Abstract
Mapping coastal bathymetry from remote sensing becomes increasingly more attractive for the coastal community. It is facilitated by a rising availability of drone and satellite data, advances in data science, and an open-source mindset. Coastal bathymetry, but also wave directions, celerity and near-surface currents can simultaneously be derived from aerial video of a wave field. However, the required video processing is usually extensive, requires skilled supervision, and is tailored to a fieldsite. This study proposes a video-processing algorithm that resolves these issues. It automatically adapts to the video data and continuously returns mapping updates and thereby aims to make wave-based remote sensing more inclusive to the coastal community. The code architecture for the first time includes the dynamic mode decomposition (DMD) to reduce the data complexity of wavefield video. The DMD is paired with loss-functions to handle spectral noise and a novel spectral storage system and Kalman filter to achieve fast converging measurements. The algorithm is showcased for fieldsites in the USA, the UK, the Netherlands, and Australia. The performance with respect to mapping bathymetry was validated using ground truth data. It was demonstrated that merely 32 s of video footage is needed for a first mapping update with average depth errors of 0.9–2.6 m. These further reduced to 0.5–1.4 m as the videos continued and more mapping updates were returned. Simultaneously, coherent maps for wave direction and celerity were achieved as well as maps of local near-surface currents. The algorithm is capable of mapping the coastal parameters on-the-fly and thereby offers analysis of video feeds, such as from drones or operational camera installations. Hence, the innovative application of analysis techniques like the DMD enables both accurate and unprecedentedly fast coastal reconnaissance. The source code and data of this article are openly available.
Highlights
Observations of near-shore hydrodynamics and bathymetry are used for various purposes: to study and manage the coast [1,2,3], to update early warning systems [4], to monitor swimmer safety [5,6,7], for dredging-and-dumping surveillance [8], and military landing operations [9]
A straightforward approach to map coastal hydrodynamics and bathymetry is via videobased remote sensing of a wave-field
After decomposing image sequences of Duck (Figure 6a) into 16 dynamic modes, the modes were normalized to global one-component phase images (GOCPI) and filtered for frequency and resolution
Summary
Observations of near-shore hydrodynamics and bathymetry are used for various purposes: to study and manage the coast [1,2,3], to update early warning systems [4], to monitor swimmer safety [5,6,7], for dredging-and-dumping surveillance [8], and military landing operations [9]. An observation of hydrodynamics or bathymetry with areal coverage, a map, is thereby often beneficial if not a prerequisite to recognize relevant spatial details. A straightforward approach to map coastal hydrodynamics and bathymetry is via videobased remote sensing of a wave-field. In comparison to in-situ measurements, video-based remote sensing is less accurate; data acquisition is less labor-intensive, and measurements have high spatial coverage by default. Different instruments and video-processing methods are used to map hydrodynamics and bathymetry. Videos may be recorded with stationary cameras [10], aircrafts [11], UAVs/drones [12,13,14,15,16], (navigational) X-Band radars [17,18,19,20,21], or satel-
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