CIRN Quantitative Coastal Imaging Toolbox

Bruder, Brittany; Brodie, Katherine L.

doi:10.1016/j.softx.2020.100582

Cited by 27 publications

(10 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Videos were taken in 4K (3840 by 2160 pixels at 30 frames per second). Images were extracted from the videos at 5 Hz centred on the rig, georectified using a flat plane at the still-water elevation in NAVD88 using multiple ground-control points following Bruder & Brodie (2020), and put into the coordinate system (figures 1 b , c and 2 e , f ). Georectified images have spatial resolution m.…”

Section: Methodsmentioning

confidence: 99%

Cross-shore wind-induced changes to field-scale overturning wave shape

et al. 2023

Self Cite

View full text Add to dashboard Cite

The shape of depth-limited breaking-wave overturns is important for turbulence injection, bubble entrainment and sediment suspension. Overturning wave shape depends on a nonlinearity parameter $H/h$ , where $H$ is the wave height, and $h$ is the water depth. Cross-shore wind direction (offshore/onshore) and magnitude affect laboratory shoaling wave shape and breakpoint location $X_{{bp}}$ , but wind effects on overturning wave shape are largely unstudied. We perform field-scale experiments at the Surf Ranch wave basin with fixed bathymetry and $\approx 2.25$ m shoaling solitons with small height variations propagating at $C=6.7\ \mathrm {m}\ \mathrm {s}^{-1}$ . Observed non-dimensional cross-wave wind $U_w$ was onshore and offshore, varying realistically ( $-1.2 < U_{w}/C < 0.7$ ). Georectified images, a wave staff, and lidar are used to estimate $X_{{bp}}$ , $H/h$ , overturn area $A$ and aspect ratio for 22 waves. The non-dimensionalized $X_{{bp}}$ was inversely related to $U_{w}/C$ . The non-dimensional overturn area and aspect ratio also were inversely related to $U_{w}/C$ , with smaller and narrower overturns for increasing onshore wind. No overturning shape dependence on the weakly varying $H/h$ was seen. The overturning shape variation was as large as prior laboratory experiments with strong $H/h$ variations without wind. An idealized potential air flow simulation on steep shoaling soliton shape has strong surface pressure variations, potentially inducing overturning shape changes. Through wave-overturning impacts on turbulence and sediment suspension, coastal wind variations could be relevant for near-shore morphology.

show abstract

Section: Methodsmentioning

confidence: 99%

Cross-shore wind-induced changes to field-scale overturning wave shape

et al. 2023

Self Cite

View full text Add to dashboard Cite

show abstract

“…For example, the image shown in Figure 1b was mapped into a local coordinate system aligned with the orientation of the shoreline (Figure 1c). Complete details on camera calibrations and projections for coastal imaging applications are available in the literature (e.g., [27,57,59,60]), and CIRN software tools for conducting calibrations and projections are described in Section 3.…”

Section: Coastal Imaging Methodologymentioning

confidence: 99%

“…The latter three toolboxes descend directly from CIL codes and are largely used for data handling and organization, as well as support photogrammetry functions. The Quantitative Coastal Imaging Toolbox [60] provides the foundational codes for initial processing and rectification of imagery data. The Toolbox includes codes to produce geo-rectified images of oblique imagery from land-based multi/single-camera stations or stationary UAS.…”

Section: Cirn Code Repositorymentioning

confidence: 99%

The Coastal Imaging Research Network (CIRN)

Palmsten

Brodie

2022

Remote Sensing

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Reference source not found.. The image georectification, and subsequent analyses, was done using MATLAB, with some of the scripts provided by the Coastal Imaging Network ( [55]). These scripts were modified and adapted to the specific requirements and environmental settings of the study area.…”

Section: Image Georectificationmentioning

confidence: 99%

Investigating the Performance of Timex Intervals for Shoreline Edge Detection: A Sensitivity Analysis from a Low-Cost Coastal Monitoring System in Brandon Bay, Ireland

Nuyts,

Farrell,

Nash

et al. 2024

Preprint

View full text Add to dashboard Cite

Remote video imagery using shoreline edge detection is widely used in coastal monitoring in order to acquire measurements of nearshore and swash processes. Some of these systems have very limited flexibility due to their rigid structures and require considerable investment in hardware. As such, there is a need for an autonomous low-cost system (~€500) that can be rapidly deployed in the field, while still producing the outcomes required for coastal monitoring. This research presents a sensitivity analysis of time-lapse intervals for two low-cost cameras located in a remote coastal area, overlooking a dissipative beach-dune system. The analysis shows that RMSD in the detected shoreline is similar to other studies for intervals ranging between 1 second and 30 seconds (i.e., RMSDmax for Camera 1 = 2.8 m and Camera 2 = 1.3 m). The increase of the sampling interval from 1 s to 30 s is shown to have no significant adverse effect on the precision of shoreline detection. The research found that the limitations of battery life and memory storage substantially decrease when sampling intervals increase without an effect on the data quality. This also has a positive effect for low-cost systems and removes the requirement for external battery and memory storage. On the systems used, the battery life increased from ~10 days to ~100 days when the sampling interval was increased from 1 to 5 s., As such, the current standard of producing Timex images from 1 s sampling intervals over 10 minutes can be changed depending on the application and requirements needed for analysis.

show abstract

CIRN Quantitative Coastal Imaging Toolbox

Cited by 27 publications

References 18 publications

Cross-shore wind-induced changes to field-scale overturning wave shape

Cross-shore wind-induced changes to field-scale overturning wave shape

The Coastal Imaging Research Network (CIRN)

Investigating the Performance of Timex Intervals for Shoreline Edge Detection: A Sensitivity Analysis from a Low-Cost Coastal Monitoring System in Brandon Bay, Ireland

Contact Info

Product

Resources

About