Application of LIDAR for the Investigation and Monitoring of Geologic Hazard

Li-Dong Li Feng-Guang Sheng CHINA RAILWAY SIYUAN AND DESIGN GROUP Co. Ltd.,

Wuhan 430063，China e-mail: [email protected]

ABSTRACT Geologic hazards, such as landslides and collapses, frequently occur in mountainous areas of complex engineering geology. For this reason, in railroad and highway construction projects, one of the important tasks of engineering geology is to survey adverse geologic conditions along the construction line, and thus to identify their distribution range, scale as well as its potential side-effect and impact on the projects. Traditional manual surveying method assisted by aerial and satellite images has displayed many shortcomings, such as heavy workload, long cycle, low efficiency, low precision and big hidden danger, which makes it difficult to meet the improving requirements for quality, safety and efficiency of the engineering construction. In this case, Light Detection And Ranging (LIDAR) is proposed as it enables investigators to obtain three-dimensional information from the unfavorable geology in a quick way and to identify its scale, spatial distribution range as well as development, therefore providing accurate data for engineering geological survey. KEYWORDS: Light Detection And Ranging; engineering geological survey;adverse geology; investigation and monitoring; three-dimensional information

INTRODUCTION Railroad and highway have been developing rapidly in mountainous areas of China. However, due to complex terrain and geography, engineering geological conditions have always been complicated in these areas, especially affected by geologic hazards, which take in forms of landslides, collapses, perilous rocks and rockfall and occur quite frequently[1]. Consequently, concerning railroad and highway construction projects in mountainous areas, the major task of the engineering investigation is to effectively identify the development and distribution range of the vicinal adverse geology so as to acquire its accurate location, scale, engineering hazard and impacts[2]. Traditional remote sensing technology has been playing an important role in engineering geological surveying of railways and highways. However, due to its low image resolution, it is incapable of acquiring precise engineering geological data from micro-landforms and small or medium-sized geologic bodies, and may cause problems like heavy workload, high cost, low efficiency, large hidden danger and low precision in field investigation. These facts show that with the improving requirements for construction quality, safety and efficiency, traditional surveying technology has been greatly challenged. Moreover, this phenomenon continues with the acceleration of railway speed, as it not only gives rise to deformations in existing subgrades and slopes, but also brings difficulties to the surveying of unfavorable geology and potential security problems.

- 1399 -

Vol. 22 [2017], Bund. 04 1400

Laser scanning is an advanced and high-precision three-dimensional automatic scanning technology that has emerged in recent years[3]. Using high-speed laser scanning measurement, it is capable of obtaining massive 3D coordinate data from the surface of the observed object with high resolution. It is, therefore, a new technical means for quick recording of the 3D information of objects[4], and in the meantime, it pushes the acquisition of spatial 3D data towards the direction of becoming real-time, dynamic, integrated, digital and more intelligent. To sum up, 3D laser radar scanning technology helps researchers to obtain the 3D information of geological structures and adverse geologic bodies in a quick and effective manner, therefore perceiving the accurate scale, spatial location, distribution range and etc.

STRUCTURE AND TECHNICAL FEATURES OF LIGHT DETECTION AND RANG SYSTEM Light Detection and Rang (LiDAR) is a surveying method that uses laser light as carrier wave. As an electromagnetic radiation, the wavelength of laser light is much shorter than centimeter wave and millimeter wave. Information can be carried by its amplitude, frequency, phase and polarization[5]. A LiDAR system principally consists of a laser, an optical system (usually operated in oscillating or rotating scanning system), a receiver (usually photomultiplier), a GPS (Global Positioning System), an IMU (Inertial Measurement Unit), a flight planning and management system and a data acquisition and storage system. It can transmit, receive and post-position signals. One of the advantages of LiDAR is that it has a narrow laser beam divergence, which allows its energy to concentrate, thereby offering high detection sensitivity and resolution. Currently, there are many different types of LiDARs, and in terms of different carrying platforms, they can be divided into handheld laser radar, ground-fixed laser radar, vehicle-borne laser radar, airborne laser radar, ship-borne laser radar, space- borne laser radar and so on. LiDAR is a space surveying system that integrates laser ranging technology, computer technology, inertial measurement unit (MU) / D and differential positioning technology of GPS to acquire data and generate accurate DEM[6]. It operating principle is that a beam of discrete pulse of light emitted and fired from a laser transmitter hits on the target object and reflects to a receiver, which can accurately measure the propagation time of the light pulse from emission to reflection. As the light speed is known, the calculation of propagation time can be converted to the measurement of distance between the transmitter and reflector. Combining the height and scanning angle of the laser transmitter, the coordinates X, Y and Z of each ground facular can be accurately calculated from the laser position obtained from GPS and the laser direction obtained from INS. Meanwhile, the laser pulse can partially penetrate forest, allowing us to obtain 3D surface terrain data with high-precision directly. The data after being processed by relevant software generates contour map of high-precision digital terrain model. It is a low cost and highly efficient spatial data acquisition method, which possesses the advantages that are unable to be achieved by traditional photogrammetry and conventional land surveying technology. The 3D laser scanning technology opens up a new way to acquire spatial data of objects. It is characterized by the function of being able to collect massive 3D point data of target surface in a continuous, automatic and fast way, including: 1) can realize fast and real-time data acquisition; 2) can obtain huge amount of data with high precision; 3) is highly automatic and can be operated around clock; 4) is all digital, where information can be transmitted, processed and expressed easily[4].

Vol. 22 [2017], Bund. 04 1401

APPLICATION OF LIDAR IN ENGINEERING GEOLOGICAL SURVEY After the process of classification and devegetation, laser point cloud data is able to represent actual terrain and landform, promoting remote sensing interpretation of engineering geological factors. Using laser detection and ranging system installed on airborne and automobile carriers, we measured the 3D coordinates of our target object and obtained LiDAR data image and digital elevation model, which allows us to gain concrete data of spatial distribution of unfavorable geologic bodies, including their structures, landslides, collapses and talus and to perform remote sensing interpretation, from qualitative to quantitative, thereby providing us basis for determining the scale, size and development, and designing regulation measures for the unfavorable geologic body. The specific steps are as follows (Figure 1): (1) LiDAR data acquisition: The target object is scanned by airborne, vehicle-borne or hand-held LiDAR, and imaged by high-resolution CCD camera. The acquired data include original laser point cloud data, high-resolution images, inertial Measurement Instrument (IMU) data, airborne GPS data and ground station GPS data. (2) LiDAR data processing: The data processing includes orientation, correction and coordinate transformation of laser point cloud data. The corrected laser point cloud data is WGS84 in the coordinate system, which can be converted according to engineering requirement. Specifically, the step is to measure the coordinates of the survey area, where a reflection target is set up. By acquiring the local coordinates of the reflection target and the survey area, we were able to work out the transformation relation between the two coordinates, and then based on the relation, we switched the colored laser spot cloud in local coordinate system to the coordinate system of the survey area. (3) LiDAR data classification: Relevant softwares were applied to classify and hierarchically display the original laser point cloud data in accordance with surface, vegetation, buildings, roads, bridges and so on, so as to meet the needs of different specialties. (4) Devegetation: After the classification of laser point cloud data, we removed the laser spot reflecting vegetation from the point cloud data to ensure that the laser point cloud data is from actual surface. This process facilitated our digital earth modeling (DOM) and digital elevation modeling (DEM). (5) Application of surface laser point cloud data: DOM, DEM, orthophotograph and DEM of mountain shadow map were generated based on surface laser point cloud data. Applying DOM and DEM, we then created 3D topographic terrain of the survey area and represented its scene; using orthophotograph and mountain shadow map, we performed remote sensing interpretation on faulted structures, lithology and other unfavorable bodies (landslides, collapses, talus, mudflows and karsts), which allows us to gain spatial position information (scale, size, shape, etc.) of the engineering geological factors and automatic generation of the cross-section of the adverse geologic bodies.

Vol. 22 [2017], Bund. 04 1402

LiDAR data collection

LiDAR data processing

LiDAR data classification

Devegetation

Laser spot cloud data from the surface

DOM DEM Orthophotograph

3D terrain modeling Collect spatial information of the adverse geologic bodies

Figure 1: Flow chart showing how information about engineering geological factors is acquired from LiDAR The application of LiDAR for monitoring the adverse geology is mainly achieved through the following functions: (1) Micro-topography and other concrete features of geo-hazards such as landslides are acquired with rapidity and accuracy; (2) Topographic parameters such as fine surface roughness, slope, slope aspect and others are quantitatively gathered[8]; (3) From multi-period data, subtle variations in topography and landform are precisely collected[7]. These offer effective technical support for the investigation and monitoring of landslides and other geological hazards. In railway and highway operation and maintenance, hazards like subgrade settlement and slope deformation occur frequently. Manual investigation into the hazards on the existing lines not only poses great security threat to the operators, but also has difficulty in meeting the depth and width requirements of the investigation. However, a multi-temporal three-dimensional scanning of the existing subgrade and side slope performed by airborne or vehicle-borne LiDAR can detect their current situation effectively and determine the embankment area of the subgrade and the unstable area of the side slope in a quick way. Because of this, it is capable of providing first-hand information for eliminating security risks along the existing lines. Vol. 22 [2017], Bund. 04 1403

APPLICATION OF LIDAR IN BALUNTAI RAILWAY, XINJIANG

Engineering survey Baluntai Railway is located in Baluntai town of Hejing County in Bayingolin Mongol Autonomous Prefecture, the central region of Xinjiang Uygur Autonomous Region in China. An approximately 95km in length, this railway line extends from Baluntai Station, an existing station in the Southern Xinjiang Railway. Running along its existing line, it turns southwestward in switchback curve after crossing China National Highway 216, China National Highway 218 and Ulastai River Valley, and then winds northwestward along Habuqihansala river valley. The route enters the northern part of Yulduz Basin via Tuolateguole, and continues to stretch northwestward along the piedmont terraces of the north bank of Kaidu River near Bayanbulak Grassland National Nature Reserve to Yiergenbulak, beside China National Highway 218.

Landform and water system The major geomorphic units along Balunta-Yiergenbulak line are mountain area of mountains in Middle Tianshan Mountains, hilly area of mountain ridge in Middle Tianshan Mountains and basin area (plain area). ① The mountain area of mountains in Middle Tianshan Mountains: It is located between Baluntai and Kaiteledaban, between 1800~3200m in elevation; it has a relative altitude between 500~1000m, and appears EW in general; the river valleys are criss-crossing, presenting the form of “V”; the longitudinal slope of the gully bed is steep, with strong downcutting, steep terrain on both banks, natural slope between 50 º~70 º, and fast flowing rives, making transportation difficult. This area is mainly covered by rocks, which are weathered and eroded due to long-time exposure, and thin vegetation, presenting semi-desertification landscape. ② The hilly area of mountain ridge in Middle Tianshan Mountains: This area is located between Kaiteledaban and Yiergen. It is between 3000~3200m in elevation, and between 20 ~ 50m in relative altitude, developing undulating terrain, which is relatively flat. The hilly area is made of small hills mostly, which are covered by lush vegetation and abundant with water. Due to this, it becomes mountain pasture of Hejing County. ③ Basin area (plain area): This area is located between Kaiteledaban and the mountain area of Yiergen. The highest elevation is 2574m. It presents a nearly east-west distribution, tilting westward, with irregular shapes. The terrain inside the basin is flat and wide, making it suitable for ranches. Besides, large area of swamps and wetlands are developed along Kaidu riverbanks at the southern part of the basin. The waters in the survey area belong to Kaidu river system. They present in dendritic water system, mainly supplied by snowmelt of Glacier and spring water. The Kaiteledaban Mountain near the route CK56+500 is the watershed, from where the water flows westward into Kaidu River, eastward into Habuqihansala, Naimenwusu and Guole River of Ulastai, and finally into Kaidu River via Huangshui Gully.

Geologic lithology and structure The lithology of the survey area is complex. Before the route section CK61, the surface is covered with sparse vegetation, causing bedrocks exposed to the open air. After the section CK61, it Vol. 22 [2017], Bund. 04 1404 is extensively covered with soil layer of Cenozoic Quaternary system. The exposed strata are Middle Tianshan Mountains mainly composed of Quaternary, Xiyu formation of Tertiary, Carboniferous system and Proterozoic, among which Caledonian and Variscan magmatic rocks are intruded. The lithology of Proterozoic are medium and high metamorphic rocks such as gneiss, quartzite, quartz plate and etc.; the Carboniferous system is composed of sandstones, sandy conglomerates, siltstones and limestones, etc.; the Tertiary is mainly consisted of conglomerates, sandy mudstones, muddy fine sandstones and sandy conglomerates, among which the conglomerates are of poor sorting, good cementing and complex composition; the Quaternary is mainly composed of alluvial-proluvial sand- gravel layers, silty clays, silts, eolian sands and so on. The directions of faulted structures in the survey area are mainly NWW and NW. There are many fractured rock masses along the fault zone and significant changes in the attitude of beds with folds developed in some local area. The faults that have great effect on the route are Zulaoyin-Wulahugaote fault and faults at CK4 + 500, CK25 + 600, CK38 + 300 and CK50 + 400.

Survey of adverse geologic conditions Adverse geology along the railway line is well-developed, including landslides, karst, bedding, mudflow, perilous rocks, rockfall, collapses, sandstorm and snow hazard, and the latter six are the most common hazards, and generation and development of which are frequently determined by tectonics, lithology, climate geomorphology as well as hydrogeological conditions. Preliminary and location surveys are carried out in the field, based on the interpretation of remote sensing. To be concrete, the remote sensing, making use of DEM of LiDAR data, shadow mapping and orthophotograph, is capable of interpreting the adverse geologic conditions along the whole line, and sending the results to field geologists, which provides guidance for on-site engineering geological survey. (Figure 2)

Figure 2: Orthophotograph interpretation of Bailuntai Station Mudflows Mountains in the survey area are characterized by thin vegetation, steepness and big longitudinal gradient of the gully, making this area more prone to the formation and flow of mudslides, which are of frequent occurrence, large scale, and superimposed development, with certain mobility. A total of 38 mudflow gullies and ancient mudflow gullies are discovered by remote sensing interpretation. They are mostly valley-type and hill-type mudflows formed of various deposits, including mostly colluvium and deluvium result from adverse geologic phenomena of avalanche and collapse, and a small amount of weathered bedrock. The mudflow that exerts the most significant impact on the Vol. 22 [2017], Bund. 04 1405 construction of the railway line is mainly located in the banks of Habuqihan River, which lies before the section of CK45+000, presenting in stripped development along the river and gully valley. (Figure 3).

Figure 3: Striograph and on-sites photos of mudflows

Perilous rocks, rockfalls and collapses Perilous rocks, rockfalls and collapses are a commonplace mainly distributed in areas before the section CK33 between the sections of Husitai station and Baluntai Station of Southern Xinjiang Railway, along 218 national highway and pasture lane of Habuqihan river valley, and other steep slope area near the traffic line of mountains zone and escarpments of natural slopes. Due to long-time exposure, rocks are intensely weathered and denudated and present fracture development with fragmented rockmass, making them prone to collapse under the effects of rainfall, earthquake and frost heaving. A total of 42 development areas of dangerous rocks, rockfalls and collapses are interpreted by the remote sensing and are verified by field investigation (Figure 4). Concerning the security of the line, tunnels are shifted and constructed to avoid the unstable valley area and development areas of large-scale of perilous rocks and collapses. Vol. 22 [2017], Bund. 04 1406

Figure 4: Orthophotograph mapping the development area of collapse

Landslides There are only few landslides in the survey area. The remote sensing interpretation shows 6 spots of small-scale landslide, which are all been verified in the field survey. The largest one is 90m long and 55m wide, developed on the right side of Habuqihan River line, and has been avoided by making a detour (Figure 5).

Figure 5: Orthophotograph capturing landslides Sandstorm In the CK87 ~ CK95 sections, the aeolian landform is developed and mainly composed of aeolian sand dune, which is formed by sand source accompanied with monsoon. It is distributed towards the northwest along Kaidu valley and deposited on windward hillside and riverbank, developing semi- Vol. 22 [2017], Bund. 04 1407 fixed sand lands and dunes, with the height of the latter generally less than 5m and local height 7 to 8 meters (Figure 6).

Figure 6: Striograph mapping aeolian accumulated dunes

CONCLUSIONS As a newly emerged surveying technology, LiDAR has been widely applied in military affairs, electricity, highway, water resource systems and so on. Its application in engineering geological survey, especially hazard investigation and monitoring presents apparent advantages, which mainly include: (1) The mountain shadow mapping performed by LiDAR can provide precise microtopographic features. In particular, different azimuth of the mountain shadow map can present different microtopographic features in different directions, which breaks through the barriers of our understanding of geological hazards caused by low data accuracy in previous researches. (2) The gradient image, surface roughness image and orthoimage captured by LiDAR provide quantitative geomorphologic parameters for the identification and analysis of geologic hazards, and scientific basis for delineation of landslides and surface subsidence boundary. (3) Using LIDAR data obtained at different periods to monitor slope deformation and ground subsidence help us grasp the developing trend and characteristics of slope deformation and ground subsidence in a certain period of time, and accurately measure the deformation, improving the monitoring efficiency and accuracy of slope deformation and ground subsidence. Our specific engineering case shows that the application of LiDAR technology in engineering geological survey, especially in the investigation and monitoring of geologic hazards proves to be fast, convenient and of high precision. Compared with traditional remote sensing technologies, it has certain irreplaceable advantages, which makes it a popular method with promising application prospect.

Vol. 22 [2017], Bund. 04 1408

REFERENCES 1. Hu Houtian: “Collapse and falling rocks” [M] Beijing: Railway Press of China, 1989. 2. Railway Ministry of China: “Code for Unfavorable Geological Condition Investigation of Railway Engineering” (TB10027-2012) [S]. Beijing: China Railway Publishing House, 2012. 3. Lai Xudong: “Basic Principle and Application of Airborne Laser Radar” [M]. Beijing: Publishing House of Electronics Industry, 2010. 4. Zhang Huixia, Zhu Wenbo: “Data Processing Theory and Application of 3D Laser Scanning” [M]. Beijing: Publishing House of Electronics Industry, 2012. 5. Zhang Xiaohong: “Airborne Laser Radar Technology and Methods of Measurement ”[M]. Wuhan: Wuhan University Press, 2007. 6. Han Gaixin: “Discussion on Application of LIDAR Technique in Railway Surveying and Design” [J]. Railway Investigation and Surveying, 2008(3): 1-3. 7. Liu Shengwei, Guo Dahai: “Application of Airborne Lidar Technology in Landslide Investigation and Monitoring of Three Gorges Reservoir Area” [J]. China Geology, 2012, 39(2): 507-514. 8. Gao shan: “Application of 3D Visualization Remote Sensing Interpretation Technology in Geological Survey of Xiang –Pu Railway Long Tunnels”[J]. Journal of Railway Engineering, 2011(11): 13-18. 9. Liu Shien, Guo Hong, Wang Jinshu, Chen Yingchao, Li Jiangfei: “The Geologic Condition and Fracturing Technique of Tight Sand Gas Reservoir in Xujiahe Formation” Electronic Journal of Geotechnical Engineering, 2015 (20.28) pp 13465-13476. Available at ejge.com. 10. Chen Xuguang, Xu Wei, Mei Yu: “The Mechanism of Geo-Hazards and Its Geotechnical Management in Scenic Area-Taking Laoshan as an Example” Electronic Journal of Geotechnical Engineering, 2013(18.P): 3451-3460. Available at ejge.com. 11. Salah Sadek, Ramzi Ramadan, and Hani Naghi: “A GIS-Based Landslide Hazard Framework for Road Repair and Maintenance” Electronic Journal of Geotechnical Engineering, 2005: Paper 0604. Available at ejge.com. 12. Da Chen, Weidong Song, and Shuai Cao: “Numerical Simulation of Land Subsidence and Its Field Monitoring Using Caving Method for an Iron Mine” Electronic Journal of Geotechnical Engineering, 2016 (21.06) pp 2215-2230. Available at ejge.com. 13. Hossein Moayedi, Bujang B. K. Huat, Fatemeh Moayedi, Thamer Ahmad Mohammad Ali, and Arash Ansari Moghaddam: “23 Years Water Level Monitoring through Earthfill Dam (Case Study)” Electronic Journal of Geotechnical Engineering, 2011(16.A): 41-58. Available at ejge.com. 14. Guangqian Du and Wenjuan Zuo: “Dynamic Monitoring and Analysis of Pile-Anchor Support in Excavation Process” Electronic Journal of Geotechnical Engineering, 2015 (20.25): 12671-12678. Available at ejge.com. 15. Mo-Wen Xie, Yan-Chang Jia, Fu-Xia Lv, Bo He, Sheng-Xiang Chang: “A Comprehensive Information System For Landslide Monitoring Based on a Three- Dimensional Geographic Information System” Electronic Journal of Geotechnical Engineering, 2014(19.W): 6929-6937. Available at ejge.com Vol. 22 [2017], Bund. 04 1409

16. Qiang Yue and Jie Yuan: “GPS Monitoring Landslide Displacement Prediction Using Nonlinear Analysis and Back-Propagation Neural Network” Electronic Journal of Geotechnical Engineering, 2016 (21.12), pp 4305-4316. Available at ejge.com. 17. Xiaojun Dai, Jianjun Liu, and Yuan Li: “Analysis of Roadbed Deformation Monitoring of Cangzhou Section of Beijing to Shanghai Express Railway” Electronic Journal of Geotechnical Engineering, 2015 (20.2) pp 814-. Available at ejge.com 18. Mo-Wen Xie, Yan-Chang Jia, Fu-Xia Lv, Bo He, Sheng-Xiang Chang: “A Comprehensive Information System For Landslide Monitoring Based On A Three- Dimensional Geographic Information System” Electronic Journal of Geotechnical Engineering, 2016(21.09): 3065-3087. Available at ejge.com 19. Ganesh Mainali, Erling Nordlund, Sven Knutsson, Hans Thunehed: “Tailings Dams Monitoring in Swedish Mines using Self-Potential and Electrical Resistivity Methods” Electronic Journal of Geotechnical Engineering, 2015 (20.13) pp 5859-5875. Available at ejge.com. 20. Xiaojun Dai, Jianjun Liu, and Yuan Li: “Analysis of Roadbed Deformation Monitoring of Cangzhou Section of Beijing to Shanghai Express Railway” Electronic Journal of Geotechnical Engineering, 2008(13.K): 1-15. Available at ejge.com. 21. Guangqian Du and Wenjuan Zuo: “Dynamic Monitoring and Analysis of Pile-Anchor Support in Excavation Process” Electronic Journal of Geotechnical Engineering, 2015 (20.25): 12671-12678. Available at ejge.com. 22. Da Chen, Weidong Song, and Shuai Cao: “Numerical Simulation of Land Subsidence and Its Field Monitoring Using Caving Method for an Iron Mine” Electronic Journal of Geotechnical Engineering, 2016 (21.06) pp 2215-2230. Available at ejge.com. 23. Mo-Wen Xie, Yan-Chang Jia, Fu-Xia Lv, Bo He, Sheng-Xiang Chang: “A Comprehensive Information System For Landslide Monitoring Based on a Three- Dimensional Geographic Information System” Electronic Journal of Geotechnical Engineering, 2014(19.W): 6929-6937. Available at ejge.com 24. Qing-rui Lu, Kai Zhao, Perino Andrea, and Barla Giovanni: “Three-dimensional Stability Analysis of Fine Grained Tailings Dam with Complex Terrain by Means of Up-stream Method” Electronic Journal of Geotechnical Engineering, 2016 (21.10), pp 3965-3978. Available at ejge.com. 25. Lei Bu and Han-min Zhou: “Three-Dimensional Stability Analysis of Fine Grained Tailings Dam with Complex Terrain by Means of Up-stream Method” [J] Electronic Journal of Geotechnical Engineering, 2016(21): 3905-3918. Available at ejge.com. 26. Kalatehjari R, Ali N. “A review of three-dimensional slope stability analyses based on limit equilibrium method.” [J] Electronic Journal of Geotechnical Engineering, 2013(18): 119-134. Available at ejge.com. 27. Liangfu Xie, Cong Sun, Xubin Ren: “Analysis of Reservoir Landslide Deformation Mechanism Based on the Three-Dimensional Fluid-Structure Coupling” Electronic Journal of Geotechnical Engineering, 2014(19.I): 2023-2032. Available at ejge.com.

Editor’s note. This paper may be referred to, in other articles, as: Li-Dong Li and Feng-Guang Sheng: “Application of LIDAR for the Investigation and Monitoring of Geologic Hazard” Electronic Journal of Geotechnical Engineering, 2017 (22.04), pp 1399-1410. Available at ejge.com.