This paper used dynamic image analysis (DIA) to analyze the grain size and shape data of beach surface sediments in Bao’ding Bay, eastern Hainan Island, China, and explored the effects of sediment transport modes and beach morphology on the grain size–shape trend curves. This paper adopted a method of combining grain size cumulative frequency curves and grain size–shape trend curves to identify three sediment transport modes: suspension, saltation, and traction, and analyzed the characteristics of the grain size–shape trend curves under different beach morphologies (reflective, intermediate, and dissipative). This paper found that the grain size–shape trend curves can effectively indicate the sediment transport modes and improve the accuracy of subpopulation division; the grain size–shape trend curves showed different distribution characteristics in the cross-shore and longshore directions, which are closely related to the beach’s morphology and sediment transport direction. This paper provides a new method and idea for studying beach sediment transport and sedimentary environment using sediment grain size and shape data.

Particle morphology is an important feature of sediments that reflects their transport history and depositional environment. In this study, we used dynamic image analysis (DIA) to measure the size and shape of beach sediments on the east and west coasts of Hainan Island in South China Sea. DIA is a fast and accurate method that can capture and analyze a large number of sediment particles in real-time. We extracted morphological descriptors of each particle, such as equivalent diameter, sphericity, aspect ratio and symmetry, and their distributions based on volume and number. We performed multivariate analysis on the particle morphological data, including alpha diversity, statistical analysis and fingerprint techniques. We found that the Shannon index, calculated by the number distribution of sediment particle morphology, can effectively discriminate between the two sites, reflecting different sediment sources, transport processes and depositional conditions. We also established a composite fingerprint based on seven morphological parameters and diversity indices, which can accurately distinguish between aeolian and hydraulic sedimentary environments. Our study demonstrates the potential application of DIA in identifying sedimentary environments and establishing sediment fingerprints. This can help us understand the sediment transport processes and depositional mechanisms in coastal areas.