Mapping coastal wetlands' spatial distribution and spatiotemporal dynamics is crucial for ecological conservation and restoration efforts. However, the high hydrological dynamics and steep environmental gradients pose challenges for precise mapping. This study developed a new method for mapping coastal wetlands using time-series remote sensing images and a deep learning model. Precise mapping and change analysis were conducted in the Liaohe Estuary Reserve in 2017 and 2022. The results demonstrated the superiority of Temporal Optimize Features (TOFs) in feature importance and classification accuracy. Incorporating TOFs into the ResNet model effectively combined temporal and spatial information, enhancing coastal wetland mapping accuracy. Comparative analysis revealed ecological restoration trends, emphasizing artificial restoration's predominant role in salt marsh vegetation rehabilitation. These findings provide essential technical support for coastal wetland ecosystem monitoring and contribute to the study of sustainability under global climate change.

Coastal wetlands are important areas with valuable natural resources and diverse biodiversity. Due to the influence of both natural factors and human activities, the landscape of coastal wetlands undergoes significant changes. It is crucial to systematically monitor and analyze the dynamic changes in coastal wetland cover over a long-term time series. In this paper, a long-term time series coastal wetland remote sensing classification process was proposed, which integrated feature selection and sample migration. Utilizing Google Earth Engine (GEE) and Landsat TM/ETM/OLI remote sensing image data, the selected feature set is combined with the sample migration method to generate the training sample set for each target year. The Simple Non-Iterative Clustering-Random Forest (SNIC-RF) model was ultimately employed to accurately map wetland classes in the Liaohe Estuary from 1985 to 2023 and quantitatively evaluate the spatio-temporal pattern change characteristics of wetlands in the study area. The findings indicate that: (1) After feature selection, the accuracy of the model reached 0.88, and the separation of the selected feature set was good. (2) After sample migration, the overall accuracy of sample classification in the target year ranged from 87 to 94%, along with Kappa coefficients of 0.84 to 0.92, thereby ensuring the validity of classification sample migration. (3) SNIC-RF classification results showed better performance of wetland landscape. Compared with RF classification, the overall classification accuracy was increased by 0.69–5.82%, and the Kappa coefficient was increased by 0.0087–0.0751. (4) From 1985 to 2023, there has been a predominant trend of natural wetlands being converted into artificial wetlands. In recent years, this transition has occurred more gently. Finally, this study offers valuable insights into understanding changes and trends in the surface ecological environment of the Liaohe Estuary. The research method can be extended to other types of wetland classification and the comprehensive application of coastal wetland in hydrology, ecology, meteorology, soil, and environment can be further explored on the basis of this research, laying strong groundwork for shaping policies on ecological protection and restoration.

Coastal wetland ecosystems, shaped by the interaction between ocean and land, play a pivotal role in blue carbon sequestration. Understanding the distribution and evolutionary patterns of complex wetland types is essential for advancing wetland conservation, exploring blue carbon potentials, and supporting national carbon neutrality goals. This study utilized the Google Earth Engine (GEE) cloud platform alongside Sentinel-1 synthetic aperture radar data (SAR), Sentinel-2 multispectral data, Digital Elevation Model (DEM) data, and Unmanned Aerial Vehicle (UAV) data to develop a feature preference algorithm integrating the Minimum Redundancy Maximum Relevance (MRMR) and Random Forest (RF) methods. Using eCognition, ArcMap, and ENVI platforms, we achieved an object-oriented classification of the Liaohekou Shuangtaizi wetlands. Results revealed that: (1) The MRMR-RF optimized 40 features, ranked by importance as Sentinel-2 spectral > Sentinel-1 index > Sentinel-1 radar > topographic > Sentinel-1 texture; (2) Six sets of comparison schemes were established, and the classification scheme based on the MRMR-RF model achieved the best classification performance, with an overall accuracy of 90.89% and a Kappa coefficient of 0.9; (3) The Liaohekou Estuary wetland was predominantly composed of Phragmites australis (P.australis), shallow sea, and tidal flat, followed by cropland, rivers, breeding pools, Suaeda salsa (S.salsa), reservoirs, puddles, bare soil, and building sites as secondary components; (4) Between 2000 and 2023, the wetland area of different types in the study area changed significantly, with the changes mainly concentrated in the coastal aquaculture areas, tidal flat areas, and S.salsa growth areas.

As ecologically sensitive interfaces shaped by the interplay of land and sea, coastal zones demand close attention. Uncovering the spatiotemporal evolution of ecosystem service value (ESV) and the intricate interrelations among ecosystem service (ES) functions is imperative for the informed governance of human–land interactions and for fostering sustainable regional development. This study analyzes the spatiotemporal evolution of ESV based on the modified equivalent factor table, combining the Geo-information Tupu, Markov transfer model, and standard deviation ellipse. Additionally, we introduce an ecosystem service tradeoff degree (ESTD) to assess the tradeoffs and synergies among various ESs, and we utilize GeoDetector to elucidate the driving forces behind the spatial disparities in ESV. Our findings reveal that (1) Although the land use composite index in the Liaoning coastal economic belt (LCEB) increased, the pace of land use transformation demonstrated a trend toward stabilization over the study duration. (2) Between 2000 to2020, ESV initially declined but subsequently experienced an upward rebound, resulting in a net gain of approximately 48 billion yuan. Spatial analysis indicated continuous enlargement of the standard deviation ellipse, with its centroid consistently located within Yingkou City and a gradual directional shift toward the southwest. (3) The dominant relationship among ESs showed synergy, with notable tradeoffs between hydrological regulation and other services. (4) Topography and climate factors were the primary drivers of spatial heterogeneity of ESV in the LCEB. The research provides spatial decision support for optimizing the ecological security pattern of the coastal zone.

The United Nations has proposed Sustainable Development Goal 15 (SDG15), which emphasizes the importance of sustainable land development. This study aims to use remote sensing data to build spectral index feature dataset based on Google Earth Engine platform, dig deep spectral features of ground objects, and use random forest algorithm to extract land use type distribution data in the Bohai Rim region from 2000 to 2020. Meanwhile, combined with land use transfer map and landscape pattern index, the spatio-temporal pattern of land use was quantitatively analyzed. Finally, the sustainable development level of land was evaluated quantitatively from three aspects: forest resource sustainability, wetland resource sustainability and land system sustainability. The results show that : (1) The overall accuracy and kappa coefficient of land use classification achieved by GEE and the RF algorithm were 0.94 and 0.92, respectively. (2) From 2000 to 2020, the main land use type in the study area is cropland, accounting for 33% of the total, and the impervious has significantly expanded, increasing by 9588.01 square kilometers, mainly from cropland, barren and water. (3) In relation to SDG15, forest resources exhibited poor stability, wetland resources demonstrated a steady recovery, SDG15.3.1 revealed that the problems of land degradation exist in various provinces and cities around the Bohai Sea, and the stability of land system is poor between 2000 and 2020. This paper can provide a valuable reference for land use management and ecological remediation in the Bohai Rim region.

The contradiction between the development of marine resources and ecological protection in Zhejiang is prominent, which restricts the high-quality and continuous supply of marine ecological products. As a result, the efficient maintenance of the ecological security pattern of inshore waters relies on the scientific division of marine ecological function zones and the management and maintenance of zoning areas. This paper constructs a technical framework for marine ecological function zoning from the aspects of marine ecosystem health, marine ecosystem diversity and economic and social development balance using GIS-based overlay analysis and the three-dimensional matrix discriminant model to divide marine ecological function zones in the managed waters of Zhejiang Province. The results indicate that the managed sea areas in Zhejiang Province are divided into ten marine ecological function areas, including three marine ecological supply areas (54%), four marine mariculture and resting areas (24%) and three marine ecological restoration areas (22%). As per the zoning results of the marine ecological function, this study further proposes protection and management strategies for different zoning units, in the hopes of providing technical support and practical reference for the division and management of marine ecological function areas in other sea areas.