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.

The high dimensionality of hyperspectral images (HSIs) brings great difficulty for their later data processing. Band selection, as a commonly used dimension reduction technique, is the selection of optimal band combinations from the original bands, while attempting to remove the redundancy between bands and maintain a good classification ability. In this study, a novel hybrid filter-wrapper band selection method is proposed by a three-step strategy, i.e., band subset decomposition, band selection and band optimization. Based on the information gain (IG) and the spectral curve of the hyperspectral dataset, the band subset decomposition technique is improved, and a random selection strategy is suggested. The implementation of the first two steps addresses the problem of reducing inter-band redundancy. An optimization strategy based on a gray wolf optimizer (GWO) ensures that the selected band combination has a good classification ability. The classification performance of the selected band combination is verified on the Indian Pines, Pavia University and Salinas hyperspectral datasets with the aid of support vector machine (SVM) with a five-fold cross-validation. By comparing the proposed IG-GWO method with five state-of-the-art band selection approaches, the superiority of the proposed method for HSIs classification is experimentally demonstrated on three well-known hyperspectral datasets.

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.

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.

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.