient landslides on the Tibetan Plateau that are slowly moving, with minimal displacement during the dry season but accelerated sliding during the rainy season [10,11]. These ancient landslides may also slide intermittently for several decades or even centuries [4]. Alternatively, they may experience rapid acceleration within a short period of time and catastrophic failure, leading to extensive destruction and fatalities [12,13,14]. Previous studies have indicated that short-term heavy rainfall is a key factor in the reactivation of ancient landslides [6,15], but the deformation behavior and reactivation mechanism of seasonal rainfall-induced ancient landslides, which are prerequisites for mitigating the hazards of landslide reactivation, are still not well understood [16,17,18].
Interferometric synthetic aperture radar (InSAR) is a measurement technique based on active microwave remote sensing that has been widely applied in the study of active landslides [19]. InSAR technology has the capability to capture ground deformations at the centimeter to millimeter level [20,21,22,23]. Time-series InSAR methods, such as the small baseline subset InSAR (SBAS-InSAR), can trace the historical deformation processes of landslides over time [23,24]. The SBAS-InSAR technique, with its excellent deformation-detection ability, has been used for studying surface deformation, especially landslide detection and reconstruction of the landslide evolution process [25], offering valuable information for analyzing the patterns and causes of active landslides. In recent years, small unmanned aerial vehicles (UAVs) have been increasingly utilized in the study of individual landslide deformations. They can acquire high-resolution optical orthophotos and precise digital surface model (DSM) data [26].
In general, ancient landslides are reactivated along pre-existing slip zones that have reached the residual state [27,28]. Therefore, understanding the mechanism of residual strength evolution in slip zones is a prerequisite for the stability assessment and engineering design of mitigation measures for ancient landslides [29,30,31]. The ring shear apparatus can reach almost unlimited shear displacements and is, therefore, widely used to assess the residual strength and resistance of soils [32,33].
In this paper, we utilized the Dandu ancient landslide, which is located upstream of the Xianshui River on the eastern Tibetan Plateau, as a typical case. Field surveys, unmanned aerial vehicle (UAV) mapping, ring shear experiments, and InSAR deformation monitoring were employed to study the deformation behavior and reactivation mechanism. The study results are important references for the disaster risk prevention of ancient landslide reactivation on the eastern Tibetan Plateau.

2. Study Area

2.1. Geological Setting

The Dandu landslide is situated in Luhuo County, Sichuan Province, China, on the eastern margin of the Tibet Plateau, upstream of the Xianshui River, at 100°27′14.00″E, 31°33′16.40″N. The G317 national road passes over the front edge of the landslide. The elevation of the surrounding mountain tops in the study area ranges from 3700 to 4000 m, and there is a relative elevation difference of 800–1000 m from the mountain peaks to the valley.
The Xianshui River active fault, the southwestern boundary fault of the Bayan Har block [34], passes through the middle of the landslide (Figure 2). The Xianshui River Fault, the most active fault on the Tibetan Plateau, is a left-trending strike-slip fault with a total length of approximately 350 km, an overall strike of 320~330°, and an average sliding rate of 10 mm/a [34,35,36]. Since 1725, the Xianshui River Fault has been involved in a total of 9 earthquakes with magnitudes higher than Ms 7.0, as well as 17 earthquakes with magnitudes of Ms 6–6.9 [35]. Notable examples include the Ms 7.5 earthquake in 1816 and the Ms 7.6 earthquake in Fuhuo County in 1973 [35,36]. These historical earthquakes have triggered numerous large-scale ancient landslides that are similar to the Dandu landslide [1].
The climate type in the study area is sub-humid, which is typical in the Tibet Plateau, as the dry and rainy seasons are distinct. The average annual precipitation in the region is 672.8 mm. The majority of rainfall occurs in the rainy season, which spans from May to September and accounts for approximately 86.4% of the average annual precipitation [1,37].

2.2. Features of Ancient Landslide

The Dandu ancient landslide was a rockslide triggered by a historic earthquake. In plan view, the ancient landslide has a “long-tongued” shape, and its rear edge exhibits a “crown” geomorphology [38,39]. The longitudinal length of the landslide measures approximately 1180 m, while the average width is approximately 650 m. The total surface area of the landslide is about 70 × 104 m2. The thickness of the landslide mass ranges from 20 to 30 m, for a total volume of approximately 1600 × 104 m3. The profile of the landslide is characterized by stepped slopes, with an inclination ranging from 10° to 15°.
The lithology of the Dandu landslide area consists of basalt, sandstone, limestone, and slate of the Rugenian formation (T3r2). The origin of the strata is 300°∠75°, which was affected by the fault; the rock structure was broken, and the slickensides of the bedrock formed by extrusion and shearing are obvious. The lithology of the landslide mass is a mixture of soil and rock, with 30% to 60% gravel content and a gravel grain size of 5–20 cm. The lithology of the gravel is metamorphic sandstone and limestone, and most of the basalt in the landslide has been weathered into soil. Giant boulders, up to 6 m in diameter, are distributed in the channel at the leading edge of the landslide and in the middle of the landslide, and the boulder lithology is mainly weather-resistant limestone.

3. Materials and Methods

3.1. Field Investigation and UAV Photography

A field investigation was conducted to observe and study the deformation features, such as cracks and scarps, of the Dandu landslide. Additionally, the field characteristics of the slip zone soil were examined. The orthophotos were taken using a UAV (DJI Mavic 3E, manufactured by DJI Innovation Technology Co. in Shenzhen, China). The DJI Mavic 3E was equipped with a wide-angle camera with an effective pixel of 20 million, and it adopted network RTK for precise positioning, supporting high-precision, high-efficiency surveying and mapping operations (Figure 3a). The South Surveying and Mapping Company’s reference station was used, and the UAV was 300–2000 m from the reference station during the flight. On 18 October 2022, four flights were conducted at a flight altitude of 300 m and a flight area of about 3.5 km2, obtaining a total of 480 images (Figure 3b), with photo resolutions of 5472 × 3078. The planned routes have a 70 percent longitudinal overlap and a 50 percent transverse overlap. Furthermore, a digital surface model (DSM) was created using the DJI Smart Map (3.7.0) software (Figure 3c,d). The DSM, with a resolution of 0.5 m, synthesized the collected data and provided a comprehensive understanding of the morphological characteristics of the landslide and its recent deformation behavior.

3.2. Deformation Monitoring by InSAR

Taking into account the well-developed summer grasslands and shrubbery on the surface of the Dandu landslide, this study utilized the computation method of SBAS-InSAR. Compared to traditional persistent scatterer (PS-InSAR) algorithms, SBAS-InSAR technology utilizes short temporal and spatial baseline sets, improving the decorrelation issues caused by a single master image and enhancing the spatial coverage density of the measurement points [22]. Simultaneously, SBAS-InSAR technology holds advantages for measuring rapid deformations in landslides over short periods. The basic principle was as follows [40].
(1)
N + 1 views of SAR images covering the study area were obtained, acquired at times: t0, t1, …, tn. Suitable spatial–temporal baseline thresholds were set to register the slave images with the master images. The interferometric pairs were obtained accordingly, at a number M.
(2)
M pairs of interferometric pairs were used to generate time-series interferograms for multi-master images.
(3)
The regression algorithm was applied to the deformation dataset to estimate and remove the elevation residuals; the residual phases, such as noise and atmospheric delays, were separated according to the selected combined filter methods.
(4)
The deformation time series was reconstructed using the small baseline set time series deformation solution model. With t0 as the reference moment, the differential interferometric phase was acquired during data processing with observed quantities. Time ti was the relative time i to t0 (0 < i < N) and obtained unknown quantities, and the interferometric phase value of the image element (r, c) was:
δ φ i r , c = φ t B , r , c − φ t A , r , c ≈ 4 π λ d t B , r , c − d t A , r , c
where λ is the radar wavelength and d(tB, r, c,) and d(tA, r, c,) are the deformation of pixels traveling at moments tB and tA along the radar line-of-sight direction, respectively.
The deformation characteristics of the Dandu landslide were analyzed using a total of 149 Sentinel-1A SAR images acquired in ascending geometry. These images were collected from 7 January 2018 to 24 December 2022. The detailed parameters of the SAR dataset are shown in Table 1. The SAR image from 9 October 2020 was chosen as the master image. The remaining SAR images were registered with the master image to ensure azimuthal registration accuracy to within one-thousandth of a pixel. The spatial vertical baseline lengths between SAR data were mostly within 200 m, with the longest being 266.5 m. The time intervals for InSAR computations ranged from a maximum of 48 d to a minimum of 12 d, with a total of 441 interferometric data pairs (Figure 4).

3.3. Ring Shear Test of Slip Zone Soil

In this study, an ARS-3 ring shear machine from Wille-Geotechnik, Germany [41], was utilized. The machine featured a circular shear box with an inner diameter of 50 mm, an outer diameter of 100 mm, and a height of 25 mm. The normal stress and the torque were controlled by the servo-actuated loading piston and the servo hydraulic motor, respectively. The maximum axial pressure of the machine was 10 kN; the maximum shear stress was 1000 kPa; and the maximum shear rate was 100 mm/min. Transducers installed in the pressurized system measured the shear stress and normal stresses, and the test data was automatically collected by a data acquisition device and transferred to a computer (Figure 5). During shearing, the hanging wall was fixed, while the foot wall with the shear box led to a shear surface in the vicinity of the shearing gap.
Field investigations revealed that the Dandu landslide sheared out from the bed of the Xianshui River, where slip zone soil can be seen. The basic physical properties of the slip zone soil are presented in Table 2 (Figure 6). The slip zone is predominantly composed of gravelly, gray-green silt-clay, which is a result of the compression, kneading, and argillization of basalt and sericite slate. The thickness of the slip zone measures between 0.2 and 0.3 m and exhibits clear slickensides (Figure 7c,d). Additionally, there is a significant amount of groundwater outflow along the slip surface (Figure 7e). The dry slip zone is characterized by its dense and hard composition. However, the water-soaked slip zone has a mud-like soil with considerably low strength.
In this study, a total of eight groups of remolded samples were tested, each with different normal stresses. The particle size of the samples used in the tests was less than or equal to 2 mm. Four samples were tested at natural moisture content (10%), and the other four samples needed to be saturated. The water content of the saturated samples was approximately 21%. After saturation was complete, the sample was placed in the shear box. Prior to each shear test, the samples were consolidated for 24 h, then sheared to reach their residual states at a shear rate of 0.2 mm/min. The ring shear tests were conducted following the standard geotechnical test methods (GB/T50123-2019) [42]. The specific testing scheme for the ring shear tests is provided in Table 3.

4. Results

4.1. Reactivation Features and Zonation of the Landslide

The Dandu ancient landslide can be divided into three zones based on deformation and cumulative displacement (Figure 7a and Figure 8). Zone Ⅰ, an ancient landslide, was initially triggered by a historic earthquake, and the main scarp is clear. Zone II is a secondary landslide that formed due to the reactivation of the ancient landslide. Zone II can be further divided into two subzones, namely, II-1 and II-2.
Zone II-1 is located on the left side of the front edge of the ancient landslide and is a rotational landslide. The landslide has a longitudinal length of 360 m and a transverse width of 300 m, with an area of about 9 × 104 m2, a thickness of 15 to 25 m, and a volume of about 150 × 104 m3 (Figure 7). Currently, significant deformation can be observed in Zone II-1, characterized by a steep scarp measuring 10–15 m in height. Additionally, numerous cracks have developed in this zone (Figure 7b, Figure 8 and Figure 9).
Zone II-2, located on the right side of the front edge of the ancient landslide, is relatively stable compared to Zone II-1. The longitudinal length of the landslide is 540 m, and the transverse width ranges from 250 to 320 m. The total area affected by the landslide is approximately 13 × 104 m2. The thickness of the landslide ranges from 15 to 25 m, and the volume of the landslide is estimated to be around 200 × 104 m3.
Zone III is a tertiary landslide formed by reactivation along the front edge of Zone II-1, with a scarp height of 5 m, a longitudinal length of 100 m, a transverse width of 100~250 m, an area of about 1.5 × 104 m2, a thickness of about 20m, and a landslide volume of about 30 × 104 m3 (Figure 7a,b and Figure 8). The deformation was strongest in Zone III, where there are several tension cracks 10–30 cm wide and extending over 10 m in the middle and lower parts of the landslide, which has led to ground uplift and destruction of the retaining wall at the landslide toe. The original G317 road also suffered significant damage due to landslide deformation. As a result, a new G317 road had to be constructed on the opposite bank of the Xianshui River. Currently, zone III is still creeping, squeezing the channel of the Xianshui River, and, as the foot of the landslide continues to be eroded by the river, sliding of the landslide could accelerate and pose a threat to the new G317 road (Figure 7, Figure 8 and Figure 9).

4.2. Deformation Characteristics Monitored by InSAR

The SBAS-InSAR results in Figure 7 indicate deformation rates ranging from −45 to 19 mm/a from 2018 to 2022. The negative values represent deformation, with a moving trend against the sensors. It can be observed that most of the Dandu ancient landslide is in a stable state. However, significant deformations have been observed at the front edge of the landslide, specifically in zone II-1 and zone III, over the past five years. The highest deformation rate is observed in Zone III, with an approximate rate of 45 mm/y. Zone II-1 shows a deformation rate of approximately 25 mm/y. In contrast, the deformations in zone I and zone II-2 were relatively small, both less than 10 mm/y. These deformation characteristics, observed through InSAR monitoring, align well with the findings from field investigations (Figure 7). The reactivation of ancient landslides exhibits multiple periods and multiple zones of deformation, with lower-order sequences showing higher deformation rates and poorer stability (Figure 8 and Figure 10).
The analysis of the Dandu landslide involved selecting profile B-B’ along the main sliding direction (Figure 10). A profile map of the deformation rate was then generated (Figure 11). The results indicate that the deformation rate varies significantly along the sliding direction. The high values, represented by negative values, are primarily concentrated at the front edge of the landslide. This observation suggests that the landslide predominantly exhibits overall tensile sliding.
To further investigate the dynamic behavior of the landslide in response to seasonal changes, we selected three points (labeled 1, 2, and 3) in Zones I, II-1, and III of the landslide (Figure 10 and Figure 11). These points were then subjected to time series analysis using the finite-difference formula to determine the velocity of their movements.
v i = ( d i + 1 − d i ) / ( t i + 1 − t i )
In Equation (2), vi represents the velocity of point P, at time ti, and di and di+1 represent the cumulative displacements of the points at times ti and ti+1, respectively.
The findings indicate that the maximum cumulative displacements observed at points 1, 2, and 3 between 2018 and 2022 were approximately 220 mm, 100 mm, and 20 mm, respectively. These points exhibited movement patterns that followed annual cycles consistently throughout the years.
The sliding motion initiated an acceleration phase with the onset of rainy seasons and significantly decelerated during dry seasons (Figure 12). A substantial amount of deformation was observed from mid-June to mid-December, while minimal displacements occurred during the dry season from mid-December to mid-June. These observations suggest that seasonal rainfall plays a significant role in triggering the reactivation and deformation of the Dandu landslide.
Based on the data presented in Figure 12b, it can be observed that even though the wet season consistently begins in mid-May each year, the acceleration of the landslide does not occur until a few weeks later, typically in early June. However, the specific time lag varies from year to year. The maximum recorded time lags are 24 days, 30 days, 34 days, and 26 days for the years 2018, 2019, 2020, and 2022, respectively (Figure 12b). It should be noted that no significant acceleration was observed in 2021. While it is possible that displacements may occur before the satellites capture the deformation, considering the minimum revisit period of Sentinel-1A datasets, which is 12 days, it can be inferred that the range of time lags fell between 12–24 days, 18–30 days, 22–34 days, and 14–26 days for the years 2018, 2019, 2020, and 2022, respectively.

4.3. Shear Strength of the Slip Zone

The ring shear tests showed that the stress–displacement curves exhibit significant strain-softening characteristics (Figure 13a,b). Shear stress can quickly reach its peak value (usually at 10 mm displacement) with relatively small displacements and then gradually decline to the residual state. Under saturated conditions, the shear strength decreased significantly compared to that under natural conditions (Figure 13c,d). Specifically, the peak internal friction angle decreased from 15.1° to 9.5°, a reduction of 37%. The peak cohesion also decreased from 33.4 kPa to 12.1 kPa, a reduction of 64%. Similarly, the residual internal friction angle decreased from 9.5° to 6.8°, a reduction of 28%, and the residual cohesion decreased from 14.2 kPa to 8.0 kPa, a reduction of 44%. In the saturated state, the shear surface was smoother (Figure 14), indicating that the presence of water not only softened the slip zone, thereby reducing the shear strength, but also promoted the directional arrangement of particles, leading to a reduction in the roughness of the shear surface. The smoother shear surface contributed to lowering the shear strength of the slip zone.

5. Discussion

5.1. Active Faults Are the Driving Forces for the Formation of Landslide Cracks

Previous studies have claimed that creeping of active faults controls the local stress field and affects slope stability [43,44]. On the one hand, the field investigation found that the Xianshui River Fault passes through the middle of the Dandu landslide, and the horizontal slip rate of this fault has, since the Holocene, reached 10–20 mm/a [34,35,36]; its influence on the ancient landslide cannot be ignored. On the other hand, the Xianshui River Fault is prone to frequent earthquakes, with nine earthquakes of magnitudes of Ms ≥ 7.0 occurring along the fault since 1725. Notable examples include the Ms 7.5 earthquake in 1816 and the Ms 7.6 earthquake in Fuhuo County in 1973 [34,35,36]. These seismic events likely contributed to the formation of landslide cracks [45]. Therefore, it can be inferred that landslide cracks, formed by fault creeping and seismic activity, provide a preponderance of infiltration paths for rainwater and are key factors in landslide reactivation (Figure 15).

5.2. Pre-Existing Slip Zones Are the Essence of Landslide Reactivation

Ancient landslides tend to reactivate along pre-existing slip zones that have reached residual states [29,30]. Previous studies have confirmed, via experiments and back analysis, that when a landslide is reactivated, the initiation strength is essentially equal to the residual strength of the slip zone [46,47]. The Dandu landslide has been creeping for at least 10 years [48], and long-term creeping has induced a gradual directional alignment of the soil particles on the slip surface [31], which will lead to a gradual decrease in shear strength (Figure 15).

5.3. Rainfall Is a Trigger Factor for Landslide Reactivation

Rainfall is a crucial trigger factor for landslide reactivation [49]. The InSAR monitoring results show that the deformation characteristics of the Dandu landslide respond very well to the rainy season, although there is some lag. Rainwater infiltrates into the landslide mass through cracks, which not only generates pore water pressure and reduces the effective stresses [50] but, more importantly, severely weakens the shear strength of the slip zone. Field investigations have also revealed the presence of multiple springs in the front portion of the landslide. Particularly, groundwater was observed flowing along the shear outlets of the landslide. The dry slip zone was dense and compact, while the water-soaked slip zone was muddy and exhibited low strength. Ring shear tests further confirmed that the strength of the slip zone in the Dandu landslide decreased significantly with the increasing water content.
It was also found that it takes time for rainwater to infiltrate into the slip zone [44,51], which explains why the response of landslide deformation to rainfall lags by several weeks. Furthermore, this may also explain why many landslides occur during the dry season [44,52], approximately a month after the rainy season, on the eastern margin of the Tibetan Plateau. In the past, it was commonly assumed that these landslides were not directly related to rainfall [53].

6. Conclusions

Taking the Dandu ancient landslide as a typical case, field surveys, ring shear experiments, and InSAR monitoring were performed to investigate the deformation behavior and reactivation mechanism. The following conclusions were drawn:
  • The reactivation of the Dandu ancient landslides exhibits multiple periods and multiple zones of deformation, with lower-order sequences showing higher deformation rates and poorer stability. The deformation rates in zones III, II-1, and I were 40 mm/a, 20 mm/a, and less than 10 mm/a, respectively.
  • The deformation characteristics of the Dandu landslide respond very well to seasonal rainfall. The sliding motion starts to accelerate after the rainy season arrives and decelerates substantially when the dry season arrives. However, this response generally lags by several weeks.
  • The cracks in the landslide, formed by fault creeping and seismic activity, provide pathways for rainwater infiltration, ultimately reducing the shear resistance of the slip zone and causing the reactivation and deformation of the Dandu landslide. Meanwhile, rainfall infiltration takes time, which is why the response of landslide deformation to rainfall lags by several weeks.

Author Contributions

S.R. and Y.Z. framed the study plan and wrote the paper; S.R. and J.L. carried out field surveys; Z.Z. conducted the InSAR data treatment; X.L. processed rainfall data; and C.T. processed some figures. 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 (Grant Nos. 41731287 and 42307229), the Key Laboratory of Airborne Geophysics and Remote Sensing Geology Foundation (No. 2023YFL22), and the Key Research and Development Program in Ningxia, China (No. 2021BEG03118).

Data Availability Statement

The Sentinel-1 datasets used in this study were provided by Copernicus and ESA, and the precipitation data were obtained from the National Meteorological Science Data Center of China free of charge. Other data presented in this paper are available on request from the corresponding author.

Acknowledgments

The authors would like to acknowledge the assistance of Dong Wenping and Guo Changbao in conducting the experiments and field surveys. The figures in this study were created using ArcGIS 10.7 and CorelDRAW X7 software. The authors also thank editors and reviewers for their insightful comments and suggestions.

Conflicts of Interest

The authors declare that there are no conflict of interest.

References

  1. Guo, C.; Montgomery, D.R.; Zhang, Y.; Wang, K.; Yang, Z. Quantitative assessment of landslide susceptibility along the Xianshui fault zone, Tibetan Plateau, China. Geomorphology 2015, 248, 93–110. [Google Scholar] [CrossRef]
  2. Chen, J.; Zhou, W.; Cui, Z.; Li, W.; Wu, S.; Ma, J. Formation process of a large paleolandslide-dammed lake at Xuelongnang in the upper Jinsha river, SE Tibetan Plateau: Constraints from OSL and 14C dating. Landslides 2018, 15, 2399–2412. [Google Scholar] [CrossRef]
  3. Bao, Y.; Zhai, S.; Chen, J.; Xu, P.H.; Zhou, X.; Zhan, J.; Zhang, W.; Zhou, X. The evolution of the Samaoding paleo landslide river blocking event at the upper reaches of the Jinsha river, Tibetan Plateau. Geomorphology 2019, 351, 106970. [Google Scholar]
  4. Lacroix, P.; Handwerger, A.L.; Bièvre, G. Life and death of slow-moving landslides. Nat. Rev. Earth Environ. 2020, 1, 404–419. [Google Scholar] [CrossRef]
  5. García-Delgado, H. The San Eduardo Landslide (Eastern Cordillera of Colombia): Reactivation of a deep-seated gravitational slope deformation. Landslides 2020, 17, 1951–1964. [Google Scholar] [CrossRef]
  6. Guo, C.; Zhang, Y.; Li, X.; Ren, S.; Yang, Z.; Wu, R.; Jin, J. Reactivation of giant Jiangdingya ancient landslide in Zhouqu County, Gansu Province, China. Landslides 2020, 17, 179–190. [Google Scholar] [CrossRef]
  7. He, K.; Ma, G.; Hu, X.; Liu, B. Failure mechanism and stability analysis of a reactivated landslide occurrence in Yanyuan City, China. Landslides 2021, 18, 1097–1114. [Google Scholar] [CrossRef]
  8. Yang, X.; Jiang, Y.; Zhu, J.; Ding, B.; Zhang, W. Deformation characteristics and failure mechanism of the Moli landslide in Guoye Town, Zhouqu County. Landslides 2023, 20, 789–800. [Google Scholar] [CrossRef]
  9. Dai, K.; Li, Z.; Xu, Q.; Tomas, R.; Li, T.; Jiang, L.; Zhang, J.; Yin, T.; Wang, H. Identification and evaluation of the high mountain upper slope potential landslide based on multi-source remote sensing: The Aniangzhai landslide case study. Landslides 2023, 20, 1405–1417. [Google Scholar] [CrossRef]
  10. Xie, M.; Zhao, W.; Ju, N.; He, C.; Huang, H.; Cui, Q. Landslide evolution assessment based on InSAR and real-time monitoring of a large reactivated landslide, Wenchuan, China. Eng. Geol. 2020, 277, 105781. [Google Scholar] [CrossRef]
  11. Yan, Y.; Guo, C.; Li, C.; Yuan, H.; Qiu, Z. The Creep-Sliding Deformation Mechanism of the Jiaju Ancient Landslide in the Upstream of Dadu River, Tibetan Plateau, China. Remote Sens. 2023, 15, 592. [Google Scholar] [CrossRef]
  12. Iverson, R.M.; George, D.L.; Allstadt, K.; Reid, M.E.; Collins, B.D.; Vallance, J.W.; Schilling, S.P.; Godt, J.W.; Cannon, C.M.; Magirl, C.S.; et al. Landslide mobility and hazards: Implications of the 2014 Oso disaster. Earth Planet. Sci. Lett. 2015, 412, 197–208. [Google Scholar] [CrossRef]
  13. Palmer, J. Creeping earth could hold secret to deadly landslides. Nature 2017, 548, 384–386. [Google Scholar] [CrossRef] [PubMed]
  14. Agliardi, F.; Scuderi, M.M.; Fusi, N.; Collettini, C. Slow-to-fast transition of giant creeping rockslides modulated by undrained loading in basal shear zones. Nat. Commun. 2020, 11, 1352. [Google Scholar] [CrossRef] [PubMed]
  15. Zhang, Y.; Meng, X.; Novellino, A.; Dijkstra, T.; Chen, G.; Jordan, C.; Li, Y.; Su, X. Characterization of pre-failure deformation and evolution of a large earthflow using InSAR monitoring and optical image interpretation. Landslides 2022, 19, 35–50. [Google Scholar] [CrossRef]
  16. Bayer, B.; Simoni, A.; Mulas, M.; Corsini, A.; Schmidt, D. Deformation responses of slow moving landslides to seasonal rainfall in the Northern Apennines, measured by InSAR. Geomorphology 2018, 308, 293–306. [Google Scholar] [CrossRef]
  17. Luna, L.V.; Korup, O. Seasonal landslide activity lags annual precipitation pattern in the Pacific Northwest. Geophys. Res. Lett. 2022, 49, e2022GL098506. [Google Scholar] [CrossRef]
  18. Liu, Y.; Qiu, H.; Yang, D.; Liu, Z.; Ma, S.; Pei, Y.; Zhang, J.; Tang, B. Deformation responses of landslides to seasonal rainfall based on InSAR and wavelet analysis. Landslides 2022, 19, 199–210. [Google Scholar] [CrossRef]
  19. Zhao, C.; Kang, Y.; Zhang, Q.; Lu, Z.; Li, B. Landslide identification and monitoring along the Jinsha River catchment (Wudongde reservoir area), China, using the InSAR method. Remote Sens. 2018, 10, 993. [Google Scholar] [CrossRef]
  20. Casu, F.; Manzo, M.; Lanari, R. A quantitative assessment of the SBAS algorithm performance for surface deformation retrieval from DInSAR data. Remote Sens. Environ. 2006, 102, 195–210. [Google Scholar] [CrossRef]
  21. Ferretti, A.; Savio, G.; Barzaghi, R.; Borghi, A.; Musazzi, S.; Novali, F.; Prati, C.; Rocca, F. Submillimeter Accuracy of InSAR Time Series: Experimental Validation. IEEE Trans. Geosci. Remote Sens. 2007, 45, 1142–1153. [Google Scholar] [CrossRef]
  22. Tizzani, P.; Berardino, P.; Casu, F.; Euillades, P.; Manzo, M.; Ricciardi, G.P.; Zeni, G.; Lanari, R. Surface deformation of Long Valley caldera and Mono Basin, California, investigated with the SBAS-InSAR approach. Remote Sens. Environ. 2007, 108, 277–289. [Google Scholar] [CrossRef]
  23. Zhu, Y.; Yao, X.; Yao, L.; Zhou, Z.; Ren, K.; Li, L.; Yao, C.; Gu, Z. Identifying the Mechanism of Toppling Deformation by InSAR: A Case Study in Xiluodu Reservoir, Jinsha River. Landslides 2022, 19, 2311–2327. [Google Scholar] [CrossRef]
  24. Su, X.; Zhang, Y.; Meng, X.; Rehman, M.U.; Khalid, Z.; Yue, D. Updating Inventory, Deformation, and Development Characteristics of Landslides in Hunza Valley, NW Karakoram, Pakistan by SBAS-InSAR. Remote Sens. 2022, 14, 4907. [Google Scholar] [CrossRef]
  25. Sandwell, D.; Price, E. Phase gradient approach to stacking interferograms. J. Geophys. Res. Solid Earth 1998, 103, 30183–30204. [Google Scholar] [CrossRef]
  26. Turner, D.; Lucieer, A.; De Jong, S.M. Time Series Analysis of Landslide Dynamics Using an Unmanned Aerial Vehicle (UAV). Remote Sens. 2015, 7, 1736–1757. [Google Scholar] [CrossRef]
  27. Handwerger, A.L.; Fielding, E.J.; Huang, M.H.; Bennett, G.L.; Liang, C.; Schulz, W.H. Widespread initiation, reactivation and acceleration of landslides in thenorthern California Coast Ranges dueto extreme rainfall. J. Geophys. Res. Earth Surf. 2019, 124, 1782–1797. [Google Scholar] [CrossRef]
  28. Schulz, W.H.; Wang, G. Residual shear strength variability as a primarycontrol on movement of landslides reactivated by earthquake-induced groundmotion: Implications for coastal Oregon, U.S. J. Geophys. Res. Earth Surf. 2014, 119, 1617–1635. [Google Scholar] [CrossRef]
  29. Skempton, A.W. Residual strength of clays in landslides, folded strata and the laboratory. Géotechnique 1985, 35, 3–18. [Google Scholar] [CrossRef]
  30. Di Maio, C.; Scaringi, G.; Vassallo, R. Residual strength and creep behaviour on the slip surface of specimens of a landslide in marine origin clay shales: Influence of pore fluid composition. Landslides 2015, 12, 657–667. [Google Scholar] [CrossRef]
  31. Wen, B.P.; Jiang, X.Z. Effect of gravel content on creep behavior of clayey soil at residual state: Implication for its role in slow-moving landslides. Landslides 2017, 14, 559–576. [Google Scholar] [CrossRef]
  32. Sassa, K.; Fukuoka, H.; Wang, G.; Ishikawa, N. Undrained dynamic-loading ring-shear apparatus and its application to landslide dynamics. Landslides 2004, 1, 1–5. [Google Scholar] [CrossRef]
  33. Hu, W.; Li, Y.; Xu, Q.; Huang, R.; McSaveney, M.; Wang, G.; Fan, Y.; Wasowski, J.; Zheng, Y. Flowslide high fluidity induced by shear-thinning. J. Geophys. Res. Solid Earth 2022, 127, e2022JB024615. [Google Scholar] [CrossRef]
  34. Bai, M.; Chevalier, M.L.; Pan, J.; Replumaz, A.; Philippe, H.L.; Marianne, M.; Li, H. Southeastward increase of the late Quaternary slip-rate of the Xianshui fault, eastern Tibet. Geodynamic and seismic hazard implications. Earth Planet. Sci. Lett. 2018, 485, 19–31. [Google Scholar] [CrossRef]
  35. Zhang, Y.; Yao, X.; Yu, K.; Du, G.; Guo, C. Late-Quaternary slip rate and seismic activity of the Xianshui fault zone. Acta Geol. Sin. 2016, 90, 525–536. [Google Scholar] [CrossRef]
  36. Chevalier, M.L.; Leloup, P.H.; Replumaz, A.; Pan, J.; Métois, M.; Li, H. Temporally constant slip rate along the Ganzi fault, NW Xianshui fault system, eastern Tibet. GSA Bull. 2017, 130, 396–410. [Google Scholar] [CrossRef]
  37. Liu, X.; Zhang, Y.; Guo, C.; Wu, R.; Ren, S.; Shen, Y. Development characteristics and evolution process of the Garazong ancient rockslide along the Xianshuihe River in western Sichuan. Acta Geol. Sin. 2019, 93, 1767–1777, (In Chinese with English Abstract). [Google Scholar]
  38. Hungr, O.; Leroueil, S.; Picarelli, L. The Varnes classification of landslide types, an update. Landslides 2014, 11, 167–194. [Google Scholar] [CrossRef]
  39. Cruden, D.M.; Varnes, D.J. Landslide types and processes. In Landslides Investigation and Mitigation; Turner, A.K., Schuster, R.L., Eds.; Special Report 247; Transportation Research Board, US National Research Council: Washington, DC, USA, 1996; Chapter 3; pp. 36–75. [Google Scholar]
  40. Berardino, P.; Fornaro, G.; Lanari, R.; Sansosti, E. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE Trans. Geosci. Remote Sens. 2002, 40, 2375–2383. [Google Scholar] [CrossRef]
  41. Ren, S.; Zhang, Y.; Li, J.; Li, J.; Liu, X.; Wu, R. A new type of sliding zone soil and its severe effect on the formation of giant landslides in the Jinsha River tectonic suture zone, China. Nat. Hazards 2023, 117, 1847–1868. [Google Scholar] [CrossRef]
  42. GB/T50123-2019; Standard for Geotechnical Testing Method. National Standard of the People’s Republic of China: Beijing, China, 2019. (In Chinese)
  43. Scheingross, J.S.; Minchew, B.M.; Mackey, B.H. Fault-zone controls on the spatial distribution of slow-moving landslides. Geol. Soc. Am. Bull. 2013, 125, 473–489. [Google Scholar] [CrossRef]
  44. Zhang, Y.; Ren, S.; Liu, X.; Guo, C.; Li, J.; Bi, J.; Ran, L. Reactivation mechanism of old landslide triggered by coupling of fault creep and water infiltration: A case study from the east Tibetan Plateau. Bull. Eng. Geol. Environ. 2023, 82, 291. [Google Scholar] [CrossRef]
  45. Yin, Y.; Wang, W.; Zhang, N.; Yan, J.; Wei, Y. The June 2017 Maoxian landslide: Geological disaster in an earthquake area after the Wenchuan Ms 8.0 earthquake. Sci. China Technol. Sci. 2017, 60, 1762–1766. [Google Scholar] [CrossRef]
  46. Stark, T.D.; Hussain, M. Shear Strength in Preexisting Landslides. J. Geotech. Geoenviron. Eng. 2010, 136, 957–962. [Google Scholar] [CrossRef]
  47. Ren, S.; Zhang, Y.; Xu, N.; Wu, R.; Liu, X.; Du, G. Mobilized strength of gravelly sliding zone soil in reactivated landslide: A case study of a giant landslide in the north-eastern margin of Tibet Plateau. Environ. Earth Sci. 2021, 80, 434. [Google Scholar] [CrossRef]
  48. Wang, D.; Xiao, H.; Li, M. Discussion on the reconstruction method of landslide geological model before failure: A case study of Laohuzui landslide in Xianshui Fault Zone. Hydrogeol. Eng. Geol. 2013, 40, 111–116, (In Chinese with English Abstract). [Google Scholar]
  49. Floris, M.; Bozzano, F. Evaluation of landslide reactivation: A modified rainfall threshold model based on historical records of rainfall and landslides. Geomorphology 2008, 94, 40–57. [Google Scholar] [CrossRef]
  50. Xu, Y.; Kim, J.; George, D.L.; Lu, Z. Characterizing Seasonally Rainfall-Driven Movement of a Translational Landslide using SAR Imagery and SMAP Soil Moisture. Remote Sens. 2019, 11, 2347. [Google Scholar] [CrossRef]
  51. Tu, G.; Huang, D.; Deng, H. Reactivation of a huge ancient landslide by surface water infiltration. J. Mt. Sci. 2019, 16, 806–820. [Google Scholar] [CrossRef]
  52. Tian, S.; Chen, N.; Wu, H.; Yang, C.; Rahman, M. New insights into the occurrence of the baige landslide along the Jinsha river in Tibet. Landslides 2020, 17, 1207–1216. [Google Scholar] [CrossRef]
  53. Notti, D.; Wrzesniak, A.; Dematteis, N.; Lollino, P.; Fazio, N.L.; Zucca, F.; Giordan, D. A multidisciplinary investigation of deep-seated landslide reactivation triggered by an extreme rainfall event: A case study of the Monesi di Mendatica landslide, Ligurian Alps. Landslides 2021, 18, 2341–2365. [Google Scholar] [CrossRef]
Remotesensing 15 05538 g001
Figure 1. (a) Location of the study area on the Tibetan Plateau; (b) distribution of ancient landslides and reactivated landslides on the east Tibetan Plateau. The SRTM DEM was freely downloaded from USGS (https://earthexplorer.usgs.gov/, accessed on 10 October 2021), and the fault data were collected from the Geological Cloud, China Geological Survey (http://geocloud.cgs.gov.cn, accessed on 18 March 2019).
Figure 1. (a) Location of the study area on the Tibetan Plateau; (b) distribution of ancient landslides and reactivated landslides on the east Tibetan Plateau. The SRTM DEM was freely downloaded from USGS (https://earthexplorer.usgs.gov/, accessed on 10 October 2021), and the fault data were collected from the Geological Cloud, China Geological Survey (http://geocloud.cgs.gov.cn, accessed on 18 March 2019).
Remotesensing 15 05538 g001
Remotesensing 15 05538 g002
Figure 2. A geological background map of the Dandu landslide. Qp: Holocene alluvium; Qh: Pleistocene alluvium; T3ln1: metamorphic sandstone of the lower part Lianghekou Formation in the Upper Triassic; T3ln2: Metamorphic sandstone of the middle part of Lianghekou Formation in the Upper Triassic; T3r1: metamorphic sandstone interbedded with slate of the lower part of Ruganian Formation in the Upper Triassic; T3r2: basalt, sandstone, limestone, and slate of the upper part of Rugenian Formation in the Upper Triassic.
Figure 2. A geological background map of the Dandu landslide. Qp: Holocene alluvium; Qh: Pleistocene alluvium; T3ln1: metamorphic sandstone of the lower part Lianghekou Formation in the Upper Triassic; T3ln2: Metamorphic sandstone of the middle part of Lianghekou Formation in the Upper Triassic; T3r1: metamorphic sandstone interbedded with slate of the lower part of Ruganian Formation in the Upper Triassic; T3r2: basalt, sandstone, limestone, and slate of the upper part of Rugenian Formation in the Upper Triassic.
Remotesensing 15 05538 g002
Remotesensing 15 05538 g003
Figure 3. UAV image acquisition and processing: (a) unmanned aerial vehicle (DJI Mavic 3E); (b) planned flight routes; (c) orthophoto generated by DJI Smart Map software with a resolution of 0.1 m; (d) digital surface model (DSM) with a resolution of 0.5 m.
Figure 3. UAV image acquisition and processing: (a) unmanned aerial vehicle (DJI Mavic 3E); (b) planned flight routes; (c) orthophoto generated by DJI Smart Map software with a resolution of 0.1 m; (d) digital surface model (DSM) with a resolution of 0.5 m.
Remotesensing 15 05538 g003
Remotesensing 15 05538 g004
Figure 4. Spatial perpendicular baseline of the 149 SAR images in the SBAS-InSAR process.
Figure 4. Spatial perpendicular baseline of the 149 SAR images in the SBAS-InSAR process.
Remotesensing 15 05538 g004
Remotesensing 15 05538 g005
Figure 5. Schematic illustration of the ring shear test. (a) ARS-3 ring shear apparatus; (b) soil sample in the shear box; (c) size of the test specimen; and (d) shear surface.
Figure 5. Schematic illustration of the ring shear test. (a) ARS-3 ring shear apparatus; (b) soil sample in the shear box; (c) size of the test specimen; and (d) shear surface.
Remotesensing 15 05538 g005
Remotesensing 15 05538 g006
Figure 6. Particle size distribution of the slip zone.
Figure 6. Particle size distribution of the slip zone.
Remotesensing 15 05538 g006
Remotesensing 15 05538 g007
Figure 7. Reactivation features of the Dandu landslide: (a) overall view of the Dandu landslide; (b) deformation zone at the front of the landslide; (c) slip zone at the landslide’s toe; (d) slickensides on the slip surface; (e) groundwater outflow along the slip zone; and (f) cracks in the front of the landslide.
Figure 7. Reactivation features of the Dandu landslide: (a) overall view of the Dandu landslide; (b) deformation zone at the front of the landslide; (c) slip zone at the landslide’s toe; (d) slickensides on the slip surface; (e) groundwater outflow along the slip zone; and (f) cracks in the front of the landslide.
Remotesensing 15 05538 g007
Remotesensing 15 05538 g008
Figure 8. Engineering geological map of the Dandu landslide.
Figure 8. Engineering geological map of the Dandu landslide.
Remotesensing 15 05538 g008
Remotesensing 15 05538 g009
Figure 9. Engineering geological profile of the Dandu landslide (see A-A′ profile in Figure 8).
Figure 9. Engineering geological profile of the Dandu landslide (see A-A′ profile in Figure 8).
Remotesensing 15 05538 g009
Remotesensing 15 05538 g010
Figure 10. Deformation rate map of the Dandu landslide monitored by SBAS-InSAR from 2018 to 2022.
Figure 10. Deformation rate map of the Dandu landslide monitored by SBAS-InSAR from 2018 to 2022.
Remotesensing 15 05538 g010
Remotesensing 15 05538 g011
Figure 11. Deformation rate along the B-B′ profile (B-B′ profile in Figure 10).
Figure 11. Deformation rate along the B-B′ profile (B-B′ profile in Figure 10).
Remotesensing 15 05538 g011
Remotesensing 15 05538 g012
Figure 12. Relationship between landslide displacement and precipitation: (a) the gray line at the bottom represents daily precipitation, and the blue line represents accumulated precipitation; (b) relationship among landslide cumulative deformation, deformation rate, and time at point 1; (c) relationship among landslide cumulative deformation, deformation rate, and time at point 2; and (d) relationship among landslide cumulative deformation, deformation rate, and time at point 3. The positions of points 1, 2, and 3 are shown in Figure 8.
Figure 12. Relationship between landslide displacement and precipitation: (a) the gray line at the bottom represents daily precipitation, and the blue line represents accumulated precipitation; (b) relationship among landslide cumulative deformation, deformation rate, and time at point 1; (c) relationship among landslide cumulative deformation, deformation rate, and time at point 2; and (d) relationship among landslide cumulative deformation, deformation rate, and time at point 3. The positions of points 1, 2, and 3 are shown in Figure 8.
Remotesensing 15 05538 g012
Remotesensing 15 05538 g013
Figure 13. Shear stress against shear displacement curves and shear strength failure envelopes for the slip zone of the Dandu landslide: (a) shear stress against shear displacement curves; initial water content was 10%; (b) shear stress against shear displacement curves; saturated water content was approximately 21%; (c) shear strength failure envelopes; initial water content was 10%; and (d) shear strength failure envelopes; saturated water content was approximately 21%.
Figure 13. Shear stress against shear displacement curves and shear strength failure envelopes for the slip zone of the Dandu landslide: (a) shear stress against shear displacement curves; initial water content was 10%; (b) shear stress against shear displacement curves; saturated water content was approximately 21%; (c) shear strength failure envelopes; initial water content was 10%; and (d) shear strength failure envelopes; saturated water content was approximately 21%.
Remotesensing 15 05538 g013
Remotesensing 15 05538 g014
Figure 14. Shear surface characteristics: (a) the initial water content was 10%; (b) the saturated water content was approximately 21%.
Figure 14. Shear surface characteristics: (a) the initial water content was 10%; (b) the saturated water content was approximately 21%.
Remotesensing 15 05538 g014
Remotesensing 15 05538 g015
Figure 15. A model of ancient landslide reactivation triggered by seasonal rainfall.
Figure 15. A model of ancient landslide reactivation triggered by seasonal rainfall.
Remotesensing 15 05538 g015
Table 1. Parameters of SAR data.
Table 1. Parameters of SAR data.
Satellite Track Date Range Number of Images Revisit Cycle (Days) Resolution (m) Angle of Incidence
(°)
Azimuth Range
Sentinel-1P267 January 2018–
24 December 2022		14912/2413.982.3334.71
Table 2. Basic physical properties of the slip zone soil of the Dandu landslide.
Table 2. Basic physical properties of the slip zone soil of the Dandu landslide.
Dry Density (g/cm3) Plastic Limit (%) Liquid Limit (%) Plasticity Index (IP) Particle Size Distribution (mm, %)
<0.005 0.005~0.075 0.075~2 >2
1.80~1.8821.1~21.537.0~37.715.9~16.216263820
Table 3. Ring shear test scheme for slip zone soil of the landslide.
Table 3. Ring shear test scheme for slip zone soil of the landslide.
Sample Number Dry Density ρ (g/cm3) Normal Stress (σn/kPa) Initial Water Content Particle Size Distribution (mm, %)
<0.005 0.005~0.075 0.075~2
DD011.8310010%203347
DD021.84200
DD031.82400
DD041.84800
DD051.81100Saturated
DD061.82200
DD071.80400
DD081.80800
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Ren, S.; Zhang, Y.; Li, J.; Zhou, Z.; Liu, X.; Tao, C. Deformation Behavior and Reactivation Mechanism of the Dandu Ancient Landslide Triggered by Seasonal Rainfall: A Case Study from the East Tibetan Plateau, China. Remote Sens. 2023, 15, 5538. https://doi.org/10.3390/rs15235538

AMA Style

Ren S, Zhang Y, Li J, Zhou Z, Liu X, Tao C. Deformation Behavior and Reactivation Mechanism of the Dandu Ancient Landslide Triggered by Seasonal Rainfall: A Case Study from the East Tibetan Plateau, China. Remote Sensing. 2023; 15(23):5538. https://doi.org/10.3390/rs15235538

Chicago/Turabian Style

Ren, Sanshao, Yongshuang Zhang, Jinqiu Li, Zhenkai Zhou, Xiaoyi Liu, and Changxu Tao. 2023. "Deformation Behavior and Reactivation Mechanism of the Dandu Ancient Landslide Triggered by Seasonal Rainfall: A Case Study from the East Tibetan Plateau, China" Remote Sensing 15, no. 23: 5538. https://doi.org/10.3390/rs15235538

APA Style

Ren, S., Zhang, Y., Li, J., Zhou, Z., Liu, X., & Tao, C. (2023). Deformation Behavior and Reactivation Mechanism of the Dandu Ancient Landslide Triggered by Seasonal Rainfall: A Case Study from the East Tibetan Plateau, China. Remote Sensing, 15(23), 5538. https://doi.org/10.3390/rs15235538

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop