Analysis of Tidal Cycle Wave Breaking Distribution Characteristics on a Low-Tide Terrace Beach Using Video Imagery Segmentation

Wave breaking is a fundamental process in ocean energy dissipation and plays a crucial role in the exchange between ocean and nearshore sediments. Foam, the primary visible feature of wave breaking areas, serves as a direct indicator of wave breaking processes. Monitoring the distribution of foam via remote sensing can reveal the spatiotemporal patterns of nearshore wave breaking. Existing studies on wave breaking processes primarily focus on individual wave events or short timescales, limiting their effectiveness for nearshore regions where hydrodynamic processes are often represented at tidal cycles. In this study, video imagery from a typical low-tide terrace (LTT) beach was segmented into four categories, including the wave breaking foam, using the DeepLabv3+ architecture, a convolutional neural networks (CNNs)-based model suitable for semantic segmentation in complex visual scenes. After training and testing on a manually labelled dataset, which was divided into training, validation, and testing sets based on different time periods, the overall classification accuracy of the model was 96.4%, with an accuracy of 96.2% for detecting wave breaking foam. Subsequently, a heatmap of the wave breaking foam distribution over a tidal cycle on the LTT beach was generated. During the tidal cycle, the foam distribution density exhibited both alongshore variability, and a pronounced bimodal structure in the cross-shore direction. Analysis of morphodynamical data collected in the field indicated that the bimodal structure is primarily driven by tidal variations. The wave breaking process is a key factor in shaping the profile morphology of LTT beaches. High-frequency video monitoring further showed the wave breaking patterns vary significantly with tidal levels, leading to diverse geomorphological features at various cross-shore locations.