Abstract
The study of coastal processes is critical for the protection and development of beach amenities, infrastructure, and properties. Many studies of beach evolution rely on data collected using remote sensing and show that beach evolution can be characterized by a finite number of “beach states”. However, due to practical constraints, long-term data displaying all beach states are rare. Additionally, when the dataset is available, the accuracy of the classification is not entirely objective since it depends on the operator. To address this problem, we collected hourly coastal images and corresponding tidal data for more than 20 years (November 1998–August 2019). We classified the images into eight categories according to the classic beach state classification, defined as (1) reflective, (2) incident scaled bar, (3) non-rhythmic, attached bar, (4) attached rhythmic bar, (5) offshore rhythmic bar, (6) non-rhythmic, 3-D bar, (7) infragravity scaled 2-D bar, (8) dissipative. We developed a classification model based on convolutional neural networks (CNN). After image pre-processing with data enhancement, we compared different CNN models. The improved ResNext obtained the best and most stable classification with F1-score of 90.41% and good generalization ability. The classification results of the whole dataset were transformed into time series data. MDLats algorithms were used to find frequent temporal patterns in morphology changes. Combining the pattern of coastal morphology change and the corresponding tidal data, we also analyzed the characteristics of beach morphology and the changes in morphodynamic states.
Highlights
Changes in beach morphology are the result of the interaction between ocean dynamic factors and topographic dynamic factors
A beach state classification model requires datasets that are dense in time and extensive in space
Beach states are usually classified into eight categories based on the general understanding of beach morphodynamics, and six categories could be observed for the beach analyzed
Summary
Changes in beach morphology are the result of the interaction between ocean dynamic factors (waves and tides) and topographic dynamic factors (sediment and topography).Understanding beach response to waves and currents is necessary to improve predictions of beach change.The classification of beach morphology, the so-called “beach state”, is one of the steps undertaken by researchers to characterize typical beach behavior and to identify transitions between states. Most researchers base beach classification on expert judgment of available images or using statistics and multivariate analysis of key environmental factors (such as wave height, wave period, sand particle size, beach slope, tidal range, and bay state). A beach state classification model requires datasets that are dense in time (many observations over the long-term are needed) and extensive in space (a large portion of the beach needs to be available for analysis). The classification of the images was entirely dependent on experts, which implies a heavy workload and an element of subjective uncertainty. To address these aspects, we used a long-term dataset of images of an embayed beach in New Zealand. The images cover almost the entire length of the beach and were collected hourly for over 20 years
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