Mapping fault geomorphology with drone-based lidar

Authors

  • Guy Salomon School of Earth and Ocean Sciences, University of Victoria
  • Theron Finley School of Earth and Ocean Sciences, University of Victoria, Victoria, British Columbia, Canada
  • Edwin Nissen School of Earth and Ocean Sciences, University of Victoria, Victoria, British Columbia, Canada
  • Roger Stephen School of Earth and Ocean Sciences, University of Victoria, Victoria, British Columbia, Canada
  • Brian Menounos Department of Geography, Earth, and Environmental Sciences, University of Northern British Columbia, Prince George, British Columbia, Canada

DOI:

https://doi.org/10.26443/seismica.v3i1.1186

Keywords:

lidar, uls, geomorphology, active faults, uav, drone

Abstract

The advent of sub-meter resolution topographic surveying has revolutionized active fault mapping. Light detection and ranging (lidar) collected using crewed airborne laser scanning (ALS) can provide ground coverage of entire fault systems but is expensive, while Structure-from-Motion (SfM) photogrammetry from uncrewed aerial vehicles (UAVs) is popular for mapping smaller sites but cannot image beneath vegetation. Here, we present a new UAV laser scanning (ULS) system which overcomes these limitations to survey fault-related topography cost-effectively, at desirable spatial resolutions, and even beneath dense vegetation. In describing our system, data acquisition and processing workflows, we provide a practical guide for other researchers interested in developing their own ULS capabilities. We showcase ULS data collected over faults from a variety of terrain and vegetation types across the Canadian Cordillera and compare them to conventional ALS and SfM data. Due to the lower, slower UAV flights, ULS offers improved ground return density (~260 points/m2 for the capture of a paleoseismic trenching site and ~10–72 points/m2 for larger, multi-kilometer fault surveys) over conventional ALS (~3–9 points/m2) as well as better vegetation penetration than both ALS and SfM. The resulting ~20–50 cm-resolution ULS terrain models reveal fine-scale tectonic landforms that would otherwise be challenging to image.

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2024-06-05

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Salomon, G., Finley, T., Nissen, E., Stephen, R., & Menounos, B. (2024). Mapping fault geomorphology with drone-based lidar. Seismica, 3(1). https://doi.org/10.26443/seismica.v3i1.1186

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