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.

The intertidal beach profile provides a fundamental representation of beach morphology and serves as a key indicator of shoreline morphodynamics. To enable frequent and accurate mapping of intertidal beach profiles, this study proposes an automated reconstruction framework that integrates single-pixel image columns with a stacked bidirectional long short-term memory (Bi-LSTM) network. Time-exposure imagery, commonly referred to as Timex imagery, acquired from a shore-based video monitoring station at Xisha Bay, China, is used as the primary data source, while wave records obtained from a wave buoy are incorporated to assign elevations to the detected waterline breakpoints, thereby enabling automatic beach profile reconstruction. The stacked Bi-LSTM network is trained for land–sea segmentation and waterline breakpoint localization. achieving the best performance among the tested methods, with precision, recall, accuracy, and F1 score values of 0.951, 0.894, 0.978, and 0.903, respectively, and a mean breakpoint localization error of 2.23 pixels. Breakpoint elevations were then estimated using a local slope–wave setup attribution model. Validation against field-measured topographic data from four fixed profiles and three survey periods showed good agreement between the reconstructed and measured profiles, with a period-based root mean square error (RMSE) of 0.212 ± 0.080 m. When all validation points were combined, the reconstructed elevations showed strong agreement with the measured elevations, with a coefficient of determination (R²) of 0.988 and an overall RMSE of 0.24 m. The profile comparisons further showed that the reconstructed profiles generally captured the overall profile shape and cross-shore morphological pattern of the measured profiles, although reconstruction accuracy varied among the four fixed profiles. These differences demonstrate that camera viewing angle, field-of-view position, camera-to-profile distance, and image quality are important factors influencing video-derived beach profile reconstruction. These results indicate that the proposed method can directly reconstruct fixed intertidal beach profiles from shore-based Timex imagery without generating a digital elevation model of the entire intertidal zone. It provides a practical tool for high-frequency monitoring of intertidal profile morphology and supports the quantitative analysis of beach erosion–accretion dynamics.