Quantifying Optically Derived Two-Dimensional Wave-Averaged Currents in the Surf Zone

Remote-Sensing Measurements of Wave Breaking at Two Pacific Northwest Jettied Inlets

Breaking waves constitute one of the main environmental stressors on coastal structures as well as a leading hazard to navigation in nearshore regions. In this paper, we use camera-based methods to measure wave breaking over two jetty systems in the Pacific Northwest; the North jetty of the Columbia River, Washington state, and the two jetties of Coos Bay, Oregon, as well as over three nearby nearshore dredge disposal areas. Data were collected using the “brightest” images and Argus camera technology over a span of 847 days for the Columbia River and 202 days for Coos Bay. Wave breaking over the Columbia north jetty reached 100% for wave heights greater than 3 m and for tides above mid-tide level and was concentrated on the seaward half of the jetty. For Coos Bay, the south jetty saw substantially more breaking than the north one with the worst overtopping occurring mid-jetty and seeming to be associated with sediment transport through the jetty and into the inlet, as well as possibly the navigation channel. Wave breaking at the Coos Bay inlet mouth was enhanced during ebb flow conditions. Argus imagery analysis showed no evidence of enhanced breaking over any of the three dredge material placement sites.

Convolutional Neural Network and Optical Flow for the Assessment of Wave and Tide Parameters from Video Analysis (LEUCOTEA): An Innovative Tool for Coastal Monitoring

Coastal monitoring is a topic continuously developing, which has been applied using different approaches to assess the meteo-marine features, for example, to contribute to the development of improved management strategies. Among these different approaches, coastal video monitoring coupled with recent machine learning and computer vision techniques has spread widely to assess the meteo-marine features. Video monitoring allows to obtain large spatially and temporally datasets well-distributed along the coasts. The video records can compile a series of continuous frames where tide phases, wave parameters, and storm features are clearly observable. In this work, we present LEUCOTEA, an innovative system composed of a combined approach between Geophysical surveys, Convolutional Neural Network (CNN), and Optical Flow techniques to assess tide and storm parameters by a video record. Tide phases and storm surge were obtained through CNN classification techniques, while Optical Flow techniques were used to assess the wave flow and wave height impacting the coasts. Neural network predictions were compared with tide gauge records. Furthermore, water levels and wave heights were validated through spatial reference points obtained from pre-event topographic surveys in the proximity of surveillance cameras. This approach improved the calibration between network results and field data. Results were evaluated through a Root Mean Square Error analysis and analyses of the correlation coefficient between results and field data. LEUCOTEA system has been developed in the Mediterranean Sea through the use of video records acquired by surveillance cameras located in the proximity of south-eastern Sicily (Italy) and subsequently applied on the Atlantic coasts of Portugal to test the use of action cameras with the CNN and show the difference in terms of wave settings when compared with the Mediterranean coasts. The application of CNN and Optical Flow techniques could represent an improvement in the application of monitoring techniques in coastal environments, permitting to automatically collect a continuous record of data that are usually not densely distributed or available.

Research on Ship Trajectory Classification Based on a Deep Convolutional Neural Network

With the aim of solving the problems of ship trajectory classification and channel identification, a ship trajectory classification method based on deep a convolutional neural network is proposed. First, the ship trajectory data are preprocessed using the improved QuickBundle clustering algorithm. Then, data are converted into ship trajectory image data, a dataset is established, a deep convolutional neural network-based ship trajectory classification model is constructed, and the manually annotated dataset is used for training. The fully connected neural network model and SVM model with latitude and longitude data as input are selected for comparative analysis. The results show that the ship trajectory classification model based on a deep convolutional neural network can effectively distinguish ship trajectories in different waterways, and the proposed method is an effective ship trajectory classification method.

The Coastal Imaging Research Network (CIRN)

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.

Shoreline Detection Accuracy from Video Monitoring Systems

Video monitoring has become an indispensable tool to understand beach processes. However, the measurement accuracy derived from the images has been taken for granted despite its dependence on the calibration process and camera movements. An easy to implement self-fed image stabilization algorithm is proposed to solve the camera movements. Georeferenced images were generated from the stabilized images using only one calibration. To assess the performance of the stabilization algorithm, a second set of georeferenced images was created from unstabilized images following the accepted practice of using several calibrations. Shorelines were extracted from the images and corrected with the measured water level and the computed run-up to the 0 m contour. Image-derived corrected shorelines were validated with one hundred beach profile surveys measured during a period of four years along a 1.1 km beach stretch. The simultaneous high-frequency field data available of images and beach surveys are uncommon and allow assessing seasonal changes and long-term trends accuracy. Errors in shoreline position do not increase in time suggesting that the proposed stabilization algorithm does not propagate errors, despite the ever-evolving vegetation in the images. The image stabilization reduces the error in shoreline position by 40 percent, having a larger impact with increasing distance from the camera. Furthermore, the algorithm improves the accuracy on long-term trends by one degree of magnitude (0.01 m/year vs. 0.25 m/year).

A Self-Adaptive Method for Mapping Coastal Bathymetry On-The-Fly from Wave Field Video

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.

Wave-Filtered Surf Zone Circulation under High-Energy Waves Derived from Video-Based Optical Systems

This paper examines the potential of an optical flow video-based technique to estimate wave-filtered surface currents in the nearshore where wave-breaking induced foam is present. This approach uses the drifting foam, left after the passage of breaking waves, as a quasi-passive tracer and tracks it to estimate the surface water flow. The optical signature associated with sea-swell waves is first removed from the image sequence to avoid capturing propagating waves instead of the desired foam motion. Waves are removed by applying a temporal Fourier low-pass filter to each pixel of the image. The low-pass filtered images are then fed into an optical flow algorithm to estimate the foam displacement and to produce mean velocity fields (i.e., wave-filtered surface currents). We use one week of consecutive 1-Hz sampled frames collected during daylight hours from a single fixed camera located at La Petite Chambre d’Amour beach (Anglet, SW France) under high-energy conditions with significant wave height ranging from 0.8 to 3.3 m. Optical flow-computed velocities are compared against time-averaged in situ measurements retrieved from one current profiler installed on a submerged reef. The computed circulation patterns are also compared against surf-zone drifter trajectories under different field conditions. Optical flow time-averaged velocities show a good agreement with current profiler measurements: coefficient of determination (r2)= 0.5–0.8; root mean square error (RMSE) = 0.12–0.24 m/s; mean error (bias) =−0.09 to −0.17 m/s; regression slope =1±0.15; coherence2 = 0.4–0.6. Despite an underestimation of offshore-directed velocities under persistent wave breaking across the reef, the optical flow was able to correctly reproduce the mean flow patterns depicted by drifter trajectories. Such patterns include rip-cell circulation, dominant onshore-directed surface flow and energetic longshore current. Our study suggests that open-source optical flow algorithms are a promising technique for coastal imaging applications, particularly under high-energy wave conditions when in situ instrument deployment can be challenging.