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 goal of “double carbon” puts forward higher requirements for the control of transport carbon emissions, and the exploration of transport carbon emission modelling driven by big data is an important attempt to reduce carbon accurately. Based on the land Vehicle Miles Traveled data (VMT) and the sea Automatic Identification System (AIS) data, this study establishes a refined, high-resolution carbon emission measurement model that incorporates the use of motor vehicles and ships from a bottom-up approach and analyzes the spatial distribution characteristics of land and sea transport carbon emissions in Tianjin using geospatial analysis. The results of the study show that (1) the transportation carbon emissions in Tianjin mainly come from land road traffic, with small passenger cars contributing the most to the emissions; (2) high carbon emission zones are concentrated in economically developed, densely populated, and high road network density areas, such as the urban center Binhai New Area, and the marine functional zone of Tianjin; (3) carbon emission values are generally higher in the segments where ports, airports, and interchanges are connected. The transportation carbon emission measurement model developed in this study provides practical, replicable, and scalable insights for other coastal cities.