Pinliang Dong and Qi Chen: LiDAR Remote Sensing and Applications

Mathematical Geosciences -     

Bias Correction for View-limited Lidar Scanning of Rock Outcrops for Structural Characterization

Lidar is a remote sensing technology that uses time-of-flight and line-of-sight to calculate the accurate locations of physical objects in a known space (the known space is in relation to the scanner). The resultant point-cloud data can be used to virtually identify and measure geomechanical data such as joint set orientations, spacing and roughness. The line-of-sight property of static Lidar scanners results in occluded (hidden) zones in the point-cloud and significant quantifiable bias when analyzing the data generated from a single scanning location. While the use of multiple scanning locations and orientations, with merging of aligned (registered) scans, is recommended, practical limitations often limit setup to a single location or a consistent orientation with respect to the slope and rock structure. Such setups require correction for measurement bias. Recent advancements in Lidar scanning and processing technology have facilitated the routine use of Lidar data for geotechnical investigation. Current developments in static scanning have lead to large datasets and generated the need for automated bias correction methods. In addition to the traditional bias correction due to outcrop or scanline orientation, this paper presents a methodology for correction of measurement bias due to the orientation of a discrete discontinuity surface with respect to the line-of-sight of the Lidar scanner and for occlusion. Bias can be mathematically minimized from the analyzed discontinuity orientation data.     

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

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.     

Using tree stems in multi-temporal terrestrial lidar scanning data to monitor landslides on vegetated slopes

Landslides - Terrestrial lidar scanning (TLS) has become a widely accepted expert tool for monitoring geohazards on bare or sparsely vegetated slopes through change detection. While trees can be an...     

Rockfall risk management using a pre-failure deformation database

Terrestrial laser scanning (TLS) monitoring has been used to estimate the location, volume, and kinematics of a variety of small magnitude rockfalls before failure (1–1000 m³ range), and in some cases, potential failure time has been assessed through the application of inverse velocity methods. However, our current understanding of rock slope pre-failure behavior for this magnitude range and prediction ability is based on observations of a small number of failure case histories. In this study, a pre-failure deformation database was constructed for rockfall volumes exceeding 0.1 m³, observed over a 1252-day study interval at the Goldpan rock slope, British Columbia, Canada, in order to better understand the pre-failure behavior of rock slopes and provide an empirical means of estimating temporal failure ranges. Repeated TLS datasets were acquired at an average scanning interval of 2–3 months. A total of 90 rockfall events were recorded at this site, during this time period, of which 64 (71%) exhibited measurable deformation prior to failure. Classification of rockfalls by volume suggests that a scale dependency may exist, as deformation was detected for a greater proportion of rockfalls >?5 m³ (92%) than for smaller rockfalls in the range of 0.1–0.5 m³ (61%). A lower rate of pre-failure deformation detection was also reported for planar sliding failures as compared with wedge or toppling failures, suggesting that deformation was less easily detected for these failure types. This study proposes and implements a framework for rockfall assessment and forecasting that does not require continuous monitoring of deformation.     

Effects of sampling interval on the frequency - magnitude relationship of rockfalls detected from terrestrial laser scanning using semi-automated methods

Using change detection and semi-automated identification methods, it is possible to extract detailed rockfall information from terrestrial laser scanning data to build a database of events, which can be used in the development of the frequency-magnitude relationship for a slope. In this study, we have applied these methods to the White Canyon, a hazardous slope that presents rockfall hazards to the CN Rail line in British Columbia, to build a database of rockfalls including their locations, volumes, and block shapes. We identified over 1900 rockfall events during a 15-month period, ranging in volume from 0.01 to 45 m³. The frequency of these events changed throughout the year, with the highest periods of activity occurring over the winter months. We investigated how the sampling interval, or duration between scans, can affect how the rockfalls are identified, and therefore the frequency-magnitude relationship for the slope using datasets with fewer scans. We show that as the duration between scans becomes larger, fewer rockfalls are detected, as multiple events that have occurred in the same location cluster together into a single event. The results of this study can be used to assist the railways in planning the appropriate number and duration between future scans, in order to capture frequency-magnitude data for the slope with a desired level of detail.