12 September 2021On the use of suborbital drone imaging and in-situ calibrations of the water surface for characterization of water waves: a space-time imaging approach
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Remote sensing technologies are useful tools when gathering spatial and temporal information about dynamic coastal regions. A method is presented using an in-situ space-time drone imaging technique for evaluating wave periods and wavelengths of surface water waves in shallow urban coastal water environments. High-definition drone video imagery (viewed near-nadir) of shallow water waves was acquired and time synchronized with littoral video imagery. Drone video imagery records of a wave patch with reflected sun glint and non-affected sun glint surface water waves facets, staff gauges, and a simultaneously deployed line target were used to determine wave periods and wavelengths. Time series analyses was applied to the video derived time series imagery. Wave energy spectrums can be extracted and used to simulate synthetic images using a gravity wave model based upon a Weibull probability distribution that simulates the sea state. Applications in shallow water coastal environments continue to benefit from knowledge of wind driven water waves. Data extracted from the Banana River in Florida was used to determine the procedure and techniques.
Jennifer L. Closson andCharles R. Bostater Jr.
"On the use of suborbital drone imaging and in-situ calibrations of the water surface for characterization of water waves: a space-time imaging approach", Proc. SPIE 11857, Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2021, 118570E (12 September 2021); https://doi.org/10.1117/12.2601716
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Jennifer L. Closson, Charles R. Bostater Jr., "On the use of suborbital drone imaging and in-situ calibrations of the water surface for characterization of water waves: a space-time imaging approach," Proc. SPIE 11857, Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2021, 118570E (12 September 2021); https://doi.org/10.1117/12.2601716