ORIGINAL PAPER 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 Received: 29 August 2010 / Accepted: 2 June 2011 / Published online: 18 June 2011 Ó Springer Science+Business Media B.V. 2011 Abstract Highways and railroads situated within rugged terrain are often subjected to the hazard of rockfalls. The task of assessing roadside rockmasses for potential hazards typ- ically 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 char- acter of the rockmass, as detailed in this paper. Keywords Lidar Á Rockfall Á Rockmass Á Characterization Á Risk Á Hazard M. J. Lato Norwegian Geotechnical Institute, Oslo, Norway M. S. Diederichs (&) Á D. J. Hutchinson Á R. Harrap Geological Sciences and Geological Engineering, Queen’s University, Kingston, ON, Canada e-mail: mdiederi@geol.queensu.ca 123 Nat Hazards (2012) 60:831–864 DOI 10.1007/s11069-011-9872-y