Coastal regions are vulnerable ecosystems where monitoring soil salinity and vegetation coverage dynamics is critical for environmental management and conservation. This study introduces a multi-sensor data fusion approach, integrating Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 multispectral imagery with advanced machine learning techniques, specifically a Convolutional Neural Network (CNN)-based classification model. This approach enables precise mapping of coastal salt-affected soil and vegetation patterns, addressing spatial heterogeneity and dynamic environmental conditions. The analysis has been conducted for a coastal region in China, where derived features such as Normalized Difference Vegetation Index (NDVI), salinity indices, and SAR-based soil moisture proxies have been used as inputs to the CNN model. The model achieved an overall accuracy of 87% and a kappa coefficient of 0.82, outperforming traditional classification methods by leveraging spatial feature learning and data augmentation. Temporal NDVI trends revealed seasonal vegetation dynamics, while predicted soil moisture patterns showed strong alignment with observed ecological conditions. The results indicate that saline soils dominate the study area, with non-saline soils and vegetated areas exhibiting scattered and localized distributions. These findings demonstrate the potential of integrating multi-sensor remote sensing with advanced machine learning techniques for coastal monitoring, providing a robust framework for sustainable land use planning and ecological management.

Urban green spaces (UGS) are essential for residents’ well-being, environmental quality, and social cohesion. However, previous studies have typically employed undifferentiated analytical frameworks, overlooking UGS types and failing to adequately measure the structural disparities of different UGS types within residents’ walking distance. To address this, this study integrates Gaussian Two-Step Floating Catchment Area models, Simpson’s index, and the Gini coefficient to construct an accessibility–diversity–equality assessment framework for UGS. This study conducted an analysis of accessibility, diversity, and equity for various types of UGSs under pedestrian conditions, using the high-density city of Shenzhen, China as a case study. Results reveal high inequality in accessibility to most UGS types within 15 min to 30 min walking range, except residential green spaces, which show moderate-high inequality (Gini coefficient: 0.4–0.6). Encouragingly, UGS diversity performs well, with over 80% of residents able to access three or more UGS types within walking distance. These findings highlight the heterogeneous UGS supply and provide actionable insights for optimizing green space allocation to support healthy urban development.

Urban underground space (UUS) provides innovative solutions to urban challenges such as overpopulation, resource scarcity, and environmental issues. However, UUS's lack of comprehensive evaluation standards hinders its sustainable development. This study systematically introduces a novel framework for evaluating green ecological UUS, integrating methodologies such as factor analysis, the analytic hierarchy process (AHP), and the Delphi method. The framework assesses UUS projects across their lifecycle, focusing on energy efficiency, resource conservation, and environmental protection. Validation through real-world projects, including Chengdu's Tianfu Square and East Railway Station, demonstrated its practical utility, classifying both projects as two-star ecological grades. Real-world projects, such as Chengdu's Tianfu Square and East Railway Station, validated the framework, classifying them into two-star ecological grades based on the proposed standards. This research provides a valuable theoretical and practical tool for establishing a robust evaluation standard for sustainable UUS development, contributing to global urban sustainability efforts.

Dense buildings in modern cities make street pollutants more and more difficult to disperse, and the exposure level of urban residents to pollutants has increased significantly. This study focuses on the spatial and temporal distribution of ambient air pollution in urban streets and the relationship between street morphology and pollutant dispersion, and explores ways to reduce ambient air pollution in streets through spatial design. The study used the CityGrid urban grid data monitoring station to conduct an empirical study on an urban trunk road in Wuhan during the winter of 2020 to 2021, monitoring pollutants including NO2, O3, PM2.5, and PM10. The results showed that pollutant concentration changes in the near-road environment are affected by a combination of meteorological elements and traffic flow characteristics. The wind parallel to the road is more favorable to evacuate pollutants inside the street canyon, and the ambient wind above the canyon perpendicular to the road causes vortices in the canyon that cause gaseous pollutants to accumulate on the leeward side of the canyon. The distribution of particulate pollutants is mainly related to the distance of road pollutant sources. Building access ventilation can effectively evacuate street pollutants, and NO2 and O3 decay to stable levels in shorter distance from the road than PM2.5 and PM10 in downwind direction. The future urban street planning can effectively alleviate street pollution levels through strategies such as street canyon morphology control, green landscape facilities arrangement, increasing the building interface opening, and building bottom overhead.