Image Identification and Modeling of Surf-Zone Wave Breaking

Project Title: Image Identification and Modeling of Surf-Zone Wave Breaking

Research Advisor: Dr. Katherine Brodie ( is a Senior Research Oceanographer at ERDC’s Coastal Observation and Analysis Branch at the Field Research Facility (FRF) in Duck, NC. Dr. Brodie leads the littoral remote sensing research group within ERDC and is a co-founder of the Coastal Imaging Research Network. Additional mentorship will be provided by Drs. Spicer Bak (, Tyler Hesser (, and Matthew Farthing (

Proposed Length of Time: 2 years, with option to extend 1 year.

Location: Duck, NC

Project Background: Wave breaking is a dominant energy input into the surf-zone. Breaking wave properties can vary in time and space, driving variability in water levels and circulation patterns, with breaking type effecting the rate of energy dissipation across the surf-zone and injection of turbulence towards the seabed.  Wave breaking is often parameterized in numerical models using coefficients that are tuned to best match observations as opposed to explicitly defined based on the wave breaking processes.  Recent experiments using lidar observations of individual breaking waves, have revealed new information on the complex kinematics and shape evolution of spilling and plunging breakers and propagating bores, which have implications for how we model wave transformation in the surf-zone (Martins et al., 2018; O’Dea et al., 2021; Carini and Chickadel, 2021a, b).  These detailed lidar data sets, while useful for understanding the fundamental physics of surf-zone waves, are challenging to collect over the broad time-periods and range of conditions needed to fully calibrate or evaluate the skill of new parameterizations of wave breaking in numerical models.  Breaking waves, however, are also a dominant signature in video imagery of the coastal zone, which can easily be collected from shore-mounted locations.  For example, Moris et al., 2021 demonstrated how image-based observations of phase-resolved wave breaking in a laboratory environment could be used to improve the calibration of wave breaking in a one-dimensional Boussinesq model.

The Argus tower at the FRF (Holman and Stanley 2007) has collected video imagery of the surf-zone for over 30 years, and image products are analyzed in near-realtime to provide estimates of surf-zone bathymetry (e.g. Holman et al., 2013) and currents (e.g. Chickadel et al., 2003; Anderson et al., 2021).  These observations have been integrated to provide boundary conditions (Bak et al., 2020) and ground-truth observations to evaluate the skill of near-realtime coastal numerical models running within ERDC’s Coastal Model Testbed (CMTB); but information on individual breaking waves is presently not extracted from the imagery.  Recent efforts within the community have explored the use of machine learning algorithms to identify the location of breaking waves in coastal imagery (Stringari and Power, 2019; Stringari et al., 2019; Kim et al., 2020; Stringari et al., 2021, Saez et al., 2021), with many of these algorithms being made available to the community for continued testing and development.  Buscombe and Carini, 2019 have also demonstrated the use of deep convolutional neural networks (CNNs) to classify wave breaking type from very close-range monochrome infrared imagery of the surf-zone.  While there are pros and cons to many of the aforementioned approaches, no algorithm exists which can robustly identify the location of active wave breaking simultaneous to wave breaking type from broad-view imagery of the surf-zone.

Project Goals: This research opportunity focuses on the development and application of techniques to automatically identify actively breaking waves (location, breaking type) in remotely-sensed coastal imagery data in order to improve fundamental knowledge and modeling of surf-zone hydrodynamic processes.  It is expected that successful candidates will evaluate state-of-the-art approaches, building on them to develop a rigorous approach to identify breaking wave location and type, that can be automated and run in near-realtime (and on historical datasets).  The applicant should then propose to use these expansive observations of wave breaking within the analysis framework of the CMTB to improve the representation of wave breaking or any related hydrodynamic process in phase-resolved and/or phase-averaged numerical wave and circulation models. 

Anticipated Skillsets: Ph.D. in oceanography, coastal engineering, geology, geography, earth science, marine science, civil & environmental engineering, applied mathematics, computer science, data science, or related fields. Experience utilizing machine-learning algorithms and working with remotely sensed data (especially imagery) of coastal regions and/or phase-resolved and phase-averaged numerical wave models is preferred.  Demonstrated strong programing capability in Python (or Matlab with a willingness to learn Python) is highly valued.


Anderson, D., Bak, A. S., Brodie, K. L., Cohn, N., Holman, R. A., & Stanley, J. (2021). Quantifying Optically Derived Two-Dimensional Wave-Averaged Currents in the Surf Zone. Remote Sensing13(4), 690.

Bak, A. S., Brodie, K. L., Hesser, T. J., & Smith, J. M. (2019). Applying dynamically updated nearshore bathymetry estimates to operational nearshore wave modeling. Coastal Engineering145, 53-64.

Buscombe, D., & Carini, R. J. (2019). A data-driven approach to classifying wave breaking in infrared imagery. Remote Sensing11(7), 859.

Carini, R. J., Chickadel, C. C., & Jessup, A. T. (2021a). Surf Zone Waves at the Onset of Breaking: 1. LIDAR and IR Data Fusion Methods. Journal of Geophysical Research: Oceans126(4), e2020JC016934.

Carini, R. J., Chickadel, C. C., & Jessup, A. T. (2021b). Surf Zone Waves at the Onset of Breaking: 2. Predicting Breaking and Breaker Type. Journal of Geophysical Research: Oceans126(4), e2020JC016935.

Chickadel, C. C., Holman, R. A., & Freilich, M. H. (2003). An optical technique for the measurement of longshore currents. Journal of Geophysical Research: Oceans108(C11).

Holman, R. A., & Stanley, J. (2007). The history and technical capabilities of Argus. Coastal engineering54(6-7), 477-491.

Holman, R., Plant, N., & Holland, T. (2013). cBathy: A robust algorithm for estimating nearshore bathymetry. Journal of Geophysical Research: Oceans118(5), 2595-2609.

Kim, J., Kim, J., Kim, T., Huh, D., & Caires, S. (2020). Wave-tracking in the surf zone using coastal video imagery with deep neural networks. Atmosphere11(3), 304.

Martins, K., Blenkinsopp, C. E., Deigaard, R., & Power, H. E. (2018). Energy Dissipation in the Inner Surf Zone: New Insights From LiDAR‐Based Roller Geometry Measurements. Journal of Geophysical Research: Oceans123(5), 3386-3407.

Moris, J. P., Catalán, P. A., & Cienfuegos, R. (2021). Incorporating wave-breaking data in the calibration of a Boussinesq-type wave model. Coastal Engineering168, 103945.

O'Dea, A., Brodie, K., & Elgar, S. (2021). Field Observations of the Evolution of Plunging‐Wave Shapes. Geophysical Research Letters48(16), e2021GL093664.

Sáez, F. J., Catalán, P. A., & Valle, C. (2021). Wave-by-wave nearshore wave breaking identification using U-Net. Coastal Engineering, 104021.

Stringari, C. E., & Power, H. E. (2019). The fraction of broken waves in natural surf zones. Journal of Geophysical Research: Oceans124(12), 9114-9140.

Stringari, C. E., Harris, D. L., & Power, H. E. (2019). A novel machine learning algorithm for tracking remotely sensed waves in the surf zone. Coastal Engineering147, 149-158.

Stringari, C. E., Guimarães, P. V., Filipot, J. F., Leckler, F., & Duarte, R. (2021). Deep neural networks for active wave breaking classification. Scientific Reports11(1), 1-12.

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