Abstract
This paper will provide an overview of a monitoring approach that has proven critical for the long-term monitoring and assessment of high-density landslide areas along pipelines at a regional or system-wide scale: the use of repeat Light Detection and Ranging (LiDAR) surveys (i.e., LiDAR Change Detection Analysis). The strength of using LiDAR is the ability to “see” through trees and vegetation and produce a bare earth digital elevation model of a given slope. As such, LiDAR is one of the most powerful tools for identifying landslides where tree cover is present, such as in the Appalachian Basin region. Repeated acquisition of LiDAR data over time while encompassing the same area can be used for the identification of new or developing landslides, as well as for monitoring movement within known landslides. LiDAR Change Detection Analysis compares successive LiDAR datasets and shows the apparent changes in elevation between the two datasets. By comparing the ground elevations between two datasets, areas where ground movement has occurred since the previous dataset can be identified. This information can aid in identifying, revising, and prioritizing potential landslide threats over large areas.
A case study will be presented from the Appalachian Basin region of the United States, where there exists a high incidence of landslides. The case study will discuss how LiDAR Change Detection Analysis was used each year over a 4-year period along approximately 1,400 miles of pipeline to monitor for landslide movement and formation. The case study will summarize the total number per year and average number per mile of right-of-way per year, of landslides with indications of movement as well as newly identified landslides. In addition, the case study will explore possible correlations between annual regional precipitation and annual landslide movement.
