Wetlands are dubbed the “kidneys of the earth” and are involved in climate regulation, carbon sequestration, ecological balance preservation, and reducing the surface water pollution. Ongoing economic development has introduced pressing challenges to wetland environments. In this context, extracting coastal wetland information and monitoring the dynamic changes are essential. Using long-term sequence Sentinel-2 satellite remote sensing images and field observations, this research proposed a Dynamic Bayesian Network classification model framework based on conjugate gradient updates. We compared the wetland feature extraction effects of the Fletcher–Reeves and the Polak–Ribière–Polyak algorithms of the conjugate gradient. Then, remote sensing combined with the FRDBN classification model was used to extract the information pertinent to wetland feature types and changes in wetland areas and analyze alterations in the distribution characteristics of land cover types. The results showed that the FRDBN model achieved high accuracy (above 96%), and kappa coefficients exceeded 0.96. Long-term monitoring revealed that the area of wetlands increased by 0.85 × 104 hm² from 2016 to 2021. Non-aquatic land cover types exhibited pronounced dynamic changes, with the area of change representing 58–69% of the monitored total. Specifically, the transition between salt marsh vegetation and artificial wetlands was relatively obvious. The FRDBN model provides a new method for extracting wetland feature information. Wetland protection, dynamic monitoring, and carbon sink research can provide robust technology support, facilitating investigations into coastal salt marsh carbon sinks and technological advances in carbon sink assessment.

Ocean observation is the basis for understanding and studying marine science. In recent years, the application of autonomous ships (AS), including Unmanned Surface Vessels (USVs), Autonomous Underwater Vehicles (AUVs), and Remotely Operated Vehicles (ROVs), in ocean observation has gained significant traction due to their capability to perform maritime autonomous tasks of oceans efficiently and safely in challenging marine environments.Compared with traditional technical means, the unique technical capability of ASs in marine environment observation is the ability to maneuver on demand under the influence of complex marine environments. Therefore, giving full play to its controllable maneuverability and realizing its perception, task decision-making, path planning, control, and perception data analysis is the key to its application. Equipped with advanced sensors and instruments, these vessels can gather critical ocean data over large areas and long durations, providing invaluable insights for marine scientists.This editorial aims to highlight the latest advancements in AS technology and their implications for ocean science, particularly the integration of Artificial Intelligence (AI) and Machine Learning (ML). These innovations have the potential to greatly enhance the efficiency and accuracy of ocean observation, transforming the field of marine science.This Research Topic comprises eleven high-quality papers, each contributing to many different aspects of autonomous ships (AS) for ocean observation. In the realm of enhanced data with spatial information from port facilities, they created semantic connections among ships, berths, and waterways. This approach enhances ship identification and berth allocation, improving decision-making for intelligent maritime systems.In summary, these collective efforts underscore a comprehensive approach to advancing maritime research and technology. By leveraging the capabilities of Autonomous Ships (ASs) and integrating sophisticated modeling, autonomous systems, image processing, and data analysis techniques, researchers are addressing complex challenges in marine science. These advancements not only enhance our ability to monitor and understand marine environments more effectively but also improve the efficiency and safety of oceanographic research. The integration of AI ML within AS technology exemplifies how innovation is transforming ocean observation, offering valuable insights into oceanic systems and facilitating better management of marine resources.Author contributions XZ, YC, GB, DW, YG, CW: Writing -original draft, Writing -review & editing.

Introduction: Human socioeconomic activities flourish in coastal zones, and the rational use and protection of spatial resources and environments are vital for the sustainable development of coastal cities. To promote optimal resource allocation across ecological, residential, and production spaces (hereafter referred to as the “triple space”), an investigation into their functional coupling coordination is crucial for optimizing the allocation of ecological, living, and production space resources.Methods: In this study, we developed an evaluation index system encompassing multiple aspects such as ecological supply and purification, residential security, and production supply. We constructed coupling coordination degree models for the triple space to quantitatively measure pairwise functional coupling and overall coordination in the coastal zone of Ningbo. Spatial characteristics and evolution patterns were analyzed using global and local Moran indexes. We analyzed the overall variation in functional coupling coordination across the coastal zone of Ningbo and correlations among evaluation units.Results: The findings are summarized as follows: First, a cross-coupling relationship exists with coupling coordination degrees ranging from 0.21 to 0.66 across the coastal zone of Ningbo, showing significant spatial variation (high in the south and north and low in the middle), with local coordination centers in Cixi City and Xiangshan County. Coupling coordination generally increased from 2000 to 2020. Secondly, pairwise functional coupling coordination varies, with relatively low coordination between ecology and residential areas, increasing coordination between ecology and production, and rapidly rising coordination between residential and production areas, which is currently at high levels. Third, the coupling coordination degree exhibits an overall random spatial distribution pattern without distinct clustering or discrete effects.Discussion: According to the local Moran index, land and sea areas of Yinzhou District show high-value polarization, failing to drive surrounding development; Zhenhai and Beilun districts exhibit low-value clustering with potential for optimization; and Cixi City and Ninghai County are high-value clustering areas, indicating strong coupling coordination. This study provides a new approach for exploring the intrinsic relationship between the different triple space functions in coastal zones and valuable reference for policy formulation and planning.

Wei, B.; Suo, A., and Chen, Y., 2025. Spatiotemporal dynamics of Tieshangang Bay's ecological landscape and the response to human interference. Journal of Coastal Research, 41(6), 1059–1068. Charlotte (North Carolina), ISSN 0749-0208. Changes in ecological landscape patterns are clearly influenced by human interference. Quantitatively evaluating human interference (HI) will facilitate the formulation of policies related to ecological environmental protection and rational utilization of space resources. Here, the natural and anthropogenic landscape information of Tieshangang Bay was extracted from satellite images captured in 1990 (TM), 2000 (HJ), 2010 (SPOT5), and 2023 (GF1) and the HI index and classification criteria were established to calculate the HI of Tieshangang Bay. Fragstats4.2 was used to determine the landscape pattern indices, such as the patch density (PD), edge density (ED), mean patch fractal dimension (MPFD), and the Shannon diversity index (SHDI) of Tieshangang Bay. The landscape pattern changes and their dynamic responses to human activities were assessed using landscape pattern indices and GIS spatial analysis. Analyses showed that the area of high- and low-HI regions in Tieshangang Bay changed considerably, whereas the area of medium-HI regions showed minor changes. The medium-to-high-HI regions tended to migrate from the bottom of the Beibu Gulf toward the southern sea area. Between 1990 and 2023, the PD and ED of Tieshangang Bay constantly increased, the mean shape index (MSI) and MPFD constantly decreased, the SHDI first increased and then decreased, and the landscape pattern decreased in heterogeneity year by year, becoming regularized, monotonous, and fragmented. PD and ED were positively correlated with HI, whereas MSI and MPFD were negatively correlated with HI. The correlational analysis between human activities and landscape pattern indices suggests that changes in HI alter landscape pattern indices, with the intensity of human activities being one of the primary drivers for landscape patterns changes.