Surf Zone Waves at the Onset of Breaking: 1. LIDAR and IR Data Fusion Methods

Carini, Roxanne J.; Chickadel, C. Chris; Jessup, Andrew T.

doi:10.1029/2020jc016934

Cited by 5 publications

(11 citation statements)

References 66 publications

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“…Over the last decade, the advent of smart phones [17][18][19], action [20][21][22], web-based beach and security cameras [23][24][25][26][27], as well as low-cost, highquality optical imaging from UAS have made coastal imaging more accessible [21,28]. The ubiquity of UAS technology has also made low-cost infra-red sensors more accessible for coastal imaging applications [29,30].…”

Section: History Of Coastal Imaging and Argusmentioning

confidence: 99%

The Coastal Imaging Research Network (CIRN)

Palmsten

Brodie

2022

Remote Sensing

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The Coastal Imaging Research Network (CIRN) is an international group of researchers who exploit signatures of phenomena in imagery of coastal, estuarine, and riverine environments. CIRN participants develop and implement new coastal imaging methodologies. The research objective of the group is to use imagery to gain a better fundamental understanding of the processes shaping those environments. Coastal imaging data may also be used to derive inputs for model boundary and initial conditions through assimilation, to validate models, and to make management decisions. CIRN was officially formed in 2016 to provide an integrative, multi-institutional group to collaborate on remotely sensed data techniques. As of 2021, the network is a collaboration between researchers from approximately 16 countries and includes investigators from universities, government laboratories and agencies, non-profits, and private companies. CIRN has a strong emphasis on education, exemplified by hosting annual “boot camps” to teach photogrammetry fundamentals and toolboxes from the CIRN code repository, as well as hosting an annual meeting for its members to present coastal imaging research. In this review article, we provide context for the development of CIRN as well as describe the goals and accomplishments of the CIRN community. We highlight components of CIRN’s resources for researchers worldwide including an open-source GitHub repository and coding boot camps. Finally, we provide CIRN’s perspective on the future of coastal imaging.

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Section: History Of Coastal Imaging and Argusmentioning

confidence: 99%

The Coastal Imaging Research Network (CIRN)

Palmsten

Brodie

2022

Remote Sensing

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“…Each LIDAR wave profile is fit with a skewed-Gaussian (SG) function to provide peak locations that are robust to spurious points due to sea spray and rapid wave shape changes (red curves in Figure 3a). The SG profiles are used to define the instantaneous wave speed, denoted  c (Figure 3d), because the SG-fitted profile peaks provide a more robust estimate of wave speed than using the raw LIDAR-tracked wave peaks (Carini et al, 2021). Wavelength is estimated as   L cT , where T is the trough-to-trough wave period from the time series extracted at the location of the tracked wave peak.…”

Section: Wave Parameter Estimationmentioning

confidence: 99%

“…For plunging breakers, the onset of breaking is defined as the moment the wave crest begins to overturn. The breaker type is classified based on the unique thermal signatures of spilling and plunging breakers, shown in Figure 2, and using the semi-automated method detailed in Carini et al (2021). Along the back face of the wave, spilling breakers exhibit an unorganized patchy texture, while plunging breakers exhibit an organized streaky pattern.…”

Section: Breaker Classificationmentioning

confidence: 99%

“…Central to our methodology is the development of a spatial wave-tracking algorithm that uses the full spatial and temporal resolution of the LIDAR to provide direct measurements of wave geometry and other parameters. Part 1 of this series describes the LIDAR data quality control and interpolation procedures and details the wave-tracking algorithm (Carini et al, 2021). From the LIDAR line-scans, over 4,200 individual waves are tracked using the spatial peak-tracking method.…”

Section: Wave Parameter Estimationmentioning

confidence: 99%

“…A detailed account of the field experiment set-up and instruments can be found in the first study of this two-part series (Carini et al, 2021). Briefly, LIDAR and thermal infrared (IR) remote sensing data of surf zone breaking waves were collected on November 7 and 8, 2016, at the US Army Corps of Engineers Field Research Facility (USACE FRF) in Duck, NC.…”

Section: Field Experimentsmentioning

confidence: 99%

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Surf Zone Waves at the Onset of Breaking: 2. Predicting Breaking and Breaker Type

2021

Self Cite

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Understanding the dynamics of breaking waves in the surf zone is vital to the progress of nearshore wave and circulation models toward operational use. The goal of wave models in coastal regions is to capture relevant physical processes through explicit representation or field-validated parameterizations. Phase-resolving models, such as Simulating WAves till SHore or SWASH (Zijlema et al., 2011), predict sea surface elevation in space and time, which allows for breaking criteria, critical wave speed or wave steepness to be assessed on a wave-by-wave basis and for wave shape changes to be tracked as breakers develop. A widely used phase-averaged model, Simulating WAves Nearshore or SWAN (Booij et al., 1997), or SWAN, parameterizes energy dissipation due to wave breaking using the Battjes and Janssen (1978) bore model. This bore model determines the portion of the wave height spectra that breaks using the breaker parameter γ = H/h, the ratio of wave height to water depth. Phase-resolving models are often used for basic research on wave mechanics and as input for phase-averaged models, which are larger scale and routinely used to predict and hindcast wave conditions for research and management decisions.Traditionally, γ is estimated as a bulk statistic, using the significant wave height, H sig , or the root-mean-squared wave height, H rms , and the mean water depth, h . For example, Sallenger and Holman (1985) found

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Surface Wave and Roller Dissipation Observed with Shore-based Doppler Marine Radar

Streßer

Horstmann

Baschek

2022

Preprint

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The Earth's coastlines are facing sea level rise and increased human interventions. Predicting long-term coastal changes is of major importance to ensure efficient planning and design of coastal structures, as well as sustainable management of the coastal zone. Furthermore, a proper incorporation of nearshore processes into earth system models is required to efficiently predict the future changes of the coastal morphology, that is, coastal morphodynamics. Long-term measurements of nearshore hydrodynamics, in particular the spatial distribution of wave heights, are rarely available but often required to develop, validate, or calibrate parameterizations of nearshore processes.Breaking surface waves are important drivers of nearshore hydro-and morphodynamics. When a wave breaks, its height and thus the energy that is contained in the wave motion at the scale of the breaker decreases. The vast majority of the extracted wave energy is irreversibly converted (dissipated) to currents (slowly varying mean flow, e.g., Longuet-Higgins & Stewart, 1964), vorticity (Clark et al., 2012, turbulence (chaotic oscillations at higher frequencies, e.g., Feddersen & Trowbridge, 2005), heat (e.g., Sinnett & Feddersen, 2014), sea spray (Van Eijk et al., 2011), sound and air entrainment (Deane, 1997), and re-suspension of sediments (Voulgaris & Collins, 2000). Therefore, wave breaking and the associated wave energy dissipation link the wave energy flux to mixing and sediment transport. A "direct measurement of wave dissipation is equivalent to measuring the forcing for nearshore flow" (Holman & Haller, 2013). However, deployment and maintenance of in-situ sensors (e.g.,

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Surf Zone Waves at the Onset of Breaking: 1. LIDAR and IR Data Fusion Methods

Cited by 5 publications

References 66 publications

The Coastal Imaging Research Network (CIRN)

The Coastal Imaging Research Network (CIRN)

Surf Zone Waves at the Onset of Breaking: 2. Predicting Breaking and Breaker Type

Surface Wave and Roller Dissipation Observed with Shore-based Doppler Marine Radar

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