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Article

Topographic Changes, Surface Deformation and Movement Process before, during and after a Rotational Landslide

by
Shuyue Ma
1,
Haijun Qiu
1,2,*,
Yaru Zhu
1,
Dongdong Yang
1,
Bingzhe Tang
1,
Daozheng Wang
3,
Luyao Wang
1 and
Mingming Cao
1
1
Shaanxi Key Laboratory of Earth Surface and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China
2
Insitute of Earth Surface System and Hazards, College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China
3
State Key Laboratory of Continental Dynamics, Department of Geology, Northwest University, Xi’an 710069, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2023, 15(3), 662; https://doi.org/10.3390/rs15030662
Submission received: 20 December 2022 / Revised: 20 January 2023 / Accepted: 20 January 2023 / Published: 22 January 2023
Browse Figures
Versions Notes

Abstract

:
The deformation characteristics and instability patterns of rotational landslides are complicated. Such landslides are large and occur continuously, seriously threatening people’s lives. We used interferometry synthetic aperture radar (InSAR), digital elevation models of difference (DODs), numerical simulations, and other techniques for analyzing the topographic changes, surface deformation and movement process before, during and after a landslide. Based on the high-resolution terrain data before and after the landslide, the topographic changes were analyzed, and the active zone of the landslide was identified. The areas of the topographic changes were mainly located on the main scarp, toe and secondary landslides. The topographic changes were influenced by rainfall and rill erosion. The geomorphologically-guided InSAR interpretation method was applied to explore the displacement pattern. The deformation area in the middle of the landslide coincided with the secondary landslides. A time-series InSAR analysis revealed the dynamic evolution of the deformation before and after the landslide. Based on its evolution, the simulated landslide process included the main landslide and three secondary landslides. Based on the displacement of the longitudinal ground surface profiles, the displacement characteristics and kinematic behavior were summarized and compared with those of a single rotational landslide and multiple rotational landslides. The single rotational landslide had obvious secondary and progressive characteristics, developing into multiple rotational landslides triggered by conditions such as rainfall.

1. Introduction

Rotational landslides are a special type of disaster due to their deformation characteristics and complex failure modes [1,2,3,4]. Rotational landslides are typically characterized by arc-shaped sliding surfaces, which are deeply buried and large in scale [4,5]. The sliding mechanism of rotational landslides is complex and is particularly characterized by their strong instability and frequent recurrence [6,7,8]. Numerous rotational landslides have developed in Neogene basins, such as the Neogene claystone basins in the northeastern margin of the Qinghai-Tibet Plateau [9,10,11]. This type of landslide poses a major threat to infrastructure and human security. Therefore, it is necessary and important to understand the characteristics, developmental mechanisms, and failure mode of rotational landslides.
As a type of deep-seated gravitational landslide, rotational landslides continue for long periods of time and undergo several partial or complete reactivation events, producing large topographic changes [8]. Mastering the topographic changes of rotational landslides and monitoring their surface displacement are crucial for preventing and forecasting reactivation [12,13,14]. With the development of science and technology, unmanned aerial vehicle (UAV) remote sensing and interferometry synthetic aperture radar (InSAR) are being used to identify landslides, determine the characteristics of landslides and monitor the deformations of landslides [15,16,17,18,19,20]. UAV remote sensing technology is fast, efficient, economical and convenient. It can produce 4D products with a high-precision digital elevation model (DEM), digital orthophoto map (DOM), digital linear graph (DLG) and digital raster graphic (DRG), and is one of the important means to obtain high-resolution terrain data [21,22,23,24]. The technology has broadened the horizons and perspectives of landslide research [21,24,25,26]. InSAR technology has the technical advantages of a high accuracy, wide coverage, high degree of automation and continuous observation [27,28,29,30]. However, InSAR interpretation also faces some difficulties due to the complex topography and long evolutionary period of landslides.
To improve the accuracy of InSAR interpretation, the geomorphologically guided InSAR interpretation method was developed as a novel approach to explore the deformation evolution of large landslides [31,32]. Furthermore, it offers new perspectives for investigating the motion and sliding mechanisms of landslides. This method, based on surface geomorphological features and changes, analyzes the spatial distribution and temporal evolution of the displacement field of the landslide and judges the subsequent development of the landslide. Schlögel et al. [32] tested this method using four case studies in the Ubaye Valley in Southeastern France. Frattini et al. [8] applied InSAR data to summarize the kinematic behaviors of 133 large landslides and deep-seated gravitational slopes in the Central Italian Alps. Using the geomorphologically guided InSAR interpretation method, the complex spatial patterns of the displacement from landslides can be summarized.
In addition, using the surface deformation and topographic changes as constraints, the landslide process obtained through model inversion can better support the prevention and mitigation of geological disasters. The visual simulation of landslide motion is an intuitive and practical analysis method based on landslide model expression and sliding-motion parameters [33,34]. Numerical simulation methods include the discrete element method, depth-integrated continuum mechanics method, smoothed particle hydrodynamics and so on. Numerous developments have been achieved in the visual dynamic simulation of landslide motion via numerical simulations [35,36,37,38,39]. Zhou et al. [40] utilized Massflow to invert the Chenjia 8# landslide and quantitatively determined the accuracy of the simulation results according to the sliding distance and accumulation range. Ouyang et al. [41] used synthetic aperture radar (SAR) and remote sensing images to capture deformation and simulated landslide processes utilizing the depth-integrated continuum method to demarcate at-risk areas. Furthermore, scholars have attempted to combine numerical simulation methods and InSAR technology. Ma et al. [42] coupled InSAR and numerical modeling to analyze landslide movements and the effects of multiple loads. Roy et al. [43] used InSAR to obtain the trend of ground deformation around landslides, applied image segmentation techniques to identify the release area of landslides, and simulated the timing and path of landslides. However, in the processing step, the topographic changes needs to combined with InSAR technology before inputting the data into the numerical simulation to avoid errors; this step has been ignored by most previous studies.
In this study, we used geomorphic change detection (GCD) to analyze the topographic changes before and after the landslide and identified the unstable areas. InSAR technology was applied to trace and monitor the surface deformation evolution before and after the landslide, and the results were analyzed in combination with rainfall data. Moreover, the geomorphologically guided InSAR interpretation method was used to explain the longitudinal ground-displacement profile. Based on the active areas of the landslide, a numerical simulation was used to retrieve the landslide process. Compared with that of a single rotational landslide and multiple rotational landslides, the displacement pattern of the landslide was summarized. The mechanical failure model of the Lianluo landslide was explored. Detecting and revealing the displacement pattern from the failure mechanism deepens the connection between the deformation and the kinematic behavior of rotational landslides, providing ideas and related references for landslide research.

2. Study Area

The study area is located in Hualong county, Qinghai Province, China. It is located in the upper reaches of the Yellow River. The river flows from west to east along the southern edge of Hualong county. The river is rich in water, and the current is turbulent. The area has a semi-arid continental climate. Precipitation is less abundant and unevenly distributed seasonally. The rainy season is from June to August. The average annual precipitation is 470 mm. The land cover is dominated by shrubs and grass. The study area is located in the transition zone between the Loess Plateau and the Qinghai-Tibet Plateau (Figure 1). The study area is affected by the Qilian fold belt and has strong tectonic movements. The faults are mainly distributed in the NNW and NWW directions. The study area is dominated by basins and valleys. It is located southwest of the Hualong Cenozoic rifted basin.
The hydrogeological conditions of the study area were simple. The groundwater layers in the study area can be divided into three types according to distribution and genetic type: quaternary loose accumulation pore wet layer, bedrock fissure aquifer and seasonal frozen soil (rock) aquifer. Mudstones have good water insulation and heat insulation, and are often sandwiched with aquifers, such as sandstone and microconglomerate, but the thickness is small and the water quantity is poor. Under the influence of seasonal thawing and freezing changes, the groundwater phase changes periodically between solid and liquid.
Geological disasters frequently occur in the study area, and mainly include landslides, collapses and debris flows. The northeastern margin of the Qinghai-Tibet Plateau is one of the areas with the most concentrated distribution of rotational landslides. Among them, rotational-translational landslides are a typical type of landslide. The landslides are mostly spread along the bank slopes of valleys, such as the Yellow River and its tributaries. The rotational landslide (Lianluo landslide) investigated in the study area occurred in August 2018. The landslide was only 27 m away from the road, posing a threat to traffic safety. In addition, secondary landslides (S1, S2, S3) occurred (Figure 1c). The landslide was still in an unstable state and the possibility of reactivation was high. The volume of the main landslide was 1.5 × 106 m3. The length of the main landslide was approximately 860 m, and the width at its widest part was 470 m. The main scarp was steep. A large number of cracks were produced at the trailing edge, with widths ranging from 2 to 10 m. The length of the landslide S1 was 176 m, and the width was 205 m. The area of landslide S2 was small. The length was 103 m, and the width was 188 m. In addition, the length of the landslide S3 was 429 m, and the width was 195 m. The slope presented a stepped landform.
The strata exposed in the study area mainly include Quaternary Late Pleistocene Maran loess, Paleogene mudstone, and Quaternary Upper Pleistocene and Cenozoic Paleogene mudstone. The natural density of mudstone is 2.09~2.43 g/cm3, the water content is 14.01~20.46%, the cohesion is 0.2~700 kPa, the internal friction angle is 17.32~28.2°, and the compressive strength is 0.41~11 MPa.

3. Methods and Data

3.1. Topographic Analysis

Topography is a basal and spatial factor, which provides favorable conditions for the occurrence of landslide disasters. Topographic and geomorphic changes are very important for exploring the evolution of landslides. The terrain data before the landslide are AW3d data with a resolution of 2.5 m, and the data after the landslide are from the digital surface model (DSM) obtained by the UAV. The UAV field survey was performed in August 2020. The post-processing of the UAV data was completed using the Pix4Dmapper software. The resolution of the DOM and the DSM was 0.12 m. We used ArcGIS10.5 to unify the resolution of the terrain data before and after the landslide. The DSM analysis was performed at a resolution of 2.5 m. To explore the topographical and geomorphological changes, we used DEMs of difference (DoDs) to explore the erosion and deposition before and after the landslide. The changes were acquired using the landform change detection software GCD 7.0. The method can quantify the topographical changes, including the area, volume and soil thickness [44,45]. The workflow is shown in Figure 2. In this study, a simple minimum level of detection was used for the DoDs analysis. The threshold was 0.2 m in accordance with the operation manual of GCD 7.0 software [44,45]. In addition, based on the topography of the landslide, the topographic wetness index (TWI) data were extracted through system for automated geoscientific analyses’ (SAGA) geographic information system (GIS). We used the soil and water assessment tool (SWAT) model in ArcGIS 10.5 to extract the hydrological channels before landslides, exploring the relationship between the topographic changes and hydrological elements.

3.2. Multi-Temporal InSAR

Multi-temporal InSAR (MT-InSAR) technology selects N + 1 radar images, generates interferometric image pairs according to the time and space baseline threshold, removes the topographic effects, conducts filtering and phase-unwrapping, and finally calculates the displacement and rate of the deformation. Pixels in SAR images can be divided into two categories: point targets dominated by persistent scatterers (PSs) and distributed targets dominated by distributed scatterers (DSs) [46,47]. In MT-InSAR analysis, the mountainous area has a great deal of coverage, and the identified point targets are sparse, which can easily lead to phase-unwrapping errors [48,49]. In order to ensure the reliability of the InSAR results, it is necessary to combine point targets and distributed targets in natural scenes for joint analysis [50]. After the PSs and DSs are identified, the above point sets can be integrated and phase-unwrapped together. In GAMMA software, we use the MT-InSAR analysis algorithm combined with the PSs and DSs in the SAR images to analyze the Lianluo landslide, obtaining the deformation field of the Lianluo landslide (Figure 2). The data used were C-band Sentinel-1 data: 67 scenes of descending orbiting data before the landslide from 6 January 2015 to 18 August 2018, and 62 scenes of descending orbiting data after the landslide from 30 August 2018 to 29 December 2020 (Figure 3). The coherence coefficient was used as an index to evaluate the quality of the interference phase of the pixels [51]. The coherence coefficient ( γ ) was calculated based on the amplitude information of the SAR image as follows:
γ = ∑ n = 1 N ∑ m = 1 M μ 1 n , m μ 2 n , m ∑ n = 1 N ∑ m = 1 M μ 1 n , m 2 ∑ n = 1 N ∑ m = 1 M μ 2 n , m 2
where N and M are the dimensions of the data block used to calculate the coherence; n and m are the row and column numbers in the data block, respectively; and μ 1 (n, m) and μ 2 (n, m) are plural values at image coordinates (n, m) within the data blocks of the primary and secondary images.
In addition, rainfall is one of the most active inducing factors of landslide development. To explore the relationship between the deformation and rainfall, we calculated the index of the antecedent rainfall [52,53]:
A b = K A 1 + K 2 A 2 + K 3 A 3 + ⋯ + K n A n
where Ab is the antecedent rainfall; Aa is the daily rainfall on the a-th day before the landslide (1 ≤ a ≤ n); and K is the decay coefficient. In this study, K = 0.84 and n = 7 days.

3.3. Landslide Process Simulation

Massflow is a numerical simulation software for simulating the dynamic processes of surface disasters. It is based on the theory of continuum mechanics with depth integration and uses the improved MacCormack total variation diminishing (TVD) finite difference method [37,40]. It also considers the complex topography, so that it can be used to simulate landslides, debris flows and other geological disasters, as well as the dynamic evolution of disaster chains [54,55]. The mass and momentum equation is as follows:
E = ∂ h ∂ t + ∂ h u ∂ x + ∂ h v ∂ y
where E represents the basal entrainment rate, h represents the flow height, t represents the elapsed time, and u and v represent the depth-integrated flow velocities in the x and y directions, respectively. The complete derivation of Equation (3) has been described by Ouyang et al. [41]. Based on the terrain data and active landslide zone before and after the landslide, the area and volume of the release area can be obtained. The Coulomb friction material model was chosen. According to the Coulomb failure criterion, the basal friction stress can be expressed as follows:
τ b = c + ρ g h 1 − λ tan δ
where τ b represents the basal friction stress, c represents the cohesion, ρ represents the flow density, g represents the gravitational acceleration, h represents flow height, δ represents the basal friction angle and λ represents the pore pressure ratio. λ ranges from 0 to 1. The parameters for numerical simulation referred to the related soil parameters measured by predecessors [56,57,58]. In the numerical simulation, the soil density was set as 2200 kg/m3. The parameter values of the total cohesion, friction coefficient, and pore water pressure coefficient were set as 14.3 kPa, 0.25 and 0.5, respectively.

4. Results

4.1. Topographic Changes before and after the Landslide

Table 1 shows the topographic parameters of the main landslide and secondary landslides. The occurrence of the landslides dramatically changed the topography (Figure 4). The changes are reflected in topographic factors, such as the terrain ruggedness index, relief, slope and elevation. The high-value areas of the terrain ruggedness index, relief and slope increased after the landslide had occurred. However, the high-value and low-value areas of elevation decreased. In addition, the elevation differences on the three transverse profiles and one longitudinal profile (Figure 4d) reveal the changes in the topographic morphology and thickness (Figure 5). Figure 5a shows that the landslide along profile A–A’ is mainly eroded. Along profile D–D’, erosion and deposition can be observed four times (Figure 5d). The maximum thickness of erosion is −13.4 m. The maximum thickness of deposition is 10.1 m. In addition, Table 2 details topographic changes in the area, volume and elevation. Based on the DoDs, it can be seen that the area and volume of the erosion areas exceed those of the deposition areas. As for the volume for the main landslide, the percentage of surface lowering is 66% and that of surface raising is 34%. The erosion areas are mainly located on the main scarp of the landslide and on the secondary landslides (Figure 4d). The deposition areas are located on the toes of the landslides.
Based on the historical images (Figure 4a,b) before and after the landslide, the landslide changed the slope terrain greatly. Interestingly, there are obvious rill erosion traces in the scarp before the landslide occurred (Figure 4a). In the main scarp of the landslide, the locations of the hydrological channels (Figure 4c) and the high TWI values overlap with the area of rill erosion. This indicates that the topographic changes in the landslide were influenced by rainfall and rill erosion. Some rainfall did not infiltrate into the soil, resulting in slope runoff and the formation of rill erosion traces.

4.2. Deformation Evolution before and after the Landslide

The superposition of continuous interference can obtain the temporal and spatial deformation information of landslides and record the evolution process of landslide movement. Figure 6 shows the cumulative displacement process before and after the landslide. The geomorphologically guided InSAR interpretation method was applied to explore the deformation. The deformation areas before the landslide were mainly distributed in the scarp, middle and toe of the landslide. The cumulative displacement was mainly distributed from −175 to −306 mm. After the landslide, the eastern half of the scarp and the middle of the landslide exhibited obvious deformation, with maximum cumulative displacements as high as −290 mm and −248 mm, respectively.
In addition, we extracted longitudinal ground surface displacement profiles along the main sliding direction of the landslide (Figure 7b,c). By comparing the topography of the landslide (Figure 7a), we found that the deformation areas coincided with the scarp of the main landslide and the areas of the secondary landslides. In terms of the time series, the secondary landslides and the main landslide continuously deformed, rather than the secondary landslides occurring after the main landslide sliding. After the landslide, the deformation on the scarp of the main landslide and the secondary landslides (S2 and S3) became slow. However, the deformation of secondary landslide S1 was more severe, with a cumulative displacement of as high as −231 mm by 29 December 2020.
Figure 8 shows the deformation rate along four profiles (Figure 4b). Compared with the actual terrain, the variation in the deformation rate before and after the landslide was consistent with the elevation variation (Figure 5a–d). Along the profile D–D’, the maximum deformation rate was at the upper part of the landslide (Figure 8a). It was −122 mm/year before the landslide and −107 mm/year after the landslide. In addition, the deformation rate at toe of the landslide decreased after sliding. The increase in the landslide rate meant that the deformation velocity of the region is accelerated, whereas the decrease meant that the terrain tends to be stable. Along the profile B–B’, the deformation rate on the west went up after sliding and that on the east went down after sliding (Figure 8b). The western part of profile B–B’ passed through the secondary landslide S2.
The landslide occurred during the 2018 rainy season. Before the landslide, the displacement exhibited a linear trend (Figure 9a). After the landslide, the displacement process showed alternating slow deformation stages and accelerated deformation stages (Figure 9b). This phenomenon reflects that the looser the structure of landslide soil, the more severely the deformation of the landslide responds to rainfall. The process of the landslide seriously damaged the internal structure of the slope, caused the cracking of the rock and soil, and produced a large number of joint cracks. Rainfall further reduced the stability of the slope and stimulated deformation or reactivation. The accelerated deformation stages occurred during periods of persistent rainfall. The first accelerated deformation stage occurred during August–September 2019, with a maximum antecedent rainfall of 40 mm. The second accelerated deformation stage occurred during August–September 2020, during which the maximum antecedent rainfall was 41 mm. Precipitation collected on the surface of the slope (Figure 4a) and infiltrated the fissures. The water content and the water pressure increased, and the instability of the landslide body was accelerated. During the rainy season, rainfall further accelerates the deformation rate of the landslide, thereby increasing the risk of reactivation. By monitoring the spatial distribution and time-series dynamic evolution of the deformation after the landslide, we concluded that there is a risk of the reactivation of the landslides in the rainy season.

4.3. Landslide Process Simulation

The release area and sliding surface are two important parts of a landslide simulation. They were delimited according to the terrain changes before and after the landslide. The release area was comprehensively determined according to the active landslide areas, which were identified based on the landslide’s deformation, topography, DoDs and TWI. The sliding surface was set according to the characteristics of the rotating landslide and the actual erosion thickness. Figure 10 shows the simulated sliding process of the landslide, including secondary sliding. From the simulation, we can grasp the motion process of the landslide based on the sliding speed. The sliding of the landslide lasted for 170 s. During the main landslide, the material slid to the toe of the slope in 40 s. The speed at the landslide’s toe reached 32 m/s at 40 s. Because of the terrain, the sliding direction was deflected from 220° to 239° in the middle of the landslide. At 100 s, 130 s and 160 s, the secondary landslides S1, S2 and S3 occurred, respectively. Compared with the main landslide, the speed of the secondary landslides was smaller. The speed of the secondary landslide S1 was 5 m/s. The secondary landslide S3 lasted for 10 s. The simulated sliding range of the landslides was consistent with that of the actual landslide (Figure 4). The overlap rate of both areas is greater than 85%.

5. Discussion

5.1. Displacement Pattern of Rotational Landslide

Based on the measured displacement of a landslide along the main sliding direction, the kinematic behaviors of different rotational landslides can be revealed [8]. The difference of the sliding surface can generate different and complex displacement distributions. We explored the kinematic behavior about the rotational landslide using InSAR displacement measurements. Negative displacement values indicate that the landslide moved away from the satellite along the deep sub-circular sliding surface, whereas positive displacement values indicate that it moved toward the satellite when the rotational landslide was deposited. For different types of rotational landslides, the sub-circular sliding surface and multiple secondary slides form some proximity and different displacement patterns (Figure 11). The displacement shows that the whole landslide mass varied continuously. The pattern of the Lianluo landslide conforms to that of multiple landslides based on a single rotational landslide (Figure 11b). The curve has multiple ups and downs in the secondary sliding regions and the process is progressive and continuous. The displacement of Landslide 1 and landside 3 was adapted from Frattini et al. [8]. According to the displacement variation along the longitudinal profile of the landslide, the deformation pattern of a single rotational landslide is a relatively simple curvilinear trend (Figure 11a). In addition, there are several nearly vertical jumps in the displacement curve of landslide 3. This indicates that the second or third landslide within landslide 3 was sudden or instantaneous, as well as the occurrence of a new second or third sliding surface or discontinuous shear zones (Figure 11c). Therefore, the displacement can be used as an indicator to interpret the different failure modes of rotational landslides. In addition to identifying the displacement curve, it was necessary to distinguish the different types of landslides with the help of fieldwork, during which the depth, range of the secondary landslide and position of the second scarp were determined.
Figure 12 shows the failure mode of the Lianluo landslide. The sliding surface forms a structural feature with a high and steep scarp, flat middle, and slight warping of the leading edge. The sub-circular sliding surface of the landslide mass formed during the sliding process is one of the important signs of rotational movement [59]. Based on the mechanical properties of landslides [60,61,62,63], we think that the Lianluo landslide conformed to the stress state of circular rotation. The internal reason for the displacement and deformation of the landslide body is that its mechanical balance is broken and the sliding force exceeds the anti-sliding force of the landslide. The component force of gravity along the sliding surface is related to the thickness and weight of the landslide and the dip angle of the sliding surface. The rotation occurs around the gravity center of the landslide under the action of the torque. During the sliding process, the sliding belt of a rotational landslide often is on a large scale and produces a great deal of kinetic energy. The landslide is subjected to torsional stress due to the reverse slope tilt of the leading edge, resulting in much soil deposition in the toe of the landslide (Figure 12). Owing to the combined action of complex forces, the landslide often produces a large number of cracks and drumlins during the rotational process.

5.2. Evolution of Multiple Rotational Landslides

The landslide body rotates and slides along a sub-circular surface to form a single rotational landslide [7]. Multiple rotational landslides involve sliding along multiple sub-circular sliding surfaces in an instant or very short period, or multiple rotational slides gradually occur based on a single rotational landslide [4,32]. The landslide presents an obvious stepped landform with multiple steep slopes. The evolution from a single rotational landslide to multiple rotational landslides is shown in Figure 13. In the process of several slides, the stratigraphic structure of the landslide is deflected as a whole [1,62,63,64,65]. Rainfall infiltrates along the fissure to form a potential slip surface for the next slide (Figure 13b). In addition, there are two paths for the development of the potential slip surface: (1) The fissures at the trailing edge of the landslide continue to expand; (2) The fissures at the middle and leading edge of the landslide collapse the stability. Vick et al. [60] also mentioned fissures and faults are the initiation of rupture that eventually leads to progressive failure, including creeping, sliding and avalanching. During the evolution of multiple rotational landslides, the overall scale of the landslide become larger, but the scale of the first and second landslides decreases successively (Figure 13c).

5.3. Inducing Factors of Landslide Reactivation

The geological conditions of landslide development and external disturbance factors combined to destabilize the landslide. Neotectonic movement, the lateral erosion of rivers, the structure of steep slopes, stratigraphic lithology and groundwater action are the main factors controlling the formation of such landslides [3,6,11]. As a type of deep-seated gravitational slope, landslide development is closely related to tectonic activity and fault zones [1]. Landslides in the study area are affected by the Qilian fold belt, taking the Lianluo landslide as an example. Additionally, a large number of rotational-translational landslides are concentrated in the Neogene mudstone basin in the northeastern margin of the Qinghai-Tibet Plateau. Their activity intensity is affected by tectonic activities, and the volumes of these landslides gradually decrease from the hinterland to the marginal of the plateau [11]. A large number of slopes around fault zones form a basis for landslides. Shear deformation develops along the fissures in the slope. The structural joints and fissures formed by the tectonic movement destroy the stability of the slope.
Rainfall is the most direct factor inducing secondary landslides, especially heavy rainfall and continuous rainfall [59,66,67,68]. Xie et al. [69] conducted an experiment on slope tilting and determined an acceleration stage during slope failure caused by artificial rainfall. Artificial rainfall means that the experimentalists simulate rainfall through rainfall devices. Massey et al. [70] found that rainfall and pore pressure cause real variability and induce landslide motion by monitoring the Utiku landslide for three years. Moreover, the multiple rotational landslides can occur in winter under the effects of freezing and thawing. Freezing and thawing cycles destroy the shear strength characteristics, and fissures particularly exacerbate the process [71].
Fissures in the landslide provide channels for the rapid infiltration of water for both primary and secondary slips [45,72]. Fissures tend to form a combination surface of weak layers, fault layers and bedrock interfaces in landslides. Under the action of rainfall, the fissures in the trailing edge of the slope gradually grow and deepen, and the slope slides along the combination surface [73]. In the absence of fissures, the water pressure of the shallow pores gradually increases as the rainfall continues, while the deep part of the landslide is less affected by the rainfall. The presence of fissures leads to a large permeability coefficient in the vertical direction [74,75]. It gathers at the end of the fissures to form a transient water source, and leads to transient, high-pore water pressure with the continuous supplementation of rainfall. Thus, fissures play a critical role in the evolution of landslides from single to multiple forms.
For the Lianluo landslide, the impact factors are mainly tectonic activity, fissures and rainfall. The landslide is located in the Qilian fold belt with frequent tectonic activities (Figure 1a). Structural activities cause the structural damage of the slope and the development of cracks. In addition, rainfall is the triggering factor for both primary and secondary landslides. It was also confirmed that the Lianluo landslide occurred during the 2018 rainy season. By comparing rainfall and displacement before and after the landslide, we found that the rain accelerates displacement (Figure 9). After the landslide, the displacement is more responsive to rainfall. This phenomenon reflects that the looser the structure, the more fissures, and the greater the probability of triggering secondary landslides.

6. Conclusions

In this study, DODs, InSAR, numerical simulations and other techniques were used to analyze the topographic changes, surface deformation and movement process before, during and after the rotational landslide. The topographic changes, deformation evolution, displacement law and triggering factors were explored using the geomorphologically guided InSAR interpretation method. The displacement pattern of the landslide was summarized, and the failure model was analyzed. The following conclusions are reached:
(1)
The landslide changed the slope terrain. The changes are reflected in topographic factors, such as the terrain ruggedness index, relief, slope and elevation. The area and volume of the erosion areas are greater than those of the deposition areas for the main landslide. The percentage of surface lowering is 66% and that of surface raising is 34% by volume. The rill erosion, hydrological channels and TWI values indicate that the topographic changes in the landslide were affected by rainfall.
(2)
The deformation before the landslide was mainly distributed in the scarp, middle and toe of the landslide. The cumulative displacement was mainly distributed from −175 to −306 mm. After the landslide, it was distributed in the eastern half of the scarp and the middle of the landslide. The maximum cumulative displacement was as high as −290 mm and −248 mm, respectively. Compared with the actual terrain, the variation in the displacement rate before and after the landslide was consistent with the topographic variation. The deformation areas in the middle of the landslide coincided with the secondary landslides. The cumulative displacement of the secondary landslide S1 after the landslide was as high as −231 mm.
(3)
Before the landslide, the displacement exhibited a linear trend. After the landslide, the displacement process showed alternating slow deformation stages and accelerated deformation stages. The accelerated deformation stages occurred during the rainy season in 2019 and 2020. Rainfall further accelerates the deformation rate of the landslide, thereby increasing the risk of reactivation.
(4)
The sliding surface forms a structural feature with a high and steep scarp, flat middle and slight warping of the leading edge. The difference of the sliding surface can generate different and complex displacement distributions. The displacement pattern conforms to that of multiple landslides based on a single rotational landslide.

Author Contributions

Conceptualization, H.Q. and M.C.; methodology, S.M., Y.Z. and H.Q.; software, S.M. and D.W.; investigation, D.Y., S.M. and L.W.; writing—original draft preparation, S.M.; writing—review and editing, B.T. and H.Q. 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 no. 42271078), Natural Science Basic Research Plan for Distinguished Young Scholars in Shaanxi Province of China (Grant No. 2021JC-40), and The Second Tibetan Plateau Scientific Expedition and Research Program (STEP) (Grant No. 2019QZKK0902).

Data Availability Statement

The Sentinel-1A data used in this study were provided by the European Space Agency (ESA), https://scihub.copernicus.eu/dhus/#/home (accessed on 14 December 2022); Precise Orbit Ephemerides (POD) used in this study were provided by the ESA, https://scihub.copernicus.eu/gnss/#/home (accessed on 14 December 2022); and the SRTM DEM was freely downloaded from the website https://earthexplorer.usgs.gov/ (accessed on 14 December 2022).

Acknowledgments

The authors thank the ESA for providing free Sentinel-1A datasets. The authors also thank the Google Earth platform for providing the optical remote sensing images.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. (a) Map showing the location of the study area. (b) Geological map of the study area. (c) Three-dimensional view of the landslide.
Figure 1. (a) Map showing the location of the study area. (b) Geological map of the study area. (c) Three-dimensional view of the landslide.
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Figure 2. Workflow of research technical chain.
Figure 2. Workflow of research technical chain.
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Figure 3. Baseline plot of Sentinel-1 data: the colors of the lines indicate the coherence of each interferogram pair.
Figure 3. Baseline plot of Sentinel-1 data: the colors of the lines indicate the coherence of each interferogram pair.
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Figure 4. Geomorphological change detection. (a) Remote sensing image before landslide. (b) Remote sensing image after landslide. (c) Topographic wetness index (TWI) and hydrological channels. (d) Elevation difference before and after the landslide.
Figure 4. Geomorphological change detection. (a) Remote sensing image before landslide. (b) Remote sensing image after landslide. (c) Topographic wetness index (TWI) and hydrological channels. (d) Elevation difference before and after the landslide.
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Figure 5. Topographic changes before and after the landslide. (a–d) Elevation in the profiles (Figure 4b). (e–h) Topographic factors.
Figure 5. Topographic changes before and after the landslide. (a–d) Elevation in the profiles (Figure 4b). (e–h) Topographic factors.
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Figure 6. Cumulative displacement graphs. (a–h) Cumulative displacement before the landslide. (i–p) Cumulative displacement after the landslide.
Figure 6. Cumulative displacement graphs. (a–h) Cumulative displacement before the landslide. (i–p) Cumulative displacement after the landslide.
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Figure 7. Longitudinal ground surface displacement profile along the main sliding direction before and after the landslide.
Figure 7. Longitudinal ground surface displacement profile along the main sliding direction before and after the landslide.
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Figure 8. Line-of-sight (LOS) deformation rate before and after the landslide. (a) Along the longitudinal (D–D’). (b–d) Along transverse profiles (A–A’, B–B’ and C–C’).
Figure 8. Line-of-sight (LOS) deformation rate before and after the landslide. (a) Along the longitudinal (D–D’). (b–d) Along transverse profiles (A–A’, B–B’ and C–C’).
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Figure 9. Displacement of landslides and antecedent precipitation. (a) Before landslides. (b) After landslides.
Figure 9. Displacement of landslides and antecedent precipitation. (a) Before landslides. (b) After landslides.
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Figure 10. Numerical simulation of landslide movement. (a) Main landslide. (b) Secondary landsides.
Figure 10. Numerical simulation of landslide movement. (a) Main landslide. (b) Secondary landsides.
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Figure 11. Displacement patterns compared with other two similar rotational landslides (adapted from Frattini et al. [8]). (a) Single rotational landslide. (b) Multiple landslides from Lianluo landslide. (c) Multiple rotational landslides.
Figure 11. Displacement patterns compared with other two similar rotational landslides (adapted from Frattini et al. [8]). (a) Single rotational landslide. (b) Multiple landslides from Lianluo landslide. (c) Multiple rotational landslides.
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Figure 12. Failure mode analysis of Lianluo landslide.
Figure 12. Failure mode analysis of Lianluo landslide.
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Figure 13. Evolution of multiple rotational landslides. Two kind of potential sliding surfaces occur based on fissures from the single rotational landslide to multiple rotational landslides. (a) Unstable slope. (b) Single rotational landslide. (c) Multiple rotational landslides.
Figure 13. Evolution of multiple rotational landslides. Two kind of potential sliding surfaces occur based on fissures from the single rotational landslide to multiple rotational landslides. (a) Unstable slope. (b) Single rotational landslide. (c) Multiple rotational landslides.
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Table
Table 1. Topographic parameters of the main landslide and secondary landslides.
Table 1. Topographic parameters of the main landslide and secondary landslides.
Topographic
Parameters		Main
Lanslide		Secondary
Landslide S1		Secondary
Landslide S2		Secondary
Landslide S3
Perimeter (m)27497845211174
Area (m2)276,64631,07915,06869,416
Maximum length (m)886172109432
Maximum width (m)490208195215
L/W ratio1.810.830.562.01
Average slope (°)23.4919.8321.0220.44
Plane morphologyTongue
shape		Semicircle
shape		Semicircle
shape		Tongue
shape
Table
Table 2. Topographic changes in the landslide in terms of area, volume and thickness.
Table 2. Topographic changes in the landslide in terms of area, volume and thickness.
LandslideParametersSurface LoweringSurface Raising
Main
lanslide		Area (m2)168,01397,350
Volume (m3)971,383
±33,602		492,059
±19,470
Thickness (m)5.78
±0.20		5.05
±0.20
Secondary
landslide
S1		Area (m2)21,5757563
Volume (m3)119,116
±4315		28,707
±1512
Thickness (m)5.52
±0.20		3.80
±0.20
Secondary
landslide
S2		Area (m2)84315125
Volume (m3)31,069
±1686		10,100
±1025
Thickness (m)3.68
±0.20		1.97
±0.20
Secondary
landslide
S3		Area (m2)37,14428,825
Volume (m3)163,751
±7429		220,502
±5765
Thickness (m)4.41
±0.20		7.65
±0.20
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MDPI and ACS Style

Ma, S.; Qiu, H.; Zhu, Y.; Yang, D.; Tang, B.; Wang, D.; Wang, L.; Cao, M. Topographic Changes, Surface Deformation and Movement Process before, during and after a Rotational Landslide. Remote Sens. 2023, 15, 662. https://doi.org/10.3390/rs15030662

AMA Style

Ma S, Qiu H, Zhu Y, Yang D, Tang B, Wang D, Wang L, Cao M. Topographic Changes, Surface Deformation and Movement Process before, during and after a Rotational Landslide. Remote Sensing. 2023; 15(3):662. https://doi.org/10.3390/rs15030662

Chicago/Turabian Style

Ma, Shuyue, Haijun Qiu, Yaru Zhu, Dongdong Yang, Bingzhe Tang, Daozheng Wang, Luyao Wang, and Mingming Cao. 2023. "Topographic Changes, Surface Deformation and Movement Process before, during and after a Rotational Landslide" Remote Sensing 15, no. 3: 662. https://doi.org/10.3390/rs15030662

APA Style

Ma, S., Qiu, H., Zhu, Y., Yang, D., Tang, B., Wang, D., Wang, L., & Cao, M. (2023). Topographic Changes, Surface Deformation and Movement Process before, during and after a Rotational Landslide. Remote Sensing, 15(3), 662. https://doi.org/10.3390/rs15030662

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