As a strategy for high-quality development of China, the stable coastal wetland ecological health is directly related to social-economic level representation. Previous studies on ecosystem services have been connected to urbanization at multiple scales, but the impacts of coastal wetland on ecosystem services at different temporal-spatial scales have not been systematically studied due to the uncertain natural wetland loss. Therefore, the coastal wetland was extracted using the machine learning method based on knowledge rules utilized on Google Earth Engine (GEE) platform to map ecosystem services for land-sea interactions, and their trade-offs/synergies and hot/cold spots were quantified to reveal the relationship between spatial correlations of ecosystems. Meanwhile, the machine learning-based random forest (RF) method is used to rank the importance of drivers of regional ecosystem services, such as climate change and human activities. The results show that the constructed wetland landscape is rich, and there are many traces of man-made transformation in the coastal zone. Coastal wetland landscapes may have an impact on biodiversity, water resource management and overall ecological sustainability in the region. A maximum of the total ecosystem services is 0.82, the minimum value is 0.02, the high value in forest region, low in aquaculture ponds, precipitation at least most of the Bohai Rim region. The proposed framework can be extended to assess spatial characteristics and control factor analysis in other regions or specific ecosystem types, and the findings are expected to provide policy recommendations for the conservation and management of the Bohai Rim ecosystem.

ecosystem service, coastal wetland, machine learning, trade-offs/synergies, spatial planning