As the first marine economic zone, the coastal zone is a complex and active ecosystem, serving as an important resource breeding area. However, during the process of economic development, coastal zone resources have been severely exploited, leading to fragile ecology and frequent natural disasters. Therefore, it is imperative to analyze coastline changes and their correlation with coastal ecological space. Utilizing long-time series high-resolution remote sensing images, Google Earth images, and key sea area unmanned aerial vehicle (UAV) remote sensing monitoring data, this study selected the coastal zone of Ningbo City as the research area. Remote sensing interpretation mark databases for coastline and typical coastal ecological space were established. Coastline extraction was completed based on the visual discrimination method. With the help of the Modified Normalized Difference Water Index (MNDWI), Normalized Difference Vegetation Index (NDVI) and maximum likelihood classification, a hierarchical classification discrimination process combined with a visual discrimination method was constructed to extract long-time series coastal ecological space information. The changes and the linkage relationship between the coastlines and coastal ecological spaces were analyzed. The results show that the extraction accuracy of ground objects based on the hierarchical classification process is high, and the verification effect is improved with the help of UAV remote sensing monitoring. Through long-time sequence change monitoring, it was found that the change in coastline traffic and transportation is significant. Changes in ecological spaces, such as industrial zones, urban construction, agricultural flood wetlands and irrigation land, dominated the change in artificial shorelines, while the change in Spartina alterniflora dominated the change in biological coastlines. The change in ecological space far away from the coastline on both the land and sea sides has little influence on the coastline. The research shows that the correlation analysis between coastline and coastal ecological space provides a new perspective for coastal zone research. In the future, it can provide technical support for coastal zone protection, dynamic supervision, administration, and scientific research.

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.