Using machine learning to analyse coastal sediment characteristics from unmanned aerial vehicle data: A case study of the Dasha sandy beach, China

The transport processes of coastal sediments play a critical role in shaping coastal geomorphology, with sediment properties—such as grain size—being fundamental to understanding morphodynamics. However, the field collection and laboratory analysis of sediments are time-consuming and labour-intensive, posing great challenges for large-scale and rapid monitoring of sediment spatiotemporal variations. Unmanned aerial vehicle platforms, combined with machine learning techniques, offer a promising solution for efficiently capturing and analysing sediment characteristics. In this study, surface sediment samples were collected from Dasha Beach, a sandy beach located along the East China Sea, and a sediment type coding scheme was established to convert text-based sediment types into digitized codes. Using 10 spatial and spectral unmanned aerial vehicle datasets, along with machine learning models and traditional mathematical methods, we predicted five sediment characteristics: sediment types, sediment water content, mean grain size, sorting coefficient and skewness. Among the models tested, Random Forest demonstrated superior performance, achieving an overall accuracy of 95.65% and a Kappa coefficient of 0.78 for sediment type. For the other four continuous variables, the Random Forest model yielded an average R² of 0.86 and 0.82 on the validation and test sets, respectively, significantly outperforming traditional multiple linear regression. The study revealed five key predictors: near-infrared, red edge, digital surface model, red and slope, underscoring the necessity of integrating spatial and spectral data for accurate predictions. In contrast, variables like intensity, green and NDVI were less relevant in predicting sediment characteristics, particularly in unvegetated areas like beaches. This study highlights an efficient and accurate approach to obtaining high-resolution sediment characteristics, addressing the limitations of traditional sampling and laboratory methods while significantly reducing labour and financial costs. Its application holds considerable potential in diverse coastal environments, including remote or inaccessible regions, offering a robust framework for future sedimentological studies.