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Rockfall hazard analysis using LiDAR and spatial modeling
Geomorphology 118 (2010) 213–223
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Geomorphology j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / g e o m o r p h
Rockfall hazard analysis using LiDAR and spatial modeling Hengxing Lan a,b,⁎, C. Derek Martin b, Chenghu Zhou a, Chang Ho Lim b a b
LREIS, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China Department of Civil and Environmental Engineering, University of Alberta, Edmonton, Alberta Canada T6G 2W2
a r t i c l e
i n f o
Article history: Received 4 February 2009 Received in revised form 28 December 2009 Accepted 3 January 2010 Available online 11 January 2010 Keywords: Rockfall Hazard analysis LiDAR Spatial modeling Rockfall analyst Railway
a b s t r a c t Rockfalls have been significant geohazards along the Canadian Class 1 Railways (CN Rail and CP Rail) since their construction in the late 1800s. These rockfalls cause damage to infrastructure, interruption of business, and environmental impacts, and their occurrence varies both spatially and temporally. The proactive management of these rockfall hazards requires enabling technologies. This paper discusses a hazard assessment strategy for rockfalls along a section of a Canadian railway using LiDAR and spatial modeling. LiDAR provides accurate topographical information of the source area of rockfalls and along their paths. Spatial modeling was conducted using Rockfall Analyst, a three dimensional extension to GIS, to determine the characteristics of the rockfalls in terms of travel distance, velocity and energy. Historical rockfall records were used to calibrate the physical characteristics of the rockfall processes. The results based on a highresolution digital elevation model from a LiDAR dataset were compared with those based on a coarse digital elevation model. A comprehensive methodology for rockfall hazard assessment is proposed which takes into account the characteristics of source areas, the physical processes of rockfalls and the spatial attribution of their frequency and energy. © 2010 Elsevier B.V. All rights reserved.
1. Introduction Rockfall is one of most significant geohazards encountered by railways along the Canadian Rockies (Keegan, 2007). Railways have unusually high exposures to ground hazards because of their great length. The Canadian railway systems have experienced various ground hazards since the initial construction of railways in the late 1800s. Canadian railway industries such as the Canadian National Railway (CN Rail) and the Canadian Pacific Railway (CP Rail) have been suffering from losses caused by ground hazards including damage to infrastructure and interruption of business and environmental impacts. For example, among the losses by all railway accidents during 1992 to 2002, those caused by ground hazard are severest with a mean loss of CAD $400,000 and the longest duration of outage for each accident (Keegan, 2007). Rockfall is an important natural geomorphic process acting on steep mountain slopes (Whalley, 1984; Matsuoka and Sakai, 1999). It is a predominant type of mass movement in the Canadian Rocky Mountains (Trenhaile, 2007, p. 114). The initiation of rockfalls can usually be attributed to a combination of climatic, topographic, and vegetational factors which induce rock fracturing, opening of joints, pore pressure increases due to rainfall infiltration, freeze–thaw processes in a cold region, and chemical weathering of rock (Lim et al., 2004). Rockfalls in ⁎ Corresponding author. Department of Civil and Environmental Engineering, University of Alberta, Edmonton, Alberta Canada T6G 2W2. Tel.: +1 86 10 64889318; fax: +1 86 10 64889630. E-mail address: [email protected] (H. Lan). 0169-555X/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.geomorph.2010.01.002
the Canadian Rocky Mountains are most frequent between November and March because freeze–thaw cycles are effective for rock detachment. Once the movement of a rock is initiated, the geometry of the slope controls rockfall trajectory. In many situations rockfall hazards cannot be eliminated because their magnitude and frequency vary both spatially and temporally. The high mobility of fallen rocks is a major difference from other slope instability phenomena (Frattini et al., 2008). A limiting factor in rockfall hazard assessment is the deficiency of high-resolution geospatial data for the analysis of slope topography, rockfall detachment areas, rock block geometry, and rock traveling paths including runout distance (Hutchinson, 1988; Evans and Hungr, 1993; Dorren, 2003). The confidence of rockfall hazard assessment also depends upon the quality and quantity of available historical data (Hutchinson et al., 2006). Conventional survey methods present serious limitations for collecting spatial datasets required for rockfall modeling. The use of new technologies such as LiDAR (Light Detection And Ranging) has rapidly increased in the field of geohazard assessment. Both groundbased and airborne LiDAR surveys are now regarded as indispensable tools for dense and accurate data acquisition to facilitate detailed topographical analysis (Nagihara et al., 2004; McKean and Roering, 2004; Bellian et al., 2005; Rosser et al., 2005; Metternicht et al., 2005; Glenn et al., 2006). Ground-based LiDAR uses a reflectorless laser to capture 3D topographic data, in a high speed, high precision and low cost manner (Rosser et al., 2005). It is capable of capturing detailed topographic information of nearly vertical structures. Airborne LiDAR is suitable for generating a high-resolution digital elevation model (DEM)
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for a large area. The highly accurate topographic data (150-mm vertical accuracy) can help assess the geomorphology, geological settings, structural discontinuities and mechanical characteristics of slopes and rock blocks (Rosser et al., 2005; Metternicht et al., 2005; Chang et al., 2005, Lato et al., 2009). LiDAR data may also improve the estimation of modeling parameters and reduce model structural error for rational rockfall hazard assessment. To assess rockfall processes it is necessary to use topographic data with a resolution relevant to the scale of morphological features being examined (Glenn et al., 2006). Numerical modeling of rockfall processes on fine-scale topographic data can provide insight into the relationship between topography and rockfall behavior (Pfeiffer and Bowen, 1989; Jones et al., 2000; Guzzetti et al., 2002; Agliardi and Crosta, 2003; Dorren and Seijmonsbergen, 2003; Martin et al., 2006; Lan et al., 2007; Yilmaz et al., 2008). A three dimensional program linked to a geographic information system (GIS), Rockfall Analyst, was developed to simulate rockfall processes and the spatial distribution of their frequency, runout and energy (Martin et al., 2006; Lan et al., 2007). In this paper, we discuss a methodology of rockfall process modeling using LiDAR and spatial modeling approaches for hazard assessment along a section of the Canadian railway. We present a procedure for systematically zoning rockfall source areas. The characteristics of the 3D physical processes of rockfalls are examined in terms of traveling
distance, velocity and energy. The modeling results from two different datasets are used to interpret the effect of the resolution of topographic data on rockfall assessment. The spatial distribution of rockfall frequency and energy was simulated to provide information necessary for hazard control. 2. Study area The Canadian railway industry has a rich database of past ground hazards. Approximately 2500 reports for the CP Rail and 1000 for the CN Rail have been compiled since the beginning of the 20th century for the total railway length of 14,000 km. The database has been improved and enhanced over the past few decades to permit the analysis of individual rockfall events. The incident reports show the time of events, possible triggers, possible source areas, impacts on operations and remedial measures categorized by subdivision and site location using track mileage. A representative part of the CN Rail (mileage 8.8–9.9) in the Cascade subdivision located in southern British Columbia is selected as our study area to evaluate the railway rockfall hazard using LiDAR technology and spatial modeling (Fig. 1). Rockfalls are frequent in this section; for example, nearly ten rockfall events were recorded in 1997. The rock fall event database contains the information back to the 1940s. Both LiDAR and ordinary digital elevation models are available.
Fig. 1. Study area in the southwest of Canada. Shaded LiDAR 1-m DEM overlaid on the 20-m DEM of Canadian Digital Elevation Data (CDED). Dots and associated numbers indicate the track location mileage. Cross sections (A–A′) from the 1-m and 20-m DEMs are inset in the middle top of the map.
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Various attributes related to geology, land use, and vegetation cover have been stored in the GIS database. The record of historical rockfall events enables the calibration of the model parameters and the validation of the rockfall modeling results. Moreover, the National Climate Data and Information Archive operated and maintained by Environment Canada contains climate and weather data. Data such as daily temperature, precipitation, and snow accumulation were compiled for the weather station closest to the Cascade subdivision (Lim et al., 2004). In terms of geological and physiographical conditions, the study area is dominated by intrusive rocks consisting of quartz diorite and granodiorite with slightly metamorphosed sedimentary rocks. Similar to some other areas in the railway corridor in the Canadian Rockies, geotechnical conditions in this section such as lithology, weathering of bedrock, joint spacing and orientation, and groundwater infiltration influence the triggering of rockfalls (Wieczorek et al., 2008). The annual rockfall frequency is highest in February that corresponds to the highest number of freeze–thaw cycles. The amount of daily rainfall was also found to be a factor affecting the rockfall occurrence and frequency (Lim et al., 2004). 3. Material and methods 3.1. LiDAR survey An airborne LiDAR survey was conducted in 2004 along the studied section of the CN Rail. A DEM with a horizontal resolution of 1 m (1-m DEM) and a vertical accuracy of ±150 mm was generated from high-resolution bear-earth LiDAR point data. For comparison a coarser DEM with 20 m resolution (20-m DEM) was prepared using the Canadian Digital Elevation Data (CDED) which has a maximum resolution of 0.75 arc-sec. The 1-m DEM shows much finer topography than the 20-m DEM (Fig. 1). Both DEMs were used to derive cross
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Fig. 3. Illustration of sharp topographic contrast.
sections along A–A′ in Fig. 1. Detailed ground features can be easily identified along the section from the 1-m DEM including two railways (CN and CP) and a highway. Both datasets enable us to evaluate the effect of the different DEM inputs on rockfall kinematic variables such as runout, velocity, dispersion and frequency as well as energy distribution. 3.2. Spatial modeling Spatial modeling of rockfall hazards was performed using Rockfall Analyst, a GIS extension (Lan et al., 2007). Rockfall Analyst includes two major parts: (1) 3D rockfall trajectory simulation, and (2) raster modeling for spatial distribution of rockfalls. Because the spatial autocorrelation of factors affecting rockfalls (e.g. slope geometry, geology and vegetation) exerts control on the distribution of rockfall events in terms of their runout extent, velocity and energy distribution, a geostatistical method is used to simulate rockfall frequency, energy, velocity and trajectories. The spatial distribution of rockfall hazard distribution was investigated using the simulated distribution of rockfall frequency and energy to evaluate the potential rockfall impact to the railway operation. Spatial overlay of different types of datasets is one of the most fundamental GIS functions used in Rockfall Analyst to provide a new dataset reflecting the spatial relationships of various datasets such as slope, climate, land use and terrain types. The approach has been frequently used in mass movement research such as the assessment of landslide susceptibility and rockfall potential. Aronoff (1989) described two types of overlay operators: arithmetic and logical. Arithmetic operation was used in our study. Capabilities are available in GIS to allow us to assess the data quality and accuracy of the data during the overlay operation. For example, seven maintenance and analysis functions have been described by Aronoff (1989) for spatial data quality control: format transformation, geometric transformation, transformation between map projections, conflation, edge matching, editing of graphic elements, and line coordinate thinning. Most of these functions have been used in our studies. 3.3. Modeling procedure
Fig. 2. Rockfall hazard assessment workflow using Rockfall Analyst.
The modeling procedure is shown in Fig. 2. Firstly, potential rockfall source areas were determined using detailed topographic data from LiDAR and high-resolution (1 m) orthoscopic color aerial photographs. The identification of source areas should take account of various features such as sharp topographic contrast, slope angle, terrain type and
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vegetation cover. For example, Guzzetti et al. (2003) used a DEM and thematic maps such as land use and soil types to identify potential rockfall sources. Sharp topographical contrast was considered to identify the top portion of a steep slope as a potential rockfall detaching area. It was detected as a sudden change in the slope angle by comparing the 1-m DEM with a DEM smoothed using a mean filter. The smoothed DEM was subtracted from the original DEM. Positive values indicate the upper portion of a steep slope which could be high rockfall potential area (Fig. 3). A similar approach was also taken by Crosta and Agliardi (2003). A slope angle layer was derived from the original DEM using Spatial Analysis extension in the GIS. The terrain types were classified by spectral information of air photos.
A spatial overlay analysis with a ranking and scoring system was performed on various factor layers including the layer of the sharp topographical contrast to identify potential rockfall seeders. Each factor was classified into five different classes, and different weights were set to the factor layers based on their relative effects on rockfall initiation. Grid cells with high total scores were delineated as rockfall sources. The weight for each layer was identified through three steps: (1) Refer to the previous research work. Extensive assessment of rockfall hazards along Canadian transportation routes was initiated in 1976. A systematic way to assign and utilize five priority ratings based on the estimated probability of failure was established (Brawner and Wyllie, 1976), which took into account the geology and rock conditions, slope
Fig. 4. Identification of potential rockfall sources for the study area of the Canadian National Railway (CN). a) Reclassified sharp topographic contrast. b) Reclassified slope angle. c) Reclassified terrain types. d) Potential rockfall sources.
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Table 1 Statistics of rockfalls dispersion and runout distance from sources S1, S2 and S3. Sources
S1 S2 S3 Mean
Fig. 5. Relationship between slope angle and rockfall frequency obtained from the rockfall database and the 1-m DEM.
geometry, ditch dimensions, hydrology (seepage) and the event history, such as the number and characteristics of past events. (2) Rigorous field investigations on different environmental conditions were carried out. (3) Validate the real rockfall records. The resultant rockfall sources should cover over 95% of the known rockfall sources.
Lateral Dispersion (W/L)
Mean runout distance (m)
1-m DEM
20-m DEM
1-m DEM
20-m DEM
33% 44% 43% 40%
13% 7% 32% 17%
299 m 362 m 367 m 342 m
285 m 387 m 424 m 365 m
Once the potential rockfall sources were identified, rockfall physical processes were simulated using Rockfall Analyst by considering ground topography and calibrated mechanical parameters. A number of starting directions were assigned to each rockfall seeder which results in nearly 3000 rockfall events simulated. Once the DEM was created from the LiDAR data and CDED data and the spatial attributes of surface terrains assigned, potential rockfalls were simulated from all of the possible source areas. It involves rock detachment and fall, and subsequent bouncing, rolling, sliding and deposition. Raster images of rockfall spatial frequency, flying height (potential energy) and kinetic energy were provided based on the simulated 3D rockfall trajectories and their velocity. The resolution of each raster image is the same as that of the input DEM (i.e. 1 m for the LiDAR DEM). The simulation of rockfall processes described above using the DEM from the LiDAR survey was repeated using the 20-m DEM. Using the raster modeling function in Rockfall Analyst, the number of rockfall trajectories was calculated for
Fig. 6. Simulation of rockfall trajectories from three different sources using the (a) 1-m and (b) 20-m DEMs and longitudinal profiles of typical trajectories with velocity (c and d).
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each grid cell with an area of one square meter. Spatial geostatistical techniques were employed to analyze the trajectories and determine the rockfall spatial frequency for the whole study area. Topographic analyses provide a crucial geomorphic context for rockfall hazard (Kirby et al, 2008). To address the effect of topography, two simulations were carried out using the high-resolution 1-m DEM and the coarse resolution 20-m DEM separately. The comparison of the two modeling results was conducted in terms of critical rockfall characteristics such as runout, flying height, and velocity. Three different rockfalls source areas, S1, S2 and S3, were selected for this purpose. Hundreds of rock blocks (seeders) were set at each source with various starting directions. The W/L ratio (the ratio of the width W to the length L of the invasion area) was selected as an index of rockfalls lateral dispersion (Crosta and Agliardi, 2004). Finally, the potential impact of rockfalls on the railway tracks was evaluated using the final rockfall hazard map which takes all available raster layers into account.
4. Results 4.1. Rockfall source identification Fig. 4a–c shows the obtained geomorphological layers, and Fig. 4d shows the identified potential rockfall sources. The cells of a higher class in each layer indicate high potential for rockfall source. The final rockfall sources are characterized by sharp topographic contrast, steep slope angle and loose vegetated or bared terrain types.
4.2. Effect of topography The rockfall database was analyzed to establish the slope angles associated with the historic rockfalls. A raster image of the slope angle was created and the mean slope angle was determined for every 0.16 km (0.1 mile) along the railway. The results are plotted in Fig. 5 and
Fig. 7. Spatial distribution of (a and b) rockfall height and (c and d) velocity modeled from the (a and c) 1-m and (b and d) 20-m DEMs.
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Fig. 8. Comparison of (a) mean flying height and (b) mean velocity modeled from the 1-m and 20-m DEMs.
show that about 50% of the rockfalls occur at slope angles less than 40°, and over 84% of the rockfalls occurred at angles greater than 30°. To further examine the effect of topography on the rockfall behaviors, simulations using both 1-m and coarse 20-m DEMs were carried out (Fig. 6). The three dimensional trajectories from S1, S2 and S3 are illustrated by Green, Blue and Red respectively. Typical profiles of rockfall trajectories and their velocity are shown in the bottom of Fig. 6. Their invasion zones were defined by the lateral dispersion of trajectories and the extent of rockfall runout. Rockfall trajectories simulated on the high-resolution DEM exhibit more complex shapes than those on the coarse DEM, and their traveling velocities change more frequently (Fig. 6), indicating more
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interactions between rocks and the slope. For instance, the rockfalls shown in the circle in Fig. 6 stopped right on the track, but none of the rockfall trajectories simulated on the coarse 20-m DEM had this type of interaction. It agrees with the field investigation that rock blocks lying on railway tracks are very common once rockfall events occur. The impact area of rockfall on the railway track can be evaluated based on the information of rockfall runout and lateral dispersion. From Table 1, rockfall invasion zones obtained from the higher resolution 1-m DEM have larger lateral dispersion and shorter runout distance than those from the coarse DEM. The flying/bouncing height and velocity are two important variables of kinematics energy of rockfalls. They reveal the dynamic interaction between rock blocks and topography during their kinematic processes. The rockfalls' flying height relative to ground represents their potential energy imposing on ground surface. The velocity variable indicates the kinematic energy of rockfalls. Both control the potential damage that rockfalls might pose on the infrastructures such as railway tracks. Therefore, they are important indices for evaluating the rockfall hazard and helping design the barriers. Fig. 7 shows the spatial distribution of rock bouncing height and velocity. The simulated results from the two different datasets also point to the effect of topography on rockfall energy distribution. These rockfall kinematic variables are strongly controlled by a local 3D slope form. The decreased grid resolution of the 20-m DEM resulted in a smoother and simplified slope geometry without micro-topographic features, which absorbs less kinetic energy of rockfalls during their activities (Crosta and Agliardi, 2004). Compared to the simulation result from the 1-m DEM, the rockfalls simulated with the 20-m DEM have higher mean height and velocity (Fig. 8), suggesting that the energy prediction using the coarse DEM could be overestimated. The average height of the whole zone simulated using the 20 m DEM is twice as high as that using the 1-m DEM, but it shows an opposite trend at the area near the railway track. 4.3. Hazard assessment Fig. 9 shows the results from the simulation using the LiDAR data in which thousands of 3D trajectories were created. Fig. 10 shows the spatial distribution of the simulated rockfall frequency for the whole study area. It can be seen that the two raster images show much different spatial distribution of rockfall frequency. The rockfall frequency along the railway track was classified into 10 classes and plotted in Fig. 11. The historical rockfall database was compared to the results from both 1-m and 20-m DEMs. It is evident from Fig. 11 that the rockfall simulation from the 1-m DEM agrees better with the historical rockfall events since it captured more precise spatial distribution of rockfall occurrence. The simulated rockfall frequency from the 1-m DEM exhibits more reasonable distribution along the railway track than that from the 20-m DEM. Once the spatial distribution of rockfalls has been computed, the energy from such events is required to complete the hazard assessment (Fig. 2). Fig. 12a shows the spatial distribution of rockfall potential energy (flying/bouncing height relative to ground) and Fig. 12b shows the kinematic energy. The final result of the rockfall hazard assessment by combining all the raster layers is shown in Fig. 12c. The rockfall hazard map clearly identifies mileage 9.1 as the section of the railway with the greatest risk, which is consistent with the historical evidence. 4.4. Hazard control
Fig. 9. Rock fall trajectories modeled from Rockfall Analyst using the 1-m DEM. Rockfall sources are after Fig. 4d.
Once the hazard has been identified, the assessment for hazard control will be conducted in various ways. The rockfall protection measures require an assessment of both the height (bouncing/flying) and velocity of the rockfalls. The simulated height and velocity raster layers provide input to the design of such protective barriers. Fig. 13 shows the quantitative likelihood of rockfall kinematic variable posing
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Fig. 10. Spatial frequency of rockfalls estimated using RockFall Analyst. a) From the 1-m DEM. b) From the 20-m DEM.
on the railway track determined in terms of probability distributions. It can be seen that the rockfall velocity impacting on the railway track has a wide range. Eighty percent of rockfall events will have the velocity from 10 to 40 ms− 1 when they reach the railway track. The rockfall height passing above railway track concentrates on a small range. For most (nearly 60%) rockfalls, height is b 5 m when reaching the railway track. 5. Discussion A number of different approaches are available for simulating rockfall processes. In general, 2D models are state of the practice, such as RocFall from RocScience (http://www.rocscience.com) and Colorado Rockfall Simulation Program (CRSP; http://www.dot.state.co.us/geotech/crsp. cfm). They need to select critical 2D cross sections and are not able to capture the 3D characteristics of rockfall processes. 3D approaches, such as Rockfall Analyst, STONE (Guzzetti et al., 2002) and Pir3D (www. geociel.fr), are advanced techniques to provide robust functions in studying the complex rockfall behavior. However, none of these models deal with the issue of rock shape and fragmentation since a “lumped
mass” approach is used. High uncertainty may also arise due to the other defects of physical models. For example, without considering the fragmentations of falling rocks, the kinetic energy (also bounding and fallings) of a rock mass in the model could be overestimated. This can be overcome, to some extent, by carefully calibrating parameters such as the coefficient of restitution and friction. The statistical approaches used in Rockfall Analyst also help handle the uncertainty issues (Lan et al, 2007). The ability of Rockfall Analyst to directly interact with GIS functions makes the tool easy to use. Because a huge amount of geospatial data and historical rockfall records commonly reside in GIS databases, additional loads are unnecessary to extract information or recompile it in a form suitable for certain computer codes. The simulation results such as rock fall trajectories, runout distance, kinetic energies, and the effect of remedial measures can be managed, visualized and analyzed directly in the user friendly environment. However, intrinsic weakness exists in Rockfall Analyst in dealing with complicated rockfall processes including the lack of solution for shape, size and fragmentation issues (Lan et al., 2007). In addition to providing tools for analyzing locations exposed to rockfall hazards, work is also being undertaken to evaluate the temporal
Fig. 11. Comparison of the historical rock fall frequency impacting the railway track with the results of the RockFall Analyst simulations using the 1-m and 20-m DEMs.
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Fig. 12. Results of rockfall hazard assessment. a) Rockfall height. b) Rockfall kinematic energy. c) Hazard assessment map.
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Such information should be addressed for a more comprehensive hazard assessment. It should be recognized that the simulation results highly depend on the quality of input data and precision of model calibration. Historical records, field observations and monitoring are essential for improving hazard assessment results. Hazard control for such a complex situation will benefit from quantitative hazard assessment. This study suggests that a 5 m barrier will protect the railway from most of the rockfall hazards. 6. Conclusions
Fig. 13. Probability distribution of rockfall kinematic variables impacting on the railway track (mileage 8.8–9.6). a) Velocity. b) Height.
nature of rockfalls to identify the environmental conditions that influence the temporal frequency of rockfalls. Such information is important for allocating resources and work crew planning for railway operators. The use of the high-resolution LiDAR data has provided better results than the use of the coarser data. It demonstrates the advantage of using LiDAR technology to simulate rockfall processes and their spatial distribution. This implies that the microscopic topographic geometry of a slope strongly control rockfall processes, since the interaction between rock blocks and cell-based slope geometry determine the status of rockfall behavior (Lan et al., 2007). More realistic modeling of rockfall behaviors can be carried out using high-resolution LiDAR data, because it can capture local micro-topographic geometry of slopes and artificial features such as railway tracks. In our study, the simulated result on LiDAR data could show very detailed information of rockfall behaviors in terms of traveling type (rolling or flying) and kinematic velocity, as well as the interaction between rockfalls and the railway track. However the cost–benefit issue should also be considered. One of the concerns that frequently arise when conducting rockfall analysis is the required level of DEM resolution to provide reliable results within acceptable cost. DEM resolution controls the geometry of the slope and thus rockfall trajectories. It also controls the physics of the impact where the coefficient of restitution is linked to DEM grids. Multiple runs should be taken on the data with different resolutions in order to determine the acceptable level of data resolution. An empirical relationship could be established by means of the comparison of results between high and low resolution data. It could help explain and understand the results for some study areas where highresolution DEMs are unavailable. Effective assessment of rockfall hazard can be carried out by comprehensively considering as many factors as possible, such as the spatial distribution of rockfall sources, physical processes, frequency and energy. Engineering constructions have to be taken where a railway crosses dangerous hillslopes. Rockfall initiation is more or less related to anthropogenic factors such as road fill and road cut. For hazard assessment perspectives, minimizing rockfall seeders are also important in addition to the prediction of rockfall trajectories and kinetic energy.
Rockfalls are physically based natural slope phenomena. They are significant hazards to Canadian railways. The issue of hazard assessment arises when they pose threats on the life, properties and infrastructure. A large number of analyses at regular intervals are necessary to conduct at regional scale areas such as the linear corridor occupied by railways. The effective rockfall hazard assessment requires enabling technology. A methodology of rockfall process modeling using LiDAR and spatial modeling approaches has been discussed for hazard assessment along a section of the Canadian railway. The use of LiDAR demonstrates the advantage in the assessment of such hazards over long railway sections. It enables us to model the accurate surface geomorphology and capture the geometry of important infrastructures. The simulated result from the high-resolution LiDAR dataset shows better agreement with the historical rockfall than that from other coarse datasets. It shows reasonable distribution of rockfall frequency along the railway track and the correct location of high likelihood of rockfall occurrence, comparable to the historical records. Topographic analyses using LiDAR data can also identify potential rockfall source zones based on topographic contrast and slope angle. Although the use of LiDAR data enables effective rockfall assessments in a large scale, it might be restricted to highly hazardous areas due to the high cost. The use of 3D modeling tools such as Rockfall Analyst provides the framework for rapid assessment of rockfall hazards and understanding the geomorphic processes of rockfalls, because they deal with 3D physical processes of rockfalls and their interactions between slope topography. It helps understand the processes of rockfalls in terms of runout and dispersion, and predicts the spatial distribution of their frequency and energy. The above technologies facilitate the effective assessment and control of rockfall hazard. Once calibrated using high-resolution spatial data and historical records, the output from spatial modeling may be used for protective measures against rockfall hazards. However, some significant intrinsic characteristics of rockfall processes such as the fragmentation of a block during movement should be taken into account in the future research. Model selection, data quality, and cost–benefit issues also need to be considered. Acknowledgements This research is supported by the Canadian Railway Ground Hazard Program (RGHRP), the One Hundred Talents Program of Chinese Academy of Sciences, and the National Key Technology R&D Program of China (2008BAK50B05), 973 Program (2008CB425802). References Agliardi, F., Crosta, G.B., 2003. High resolution three-dimensional numerical modelling of rockfalls. International Journal of Rock Mechanics and Mining Sciences 40, 455–471. Aronoff, S., 1989. Geographic Information Systems: a management perspective. WDL Publ, Ottawa. 294 pp. Bellian, J.A., Kerans, C., Jennette, D.C., 2005. Digital outcrop models: applications of terrestrial scanning LiDAR technology in stratigraphic modelling. Journal of Sedimentary Research 75, 166–176. Brawner, C.O., Wyllie, D., 1976. Rock slope stability on railway projects. American Railway Engineering Association Bulletin 656, 449–474.
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Lan, H., Martin, C.D., Lim, C.H., 2007. RockFall analyst: a GIS extension for threedimensional and spatially distributed rockfall hazard modeling. Computers & Geosciences 33, 262–279. Lato, M.M.S., Diederichs, D., Hutchinson, J., Harrap, R., 2009. Optimization of LiDAR scanning and processing for automated structural evaluation of discontinuities in rockmasses. International Journal of Rock Mechanics and Mining Sciences 46, 194–199. Lim, C.H., Martin, C.D., Herd, E.P.K., 2004. Rock fall hazard assessment along railways using GIS. CD-ROM Proc. 57th Canadian Geotechncial Conference, Quebec City 2004, pp. 1–8. Martin, C.D., Lan, H., Lim, C.H., Keegan, T., Bunce, C.M., 2006. Evaluating rock fall hazard along railway corridors using GIS technology. Felsbau 24, 38–41. Matsuoka, N., Sakai, H., 1999. Rockfall activity from an alpine cliff during thawing periods. Geomorphology 28, 309–328. McKean, J., Roering, J., 2004. Objective landslide detection and surface morphology mapping using high-resolution airborne laser altimetry. Geomorphology 57, 331–351. Metternicht, G., Hurni, L., Gogu, R., 2005. Remote sensing of landslides: an analysis of the potential contribution to geo-spatial systems for hazard assessment in mountainous environments. Remote Sensing of Environment 98, 284–303. Nagihara, S., Mulligan, K.R., Xiong, W., 2004. Use of a three-dimensional laser scanner to digitally capture the topography of sand dunes in high spatial resolution. Earth Surface Processes and Landforms 29, 391–398. Pfeiffer, T.J., Bowen, T., 1989. Computer simulation of rock falls. Bulletin of the Association of Engineering Geologists 26, 135–146. Rosser, N.J., Petley, D.N., Lim, M., Dunning, S.A., Allison, R.J., 2005. Terrestrial laser scanning for monitoring the process of hard rock coastal cliff erosion. Quarterly Journal of Engineering Geology and Hydrogeology 38, 363–375. Trenhaile, A.S., 2007. Geomorphology: a Canadian Perspective, Third Edition. Oxford University Press, Oxford, pp. 114–116. Whalley, W.B., 1984. Rockfalls. In: Brunsden, D., Prior, D.B. (Eds.), Slope Instability. Wiley, Chichester, pp. 217–256. Wieczorek, G.F., Stock, G.M., Reichenbach, P., Snyder, J.B., Borchers, J.W., Godt, J.W., 2008. Investigation and hazard assessment of the 2003 and 2007 Staircase Falls rock falls, Yosemite National Park, California, USA. Natural Hazards and Earth System Sciences 8, 421–432. Yilmaz, I., Yildirim, M., Keskin, I., 2008. A method for mapping the spatial distribution of rockfall computer program analyses results using ArcGIS software. Bulletin of Engineering Geology and the Environment 67, 547–554.
Contents lists available at ScienceDirect
Geomorphology j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / g e o m o r p h
Rockfall hazard analysis using LiDAR and spatial modeling Hengxing Lan a,b,⁎, C. Derek Martin b, Chenghu Zhou a, Chang Ho Lim b a b
LREIS, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China Department of Civil and Environmental Engineering, University of Alberta, Edmonton, Alberta Canada T6G 2W2
a r t i c l e
i n f o
Article history: Received 4 February 2009 Received in revised form 28 December 2009 Accepted 3 January 2010 Available online 11 January 2010 Keywords: Rockfall Hazard analysis LiDAR Spatial modeling Rockfall analyst Railway
a b s t r a c t Rockfalls have been significant geohazards along the Canadian Class 1 Railways (CN Rail and CP Rail) since their construction in the late 1800s. These rockfalls cause damage to infrastructure, interruption of business, and environmental impacts, and their occurrence varies both spatially and temporally. The proactive management of these rockfall hazards requires enabling technologies. This paper discusses a hazard assessment strategy for rockfalls along a section of a Canadian railway using LiDAR and spatial modeling. LiDAR provides accurate topographical information of the source area of rockfalls and along their paths. Spatial modeling was conducted using Rockfall Analyst, a three dimensional extension to GIS, to determine the characteristics of the rockfalls in terms of travel distance, velocity and energy. Historical rockfall records were used to calibrate the physical characteristics of the rockfall processes. The results based on a highresolution digital elevation model from a LiDAR dataset were compared with those based on a coarse digital elevation model. A comprehensive methodology for rockfall hazard assessment is proposed which takes into account the characteristics of source areas, the physical processes of rockfalls and the spatial attribution of their frequency and energy. © 2010 Elsevier B.V. All rights reserved.
1. Introduction Rockfall is one of most significant geohazards encountered by railways along the Canadian Rockies (Keegan, 2007). Railways have unusually high exposures to ground hazards because of their great length. The Canadian railway systems have experienced various ground hazards since the initial construction of railways in the late 1800s. Canadian railway industries such as the Canadian National Railway (CN Rail) and the Canadian Pacific Railway (CP Rail) have been suffering from losses caused by ground hazards including damage to infrastructure and interruption of business and environmental impacts. For example, among the losses by all railway accidents during 1992 to 2002, those caused by ground hazard are severest with a mean loss of CAD $400,000 and the longest duration of outage for each accident (Keegan, 2007). Rockfall is an important natural geomorphic process acting on steep mountain slopes (Whalley, 1984; Matsuoka and Sakai, 1999). It is a predominant type of mass movement in the Canadian Rocky Mountains (Trenhaile, 2007, p. 114). The initiation of rockfalls can usually be attributed to a combination of climatic, topographic, and vegetational factors which induce rock fracturing, opening of joints, pore pressure increases due to rainfall infiltration, freeze–thaw processes in a cold region, and chemical weathering of rock (Lim et al., 2004). Rockfalls in ⁎ Corresponding author. Department of Civil and Environmental Engineering, University of Alberta, Edmonton, Alberta Canada T6G 2W2. Tel.: +1 86 10 64889318; fax: +1 86 10 64889630. E-mail address: [email protected] (H. Lan). 0169-555X/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.geomorph.2010.01.002
the Canadian Rocky Mountains are most frequent between November and March because freeze–thaw cycles are effective for rock detachment. Once the movement of a rock is initiated, the geometry of the slope controls rockfall trajectory. In many situations rockfall hazards cannot be eliminated because their magnitude and frequency vary both spatially and temporally. The high mobility of fallen rocks is a major difference from other slope instability phenomena (Frattini et al., 2008). A limiting factor in rockfall hazard assessment is the deficiency of high-resolution geospatial data for the analysis of slope topography, rockfall detachment areas, rock block geometry, and rock traveling paths including runout distance (Hutchinson, 1988; Evans and Hungr, 1993; Dorren, 2003). The confidence of rockfall hazard assessment also depends upon the quality and quantity of available historical data (Hutchinson et al., 2006). Conventional survey methods present serious limitations for collecting spatial datasets required for rockfall modeling. The use of new technologies such as LiDAR (Light Detection And Ranging) has rapidly increased in the field of geohazard assessment. Both groundbased and airborne LiDAR surveys are now regarded as indispensable tools for dense and accurate data acquisition to facilitate detailed topographical analysis (Nagihara et al., 2004; McKean and Roering, 2004; Bellian et al., 2005; Rosser et al., 2005; Metternicht et al., 2005; Glenn et al., 2006). Ground-based LiDAR uses a reflectorless laser to capture 3D topographic data, in a high speed, high precision and low cost manner (Rosser et al., 2005). It is capable of capturing detailed topographic information of nearly vertical structures. Airborne LiDAR is suitable for generating a high-resolution digital elevation model (DEM)
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for a large area. The highly accurate topographic data (150-mm vertical accuracy) can help assess the geomorphology, geological settings, structural discontinuities and mechanical characteristics of slopes and rock blocks (Rosser et al., 2005; Metternicht et al., 2005; Chang et al., 2005, Lato et al., 2009). LiDAR data may also improve the estimation of modeling parameters and reduce model structural error for rational rockfall hazard assessment. To assess rockfall processes it is necessary to use topographic data with a resolution relevant to the scale of morphological features being examined (Glenn et al., 2006). Numerical modeling of rockfall processes on fine-scale topographic data can provide insight into the relationship between topography and rockfall behavior (Pfeiffer and Bowen, 1989; Jones et al., 2000; Guzzetti et al., 2002; Agliardi and Crosta, 2003; Dorren and Seijmonsbergen, 2003; Martin et al., 2006; Lan et al., 2007; Yilmaz et al., 2008). A three dimensional program linked to a geographic information system (GIS), Rockfall Analyst, was developed to simulate rockfall processes and the spatial distribution of their frequency, runout and energy (Martin et al., 2006; Lan et al., 2007). In this paper, we discuss a methodology of rockfall process modeling using LiDAR and spatial modeling approaches for hazard assessment along a section of the Canadian railway. We present a procedure for systematically zoning rockfall source areas. The characteristics of the 3D physical processes of rockfalls are examined in terms of traveling
distance, velocity and energy. The modeling results from two different datasets are used to interpret the effect of the resolution of topographic data on rockfall assessment. The spatial distribution of rockfall frequency and energy was simulated to provide information necessary for hazard control. 2. Study area The Canadian railway industry has a rich database of past ground hazards. Approximately 2500 reports for the CP Rail and 1000 for the CN Rail have been compiled since the beginning of the 20th century for the total railway length of 14,000 km. The database has been improved and enhanced over the past few decades to permit the analysis of individual rockfall events. The incident reports show the time of events, possible triggers, possible source areas, impacts on operations and remedial measures categorized by subdivision and site location using track mileage. A representative part of the CN Rail (mileage 8.8–9.9) in the Cascade subdivision located in southern British Columbia is selected as our study area to evaluate the railway rockfall hazard using LiDAR technology and spatial modeling (Fig. 1). Rockfalls are frequent in this section; for example, nearly ten rockfall events were recorded in 1997. The rock fall event database contains the information back to the 1940s. Both LiDAR and ordinary digital elevation models are available.
Fig. 1. Study area in the southwest of Canada. Shaded LiDAR 1-m DEM overlaid on the 20-m DEM of Canadian Digital Elevation Data (CDED). Dots and associated numbers indicate the track location mileage. Cross sections (A–A′) from the 1-m and 20-m DEMs are inset in the middle top of the map.
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Various attributes related to geology, land use, and vegetation cover have been stored in the GIS database. The record of historical rockfall events enables the calibration of the model parameters and the validation of the rockfall modeling results. Moreover, the National Climate Data and Information Archive operated and maintained by Environment Canada contains climate and weather data. Data such as daily temperature, precipitation, and snow accumulation were compiled for the weather station closest to the Cascade subdivision (Lim et al., 2004). In terms of geological and physiographical conditions, the study area is dominated by intrusive rocks consisting of quartz diorite and granodiorite with slightly metamorphosed sedimentary rocks. Similar to some other areas in the railway corridor in the Canadian Rockies, geotechnical conditions in this section such as lithology, weathering of bedrock, joint spacing and orientation, and groundwater infiltration influence the triggering of rockfalls (Wieczorek et al., 2008). The annual rockfall frequency is highest in February that corresponds to the highest number of freeze–thaw cycles. The amount of daily rainfall was also found to be a factor affecting the rockfall occurrence and frequency (Lim et al., 2004). 3. Material and methods 3.1. LiDAR survey An airborne LiDAR survey was conducted in 2004 along the studied section of the CN Rail. A DEM with a horizontal resolution of 1 m (1-m DEM) and a vertical accuracy of ±150 mm was generated from high-resolution bear-earth LiDAR point data. For comparison a coarser DEM with 20 m resolution (20-m DEM) was prepared using the Canadian Digital Elevation Data (CDED) which has a maximum resolution of 0.75 arc-sec. The 1-m DEM shows much finer topography than the 20-m DEM (Fig. 1). Both DEMs were used to derive cross
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Fig. 3. Illustration of sharp topographic contrast.
sections along A–A′ in Fig. 1. Detailed ground features can be easily identified along the section from the 1-m DEM including two railways (CN and CP) and a highway. Both datasets enable us to evaluate the effect of the different DEM inputs on rockfall kinematic variables such as runout, velocity, dispersion and frequency as well as energy distribution. 3.2. Spatial modeling Spatial modeling of rockfall hazards was performed using Rockfall Analyst, a GIS extension (Lan et al., 2007). Rockfall Analyst includes two major parts: (1) 3D rockfall trajectory simulation, and (2) raster modeling for spatial distribution of rockfalls. Because the spatial autocorrelation of factors affecting rockfalls (e.g. slope geometry, geology and vegetation) exerts control on the distribution of rockfall events in terms of their runout extent, velocity and energy distribution, a geostatistical method is used to simulate rockfall frequency, energy, velocity and trajectories. The spatial distribution of rockfall hazard distribution was investigated using the simulated distribution of rockfall frequency and energy to evaluate the potential rockfall impact to the railway operation. Spatial overlay of different types of datasets is one of the most fundamental GIS functions used in Rockfall Analyst to provide a new dataset reflecting the spatial relationships of various datasets such as slope, climate, land use and terrain types. The approach has been frequently used in mass movement research such as the assessment of landslide susceptibility and rockfall potential. Aronoff (1989) described two types of overlay operators: arithmetic and logical. Arithmetic operation was used in our study. Capabilities are available in GIS to allow us to assess the data quality and accuracy of the data during the overlay operation. For example, seven maintenance and analysis functions have been described by Aronoff (1989) for spatial data quality control: format transformation, geometric transformation, transformation between map projections, conflation, edge matching, editing of graphic elements, and line coordinate thinning. Most of these functions have been used in our studies. 3.3. Modeling procedure
Fig. 2. Rockfall hazard assessment workflow using Rockfall Analyst.
The modeling procedure is shown in Fig. 2. Firstly, potential rockfall source areas were determined using detailed topographic data from LiDAR and high-resolution (1 m) orthoscopic color aerial photographs. The identification of source areas should take account of various features such as sharp topographic contrast, slope angle, terrain type and
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vegetation cover. For example, Guzzetti et al. (2003) used a DEM and thematic maps such as land use and soil types to identify potential rockfall sources. Sharp topographical contrast was considered to identify the top portion of a steep slope as a potential rockfall detaching area. It was detected as a sudden change in the slope angle by comparing the 1-m DEM with a DEM smoothed using a mean filter. The smoothed DEM was subtracted from the original DEM. Positive values indicate the upper portion of a steep slope which could be high rockfall potential area (Fig. 3). A similar approach was also taken by Crosta and Agliardi (2003). A slope angle layer was derived from the original DEM using Spatial Analysis extension in the GIS. The terrain types were classified by spectral information of air photos.
A spatial overlay analysis with a ranking and scoring system was performed on various factor layers including the layer of the sharp topographical contrast to identify potential rockfall seeders. Each factor was classified into five different classes, and different weights were set to the factor layers based on their relative effects on rockfall initiation. Grid cells with high total scores were delineated as rockfall sources. The weight for each layer was identified through three steps: (1) Refer to the previous research work. Extensive assessment of rockfall hazards along Canadian transportation routes was initiated in 1976. A systematic way to assign and utilize five priority ratings based on the estimated probability of failure was established (Brawner and Wyllie, 1976), which took into account the geology and rock conditions, slope
Fig. 4. Identification of potential rockfall sources for the study area of the Canadian National Railway (CN). a) Reclassified sharp topographic contrast. b) Reclassified slope angle. c) Reclassified terrain types. d) Potential rockfall sources.
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Table 1 Statistics of rockfalls dispersion and runout distance from sources S1, S2 and S3. Sources
S1 S2 S3 Mean
Fig. 5. Relationship between slope angle and rockfall frequency obtained from the rockfall database and the 1-m DEM.
geometry, ditch dimensions, hydrology (seepage) and the event history, such as the number and characteristics of past events. (2) Rigorous field investigations on different environmental conditions were carried out. (3) Validate the real rockfall records. The resultant rockfall sources should cover over 95% of the known rockfall sources.
Lateral Dispersion (W/L)
Mean runout distance (m)
1-m DEM
20-m DEM
1-m DEM
20-m DEM
33% 44% 43% 40%
13% 7% 32% 17%
299 m 362 m 367 m 342 m
285 m 387 m 424 m 365 m
Once the potential rockfall sources were identified, rockfall physical processes were simulated using Rockfall Analyst by considering ground topography and calibrated mechanical parameters. A number of starting directions were assigned to each rockfall seeder which results in nearly 3000 rockfall events simulated. Once the DEM was created from the LiDAR data and CDED data and the spatial attributes of surface terrains assigned, potential rockfalls were simulated from all of the possible source areas. It involves rock detachment and fall, and subsequent bouncing, rolling, sliding and deposition. Raster images of rockfall spatial frequency, flying height (potential energy) and kinetic energy were provided based on the simulated 3D rockfall trajectories and their velocity. The resolution of each raster image is the same as that of the input DEM (i.e. 1 m for the LiDAR DEM). The simulation of rockfall processes described above using the DEM from the LiDAR survey was repeated using the 20-m DEM. Using the raster modeling function in Rockfall Analyst, the number of rockfall trajectories was calculated for
Fig. 6. Simulation of rockfall trajectories from three different sources using the (a) 1-m and (b) 20-m DEMs and longitudinal profiles of typical trajectories with velocity (c and d).
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each grid cell with an area of one square meter. Spatial geostatistical techniques were employed to analyze the trajectories and determine the rockfall spatial frequency for the whole study area. Topographic analyses provide a crucial geomorphic context for rockfall hazard (Kirby et al, 2008). To address the effect of topography, two simulations were carried out using the high-resolution 1-m DEM and the coarse resolution 20-m DEM separately. The comparison of the two modeling results was conducted in terms of critical rockfall characteristics such as runout, flying height, and velocity. Three different rockfalls source areas, S1, S2 and S3, were selected for this purpose. Hundreds of rock blocks (seeders) were set at each source with various starting directions. The W/L ratio (the ratio of the width W to the length L of the invasion area) was selected as an index of rockfalls lateral dispersion (Crosta and Agliardi, 2004). Finally, the potential impact of rockfalls on the railway tracks was evaluated using the final rockfall hazard map which takes all available raster layers into account.
4. Results 4.1. Rockfall source identification Fig. 4a–c shows the obtained geomorphological layers, and Fig. 4d shows the identified potential rockfall sources. The cells of a higher class in each layer indicate high potential for rockfall source. The final rockfall sources are characterized by sharp topographic contrast, steep slope angle and loose vegetated or bared terrain types.
4.2. Effect of topography The rockfall database was analyzed to establish the slope angles associated with the historic rockfalls. A raster image of the slope angle was created and the mean slope angle was determined for every 0.16 km (0.1 mile) along the railway. The results are plotted in Fig. 5 and
Fig. 7. Spatial distribution of (a and b) rockfall height and (c and d) velocity modeled from the (a and c) 1-m and (b and d) 20-m DEMs.
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Fig. 8. Comparison of (a) mean flying height and (b) mean velocity modeled from the 1-m and 20-m DEMs.
show that about 50% of the rockfalls occur at slope angles less than 40°, and over 84% of the rockfalls occurred at angles greater than 30°. To further examine the effect of topography on the rockfall behaviors, simulations using both 1-m and coarse 20-m DEMs were carried out (Fig. 6). The three dimensional trajectories from S1, S2 and S3 are illustrated by Green, Blue and Red respectively. Typical profiles of rockfall trajectories and their velocity are shown in the bottom of Fig. 6. Their invasion zones were defined by the lateral dispersion of trajectories and the extent of rockfall runout. Rockfall trajectories simulated on the high-resolution DEM exhibit more complex shapes than those on the coarse DEM, and their traveling velocities change more frequently (Fig. 6), indicating more
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interactions between rocks and the slope. For instance, the rockfalls shown in the circle in Fig. 6 stopped right on the track, but none of the rockfall trajectories simulated on the coarse 20-m DEM had this type of interaction. It agrees with the field investigation that rock blocks lying on railway tracks are very common once rockfall events occur. The impact area of rockfall on the railway track can be evaluated based on the information of rockfall runout and lateral dispersion. From Table 1, rockfall invasion zones obtained from the higher resolution 1-m DEM have larger lateral dispersion and shorter runout distance than those from the coarse DEM. The flying/bouncing height and velocity are two important variables of kinematics energy of rockfalls. They reveal the dynamic interaction between rock blocks and topography during their kinematic processes. The rockfalls' flying height relative to ground represents their potential energy imposing on ground surface. The velocity variable indicates the kinematic energy of rockfalls. Both control the potential damage that rockfalls might pose on the infrastructures such as railway tracks. Therefore, they are important indices for evaluating the rockfall hazard and helping design the barriers. Fig. 7 shows the spatial distribution of rock bouncing height and velocity. The simulated results from the two different datasets also point to the effect of topography on rockfall energy distribution. These rockfall kinematic variables are strongly controlled by a local 3D slope form. The decreased grid resolution of the 20-m DEM resulted in a smoother and simplified slope geometry without micro-topographic features, which absorbs less kinetic energy of rockfalls during their activities (Crosta and Agliardi, 2004). Compared to the simulation result from the 1-m DEM, the rockfalls simulated with the 20-m DEM have higher mean height and velocity (Fig. 8), suggesting that the energy prediction using the coarse DEM could be overestimated. The average height of the whole zone simulated using the 20 m DEM is twice as high as that using the 1-m DEM, but it shows an opposite trend at the area near the railway track. 4.3. Hazard assessment Fig. 9 shows the results from the simulation using the LiDAR data in which thousands of 3D trajectories were created. Fig. 10 shows the spatial distribution of the simulated rockfall frequency for the whole study area. It can be seen that the two raster images show much different spatial distribution of rockfall frequency. The rockfall frequency along the railway track was classified into 10 classes and plotted in Fig. 11. The historical rockfall database was compared to the results from both 1-m and 20-m DEMs. It is evident from Fig. 11 that the rockfall simulation from the 1-m DEM agrees better with the historical rockfall events since it captured more precise spatial distribution of rockfall occurrence. The simulated rockfall frequency from the 1-m DEM exhibits more reasonable distribution along the railway track than that from the 20-m DEM. Once the spatial distribution of rockfalls has been computed, the energy from such events is required to complete the hazard assessment (Fig. 2). Fig. 12a shows the spatial distribution of rockfall potential energy (flying/bouncing height relative to ground) and Fig. 12b shows the kinematic energy. The final result of the rockfall hazard assessment by combining all the raster layers is shown in Fig. 12c. The rockfall hazard map clearly identifies mileage 9.1 as the section of the railway with the greatest risk, which is consistent with the historical evidence. 4.4. Hazard control
Fig. 9. Rock fall trajectories modeled from Rockfall Analyst using the 1-m DEM. Rockfall sources are after Fig. 4d.
Once the hazard has been identified, the assessment for hazard control will be conducted in various ways. The rockfall protection measures require an assessment of both the height (bouncing/flying) and velocity of the rockfalls. The simulated height and velocity raster layers provide input to the design of such protective barriers. Fig. 13 shows the quantitative likelihood of rockfall kinematic variable posing
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Fig. 10. Spatial frequency of rockfalls estimated using RockFall Analyst. a) From the 1-m DEM. b) From the 20-m DEM.
on the railway track determined in terms of probability distributions. It can be seen that the rockfall velocity impacting on the railway track has a wide range. Eighty percent of rockfall events will have the velocity from 10 to 40 ms− 1 when they reach the railway track. The rockfall height passing above railway track concentrates on a small range. For most (nearly 60%) rockfalls, height is b 5 m when reaching the railway track. 5. Discussion A number of different approaches are available for simulating rockfall processes. In general, 2D models are state of the practice, such as RocFall from RocScience (http://www.rocscience.com) and Colorado Rockfall Simulation Program (CRSP; http://www.dot.state.co.us/geotech/crsp. cfm). They need to select critical 2D cross sections and are not able to capture the 3D characteristics of rockfall processes. 3D approaches, such as Rockfall Analyst, STONE (Guzzetti et al., 2002) and Pir3D (www. geociel.fr), are advanced techniques to provide robust functions in studying the complex rockfall behavior. However, none of these models deal with the issue of rock shape and fragmentation since a “lumped
mass” approach is used. High uncertainty may also arise due to the other defects of physical models. For example, without considering the fragmentations of falling rocks, the kinetic energy (also bounding and fallings) of a rock mass in the model could be overestimated. This can be overcome, to some extent, by carefully calibrating parameters such as the coefficient of restitution and friction. The statistical approaches used in Rockfall Analyst also help handle the uncertainty issues (Lan et al, 2007). The ability of Rockfall Analyst to directly interact with GIS functions makes the tool easy to use. Because a huge amount of geospatial data and historical rockfall records commonly reside in GIS databases, additional loads are unnecessary to extract information or recompile it in a form suitable for certain computer codes. The simulation results such as rock fall trajectories, runout distance, kinetic energies, and the effect of remedial measures can be managed, visualized and analyzed directly in the user friendly environment. However, intrinsic weakness exists in Rockfall Analyst in dealing with complicated rockfall processes including the lack of solution for shape, size and fragmentation issues (Lan et al., 2007). In addition to providing tools for analyzing locations exposed to rockfall hazards, work is also being undertaken to evaluate the temporal
Fig. 11. Comparison of the historical rock fall frequency impacting the railway track with the results of the RockFall Analyst simulations using the 1-m and 20-m DEMs.
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Fig. 12. Results of rockfall hazard assessment. a) Rockfall height. b) Rockfall kinematic energy. c) Hazard assessment map.
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Such information should be addressed for a more comprehensive hazard assessment. It should be recognized that the simulation results highly depend on the quality of input data and precision of model calibration. Historical records, field observations and monitoring are essential for improving hazard assessment results. Hazard control for such a complex situation will benefit from quantitative hazard assessment. This study suggests that a 5 m barrier will protect the railway from most of the rockfall hazards. 6. Conclusions
Fig. 13. Probability distribution of rockfall kinematic variables impacting on the railway track (mileage 8.8–9.6). a) Velocity. b) Height.
nature of rockfalls to identify the environmental conditions that influence the temporal frequency of rockfalls. Such information is important for allocating resources and work crew planning for railway operators. The use of the high-resolution LiDAR data has provided better results than the use of the coarser data. It demonstrates the advantage of using LiDAR technology to simulate rockfall processes and their spatial distribution. This implies that the microscopic topographic geometry of a slope strongly control rockfall processes, since the interaction between rock blocks and cell-based slope geometry determine the status of rockfall behavior (Lan et al., 2007). More realistic modeling of rockfall behaviors can be carried out using high-resolution LiDAR data, because it can capture local micro-topographic geometry of slopes and artificial features such as railway tracks. In our study, the simulated result on LiDAR data could show very detailed information of rockfall behaviors in terms of traveling type (rolling or flying) and kinematic velocity, as well as the interaction between rockfalls and the railway track. However the cost–benefit issue should also be considered. One of the concerns that frequently arise when conducting rockfall analysis is the required level of DEM resolution to provide reliable results within acceptable cost. DEM resolution controls the geometry of the slope and thus rockfall trajectories. It also controls the physics of the impact where the coefficient of restitution is linked to DEM grids. Multiple runs should be taken on the data with different resolutions in order to determine the acceptable level of data resolution. An empirical relationship could be established by means of the comparison of results between high and low resolution data. It could help explain and understand the results for some study areas where highresolution DEMs are unavailable. Effective assessment of rockfall hazard can be carried out by comprehensively considering as many factors as possible, such as the spatial distribution of rockfall sources, physical processes, frequency and energy. Engineering constructions have to be taken where a railway crosses dangerous hillslopes. Rockfall initiation is more or less related to anthropogenic factors such as road fill and road cut. For hazard assessment perspectives, minimizing rockfall seeders are also important in addition to the prediction of rockfall trajectories and kinetic energy.
Rockfalls are physically based natural slope phenomena. They are significant hazards to Canadian railways. The issue of hazard assessment arises when they pose threats on the life, properties and infrastructure. A large number of analyses at regular intervals are necessary to conduct at regional scale areas such as the linear corridor occupied by railways. The effective rockfall hazard assessment requires enabling technology. A methodology of rockfall process modeling using LiDAR and spatial modeling approaches has been discussed for hazard assessment along a section of the Canadian railway. The use of LiDAR demonstrates the advantage in the assessment of such hazards over long railway sections. It enables us to model the accurate surface geomorphology and capture the geometry of important infrastructures. The simulated result from the high-resolution LiDAR dataset shows better agreement with the historical rockfall than that from other coarse datasets. It shows reasonable distribution of rockfall frequency along the railway track and the correct location of high likelihood of rockfall occurrence, comparable to the historical records. Topographic analyses using LiDAR data can also identify potential rockfall source zones based on topographic contrast and slope angle. Although the use of LiDAR data enables effective rockfall assessments in a large scale, it might be restricted to highly hazardous areas due to the high cost. The use of 3D modeling tools such as Rockfall Analyst provides the framework for rapid assessment of rockfall hazards and understanding the geomorphic processes of rockfalls, because they deal with 3D physical processes of rockfalls and their interactions between slope topography. It helps understand the processes of rockfalls in terms of runout and dispersion, and predicts the spatial distribution of their frequency and energy. The above technologies facilitate the effective assessment and control of rockfall hazard. Once calibrated using high-resolution spatial data and historical records, the output from spatial modeling may be used for protective measures against rockfall hazards. However, some significant intrinsic characteristics of rockfall processes such as the fragmentation of a block during movement should be taken into account in the future research. Model selection, data quality, and cost–benefit issues also need to be considered. Acknowledgements This research is supported by the Canadian Railway Ground Hazard Program (RGHRP), the One Hundred Talents Program of Chinese Academy of Sciences, and the National Key Technology R&D Program of China (2008BAK50B05), 973 Program (2008CB425802). References Agliardi, F., Crosta, G.B., 2003. High resolution three-dimensional numerical modelling of rockfalls. International Journal of Rock Mechanics and Mining Sciences 40, 455–471. Aronoff, S., 1989. Geographic Information Systems: a management perspective. WDL Publ, Ottawa. 294 pp. Bellian, J.A., Kerans, C., Jennette, D.C., 2005. Digital outcrop models: applications of terrestrial scanning LiDAR technology in stratigraphic modelling. Journal of Sedimentary Research 75, 166–176. Brawner, C.O., Wyllie, D., 1976. Rock slope stability on railway projects. American Railway Engineering Association Bulletin 656, 449–474.
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