24 Cryptic and Slow-Moving Quaternary-Active Faults in the Western United States (Posters)

Wednesday, 16 March 2022
8:30 AM - 6:00 PM
Student Union Ballroom A/B

COMPARING LIDAR DATASETS FROM AIRCRAFTS AND DRONES FOR USE IN GEOMORPHIC MAPPING OF THE DENSELY-VEGETATED SANTA YNEZ FAULT IN SOUTHERN CALIFORNIA

Paper number: 24-2
Program: Theme Sessions
Time: 8:30 AM – 6:00 PM

Authors

Abstract

Constant advances in mapping technology allow tectonic geomorphologists to more accurately evaluate geomorphic features of the landscape and their relationship to fault movement. LiDAR technology has become a highly accessible solution to creating precise 3D models of landscapes and their features. LiDAR data creates an opportunity to evaluate areas previously determined as difficult, due to lack of field accessibility and vegetation coverage.

We are using LIDAR data collected from aircraft and drones to map the geomorphic expression of the Santa Ynez fault in the western Transverse Ranges (WTR) of southern California. This fault is densely vegetated, which restricts field access and makes geomorphic and structural observations difficult. Consequently, little is known about the fault’s kinematic history and recency of faulting. We used 1 m resolution LIDAR collected from aircraft after the 2018 fires in the WTR and higher-resolution LIDAR collected from a drone at specific sites along the Santa Ynez Fault to map topographic features along the fault. The 1 m LIDAR has a resolution of 3 to 5 points per square meter, while the drone LIDAR has closer to 260 points per square meter. We present a comparison of these two data sets and show examples of differences in interpretations from each data set. In most cases, the higher resolution drone LIDAR is better for identifying small-scale features, and making more accurate measurements of subtle topography, but the benefits of drone LIDAR at a site are dependent on the amount and type of vegetation.

Geological Society of America Abstracts with Programs. Vol. 54, No. 2

doi: 10.1130/abs/2022CD-374318

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