Lina Ke

To address ecological degradation in estuarine coastal wetlands caused by the combined pressures of land–sea interactions and anthropogenic activities, we developed an improved Coastal Wetland Ecological Status Index (CWESI). Using Landsat time-series observations from 1994 to 2024, together with the coefficient of variation (CV), Theil–Sen slope estimator, Mann–Kendall significance test, and Hurst exponent, we quantified the spatiotemporal dynamics and persistence characteristics of ecological quality in the Liaohe Estuary coastal wetland. Furthermore, an optimal parameter-based geographical detector (OPGD) model was employed to investigate the relative contributions and interactive effects of natural and anthropogenic factors on ecological quality heterogeneity. The results revealed the following: (1) Ecological quality exhibited an overall fluctuating improvement trend during 1994–2024, with the mean CWESI value increasing from 0.45 to 0.52. Spatially, ecological quality displayed a decreasing gradient from inland areas towards coastal zones, with high-quality ecological patches primarily concentrated in reed wetlands and core conservation areas. (2) During the study period, the area of low-quality ecological spaces decreased by 75.23 km², whereas areas classified as Moderate or higher expanded by 74.96 km². Approximately 78.66% of the study area exhibited stable or improving ecological conditions, while only 5.23% experienced ecological degradation. (3) Driver analysis indicated that fractional vegetation cover and net primary productivity were the dominant natural factors influencing ecological quality. Their interactions with soil moisture and climatic variables exhibited significant bivariate enhancement effects. These results show that wetland ecological quality is regulated by the synergistic interactions among multiple environmental factors. Long-term ecological restoration measures, including aquaculture withdrawal and wetland rehabilitation as well as ecological water replenishment projects, acted as critical anthropogenic drivers promoting sustained ecological improvement. The proposed CWESI framework provides a robust methodological basis for dynamic monitoring, conservation planning, and adaptive ecological management of coastal wetlands facing similar environmental pressures.

The rapid urbanization of coastal zones has brought to light ecological and environmental issues at the junction between land and sea. Accurately identifying key areas for ecological restoration in coastal zones, as well as implementing projects for such protection and restoration, are effective strategies for addressing these challenges and ensuring the ecological security and stability of coastal zones. This study integrated terrestrial and marine spaces, employing the research logic of “patch (ecological sources)–network (ecological networks)–region (ecological restoration areas)” to establish a research framework for identifying key areas for ecological restoration of coastal zones. The findings presented in this paper demonstrate the following: (1) The ecological sources and ecological corridors in coastal ecological networks are primarily distributed across woodland, grassland, waters, and marine protected areas. This includes 19,233.48 km² of land ecological sources and 6099.52 km² of sea ecological sources, with the overall length of ecological corridors reaching 3154.59 km. (2) The ecological pinch points of the key areas are primarily situated in Jinzhou, Panjin, the southern part of Yingkou, and the Lushunkou district of Dalian. It is imperative to enhance the ecological functions within these regions. (3) The ecological barriers in the key areas are mainly concentrated in the central and western regions of Dalian. These areas should be rehabilitated based on land type and marine functional area classification in future endeavors. This study provides a scientific reference for the formulation and implementation of related coastal zone national ecological restoration plans.

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.

Coastal wetlands are vital ecosystems at the land–sea interface. They intercept land-based pollutants, regulate microclimates, and mediate carbon cycles. They play a significant role in enhancing carbon sequestration capacity and maintaining ecological structure and functioning. This study proposes an improved method for estimating blue carbon storage in coastal salt marsh wetlands, considering soil thickness, by utilizing an enhanced Soil Land Inference Model (SoLIM) to estimate soil thickness in coastal wetlands with a restricted number of sample points. The wetland soil thickness index is integrated into the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) blue carbon storage estimation model, ultimately enabling the estimation and visualization of blue carbon storage in the Liaohe Estuary coastal wetland. Results indicate the following: (1) The studied area’s soil thickness shows a spatial distribution pattern that becomes progressively thinner from north to south. Soil thickness is more significant in the salt marsh vegetation areas and more minor in the coastal tidal flat areas, with 52% of the region having soil thickness between 40 and 60 cm. (2) In 2023, the blue carbon stock in the study area is estimated at 389.85 × 10⁶ t, with high-value areas concentrated in the northern natural landscapes, and low-value areas in the southern coastal zone, characterized by flat terrain and human influence. The coupled soil thickness–blue carbon storage estimation model provides methodological support for refining the estimation of blue carbon storage in coastal wetlands. It also offers technical support for formulating policies on the ecological restoration, compensation, protection, and management of coastal wetlands.

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.

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.

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.

Oceans and their resources are experiencing immense pressure because of human exploitation. The intensive use of sea areas has become an important method in solving the contradiction between ocean supply and demand, thereby ensuring sustainable marine economy development, tapping potential sea-area utilization, reasonably allocating sea-area utilization structures, and increasing marine economic benefits. This paper explores the definition and connotation of intensive sea-area use and constructs an evaluation index system based on marine input intensity, marine utilization structure, marine economic benefit, and marine ecological environment. Multi-objective variable fuzzy set theory and fuzzy decision analysis methods were used to evaluate the intensive sea-area utilization in the Liaoning Coastal Economic Zone of China during 2004–2016. The spatial differentiation characteristics of intensive sea-area use were analysed using cluster analysis. The research result showed that: (1) Intensive utilization level of the Liaoning coastal economic zone has gradually increased, while it is still in a moderately weak level; (2) Sea area intensive utilization varied in degrees and fluctuates in the six cities under the jurisdiction of the Liaoning coastal economic zone; and (3) Marine input intensity, marine utilization structure, marine economic benefit, and marine sustainability indexes have increased in the cities, thereby exhibiting improvements in the Liaoning coastal economic zone.

Acceleration of urbanization has brought about a series of problems, which include irreversible changes to urban surfaces and continuous increases in land surface temperatures (LSTs). In this context, analysis of the driving factors and spatial heterogeneity of urban LST is of considerable importance for mitigating urban heat island effects and promoting healthy and comfortable urban living environments. This study explored the relationship between the spatial characteristics and driving factors of the LST by using a geographically weighted regression (GWR) model to analyze multisource data from the Xigang District of Dalian City. The results showed that the urban heat island effect in Xigang District is significant, with LSTs generally above 28°C at the end of August, mostly concentrated in the range of 3840°C. The highest LST values were detected in northern port and harbor areas; the lowest LST values occurred in mountainous forest areas. The global Morans I value was 0.994, which was indicative of a very high positive correlation, and local Morans I values formed H-H and L-L type clusters concentrated in the northern harbor area and southern mountainous area, respectively. Finally, the GWR model could reflect the spatial heterogeneity of the relationships between LST and its driving factors well. Among these, in terms of natural physical factors, digital elevation model, normalized difference vegetation index, and modified normalized difference water index data were found to be negatively correlated with LSTs in most cases; in the social dimension, the point-of-interest number and building-coverage ratio were generally positively correlated with LSTs.