Active High-Locality Landslides in Mao County: Early Identification and Deformational Rules

0. INTRODUCTION

High-locality landslides (HLs) are a special type of landslide developed on slopes at high elevations (Wang D S et al., 2018; Wang Y S et al., 2009; Chen, 1992) and feature large trailing edge heights (Mu et al., 2010). This kind of landslide usually turns into high-speed and long-distance landslides, causing catastrophic disaster chains (Liu et al., 2014). Owing to the characteristics of high locality, untraversed position, great concealment, and sudden onset, HLs often cause tremendous damage in urban regions, including casualties and facility destruction. For example, a large-scale high-locality landslide, the Touzhaigou landslide, occurred in Zhaotong City, China on 23 September 1991, and resulted in 216 deaths and a large economic loss of RMB 12 million (Xu et al., 2007). Therefore, it is vital to discover the active HLs in their early deformational stages and to understand their deformation processes and failure mechanisms for the valid prevention and mitigation of this kind of catastrophic landslide.

At present, there has been no exact definition of HLs. As shown in Table 1, a typical HL generally possesses a trailing edge height no less than 450 m (i.e., a high center of gravity) and a front higher than the slope foot. Thus, in this work a landslide is defined as a HL when it features a trailing edge height no less than 450 m, a front higher than the slope foot, and a free surface at the front.

Table  1.  Typical high-locality landslides

High-locality landslide	Occurrence time	Volume (×104 m3)	Front height (m)	Slope height (m)	Trailing edge height (m)	Reference
Zhana landslide	1943	12 700	50	400	450	Wang et al. (2018)
Lannigou landslide	1965	21 400	1 050	720	1 770	Wang et al. (2018)
Tangudong landslide	1967	9 020	110	970	1 080	Wang et al. (2018)
Touzhai landslide	1991	1 800	410	350	760	Wang et al. (2018)
Laojinshan landslide	1996	43	560	190	750	Wang et al. (2018)
Yigong landslide	2000	30 000	1 560	1 770	3 330	Wang et al. (2018)
Baishi landslide	2007	200	530	270	800	Wang et al. (2018)
Donghekou landslide	2008	1 000	270	200	470	Wang et al. (2018)
Ermanshan landslide	2010	100	680	148	828	Wang et al. (2018)
Guanling landslide	2010	175	250	230	480	Zheng (2018); Xing et al. (2014); Yin et al. (2010)
Tonghua landslide	2017	123	2012	202	2 214	Xie et al. (2020); Shao (2018)
Xinmo landslide	2017	450	85	1 103	1 188	Fan et al. (2017); Xu et al. (2017)
Note: Front height denotes the elevation difference between the front and slope foot. The trailing edge height indicates the elevation difference between the trailing edge and slope foot. Slope height signifies the elevation difference between the front and trailing edge. Note that the front and trailing edge heights of the Tonghua landslide are the absolute elevation values.

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Due to catastrophic destruction, HLs have attracted the attention of many scientists. Previous studies on HLs have mainly focused on two aspects. (1) The dynamic process of a single typical HL is simulated to understand movement features. It was suggested that HLs generally move fast and travel long distances with many disintegrated masses (Xu et al., 2015; Zhou et al., 2013; Huang et al., 2012; Hu et al., 2009). For example, Zhou et al. (2013) investigated the initiation and movement of the Donghekou landslide debris and adopted a combined numerical method to simulate the movement process. The maximum sliding velocity reached approximately 66.35 m/s, with a travel distance of approximately 2 400 m (Zhou et al., 2013). Xing et al. (2014) suggested a combined frictional-vollemy model to simulate landslide movement and indicated that the Guanling landslide slid 1 300 m with a mean velocity of 23 m/s in approximately 60 s. Kang et al. (2018) employed direct shear tests and an energy-based runout model to calculate velocities at different time stages and proposed that the Saleshan landslide was a fast-moving landslide with a long traveling distance. (2) A single HL is investigated regarding the failure mechanism. Previous studies (Wang et al., 2009; Yin et al., 2002) have suggested that HLs can be triggered by precipitation, earthquakes, and groundwater. In addition, strongly weathered and fragmented bedrock and steep terrain (e.g., valley-side slopes) also play critical roles in the formation of HLs (Rodríguez-Peces et al., 2018; Huang et al., 2012; Wu et al., 2011; Evans et al., 2009). For example, Xu et al. (2012) analyzed the dynamic process and deformation mechanism of the Ermanshan landslide and elucidated that antecedent torrential rainfall was the direct triggering factor and that the strongly weathered and fragmented basalts were the most important controlling factor. Roberti et al. (2017) employed Structure from Motion (SfM) photogrammetry to produce pre- and post-failure geomorphic maps of the Mount Meager landslide and illuminated that the landslide was induced by melting ice and snow that changed the groundwater. Wang et al. (2020) implemented a comprehensive investigation of the Tonghua landslide. Precipitation and an earthquake reactivated the landslide, and displacement was initiated after crack initiation, propagation, and coalescence (Wang et al., 2020). Li et al. (2020) utilized multisource precipitation, earthquake, and temperature data to reveal the main influencing factors and dynamic process of the Yigong landslide. The long-term freeze-thaw cycle, dry-wet cycle, and an earthquake triggered this landslide (Li et al., 2020).

Great progress has been achieved in studies on HLs; however, there are still some limitations in the present studies. (1) Present studies have generally focused on a single HL. Know-ledge of a single landslide possesses limitations when extended to other landslides and constrains the comprehensive understanding of HLs. Analysis of multiple active HLs induced by different factors at a regional scale may improve our understanding of this special type of tremendously destructive landslide. (2) Current studies have generally investigated HLs that have already occurred, and there has been no detailed investigation on potential active HLs during their early deformational stages. Due to their good concealment and sudden onset, these potential active HLs pose large threats to human lives and property. Thus, early discovery of HLs and understanding of their deformational law will contribute much to effectively reducing potential destruction and loss by the timely adoption of controlling measures.

Focusing on the above two limitations, this work makes two improvements. (1) Multiple HLs triggered by different factors at a regional scale are investigated to enrich the knowledge on the developmental rules of this particular kind of landslide. (2) Active HLs are examined in regard to their early identification and deformational law; thus, controlling measures can be implemented more timely and precisely. Furthermore, HLs are difficult to survey in the field as a result of their high locality, and the InSAR technique provides an effective alternative observational way for HLs. Thus, time-series InSAR techniques are employed in this work to identify the active HLs and to analyze their deformational laws.

Focusing on the HL-susceptible area of Mao County, this study employs multisource data (geological, topographic, meteorological, remote sensing, and site monitoring data) to investigate HLs and their deformational rules. Time-series InSAR techniques of small baseline subset (SBAS) and persistent scatterer (PS) are adopted to identify HLs and to acquire their deformational velocities. Some new viewpoints on the following three questions are proposed. (1) Where are the active HLs in Mao County? (2) Which factors control or trigger the deformation of active HLs? (3) What are the deformational laws of HLs induced by different factors? This work may enrich our knowledge on HLs regarding their developmental characteristics and rules.

1. STUDY AREA

Mao County (Fig. 1), with an area of ~3 903.28 km², is situated in the famous North-South seismic zone with frequent earthquake activities, accompanied by crustal uplifts and shocks (Wang et al., 2010). Historically, some large earthquakes have struck this area, including the 1933 M_s 7.5 Diexi Earthquake, 1976 M_s 7.2 Songpan-Pingwu Earthquake, and 2008 M_s 8.0 Wenchuan Earthquake (Shao et al., 2017; Xu et al., 2017; Ran et al., 2013; Zhang et al., 2010), and the seismic intensities of the above large earthquakes all attained or exceeded VI in Mao County (Zhao et al., 2020; Xu et al., 2017). Earthquakes not only damaged the integrity of the rock mass but also promoted rock weathering and changed groundwater mobility, which provided good conditions for the development of HLs (Han et al., 2009; King et al., 2006).

Figure  1.  Geological and tectonic setting of the study area. (a) Location of the study area in the transitional zone from the Tibetan Plateau to the Sichuan Basin. The administrative boundary of Mao County is marked with the red line. (b) Geological environment of the study area, including seismotectonics, strata, water system, and road network. (c) Great terrain variation from the Tibetan Plateau to the Sichuan Basin along with the profile AB in Subfigure (b). The symbols include: T₃zh. Zhuwo Formation of Late Triassic Age; T₃xn. Xinduqiao Formation of Late Triassic Age; T₂z. Zagunao Formation of Middle Triassic Age; T₁b. Bocigou Formation of Early Triassic Age; P₃. Upper Permian; P₂. Lower Permian; γ₂. Proterozoic granite; δ₂. Proterozoic diorite; O. Ordovician; O₂. Middle Ordovician; Є. Cambrian; Є₁. Lower Cambrian; S₁. Lower Silurian; Smx. Maoxian Formation of Silurian Age; D₃. Upper Devonian; Dwg. Weiguanqun Formation of Devonian Age; Dyl. Yuelizhai Formation of Devonian Age; γ_s^1-2. Yanshanian-Indosinian granites; C+P. union of Carboniferous and Permian; C. Carboniferous; Q_h. Holocene in the Quaternary; Q_p. Pleistocene in the Quaternary; and Z. Sinian.

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Folds and faults are present under the rugged ground surface (Wu, 2019) (Fig. 1). The Maowen fault, the most active part of the Longmenshan fault, and the southern section of the Minjiang fault dominate regional geological movements, cause broken rocks and generate a large number of cracks (Teng et al., 2018; Tang and Zhang, 2001; Bai and Wen, 1994). Moreover, the exposed strata in the study area mainly consist of the Weiguanqun Formation of Devonian Age (Dwg), the Maoxian Formation of Silurian Age (Smx), and the Bocigou (T₁b), Zagunao (T₂z) and Zhuwo (T₃zh) Formations of Triassic Age (1 : 200 000 geological map) (Fig. 1). Lithology is composed of metamorphic sandstone, quartz sandstone, marble limestone, phyllite, and carbonaceous slate that feature soft rocks or soft and hard interbedded rocks (1 : 200 000 geological map).

A dramatic terrain change is another distinctive characteristic in Mao County. Topography differs from the Chengdu Plain at an altitude of less than 500 m to the eastern edge of the Tibetan Plateau with an elevation of more than 4 000 m, for-ming a very steep terrain belt (Hu et al., 2010; Zhou and Luo, 2010) (Fig. 1). The landform is depicted by mountains and canyons (Ding et al., 2010). The northwestern part is typical of high altitudes with a height difference of 1 000–2 500 m, while the southeastern part is characterized by low elevations with a height difference of 500–1 000 m (Ding et al., 2010).

Mao County features a monsoon climate on the eastern Tibetan Plateau with an average annual rainfall of 688–811 mm from 1961 to 2015 and with the maximum daily rainfall of 104.2 mm (Jin et al., 2006; Liu and Yin, 2002; Liu and Jiao, 2000). The annual rainfall varied from 484.1 to 712.0 mm from 1981 to 2010, and a maximum monthly rainfall reached 168.1 mm (Pei et al., 2018). Rainfall exhibits obvious seasonality, and 91.8% of the annual rainfall is concentrated from April to October (Zhao et al., 2012). Continuous or heavy rain promotes rock masses softened by groundwater (Yin et al., 2002). The river system in Mao County is mainly sourced from the Tuojiang River system and Minjiang River system (Su, 2018). There are more than 170 rivers, 14 of which have catchment areas greater than 50 km² (Xiong, 2015; Li et al., 2006). Groundwater is well developed owing to the developed river system and bedrock fissure water (Su, 2018). There are 47 groundwater outlets in Mao County, with a maximum spring flow rate of 10 L/s·km² (1 : 200 000 geological map). Abundant groundwater causes strong erosion of the rock mass (Guzzetti et al., 2008).

Human engineering activity is intensive in Mao County. National Roads 213 and 347 traverse this area, and the roadbeds border a river on one side and cause excavated slopes on the other side. Many settlements are distributed on the hillsides or at the foot of the mountains; thus, a large number of town roads have been built for convenience (Feng et al., 2008). However, road and infrastructure construction has further reduced the stability of high-locality slopes and has made them very fragile under the long-term impacts of earthquakes and water erosion.

Therefore, HLs are prone to develop in Mao County due to the active seismic zone, complex geological structure, strong geological stress, steep terrain, abundant groundwater, seasonal concentrated rainfall, and frequent human activity. For example, Xinmo landslide, a famous HL, occurred in Mao County on 24 June 2017, destroyed the Xinmo Village and caused 10 people to die and 73 people to be missing (Fan et al., 2017). Thus, Mao County, especially the section along the Minjiang River, is a typical study area for HLs, and the objective of this work is to identify active HLs during their early deformational stage and to reveal the developmental rules of HLs.

2. DATA AND METHODS

2.1 Data

Seven sets of multisource data are employed in this work to identify HLs and to reveal their deformational rules (Table 2).

Table  2.  Multisource data employed in this work

Data type	Data	Date	Resolution	Data source
Image	Sentinel-1A SAR image	2018.04.20–2019.09.06	5 × 20 m	European Space Agency
	Google Earth image	2016.12.31	8 m	Google Earth
Topography	SRTM DEM	2003	30 m	USGS
Geology	Geological map	—	1 : 200 000	China Geological Survey
Seismology	Earthquake inventory	2018.02–2019.09	—	China Earthquake Administration
Meteorology	CHIRPS Satellite	2018.04.20–2019.09.06	5 km	UCSB
Field monitoring	BDS monitoring	2018.04–2019.09	—	BDS Cloud Platform
Historical landslide	Landslide inventory	Until 2019.09	—	China Geological Survey and BDS Cloud Platform
Note: SRTM. Shuttle Radar Topography Mission; USGS. United States Geological Survey; CHIRPS. Climate Hazards Group Infrared Precipitation with Station Data; UCSB. University of California Santa Barbara; and BDS. Beidou Navigation Satellite System.

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First, Sentinel-1A SAR images are adopted to recognize HLs and determine the positions and scales of various HLs. Thirty-four ascending images in the C-band (5.6 cm wavelength) from 20 April 2018 to 6 September 2019 are utilized to conduct multiple interferograms and to discover surface deformation within the monitoring period. These SAR images were shot with a 250 km swath at a spatial resolution of 5 m × 20 m.

Second, the known landslide inventories provided by the Beidou Navigation Satellite System (BDS) Cloud Platform and China Geological Survey are employed to compare with the landslide identification result obtained from the Sentinel-1A SAR images. Moreover, the BDS displacement data of a known HL (Tizicao landslide) are compared with the surface deformation results acquired from the Sentinel-1A SAR images. The field displacement data are from the five BDS monitoring sites on the Tizicao HL.

Third, five sets of multisource data are employed to establish the controlling and triggering factors of HLs and to further analyze the deformational laws of HLs. (1) Shuttle Radar Topography Mission (SRTM) DEM data are used to establish the topographic factors of slope and elevation. (2) The geological map is adopted to extract geological and tectonic factors of lithology and distance to fault. (3) Meteorological data are employed to construct the precipitation factors of 2-, 3-, 4-, 7-, 12-, and 14-day cumulative rainfall, 7-day maximum rainfall, and daily rainfall. (4) Seismic data are collected to generate the factor of peak ground acceleration (PGA). (5) Google Earth images are adopted to interpret roads and to construct the factor of distance to road. In this work, seismic data are sourced from the earthquakes with magnitudes no less than 3.0 that occurred within 400 km of Mao County.

2.2 Methods

The purpose of this work is to identify active HLs and to reveal the deformational laws of HLs. Figure 2 shows the flowchart of this work. First, SBAS-InSAR technology is employed to identify the active HLs and to determine the deformation regions of various HLs. The PS-InSAR technique is adopted to acquire the displacements and velocities of various active HLs. Moreover, an inventory of the known landslides provided by the BDS Cloud Platform and China Geological Survey is adopted to compare with the landslides recognized by InSAR technique and to determine which HLs are newly discovered in this work. Second, seven influencing factors are generated, consisting of geoenvironmental and triggering factors. Third, three typical HLs that were triggered by different factors are focused on to reveal the deformational rules of active HLs. The SBAS technique has the advantage of spatial continuity, whereas the PS technique is better at the position and displacement accuracies by using highly coherent vector points. Thus, this work integrates the advantages of SBAS and PS techniques by determining HLs and their boundaries by the SBAS technique and acquiring displacement and velocity values via the PS technique. Furthermore, active HLs are determined according to five criteria. (1) The slope exhibits obvious deformational features observed by the time-series InSAR technique, and the region of deformation is characterized by good spatial continuity and a clear and complete boundary. (2) The slope angle and the trailing edge height of the deformed slope are no lower than 10° and 450 m, respectively. The front of the slope is higher than the slope foot, and a free surface is developed at the front. (3) The slope exhibits microgeomorphologic characteristics of a landslide, e.g., free surfaces, gullies, broken rock mass, collapses, or cracks. (4) The slope is situated in the facility-sliding stratum, featuring soft rocks or soft-hard interbeds. (5) The deformation of the slope is related to rainfall, earthquakes, or road construction.

Figure  2.  Flowchart of this work. The abbreviations include: BDS. Beidou Navigation Satellite System; SRTM. Shuttle Radar Topography Mission; CHIRPS. Cclimate Hazards Group Infrared Precipitation with Station Data; PGA. peak ground acceleration; SBAS. small baseline subset; and PS. permanent scatterer.

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2.2.1   Recognition of active high-locality landslides using the SBAS-InSAR technique

The principle of the SBAS-InSAR technique is that multiple time series of independent SAR images are connected based on certain thresholds of the spatial baseline and time baseline, and then, a connection map of interference pairs is established to generate several short-baseline image sets (Bateson et al., 2015; Dong et al., 2014; Casu et al., 2006; Berardino et al., 2002). The spatial and time baselines of the SAR images in one set are small, whereas the spatial and time baselines between different sets are large, which can minimize the error and decoherence caused by viewing angle differences (Berardino et al., 2004). Differential interference is performed on each interference image pair to obtain the interferogram (Berardino et al., 2002). It is assumed that $\mathrm{\delta }\mathrm{\varnothing }\left(j\right)$ is the interference phase value of pixel P in the jth interferogram, which ignores the influences of atmospheric delay, sensor noise, and terrain error (Rosen et al., 2000), and its expression is shown in Equation (1) (Zhao et al., 2016; Arangio et al., 2014; Hou et al., 2012; Berardino et al., 2002):

where $\mathrm{\varnothing }\left({t}_{m}\right)$ and $\mathrm{\varnothing }\left({t}_{n}\right)$ indicate the phases at times ${t}_{m}$ and ${t}_{n}$, respectively, d(t_m) represents the cumulative deformation of pixel P in the radar line of sight (LOS) at time ${t}_{m}$ relative to the initial time t₀, d(t_n) indicates the cumulative deformation of pixel P in the radar LOS at time ${t}_{n}$ relative to t₀, and $\lambda$ denotes the sensor wavelength.

The interference phase ${\bf{{{ δ}} }}\boldsymbol{\psi }$(time) of pixel P in the M interference image pairs is recorded as an M-dimensional vector (Eq. 2) (Schmidt et al., 2003; Berardino et al., 2002):

Then the methods of least square regression and singular value decomposition (SVD) are used to convert the deformation phase of each image pair to the deformation phase related to a certain image and to generate the final deformation results after elimination of the atmospheric phase and noise phase (Ahmad et al., 2015; Neokosmidis et al., 2014; Jung et al., 2008; Schmidt et al., 2003; Berardino et al., 2002).

In this work, the 34 SAR images are divided into two image sets. One set covers the timeline from 20 April 2018 to 3 April 2019, and the other covers the timeline from 9 January 2019 to 6 September 2019. Considering the large terrain change in the study area, the temporal and spatial baselines should be as small as possible to prevent decoherence. However, baselines that are too small may lead to too few interferograms to obtain accurate and continuous deformation. After comprehensive consideration and multiple experiments, these SAR images are cut into small and coregistered rectangles that together cover the entire study area, and thus 287 interferograms are generated with the perpendicular baseline threshold of 2% and the maximum temporal baseline of 180 days. A multilook of 4 × 1 (the range multilook number is 4, and the azimuth multilook number is 1), combined with the Goldstein filter method, is selected to increase the signal-to-noise ratio and to improve the coherence among the interferograms.

The areas with absolute deformation velocities above 10 mm/year are determined as deformation regions. The deformation velocity threshold of 10 mm/year is determined according to two criteria. (1) The deformation regions possess good spatial continuity and exhibit clear and complete edges. (2) The deformation velocity of a deformation region is obviously larger than that of its surrounding area.

2.2.2   Velocity determination of active high-locality landslides by PS-InSAR

The principle of the PS-InSAR technique is that multiple time series of SAR images are employed to determine the permanent scatterers (PSs) that are not affected by time, space, and atmosphere (Ferretti et al., 2001). Then the selected PS points are adopted to establish the functional relationship between the surface deformation and phase difference of the signals reflected from the ground; thus, the LOS surface deformation across the entire region can be obtained (Farina et al., 2006; Ferretti et al., 2001).

The selection of PS points is carried out by the coherence coefficient threshold method (Eq. (3)) (Colesanti et al., 2002; Ferretti et al., 2001).

where M signifies the local pixel information (complex) block of the master image among the interference image pair, S represents the local information block of the slave images, ^* is a conjugate operator, m and n define the size of a local window, i and j indicate the pixel coordinates, and γ$\in$ [0, 1] represents the coherence coefficient. The closer γ is to 1, the lower the interference phase noise at a spatial point and the higher the signal-to-noise ratio at the point (Colesanti et al., 2002; Ferretti et al., 2001). The coherence coefficient value in a pixel is compared with the coherence coefficient threshold, and the pixel with the coherence coefficient value greater than the threshold is regarded as a PS point (Ferretti et al., 2001; Zebker and Villasenor, 1992).

The terrain error and atmospheric delay error at the PS points need to be eliminated to obtain the ground deformation at each PS point at each time point (Yuan et al., 2017; Cimini et al., 2012; Wang et al., 2010; Ferretti et al., 2001). SRTM DEM data are employed to remove topographic phase influence and to conduct geocoding for the SAR products. Regarding atmospheric delay errors, low-pass filtering in the space domain, and high-pass filtering in the time domain are adopted to remove the errors (Chen et al., 2012; Qi, 2009; Ferretti et al., 2001). Points with good stability and a high signal-to-noise ratio within the monitoring period are selected as the PS points (Ferretti et al., 2001). The deformation values of other points with low signal-to-noise ratios can be interpolated based on these stable PS points to acquire the deformation values of the entire region within the monitoring period (Chang et al., 2010).

In this work, 34 ascending images from 20 April 2018 to 6 September 2019 are utilized to conduct multiple interferograms and to discover surface deformation within the period. The SAR image shot on 30 August 2018 is selected as the primary image, and the 33 interferograms are divided into 25 km × 25 km grids with an overlap of 30% so that each grid includes PS points. According to the PS-InSAR deformation values within a HL, the deformation velocity of the HL can be determined.

2.2.3   Establishment of geo-environmental and triggering factors

Seven influencing factors are established from the multisource data, which are closely related to local HL occurrence and development (Guo et al., 2017; Ouyang et al., 2017; Dong et al., 2011). The seven factors are elevation, slope, lithology, fault tectonics, precipitation, PGA, and distance to roads (Table 3). The factors of elevation, slope, lithology, and fault tectonics are attributed to geoenvironmental factors, in which elevation reflects the positional characteristics of HLs. Precipitation, PGA, and distance to road belong to inducing factors, among which PGA denotes the influence of earthquake events, and distance to roads embodies the action of human activity (road construction).

Table  3.  Geological-environmental and triggering factors of high-locality landslides in Mao County

Factor type	No.	Factor	Grade
Topographic	1	Elevation (m)	Continuous
	2	Slope (°)	(1) ≤10; (2) 10–20; (3) 20–30; (4) 30–40; (5) > 40
Geologic	3	Lithology	(1) T3zh; (2) T3xn; (3) T2z; (4) T1b; (5) P3; (6) P2; (7) γ2; (8) δ2; (9) O; (10) O2; (11) Є; (12) Є1; (13) S1; (14) Smx; (15) D3; (16) Dwg; (17) Dyl; (18) γs1-2; (19) C+P; (20) C; (21) Qh; (22) Qp; (23) Z
	4	Distance to fault (km)	(1) ≤1; (2) 1–3; (3) 3–6; (4) > 6
Precipitation	5	2-day cumulative rainfall (mm)	Continuous
	6	3-day cumulative rainfall (mm)
	7	4-day cumulative rainfall (mm)
	8	7-day cumulative rainfall (mm)
	9	12-day cumulative rainfall (mm)
	10	14-day cumulative rainfall (mm)
	11	Daily rainfall (mm)
	12	7-day maximum rainfall (mm)
Seismic	13	2-day cumulative PGA (g)	Continuous
	14	3-day cumulative PGA (g)
	15	4-day cumulative PGA (g)
	16	7-day cumulative PGA (g)
	17	12-day cumulative PGA (g)
	18	14-day cumulative PGA (g)
	19	7-day maximum PGA (g)
Human engineering activity	20	Distance to road (m)	(1) ≤20; (2) 20–40; (3) 40–60; (4) 60–80; (5) 80–100; (6) 100–150; (7) > 150
T3zh. Zhuwo Formation of Late Triassic Age; T3xn. Xinduqiao Formation of Late Triassic Age; T2z. Zagunao Formation of Middle Triassic Age; T1b. Bocigou Formation of Early Triassic Age; P3. Upper Permian; P2. Lower Permian; γ2. Proterozoic granite; δ2. Proterozoic diorite; O. Ordovician; O2. Middle Ordovician; Є. Cambrian; Є1. Lower Cambrian; S1. Lower Silurian; Smx. Maoxian Formation of Silurian Age; D3. Upper Devonian; Dwg. Weiguanqun Formation of Devonian Age; Dyl. Yuelizhai Formation of Devonian Age; γs1-2. Yanshanian-Indosinian granites; C+P. union of Carboniferous and Permian; C. Carboniferous; Qh. Holocene in the Quaternary; Qp. Pleistocene in the Quaternary; and Z. Sinian.

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The factor type of precipitation includes the following three factors: daily rainfall, cumulative rainfall, and 7-day maximum rainfall (Table 3). Given the hysteresis in precipitation, the accumulated rainfall consists of 2-, 3-, 4-, 7-, 12-, and 14-day cumulative rainfall. Moreover, Mao County lies in the seismic belt; thus, earthquakes play an important role in HL deve-lopment. Multiple seismic waves have a superimposed effect on slope damage, and the accumulated seismic wave effect can cause the shattering and relaxation of rock masses and slopes (Xu et al., 2017; Yin et al., 2017; Li, 2003). Therefore, the factor type of earthquakes consists of the two factors of cumulative PGA and 7-day maximum PGA. The cumulative PGA is composed of 2-, 3-, 4-, 7-, 12-, and 14-day cumulative PGAs (Table 3).

3. RESULTS

3.1 Topographic Visibility Analysis

In an alpine-canyon region, e.g., Mao County, dramatic topographic changes can cause serious geometric distortion in SAR images, and the deformation in some areas is even completely undetectable by the SAR technique (Kropatsch and Strobl, 1990). Quantitative visibility analysis of deformation in LOS sheds insight into the areas not influenced by geometric distortion, and the deformation in these areas can be effectively detected by the InSAR technique (Guo et al., 2020; Cigna et al., 2014). The quantitative visibility analysis method of the R-index (Cigna et al., 2014) is employed in this work to discriminate the good visibility areas without geometric distortion and the geometric distortion areas including foreshortening, layover, and shaded areas. As shown in Fig. 3, the areas of good visibility, foreshortening, layover, and shaded regions are 1 990.53, 1 366.05, 429.33, and 117.09 km², respectively, occupying 51%, 35%, 11%, and 3% of the entire study area, respectively. Only the deformation in the good visibility regions is thought to be effective deformation in LOS that can be relatively accurately acquired by InSAR monitoring.

Figure  3.  Topographic visibility analysis in the radar line of sight. The abbreviations include: GV. good visibility; F. foreshortening; AL. active layover; PL. passive layover; Ash. active shade; and PSh. passive shade.

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3.2 Identification of Active High-Locality Landslides

Visibility analysis is combined with the SBAS-InSAR technique to identify active HLs. The SAR-detected deformation in the good visibility areas is regarded as effective deformation and is considered in this work. The SAR-detected deformation in the geometric distortion areas is not considered in this work. Thus, the identification results of active HLs are shown in Fig. 4, and only deformation in the good visibility areas is present. There were 19 HLs in total identified in Mao County, of which 7 were newly discovered in this work. The geoenvironmental characteristics of the 19 HLs are shown in Table 4. All HLs featured large slope heights and great trailing edge heights. Note that although the HL-8 and HL-19 landslides possessed low front heights, they were characterized by large slope heights of 721 and 529 m, respectively. Thus, once the slopes fail, the rock mass on the trailing edges travels long distances to acquire large kinetic energy and cause catastrophic destruction. These identified HLs mainly exhibit four distribution characteristics (Fig. 5). (1) They were mainly distributed on both sides of the Heishui River and Minjiang River, and 13 of them were located within 200 m of the rivers. Thus, fluvial denudation contributed to HL development. (2) Most HLs were located close to National Roads 213 and 347, and town roads were concentrated on the slopes of various HLs and even wound through the entire landslides. Therefore, human engineering activities, especially road construction, played an important role in HL development. (3) The exposed strata in the 19 HLs consisted of Weiguanqun Formation of Devonian age (Dwg), Maoxian Formation of Silurian age (Smx), and Zagunao (T₂z) Formation of Triassic age. Lithology was primarily composed of sericite phyllite, carbonaceous phyllite, and interbedded phyllite and sandstone. Phyllite and sandstone are soft rocks that are apt to be weakened and softened when absorbing water (Liu and Wang, 2019). Thus, soft lithology had an important influence on HL development. (4) Sixteen HLs were situated within 6 km of faults, and 8 of them were located within 3 km of faults. Therefore, earthquakes and fault tectonics contributed to HL generation.

Figure  4.  Active high-locality landslides in Mao County. (a) Velocity map from April 2018 to April 2019 derived from the SBAS-InSAR technique; (b) a partial enlarged map of Subfigure (a); (c) velocity map from January 2019 to September 2019 derived from the SBAS-InSAR technique; (d) a partial enlarged map of Subfigure (c). The confirmed high-locality landslides are the known landslides in the landslide inventory, and the new high-locality landslides are not included in the known landslide inventory. The identified landslides generally exhibit the same deformation direction in the radar line of sight in the two different monitoring periods.

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Table  4.  Geoenvironmental features of the 19 identified high-locality landslides

No.	Type	Front height (m)	Slope height (m)	Trailing edge height (m)	Lithology	Distance to fault (km)	Distance to river (km)	Distance to groundwater outcrop (km)
HL-1	Known	11	766	777	M, S	0.13	0.02	5.62
HL-2	New	328	293	621	M, S	0.91	0.63	5.81
HL-3	Known	288	493	781	PSL	4.62	0.56	0.49
HL-4	New	24	711	735	PSL	7.21	0.04	2.94
HL-5	Known	613	449	1062	PSL	4.43	1.90	1.35
HL-6	New	478	301	779	PSL	5.89	1.52	1.50
HL-7	Known	264	308	572	PSL	5.93	0.16	1.97
HL-8	New	9	721	730	PSL	8.64	0.02	2.92
HL-9	Known	14	966	980	PSL	10.47	2.21	5.60
HL-10	Known	29	424	453	PSL	5.94	0.15	2.60
HL-11	New	128	771	899	PSL	2.59	0.02	2.93
HL-12	Known	283	553	836	PSL	0.81	0.20	5.92
HL-13	New	15	491	506	PSL	0.44	0.04	5.42
HL-14	Known	186	264	450	PSL	2.55	0.20	5.95
HL-15	Known	31	460	491	PSL	2.91	0.02	7.68
HL-16	New	13	736	749	PSL	4.70	0.05	8.83
HL-17	Known	80	580	660	PSL	5.67	0.19	8.92
HL-18	Known	113	568	681	PSL	5.63	2.91	5.07
HL-19	Known	5	529	534	PSL	1.19	0.04	8.28
The abbreviations include: HL. high-locality landslide; M. metasandstone; S. slate; and PSL. phyllite interbedded with sandstone and limestone.

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Figure  5.  Distribution characteristics of the 19 active high-locality landslides (HLs) in Mao County. (a) Distance from HLs to rivers; (b) relationship between HLs and roads; (c) relationship between HLs and strata; (d) distance from HLs to faults; (e) a partially enlarged map of Subfigure (b). The symbols include: T₃zh. Zhuwo Formation of Late Triassic Age; T₃xn. Xinduqiao Formation of Late Triassic Age; T₂z. Zagunao Formation of Middle Triassic Age; T₁b. Bocigou Formation of Early Triassic Age; P₃. Upper Permian; P₂. Lower Permian; γ₂. Proterozoic granite; δ₂. Proterozoic diorite; O. Ordovician; O₂. Middle Ordovician; Є. Cambrian; Є₁. Lower Cambrian; S₁. Lower Silurian; Smx. Maoxian Formation of Silurian age; D₃. Upper Devonian; Dwg. Weiguanqun Formation of Devonian Age; Dyl. Yuelizhai Formation of Devonian Age; γ_s^1-2. Yanshanian-Indosinian granites; and C+P. union of Carboniferous and Permian.

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The PS-InSAR technique is adopted to determine the displacement and velocity of each HL, and the PS-detected displacement is compared with the BDS field monitoring displacement. The PS displacement values are in good agreement with the BDS displacement values, with a minimum RMSE of 6.68 mm and a maximum R² value of 0.99. The comparison is depicted in the supplementary file.

3.3 A Representative New High-Locality Landslide

HL-16 is a newly-discovered active high-locality landslide that was previously neglected. The HL is situated on the western bank of the Minjiang River, covering a deformation area of 1.46 km². Human engineering activity has been frequent on the unstable slope. Road networks have been constructed, and settlements are distributed both on the unstable slope and in the landslide-threatened area. Due to the large area and the potential threat to human lives and properties, HL-16 is selected as a case study, and its deformational characteristics are illuminated by the InSAR technique.

The landslide is located on the bank of the Minjiang River in the shape of a circle chair and with the aspect of 78°. The slope is adjacent to the Minjiang River, with a front height, slope height, and trailing edge height of 13, 736, and 749 m, respectively. The deformation area can be divided into two zones: Zone I (upper zone) and Zone II (lower zone) (Fig. 6a). Zone I was situated on a relatively steep slope with an average slope degree of ~31°, and Zone II was related to human engineering activity. Some buildings were constructed in Zone II, and roads wound through the zone. The displacement in Zone II was generally greater than -30 mm, and the deformation displacement and area in Zone II were both larger than those in Zone I. The slope downward movement in Zone II led to pressure unloading in Zone I and then caused the deformation in Zone I. Thus, the landslide, as a whole, was in traction deformation. In addition, in Zone II, there were two obvious deformation areas: Area 1 and Area 2 (Fig. 6b). The maximum deformation occurred in the central regions of both Area 1 and Area 2, i.e., in the middle of Zone II, which reached -65 mm (Fig. 6a). In contrast, the slope toe deformation was less than -30 mm. Thus, the slope in Zone II experienced an extrusion deformation.

Figure  6.  Deformation characteristics of HL-16 and threatened human engineering infrastructure. (a) Partition of the slope into Zone I (upper zone) and Zone II (lower zone). These two zones possessed different deformation displacements; (b) two obvious deformation regions in Zone II. The two regions posed threats to roads and residential areas. The background image is a Google image with a resolution of 8 m. The abbreviation includes: SBAS. small baseline subset.

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4. DISCUSSION

Landslide occurrence and deformation are controlled by geoenvironmental factors and triggered by inducing factors. The geoenvironmental factors are shown in Fig. 7, and the geoenvironmental characteristics of each HL are shown in Table 4. Elevation indicates the location of a landslide, and slope angle reflects terrain steepness and the development of free surfaces. Steep topography and a high elevation difference between the trailing edge and slope foot make for the development of HLs and determine the high gravitational potential energy of HLs (Wang et al., 2018). Lithology in Mao County includes mainly soft rocks or soft and hard interbedded rocks, and the strata are prone to sliding. Soft rock and soft-hard interbeds are characterized by low shear strength; thus, they are apt to develop weak intercalations and a sliding surface under the functions of tectonic movement, water erosion, and weathering (Guzzetti et al., 2008), thereby creating the development of HLs. Rock masses closer to a fault, cleavages and fractures are more highly developed, and the rock mass is more highly broken; thus the slope is more inclined to lose stability and to slide under an external force (Zhou et al., 2020; Tatard et al., 2010). River cutting and eroding to a slope foot generate free surfaces and enlarge the slope angle of the free faces (Liang et al., 2020; Kohv et al., 2008). In addition, the groundwater from rivers immerses and softens the rock and soil, accompanied by water-level changes and hydrodynamic pressure (Kohv et al., 2008; Yalcinkaya and Bayrak, 2003), and provides favorable environmental conditions for HL development. The positions of groundwater outcrops reflect groundwater spatial migration characteristics and indicate groundwater discharge paths (Yan et al., 2019). The rock mass around a groundwater outcrop closely contacts the groundwater and contains high water content during the long water seepage process (Yan et al., 2019). The surrounding weak strata are further softened under hydrodynamic action, and the rock mass is further weakened in shear strength (Yan et al., 2019); thus HLs develop.

Figure  7.  Geoenvironmental characteristics in Mao County. (a) Slope; (b) elevation; (c) strata; (d) distance to fault; (e) distance to river; (f) distance to groundwater outcrop. The abbreviation includes: HL. high-locality landslide. The symbols include: T₃zh. Zhuwo Formation of Late Triassic age; T₃xn. Xinduqiao Formation of Late Triassic age; T₂z. Zagunao Formation of Middle Triassic age; T₁b. Bocigou Formation of Early Triassic age; P₃. Upper Permian; P₂. Lower Permian; γ₂. Proterozoic granite; δ₂. Proterozoic diorite; O. Ordovician; O₂. Middle Ordovician; Є. Cambrian; Є₁. Lower Cambrian; S₁. Lower Silurian; Smx. Maoxian Formation of Silurian age; D₃. Upper Devonian; Dwg. Weiguanqun Formation of Devonian age; Dyl. Yuelizhai Formation of Devonian age; γ_s^1-2. Yanshanian-Indosinian granites; C+P. union of Carboniferous and Permian; C. Carboniferous; Q_h. Holocene in the Quaternary; Q_p. Pleistocene in the Quaternary; and Z. Sinian.

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There are primarily three types of inducing factors for landslides in Mao County: precipitation, earthquakes, and human engineering activity (road construction) (Fig. 8). For precipitation (Fig. 8a), various rainfall factors all presented obvious seasonal characteristics, and precipitation in Mao County is mainly concentrated from May to October. Regarding human engineering activity, a typical region is shown in Fig. 8b. Road networks were concentrated in various HLs and even wound through the entire landslides. As for earthquakes (Fig. 8c), multiple small-level earthquakes frequently occurred in June 2019 in southern Sichuan, which led to a significant increase in cumulative PGA values in June. Therefore, the deformational rule of each HL can be revealed by combining the geoenvironmental factors, triggering factors, and deformation characteristics. Note that the displacement value of each HL at a specific time is the mean displacement value of all PS points within the landslide boundary and reflects the overall deformation characteristic of the HL. In this work, three typical HLs triggered by diffe-rent factors are selected to highlight the deformational rules.

Figure  8.  Triggering factors of the active high-locality landslides (HLs) in Mao County. (a) Precipitation factors; (b) influence of road construction on HLs in a typical region; (c) earthquake factors. The abbreviation includes: PGA. peak ground acceleration.

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4.1 A Typical High-Locality Landslide Triggered by Earthquakes

The HLs induced by earthquakes were generally situated on steep slopes. The steep topography and high locality may have had an amplifying effect on seismic waves (Meric et al., 2005), and these slopes were inclined to be shattered and relaxed during earthquake events.

HL-3 is selected as a case study to highlight the deformational rule of a HL induced by earthquakes. It is located on the southern bank of the Heishui River, approximately 4.62 km away from a fault. National Road 347 crosses the slope foot. The slope angle, front height, and slope height are 40°, 288 m, and 493 m, respectively. The HL occurs in the Dwg stratum that consists of phyllite interbedded with limestone and sandstone saturated with fissure water. Groundwater in Mao County is very abundant (Ruan et al., 2015). An outcrop of groundwater is situated near HL-3 (Fig. 9) and has a maximum spring flow rate of 10 L/s·km² (geological map). The groundwater outcrop indicates abundant fissure-water content and good groundwater supply and reserve conditions. Abundant groundwater contributed to the development of HL-3. During groundwater flew and discharged, close mechanical, physical, and chemical interactions occurred between the groundwater and the surrounding rock mass, and the physical and chemical interactions resulted in mechanical effects (Tang, 2000). Then, the mechanical effects continuously deformed HL-3. The middle and lower parts of the landslide and rock mass around the front edge were highly eroded by weathering and surface runoff (Fig. 9), and the loose rock mass provided favorable conditions for groundwater penetration.

Figure  9.  Deformation characteristics of an active high-locality landslide induced by earthquakes (HL-3). (a) Displacement derived by the SBAS-InSAR technique; (b) velocity generated by the PS-InSAR technique. The base image is a Google image with a resolution of 8 m. The abbreviations include: SBAS. small baseline subset; and PS. permanent scatterer. The velocity generated by the SBAS-InSAR technique is shown in Fig. S2 in the supplementary file.

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The significant deformation area with a maximum displacement of -43 mm was located in the middle of the landslide, and the obvious deformation area with a displacement of -20– -30 mm occupied ~70% of the total deformation area (Fig. 9). Figure 10 shows the relationship between the landslide deformation velocity and the earthquake factor of 7-day cumulative PGA. The factor of 7-day cumulative PGA that lagged for 48 days after the earthquake's occurrence (48-day-lagging cumulative PGA) had a relatively synchronous change with the landslide deformation velocity. The lag effect between landslide deformation and an earthquake may be caused by the long-term influence of an earthquake on slope instability. An earthquake shook and relaxed the rock mass, which caused HL-3 to gradually become unstable and to deform under the subsequent impacts of rainwater infiltration and groundwater level fluctuations. Therefore, there was a hysteresis between an earthquake event and landslide deformation. Fewer earthquakes occurred from April 2018 to December 2018, and correspondingly, HL-3 had relatively small deformation velocities. In contrast, the earthquake frequency and number increased after January 2019, and the deformation velocity of HL-3 correspondingly increased. Moreover, HL-3 was located close to the groundwater crop, and bedrock fissure water was abundant in HL-3; thus, its deformation may have been caused by the combined effect of groundwater erosion and earthquakes.

Figure  10.  Relationship between HL-3 deformation velocity and the 7-day cumulative peak ground acceleration (PGA). The factor of 48-lagging cumulative PGA indicates that the 7-day cumulative PGA lagged for 48 days after an earthquake event. The values of landslide velocity and PGA are both normalized to 0–1.

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Therefore, the deformation of HL-3 was the result of the combined effect of earthquakes and groundwater. Seismic waves were amplified by the steep terrain and high position (Lee et al., 2010; Meric et al., 2005), which relaxed and damaged the rock mass causing it to gradually fragment (Liu et al., 2014). The relaxed and shattered rock mass was favorable to fissure water infiltration and groundwater erosion. Abundant groundwater softened the soil and rocks, increased bulk density and pore water pressure in the soil and rocks (Yin et al., 2002), and reduced the shear strength of the slip zone materials (Xia et al., 2015); thus, the stability of the slope decreased. The internal stress change in the landslide body may have lasted for some time. When the balance in the rock mass was broken, landslide deformation accordingly occurred (Xia et al., 2015).

4.2 A Typical High-Locality Landslide Triggered by Precipitation

The HLs mainly induced by precipitation were generally distributed close to water systems, and thus were susceptible to river erosion. Their bedrock mainly consisted of phyllite, a kind of rock that more easily softens when exposed to water (Liu and Wang, 2019). These HLs were mainly induced by rainfall and to some degree triggered by earthquakes and road construction.

HL-7 is selected as a case study to highlight the deformational rule of a precipitation-induced HL. It is located on the northern bank of the Heishui River. The landslide covered an area of 0.53 km² with a length of 1 277 m from the front to the back edges. The front height, slope height, trailing edge height, and average slope angle were 264, 308, 572 m, and 28°, respectively. It faced southwest with an azimuth of 204°. The front was characterized by a steep free surface with a length of 450 m and a slope angle of 36°. The microgeomorphological features of the landslide were obvious, featuring a seriously water-eroded slope surface and developed gullies. The slope was significantly eroded by weathering and surface runoff, and gullies and cracks had developed on the slope (Fig. 11). Two obvious gullies were located in the central part of the landslide. One gully was 165 long and 60 m wide (maximum width), and the other was 136 m long and 30 m wide (maximum width). The formation lithology was mainly phyllite interbedded with limestone and sandstone belonging to the Smx stratum, which is easily softened by water (Liu and Wang, 2019).

Figure  11.  Deformation characteristics of an active high-locality landslide mainly induced by precipitation (HL-7). (a) Displacement derived by the SBAS-InSAR technique. (b) Velocity generated by the PS-InSAR technique. The base image is a Google image with a resolution of 8 m. The abbreviations include: SBAS. small baseline subset; and PS. permanent scatterer. The velocity generated by the SBAS-InSAR technique is shown in Fig. S3 in the supplementary file.

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The landslide deformation was concentrated in the area with developed gullies, with a maximum displacement of 31 mm (Fig. 11). Figure 12 shows the relationship between landslide deformation and the 14-day cumulative rainfall. The deformation velocity of HL-7 consistently changed with the 14-day accumulated rainfall, which lagged for 48 days after a rainfall event. The hysteresis effect between landslide deformation and rainfall originated from the rainwater infiltration period (Min et al., 2004). Phyllite possesses a relatively high grain density, which results in a low water absorption rate and a slow rainfall permeability rate (Li, 2015; Mao et al.., 2011). Thus, the effect of precipitation on the development of HL-7 possessed obvious hysteresis. Therefore, the deformational rule of HL-7 is established. The slope was highly eroded by weathering and surface runoff, and large gullies and cracks developed on the slope, which created favorable conditions for rainwater infiltration. When rainwater slowly penetrated into the rock mass, the phyllite bedrock absorbed water becoming softer and disintegrated (Qi et al., 2012). Moreover, accompanied by the increased water content in the landslide body, the weight of rock and soil correspondingly increased (Yao et al., 2002). Thus, the shear stress of the slope decreased, and the strength and cohesion of the rock mass also decreased (Wang, 2001; Finlay et al., 1997). When the rock and soil were softened to a certain degree after a time delay in rainwater infiltration, the landslide began to deform. When rainfall decreased, the landslide returned to a stable state again.

Figure  12.  Relationship between HL-7 deformation velocity and the 14-day cumulative rainfall. The factor of 48-day-lagging cumulative rainfall indicates that the 14-day cumulative rainfall lagged for 48 days after a rainfall event. The values of landslide velocity and cumulative rainfall are both normalized to 0–1.

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4.3 A Typical High-Locality Landslide Induced by Human Engineering Activity

The lithology of the landslides induced by human engineering activity mainly consisted of phyllite interbedded with limestone and sandstone including sericite phyllite and carbonaceous phyllite. The deformation of these landslides was significantly relevant to road construction, and was also to some degree triggered by rainfall and earthquakes (Table 3).

The HL-17 landslide is selected as a case study to highlight the deformation rule of a HL induced by human engineering activity (Fig. 13). It occurred on the western bank of the Minjiang River, facing northeast with an average slope of 25°. The front height and slope height were 80 and 580 m, respectively. The trailing edge height was 660 m above the slope foot; thus, HL-17 possessed a large gravitational potential energy. The Minjiang River and National Road 213 passed through the slope foot, and a dense road network was constructed on the slope (Fig. 13). Moreover, town roads were built on the mountain, with a total length of 38.18 km within the landslide area. The gullies were distributed on the slope, and the rock mass along both sides of the roads was seriously broken and collapsed. Thus, precipitation and road construction had an important influence on HL-17 deformation. The formation lithology consisted mainly of metamorphic phyllite belonging to the Smx stratum, which is prone to softening and sliding (Liu and Wang, 2019).

Figure  13.  Deformation characteristics of an active high-locality landslide induced by human engineering activity (HL-17). (a) Displacement derived by the SBAS-InSAR technique. (b) Velocity generated by the PS-InSAR technique. The base image is a Google image with a resolution of 8 m. SBAS. small baseline subset; and PS. permanent scatterer. The velocity generated by the SBAS-InSAR technique is shown in Fig. S4 in the supplementary file.

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The significant deformation region, with a displacement larger than -60 mm, covered an area of 52 800 m². The region was situated within the road network, with the maximum displacement reaching -80 mm. Table 3 shows the levels of the factor of distance to roads, and the area of the significant deformation region falling in each level is calculated, called the area proportion of significant deformation. Figure 14 shows a strong linear relationship between the area proportion of significant deformation and the distance to roads. The area of the significant deformation region became larger when it was closer to roads. Furthermore, HL-17 deformation was dominated by road construction, and was also related to rainfall. Figure 15 shows the relationship between landslide deformation velocity and the 14-day cumulative rainfall. The deformation velocity of HL-17 generally changed consistently with the 14-day accumulated rainfall, which lagged for 48 days after a rainfall event. Additionally, from December 2018 to April 2019, rainfall was very low, whereas HL-17 maintained continuous deformation that may have been caused by the sustaining influence of the road construction. In other periods, the HL-17 deformation was triggered by both rainfall and road construction. Therefore, road construction induced the continuous deformation of HL-17, and rainfall aggravated the deformation.

Figure  14.  Relationship between the area proportion of the obvious deformation region and distance to roads for HL-17.

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Figure  15.  Relationship between HL-17 deformation velocity and 14-day cumulative rainfall. The factor of 48-day-lagging cumulative rainfall indicates that the 14-day cumulative rainfall lagged for 48 days after a rainfall event. The values of landslide velocity and cumulative rainfall are both normalized to 0–1.

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Therefore, the deformation of HL-17 was caused by the combined effect of road construction and rainfall. Road construction was intense in the HL-17 landslide area, destroying the intact rock mass (Bergillos et al., 2018) and, to some degree inducing landslide deformation. The fragmented rock mass created permeability for rainwater through fracture planes. Accompanied by rainwater scouring and infiltration, gullies developed on the slope, and the phyllite bedrock softened; thus, a weakly permeable sliding bed formed (Hafizi et al., 2010). The sliding bed was beneficial to groundwater accumulation on the top surface of the base layer, reducing friction, decreasing shear strength, and facilitating landslide deformation (Hafizi et al., 2010). In addition, the infiltrated rainfall increased the weight of the rock mass and intensified landslide deformation.

5. CONCLUSIONS

This work employs time-series InSAR techniques and multisource data to identify active HLs in Mao County and reveals the deformational rules of these HLs. This work focuses on the hidden active HLs in their early deformational stages, which is different from previous studies that generally concentrated on HLs that had already occurred. Thus, landslide-controlling measures can be implemented more timely and precisely, and the loss due to landslides may be reduced. Moreover, this work investigated 19 HLs induced by different factors at a regional scale and revealed their different deformational laws, instead of studying a single HL as has generally been conducted in previous studies; thus, knowledge regarding the developmental rules of this particular type of tremendously destructive landslide is enriched. In addition, this work suggests comprehensive criteria to identify potential active landslides. These criteria combine the features of surface deformation, disaster-pregnant environment (topography, micro-geomorphology, and lithology), and disaster-causing factors (precipitation, earthquakes, and human engineering activity), which may improve the accuracy of potential landslide determination. These comprehensive criteria are generally applicable and can be applied to other types of landslides and other disaster-intensive regions. This work may provide some clues on the prevention and control of HLs and can be applied to the determination of active HLs in other hard-hit landslide areas. Five main conclusions are drawn as follows.

(1) Nineteen active HLs were identified according to surface deformation, topography, microgeomorphology, lithology, and disaster-inducing factors, of which 7 are newly discovered. The newly discovered HL-16 was characterized by having a large scale and a large gravitational potential energy, which posed a significant threat to the surrounding people and property. Moreover, the 19 HLs all possessed good concealment in their early deformational stage of slow creeping.

(2) The HLs in Mao County were mainly triggered by three factors: earthquakes, precipitation, and road construction.

(3) The deformation in a typical HL induced by earthquakes was the combined effect of earthquakes and groundwater. Seismic waves were amplified by the steep terrain and high position, and these seismic waves shook and relaxed the rock mass. The relaxed and shattered rock mass was favorable to fissure-water infiltration and groundwater erosion. The region was characterized by abundant fissure water content and good groundwater supply and reserve conditions. Groundwater softened the soil and rocks, posed a hydrodynamic pressure on the slope, and reduced the shear strength of the rock mass. Thus, under the combined action of earthquakes and groundwater, the slope became unstable and deformed continuously.

(4) The deformational rule of a typical HL induced by precipitation is revealed. The slope was highly eroded by weathering and surface runoff, and large gullies and cracks were developed on the slope, which created favorable conditions for rainwater infiltration. When rainwater slowly penetrated into the rock mass, the phyllite bedrock absorbed water, softened and disintegrated, and formed a weak sliding surface. When the rock and soil were softened to a certain degree with a time delay after rainwater infiltration, the landslide deformed. When rainfall decreased, the landslide returned to a stable state again.

(5) The deformation of a typical HL induced by human engineering activity was caused by the combined effect of road construction and rainfall. As road construction was intensified in the landslide area, the rock mass was excavated and became relaxed. The rock mass along both sides of the roads became seriously fragmented and collapsed. Thus, road construction destroyed the intact rock mass, induced landslide deformation and increased rainwater permeability through fracture planes. Accompanied by rainwater scouring and infiltration, gullies developed on the slope, and the phyllite bedrock softened from water immersion; thus, a weakly-permeable sliding bed formed. The infiltrated rainfall increased the weight of the rock mass and intensified landslide deformation. Therefore, road construction induced continuous deformation, and rainfall aggravated this deformation.