Natural Hazards

, Volume 60, Issue 3, pp 831–864 | Cite as

Evaluating roadside rockmasses for rockfall hazards using LiDAR data: optimizing data collection and processing protocols

  • Matthew J. Lato
  • Mark S. Diederichs
  • D. Jean Hutchinson
  • Rob Harrap
Original Paper


Highways and railroads situated within rugged terrain are often subjected to the hazard of rockfalls. The task of assessing roadside rockmasses for potential hazards typically involves an on-site visual investigation of the rockmass by an engineer or geologist. At that time, numerous parameters associated with discontinuity orientations and spacing, block size (volume) and shape distributions, slope geometry, and ditch profile are either measured or estimated. Measurements are typically tallied according to a formal hazard rating system, and a hazard level is determined for the site. This methodology often involves direct exposure of the evaluating engineer to the hazard and can also create a potentially non-unique record of the assessed slope based on the skill, knowledge and background of the evaluating engineer. Light Detection and Ranging (LiDAR)–based technologies have the capability to produce spatially accurate, high-resolution digital models of physical objects, known as point clouds. Mobile terrestrial LiDAR equipment can collect, at traffic speed, roadside data along highways and rail lines, scanning continual distances of hundreds of kilometres per day. Through the use of mobile terrestrial LiDAR, in conjunction with airborne and static systems for problem areas, rockfall hazard analysis workflows can be modified and optimized to produce minimally biased, repeatable results. Traditional rockfall hazard analysis inputs include two distinct, but related sets of variables related to geological or geometric control. Geologically controlled inputs to hazard rating systems include kinematic stability (joint identification/orientation) and rock block shape and size distributions. Geometrically controlled inputs include outcrop shape and size, road, ditch and outcrop profile, road curvature and vehicle line of sight. Inputs from both categories can be extracted or calculated from LiDAR data, although there are some limitations and special sampling and processing considerations related to structural character of the rockmass, as detailed in this paper.


Lidar Rockfall Rockmass Characterization Risk Hazard 



The authors would like to extend gratitude to Kathy Kalenchuk and Dave Ball from Queen’s University. The authors would like to thank the support of Paul Mrstik, Craig Sheriff and Kresimir Kusevic from Ambercore for mobile terrestrial and helicopter LiDAR data collection and knowledgeable support. The authors would also like to thank Isabel Coderre for her countless hours of editing. As well, the authors would like to thank InnovMetric for the consultations regarding processing geological LiDAR data in PolyWorks. This research has been generously funded since 2006 by the NSERC, GEOIDE and PREA agencies with tactical support from CN and CPRail.


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Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Matthew J. Lato
    • 1
  • Mark S. Diederichs
    • 2
  • D. Jean Hutchinson
    • 2
  • Rob Harrap
    • 2
  1. 1.Norwegian Geotechnical InstituteOsloNorway
  2. 2.Geological Sciences and Geological EngineeringQueen’s UniversityKingstonCanada

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