1 Introduction

A frost mound is a common periglacial landform, which is dominated by frost heave (Nixon et al., 1983 ; Palmer and Williams, 2003). Frost heave occurs in response to ground-ice formation in the progression and regression of the seasons (Rempel, 2007). Frost mounds are a secondary disaster in cold-regions engineering, because the freezing and thawing of ground ice brings serious consequences for construction in frozen-soil regions (Ma et al., 2012 ). Frost mounds are considered to consist of ice-rich frozen soil, which through very complex physical, mechanical, and thermal changes during freeze–thaw cycles, along with a phase change in water, led to frost heave in winter or thaw settlement in summer (Pissart, 2002; Palmer and Williams, 2003, 2005). There are various thermodynamic processes induced by freezing–thawing disasters. The repeated cycles of freezing and thawing slowly weaken the structural integrity of porous and cracked soil. The direct effect of air temperature is reflected in the difference in the process of freezing and thawing, and the change of ground temperature. Therefore, climate warming is driving the increase in slope instability and may further aggravate this danger in the future (Gruber et al., 2004 ; Niu et al., 2014 , 2016). Freezing–thawing episodes are potential hazards for people and infrastructure in the engineering and transportation corridor. Laboratory tests have shown that the shear strength of ice filling discontinuities decreases with increasing air temperature (Pissart, 2002). The increase in climate warming across the QTEC over the past decade has contributed to the permafrost degradation, which has accelerated risks of freezing–thawing disasters, including slope deformation and failure. Slope deformation and failure of the permafrost zone have motivated various monitoring techniques, including total station, tension-crack monitoring, electrical resistivity tomography (ERT), terrestrial photogrammetric methods, global navigation satellite system (GNSS), slope-stability radar, interferometric synthetic aperture radar (InSAR), terrestrial laser scanning (TLS), and unmanned aerial vehicle (UAV) (Wang and French, 1995; Setkowicz, 2014; Al-Rawabdeh et al., 2016 ). At present, most studies in the QTP focus on thaw-induced slope failure, especially thaw slump. For example, steel rods spaced 10 m apart were deployed in transects transversal to the slope headwall to allow manual monitoring of slope retrogression in a permafrost region of the QTP, China (Niu et al., 2012 ). High accuracy and efficient deformation monitoring, like TLS, can help early detection of potential risks. The positioning technology of GNSS can help to improve the data accuracy of TLS and UAV (Luo et al., 2017 ).

A large number of frost mounds have been found along the Qinghai–Tibet engineering corridor (QTEC). Even with the increasing air temperature, little attention has been given over the past decade to the study of frozen disasters. In order to monitor the deformation law of frost mounds, GNSS, TLS and UAV have been deployed on the surface of frost mounds to monitor surface deformation. Freeze/thaw–induced deformation was monitored at the beginning and the end of freezing and thawing between 2014 and 2015. Furthermore, scanned surface changes of the frost mounds through comparison of multi-temporal 3D data were analyzed to reveal the deformation process of the active layer during freeze–thaw cycles. The characteristic heaving and settlement exist during freeze–thaw cycles, varying with deformations; and they can be used to analyze landslide failure. Accordingly, the aim of this study is to obtain a better understanding of the relation between destabilization and permafrost freeze–thaw.

2 Data and methods 2.1 Study area

The two frost mounds (frost mound A: 94°03′46″E, 35°39′4″N, 4,759 m a.s.l.; frost mound B: 94°03′48″E, 35°38′45″N, 4,770 m a.s.l.) are located in the northern QTEC, 1 km and 250 m northeast of the Kunlun Mountain Pass (KMP) (Figure 1). The mean slope angle of the two mounds between the bases and the upper zones of the mounds is 40°, and the height from the base to the top of the mounds is between 36 and 32 m. The total area of the two mounds is approximately 7,620 m² and 24,300 m², respectively. The QTEC stretches for about 1,120 km, is known as a critical infrastructure and passage connecting inland China and the Qinghai–Tibet Plateau (QTP), and is a naturally occurring north–south corridor in the central area of the QTP that includes important engineering infrastructure, such as the Qinghai–Tibet Railway (QTR) and the Qinghai–Tibet Highway (QTH), a high-tension line, a petroleum transmission pipeline, communication optical fiber cable, etc. (Luo et al., 2017a ).

2.2 Methods

Multiple monitoring tools were integrated to observe the deformation process of the frost mounds. GNSS, TLS, and UAV were employed to monitor the surface topography. GNSS was performed using six Trimble 5700 GNSS systems. TLS used a FARO Focus^3D X130 laser scanner, which obtains detailed, high-speed, three-dimensional document scans (Yang et al., 2013 ). DJI Inspire 1 was adopted as a UAV system to map the elevations (Weiss and Baret, 2017). GNSS, TLS, and UAV are three different techniques that can contribute in different ways to the investigation of a ground-deformation phenomenon. Over the last two decades, TLS, a revolutionary technique for the acquisition of spatial data, has gained widespread acceptance in both scientific and commercial communities as a powerful tool for topographic measurement in various geophysical disciplines. The use of UAVs in aerial imaging has enabled measurements with higher spatial resolution, improving the resolution of photogrammetric point clouds and the acquisition of 3D structural data. GNSS and TLS were deployed in the study area from May 2014 to October 2015 (05/02/2014, 10/10/2014, 05/03/2015, and 10/04/2015), while the area went through two thawing periods and a freezing period. The three freeze–thaw periods are referred to as "first thawing" (05/02/2014–10/10/2014), "freezing" (10/10/2014–05/ 03/2015) and "second thawing" (05/03/2015–10/04/2015) in the following discussion. The original recorded intensity values were extracted in a point cloud image created by the standard software Faro SCENE 5.0, and then Geomagic Studio 12 was adopted for processing these scanner data. Totals of 26 and 31 scans for each of the two frost mounds were obtained, containing more than one million points per scan. A selected point is tracked in response to displacement by the XYZ coordinates, wherein Z is the elevation of frost mounds. For examining the topographic change of the frost mounds, an elevation (Z) is compared in the same XY position point at different times. GNSS was adopted to ensure the orientation and registration of different 3D datasets in a common coordinate system. The GNSS solution, combined with TLS to generate geospatial data, was then compared to the other TLS measurements to analyze the deformation process. UAV was employed to map the terrain of the mounds, providing digital surface models (DSMs) and orthophotographs. Data processing and analysis 3D point cloud data can be divided into four stages: data collection, data registration, data processing, and comparison analysis.

3 Result and discussion 3.1 Deformation of frost mound A

Frost mound A can be divided into three zones: the upper zone, the lower zone, and the base. The zonal changes in elevation (Z) during the two thawing periods and one freezing period are shown in Figure 2. The areas of the three zones represent 39%, 28% and 33% of the total area of frost mound A. The colored scale on the right-hand side (in meters) indicates the changes in elevation, from −0.50 to +0.50 m. Radial cracks can be found in the upper zone (Figure 3). In the two thawing periods, (1) the upper zone exhibited mainly a regressive trend; but the radial cracks showed the opposite trend; (2) the lower zone indicated mainly a progressive trend; (3) the base displayed mainly a progressive trend in the freezing period, but there were significant differences in the two thawing periods. Overall, frost mound A presented mainly regressive trends in the thawing periods but a progressive trend in the freezing period (Figure 4). The seasonal retrogressive trend was caused by thaw settlement and soil erosion, and the seasonal progressive trend was probably due to frost heave and soil deposition through monitoring and survey. The upper zone was less affected by the surrounding environment. The upper soil slid to the radial cracks and the lower zone in thawing periods and then to the base. And the base can also be affected by the QTH. Therefore, the base was changing under the impact of erosion and deposition by flowing water (Figure 3).

3.2 Deformation of frost mound B

The changes in elevation (Z) of frost mound B during the two thawing periods and one freezing period are shown in Figure 5. Because the surrounding environment was more complex, frost mound B had more missing and singular values. The data quality for frost mound B was worse than that for frost mound A. The changeable climate of the QTEC and the complex surroundings is an important factor that affects data quality and accuracy (Luo et al., 2012 ). On the whole, frost mound B also presented mainly regressive trends in thawing periods and a progressive trend in the freezing period. The radial cracks also showed the opposite trend to that of the main body, whether in freezing or thawing periods. Through the examination of the two frost mounds, we found them to be dominated by frost heave after two thawing periods and one freezing period.

3.3 Deformation analysis

TLS and GNSS showed the changes in elevation deformation of frost mounds were vertical over the freeze–thaw cycles, but the surface-soil movement was diverse. In addition to thaw settlement in thawing periods, three factors—runoff, erosion and sedimentation—have strong effects on the deformation of frost mounds. Most of the material lost from the mounds in the thawing period was probably transferred into the underlying radial cracks and the base, and some was then washed into the base by rain or water because the elevation of the northern part of the base was lower (Figure 3). Except for frost heave in the freezing period, the changes in elevation of the frost mounds were susceptible to the effects of accumulated snow.

Table 1 illustrates the maximum air temperature/precipitation and total precipitation from 2014 to 2015. The total precipitation in 2015 was greater than in 2014. Precipitation in the Qinghai–Tibet Plateau is obviously less than the evaporation. In the short term, precipitation will have a direct influence on the changes in the shallow water areas, which will affect water and heat exchange; but these effects are local and short-lived. More attention may need to be devoted to analyzing the monitoring data over successive years to learn about the long-term effects of the permafrost. In addition, precipitation is a short-term stochastic process, requiring a lot of long-term data analysis to find the applicable law (Luo et al., 2017b ). The direct effect of air temperature is reflected in the difference in the process of freezing and thawing, and the change of ground temperature. All of the external climate changes are ultimately reflected in the freezing and thawing process of the soil and the changes in water and heat.

Permafrost indices can be computed using quantitative methods—such as analytical, numerical, or empirical functions—and models. Dozens of different indices have been used to evaluate the characteristics and dynamics of permafrost presence or absence, including the freezing/thawing depth, mean annual air temperature (MAAT), mean annual ground-surface temperature (MAGST), and active-layer thickness (ALT). These indices can be used to measure the intensity of freeze–thaw cycles and can be combined with deformation monitoring to analyze the terrain changes under the influence of freezing and thawing. The surface deformation measured by TLS measurement is the total displacement; and ALT can determine the range of soil layers where deformation occurs, thereby making it an important indicator of surface deformation. In 2015, the values of ALT, MAAT, and MAGST were greater than those in 2014; and the slope-terrain deformation during the thawing period was also much greater in 2015 than that in 2014. The changes in ALT and the slope terrain had a strong correlation, in which there was a positive trend in thaw settlement as the air temperature increased (Figure 6).

4 Conclusions

In recent years, field investigation has found such frost mounds in a large cluster near the KMP and also spread throughout in the QTEC. Most previous studies focused on the impact of climate change on thaw-induced slope instability, with little consideration of slope instability caused by freezing. This paper presents various deformation-monitoring technologies that were employed to monitor the frost heave and thaw settlement of two mounds along the QTEC, and to estimate their freeze/thaw–induced surface deformation. The two frost mounds exhibited mainly thaw settlement in thawing periods and frost heave in the freezing period; but frost heave dominates after repeated freeze–thaw cycles. Different zones of frost mound A showed different deformation characteristics. ALT and elevation changes were highly correlated during thaw periods. This study demonstrates that deformation measurements of the 3D-laser scanner TLS with GNSS are complementary to more traditional in situ measurements and can provide new insights into the deformation dynamics of freezing–thawing disaster. Integrated 3D-measurement technologies can achieve a better understanding and assessment of hazards in the permafrost zone. In the future, UAV could be used to detect topographical features as an alternative to expensive scanners and their necessary maintenance costs.

Acknowledgments:

This work was supported by the National Natural Science Foundation of China (41301508, 41630636). We express our gratitude to the editors and anonymous reviewers for suggestions that improved this paper.