Utilizing Temporal Airborne LiDAR to Identify and Characterize Pipeline Geohazards

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BGC Engineering Inc. (BGC) is an applied earth sciences consulting firm with offices across Canada as well as in the United States and Chile. BGC works to aid our pipeline clients in managing a variety of geotechnical and hydrotechnical hazards, employing a systematic approach to prioritize sites for inspection, monitoring, and mitigation. Pembina Pipeline Corporation (Pembina) owns and operates over 500 km of oil pipelines in the Swan Hills region of Alberta (Figure 1). BGC has been working with Pembina since 2007 to manage risks posed by geohazards to the safe operation of their pipeline network. The geotechnically challenging terrain in this area presents a significant inventory of geohazards including deep-seated landslides in bedrock, shallow to moderate depth slides and slumps within surficial soils, and stream bank erosion. Working with Pembina, BGC utilized multi-temporal Airborne Laser Scanning (ALS) data to perform topographical change detection analyses, to assist in identifying and characterizing geohazards and recognizing anthropogenic changes within, or in proximity to, the pipeline rights-of-way. BGC has used this tool as input to a screening process to prioritize sites for detailed field inspection. We also present a case in which ALS change detection was used as an immediate response to develop mitigation strategies for a pipe that had become exposed to an active landslide.

The use of ALS change detection can provide insight into slide activity, which is a critical component of geohazard characterization that is often difficult to evaluate using traditional methods. While a single ALS data acquisition is useful in identifying landforms with slide morphology, it does not provide information about slide activity, extent or changes to the topography. ALS change detection can provide positive confirmation of slide activity, and can be used to identify where active slides are noted to be impacting a pipeline. This information can be used to update the frequency of geohazard inspection or monitoring activities. Another advantage of ALS change detection is the ability to use the results as a preliminary screening tool to identify and characterize geohazards across spatially expansive regions. This analysis also allows us to identify small changes that have accumulated over several years that may not be evident from field inspections.

In this case study, BGC inspected sixty areas within a 4,800 square kilometer area of interest, covering 481 previously identified geotechnical hazards, in a period of three weeks. The analysis was used as part of a screening tool to help make more informed decisions as to which sites are in need of detailed field inspections. 2006, 2007 and 2008 ALS data were compared to 2013 and 2015 data. The quality of data was variable. 2006 to 2008 data were available as 1 m elevation grids or point clouds with an approximate density of 1.1 points per m2. Point clouds for the 2013 and 2015 data had an approximate density of 2.2 points per m2.

To perform the change detection, BGC employs sophisticated three-dimensional (3D) processing methods that maximize the information gained from the analysis and accurately quantify the reliability of the results. Using these methods, we have been able to detect ground changes as small as 20 cm in this area over a period of eight years. In order to account for the slight misalignments that exist between different ALS datasets, due to georeferencing errors during data collection, BGC employs a `fine realignment’, which adjusts the position of the ALS datasets relative to another by applying a translational and rotational shift to one dataset, which minimizes distances between the two datasets. To improve the quality of the alignment, this process is only performed in areas that are known to have been unchanged between the surveys. The limit of detectable change (LOD) is then determined for each site, based on the alignment error between the non-changing regions in the two ALS datasets. The LOD ranged from 0.20 to 0.50 m, with an average of 0.30 m for the sites analyzed in this study.

The results of the change detection analysis are typically shown as colorcontoured 3D datasets illustrating positive and negative displacements (Figure 2). Positive displacements are interpreted to represent accumulation or bulging of material (for example, at the toe of a landslide). Negative changes are interpreted as loss of material through geological processes such as erosion or slumping, or material removal (anthropogenic). Anthropogenic changes are often seen when construction activities take place such as grading of the pipeline right-of-way. The detailed 3D processing methods BGC employs, and our ability to quantify the accuracy of our measurements allows us to make interpretations about landslide movement rates based on the results of the change detection. Even when no changes above the LOD are identified, this provides useful information as it is indicative that slides are moving at a rate slower than the LOD over the comparison period.

ALS change detection is useful in confirming and complementing information acquired from field inspections. The example in Figure 3 shows an example where a landslide was noted to be impacting a pipeline. The slide area is 600 m wide and 125 m long with an approximately 120 m wide section that has been reactivated in recent years. In 2015, a bending strain anomaly was detected over an 11 m section of pipe. A site visit was conducted in 2016, where a well-defined headscarp as well as tension cracks were observed upslope of the pipeline. ALS change detection was then performed, which confirmed the movements identified in the field, and allowed BGC to aid Pembina in defining the extents and geometry of the active portion of the slide area. The location at which ground movement and slide features were identified in the field matched with the area of largest change identified in the ALS data. The change detection map shows a downward movement of the uppermost block of the slide, with accumulation of material downslope, which can be interpreted as horizontal translation of the slide’s downslope face. This information was used to develop short and long-term mitigation strategies. In the short term, the affected area of the pipeline was immediately stress relieved. The use of ALS change detection supported the eventual re-routing of the pipeline based on the identified footprint of the active slide area.

Interpretation of the change detection results requires an understanding of the capabilities and limitations of this analysis. BGC is continually working to improve our methods for processing ALS data and being able to quantify small ground changes with a high level of confidence. We continue to develop methods of communicating these results to our clients, working with them to develop strategies for the safe operation of their pipelines.

Megan van Veen obtained a BSc in Geological Engineering (2014) and a MASc in Geotechnical Engineering (2016) from Queen’s University. She currently works at BGC Engineering as a Geotechnical Engineer and is involved in various projects related to remote sensing, geohazards and slope stability. Note: Acknowledgements are given to Jamie Sorensen, Matt Lato, Joel Van Hove, Greg Hunchuk and Matt Lloyd from BGC for their contributions to this work and to Joel Babcock and Jan Bracic of Pembina Pipeline Corporation for their support.

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