Land resources are an essential foundation for socioeconomic development. Island land resources are limited, the type changes are particularly frequent, and the environment is fragile. Therefore, large-scale, long-term, and high-accuracy land-use classification and spatiotemporal characteristic analysis are of great significance for the sustainable development of islands. Based on the advantages of remote sensing indices and principal component analysis in accurate classification, and taking Zhoushan Archipelago, China, as the study area, in this work long-term satellite remote sensing data were used to perform land-use classification and spatiotemporal characteristic analysis. The classification results showed that the land-use types could be exactly classified, with the overall accuracy and Kappa coefficient greater than 94% and 0.93, respectively. The results of the spatiotemporal characteristic analysis showed that the built-up land and forest land areas increased by 90.00 km² and 36.83 km², respectively, while the area of the cropland/grassland decreased by 69.77 km². The areas of the water bodies, tidal flats, and bare land exhibited slight change trends. The spatial coverage of Zhoushan Island continuously expanded toward the coast, encroaching on nearby sea areas and tidal flats. The cropland/grassland was the most transferred-out area, at up to 108.94 km², and built-up land was the most transferred-in areas, at up to 73.31 km². This study provides a data basis and technical support for the scientific management of land resources.

Semi-enclosed bays are significantly influenced by the interactions between land and sea, as well as human activities. One notable human activity, land reclamation, impacts water exchange within these bays. However, the variability of sediment transport and cross-bay transport following reclamation remains poorly understood. This study aims to enhance the understanding of sediment dynamics and the responses of cross-bay transport to reclamation. A well-validated three-dimensional numerical model was developed in the Laizhou Bay (LB). Following reclamation, tidal currents suspended sediment concentration, and erosion increased seaward, while these factors decreased landward. In LB, surface and bottom subtidal currents flowed in opposite directions, with the direction of volume transport primarily determined by bottom currents. In the western LB, volume and sediment transport exhibited an anticyclonic pattern, with pronounced seasonal variations observed elsewhere. During summer, volume and sediment transport predominantly occurred from the northeast to the southwest. In winter, volume transport in northern LB was directed westward, while it was eastward in the southern part; sediment transport was primarily eastward. Advection played a significant role in sediment transport dynamics. The pathway of cross-bay sediment transport was primarily located in the central part of the bay. Notably, the cross-bay sediment transport flux in winter was approximately 3.5 times greater than in summer, with reclamation resulting in a reduction in cross-bay transport flux by about 22.17%.

In response to the drawbacks of low efficiency, cumbersome calculation, and easy-to-fall local optimal solutions in existing shallow water acoustic parameters inversion research, this paper proposes a shallow water acoustic parameters inversion method based on a feedback (BP) neural network model. Firstly, the theoretically predicted values of the shallow water sound pressure field are obtained through the fast field method (FFM). Secondly, a relationship model between the predicted sound pressure field and the inversion of ground sound parameter values is established based on the BP neural network model. Finally, the measured sound pressure field data are brought into the neural network model to obtain the inversion results. The application results of the method indicate that, compared to the classical simulated annealing (SA) algorithm, the BP neural network model converts the data-matching process of the optimization algorithm into the construction of a relationship model between the input data and the desired parameters, avoiding repeated matching and optimization processes. Therefore, it can directly, accurately, and efficiently output the inversion results. Under the premise of setting the same accuracy, the iteration number of the BP neural network model is reduced to 2% of the SA algorithm, cutting the calculation time to 30% of the SA algorithm. It has broad application prospects in shallow sea acoustic parameters inversion algorithms.

Introduction: The Holocene mud deposits that extend from the Yangtze River mouth to the Taiwan Strait along the Zhejiang–Fujian coast, East China Sea (ECS), have attracted considerable research attention. However, there is a lack of consensus regarding the redistribution flux of Yangtze River sediment after it enters the sea. There are many bays along the Zhejiang and Fujian coasts, but their role in the source–sink process of Yangtze River sediment is not well-documented owing to limited data.Methods: In this study, data from shallow seismic profiles, core samples, and surface sediment samples were analysed to assess the role of Sanmen Bay, the second largest bay in Zhejiang Province, in the source–sink process of Yangtze River sediments.Results: The results showed that Sanmen Bay is an important sink for Yangtze River sediments. The characteristics of the clay minerals indicate that the fine-grained sediments in Sanmen Bay are mainly derived from Yangtze River sediments. The thickness of Holocene deposits in Sanmen Bay ranges from 8.2 to 32.0 m, with an average of 23.4 m and a total sediment volume of 5.06 × 10⁴ Mt, accounting for 1% of Yangtze River sediment in this time period. The Bayesian age-depth model based on borehole accelerator mass spectrometry (AMS) ¹⁴C dating shows that a high sedimentation rate occurred in the early and late Holocene but decreased significantly in the mid-Holocene, which is similar to that of the inner shelf of the East China Sea (ECS).Conclusion: The variation in the Holocene sedimentation rate in Sanmen Bay is related to the southward transport process and mechanism of Yangtze River sediments. The results of this study provide a basis for understanding the source–sink process of sediment in large rivers.

The concentration of heavy metals in sediment is an important factor for assessing the quality of the nearshore environment. Geochemical background values are the normal concentrations of heavy metals in the natural environment. The use of different background values results in different evaluation results. Heavy metal (Cu, Pb, Zn, Cr, Cd, As, and Hg) concentration profiles along a sediment core were investigated to obtain background values and assess the depositional processes and contamination levels in Laizhou Bay. Apart from As and Hg, all other metals had similar distribution patterns and could be divided into four stages since marine isotope stage (MIS) 2. The results of the enrichment factor, geoaccumulation index, Pearson’s correlation coefficient, Nemerow pollution index, principal component analysis, and potential ecological risk index showed that element enrichment or contamination in the core sediments was absent and that all heavy metals were mainly naturally sourced. The changes in heavy metals since MIS 2 in the core sediments were predominantly related to the sedimentary environment, sediment sources, and mean grain size. The average values of the heavy metals in the U2 unit formed during the Middle Holocene can be used as reference values for background values.