68th Canadian Geotechnical Conference and 7th Canadian Permafrost Conference

APPLICATIONS OF REMOTE SENSING TECHNIQUES TO MANAGING ROCK SLOPE INSTABILITY RISK

Jean Hutchinson, Department of Geological Sciences and Geological Engineering, Queen’s University (Canada)
Matthew Lato, BGC Engineering Inc. (Canada)
Dave Gauthier, BGC Engineering Inc. (Canada)
Ryan Kromer, Department of Geological Sciences and Geological Engineering, Queen’s University (Canada)
Matthew Ondercin, Department of Geological Sciences and Geological Engineering, Queen’s University (Canada)
Megan van Veen, Department of Geological Sciences and Geological Engineering, Queen’s University (Canada)
Rob Harrap, Department of Geological Sciences and Geological Engineering, Queen’s University (Canada)

The recent development of rapid, accurate and sophisticated remote sensing tools has provided valuable rock slope change data, previously impossible to obtain. The analysis techniques discussed in this paper utilize detailed and accurate models of three-dimensional geometry developed from photographs and LiDAR point clouds. Models of the rock slope from data collected at similar times can be combined, taking advantage of data at different resolutions and collected from different vantage points and platforms. Such models can be used for remote mapping of discontinuities and lithology as has been demonstrated by others. The added value for slope stability management discussed in this paper is realized when geometrical data sets from different times are compared. Depending upon the frequency of measurements and the rate of change of the rock slope, prior to slope failure it is possible to hypothesize the slope failure mode, the potential volume of the impending failure and in some cases, to provide an accurate estimate of the time of failure. In back analysis, it is possible to determine the distribution of the source zone(s), to assess the path of movement, and to calculate the volume of the source volume and accumulated debris. The case histories presented in this paper demonstrate our enhanced ability to detect and manage the risk of rock slope failure.