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Imaging-Based Nearshore Bathymetry Measurement Using an Unmanned Aircraft System
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.
Imaging-Based Nearshore Bathymetry Measurement Using an Unmanned Aircraft System
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.
Imaging-Based Nearshore Bathymetry Measurement Using an Unmanned Aircraft System
Sun, Shih-Heng (author) / Chuang, Wei-Liang (author) / Chang, Kuang-An (author) / Young Kim, Jin (author) / Kaihatu, James (author) / Huff, Thomas (author) / Feagin, Rusty (author)
2019-01-02
Article (Journal)
Electronic Resource
Unknown
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