Rapid wave model-based nearshore bathymetry inversion with UAS measurements

Jonghyun Harry Lee1, Hojat Ghorbanidehno2, Matthew Farthing3, Ty Hesser3, Matthew P Geheran3, Katherine L Brodie4, Brittany Lynn Bruder5, Eric F Darve6 and Peter K Kitanidis7, (1)University of Hawaii at Manoa, Civil and Environmental Engineering/Water Resources Research Center, Honolulu, HI, United States, (2)Stanford University, Stanford, CA, United States, (3)US Army Corps of Engineers, Coastal and Hydraulics Laboratory, Vicksburg, United States, (4)U.S. Army Engineer Research and Development Center, Coastal and Hydraulics Laboratory, Field Research Facility, Duck, NC, United States, (5)University of Delaware, Center for Applied Coastal Research, Newark, DE, United States, (6)Stanford University, Mechanical Engineering, Stanford, CA, United States, (7)Stanford University, Department of Civil and Environmental Engineering, Stanford, CA, United States
Immediate estimation of nearshore bathymetry and beach topography is crucial for accurate prediction of nearshore wave conditions and coastal flooding events. However, direct bathymetry data collection is expensive and time-consuming while accurate airborne lidar-based survey is limited by breaking waves and decreased light penetration affected by water turbidity. Several recent efforts have been made to combine inverse modeling approaches with indirect video-based observations, but mostly collected from fixed tower-based platforms. In this work, we propose a flexible and rapid bathymetry estimation framework by utilizing a low-cost commercial off-the-shelf Unmanned Aircraft System (UAS) with a real-time batch-data inverse modeling approach that can be performed during the UAS flight. UAS-derived imagery through CBathy and structure-from-motion algorithms provides high-resolution wave celerity and beach topographic data on a single flight. pyPCGA, an open source python library for a fast and scalable variational inverse modeling, is then applied to estimate a snapshot of bathymetry and quantify its estimation uncertainty almost in real time with a nearshore spectral wave model STWAVE. To illustrate the speed and accuracy of our framework, a snapshot of nearshore bathymetry at the U.S. Army Corps of Engineer Field Research Facility (USACE-FRF) in Duck, NC was estimated using UAS data sets collected on July 22, 2016, and we compare the estimation results with direct bathymetry profiles surveyed near the UAS flight date. The computational time for the estimation is only about five minutes on a modern workstation, which is within the UAS-based data collection duration. Estimated bathymetry profiles are remarkably close to the direct survey data (RMSE = 0.3 m) within the estimation credible interval due to the additional use of inland elevation data and a Bayesian prior derived from Dean's profile. These promising results indicate the feasibility of real-time bathymetry imaging for safe marine navigation, beach nourishment project monitoring, and coastal science support.