Quantifying gravel beach morphodynamics: Uncrewed aerial vehicle−based machine learning for determining texture and sediment transport

Gravel beaches serve as natural defenses against wave energy, storm surges, and coastal hazards. Understanding their sediment dynamics requires analyzing gravel grain-size and grain-shape parameters, yet traditional field sampling, laboratory analysis, and transport-direction determination methods have long presented challenges. To overcome these limitations, this study proposes an integrated technical framework that employs machine learning algorithms to derive sediment characteristics and transport directions from multisource uncrewed aerial vehicle (UAV) datasets. Using spatial and spectral UAV datasets collected at the Fengmenkou gravel beach (Nantian Island, China), the framework derives four key grain-size and grain-shape parameters: mean grain size, sorting coefficient, skewness, and roundness. Results demonstrate that oblique quadrat images collected in the field can be orthorectified for reliable digital analysis, yielding grain-size and grain-shape parameters within acceptable error margins (mean grain-size errors <0.4Φ; sorting coefficient errors <0.3Φ). The four derived parameters exhibit consistently high accuracy on validation datasets (R2 >0.80, root mean square error [RMSE] <0.04), enabling high-resolution and spatially continuous characterization of gravel beach sediments. The resulting spatially continuous parameter fields overcome discrete sampling constraints and substantially reduce field costs, while providing improved input data for the Gao-Collins model. Enhanced data resolution and reduced edge effects extend the applicability of the model for grain-size trend analysis on gravel beaches. In addition, a UAV elevation-based approach was developed to infer sediment transport directions from surface elevation changes. This independent analysis shows potential for identifying net sediment movement and provides complementary support for grain-size trend interpretations. Collectively, this work enables spatially explicit and cost-effective characterization of gravel beach sediment dynamics, thereby improving the assessment of coastal processes using UAV data.