Poster #027, San Andreas Fault System (SAFS)

High Resolution Topography along 40 km of the Southern San Andreas Fault

Poster Image: 

Poster Presentation

2020 SCEC Annual Meeting, Poster #027, SCEC Contribution #10689
We produced a point cloud, digital surface model (DSM), and orthomosaic that cover ~ 40.5 km of strike length and ~31 km^2 area along the Southern San Andreas fault using small uncrewed areal system (sUAS) photographs and dGNSS camera positioning. The point cloud contains ~8x10^9 points (240 pts/m^2; for comparison the entire B4 LiDAR data has 5.7x10^9 points), and the DSM and orthomosaic resolutions are 10 and 5 cm; they extend from north of Painted Cyn. south to Bombay Beach. The dataset will be made publicly available on OpenTopography. We anticipate a variety of applications for the data and see the effort as a test-run for rapid, accurate, high resolution topography (HRT) generation fol...lowing surface-rupturing earthquakes.

A Sensefly eBee Plus sUAS was used to collect 15773 photographs. Camera positions were determined using dual frequency on-board dGNSS post-processed against our geodetic quality reference stations. Field work comprised 3.5 days, 22 1-hour sUAS flights, daily set-up of a reference station, and checkpoint measurement (GCPs were not used for georeferencing). Nightly preliminary processing was completed on three GPU-equipped laptops. High quality processing was performed on a cluster of five specially built workstations with 12 GPUs in ~48 hours computer time in Agisoft Metashape. Final processing includes tie point generation on the ‘highest’ setting, inclusion of camera positions, iterative bundle adjustments and removal of large uncertainty tie points, and a ~10 cm vertical translation to account for systematic bias relative to 176 independently measured checkpoints. Horizontal and vertical RMS error of the point cloud relative to the checkpoints is 2.3 and 4.5 cm, respectively.

There are a variety of applications for our dataset, including geomorphic mapping, change detection relative to B4 LiDAR and other datasets, and as pre-event data in the event of a future surface-rupturing earthquake in the area. We present initial differencing results for the 2005 B4 and our 2020 sUAS dataset showing geomorphic change and infrastructure development. Our sUAS dataset is a complimentary pre-event dataset to the B4 lidar data given their varying aperture, spatial resolution, texture, and error sources. In addition, the ability to rapidly produce HRT (~10 km^2/day by one sUAS and crew) has important potential for post-earthquake scientific response including the possibility of repeat surveys to capture post-event surface deformation.