Technical Papers
Jan 2, 2019

Imaging-Based Nearshore Bathymetry Measurement Using an Unmanned Aircraft System

Publication: Journal of Waterway, Port, Coastal, and Ocean Engineering
Volume 145, Issue 2

Abstract

An imaging-based method to estimate the nearshore bathymetry in the surf zone is described. The method uses imagery collected by an unmanned aircraft system (UAS), or a consumer drone. The UAS was flown over the area of interest to record video, and a particle image velocimetry (PIV) technique was then applied to analyze the image frames to retrieve the wave celerity. Using the shallow water approximation to the linear-wave dispersion relation, wave celerity from the imagery could be used to deduce the local water depth. After combining the water depth inversion at multiple points from within the area of interest, the bathymetry was constructed. To validate the method, water depths from 25 spatial points were surveyed with a total station during a trial in the nearshore surf zone at Freeport, Texas. The root-mean-square error (RMSE) was estimated as 0.132 m. By minimizing the RMSE, the correction factor that accounts for the wave nonlinearity in estimating wave celerity was estimated as 1.02. This new and simple approach provides simultaneous measurement of bathymetry and surface velocity field mainly in the surf zone, where breaking/broken waves and energetic sediment transport frequently dominate, and does not require a high-end UAS, resulting in greater flexibility in sampling across space and time.

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Acknowledgments

This research was, in part, supported by the National Oceanic and Atmospheric Administration Texas Sea Grant College Program, with a project title “Study of Wetland Erosion due to Storms through Combined Field, Laboratory, and Numerical Investigations.” Data used in generating results in this study will be available upon request by contacting the corresponding author at [email protected].

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Go to Journal of Waterway, Port, Coastal, and Ocean Engineering
Journal of Waterway, Port, Coastal, and Ocean Engineering
Volume 145Issue 2March 2019

History

Received: Dec 29, 2017
Accepted: Aug 28, 2018
Published online: Jan 2, 2019
Published in print: Mar 1, 2019
Discussion open until: Jun 2, 2019

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Affiliations

Shih-Heng Sun [email protected]
Graduate Student, Dept. of Ocean Engineering, Texas A&M Univ., College Station, TX 77843. Email: [email protected]
Wei-Liang Chuang [email protected]
Postdoctoral Researcher, Zachry Dept. of Civil Engineering, Texas A&M Univ., College Station, TX 77843. Email: [email protected]
Kuang-An Chang, M.ASCE [email protected]
Professor, Zachry Dept. of Civil Engineering and Department of Ocean Engineering, Texas A&M Univ., College Station, TX 77843 (corresponding author). Email: [email protected]
Jin Young Kim [email protected]
Graduate Student, Zachry Dept. of Civil Engineering, Texas A&M Univ., College Station, TX 77843. Email: [email protected]
James Kaihatu, A.M.ASCE [email protected]
Professor, Zachry Dept. of Civil Engineering, Texas A&M Univ., College Station, TX 77843. Email: [email protected]
Thomas Huff [email protected]
Graduate Student, Dept. of Ecosystem Science and Management, Texas A&M Univ., College Station, TX 77843. Email: [email protected]
Rusty Feagin [email protected]
Professor, Dept. of Ecosystem Science and Management, Texas A&M Univ., College Station, TX 77843. Email: [email protected]

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