Urban “blue-green” space is the basic element of urban natural ecological space. The rapid urbanization process has a significant impact on the pattern of urban blue-green space and seriously threatens the urban ecological environment. It is of great significance for urban ecological protection and sustainable development to study the change in characteristics of blue and green space during urban expansion. In this paper, a comprehensive method of remote sensing image, landscape pattern analysis, sector analysis, and coupling analysis is applied to analyze the spatio-temporal evolution characteristics and coordinated development level of the blue-green space and built-up area in central Wuhan from 1987 to 2018. The results showed that the land use and landscape pattern in the central city of Wuhan underwent a drastic change process, and the dominant change was the occupation of blue-green space by urban built-up areas, which led to the increase of landscape fragmentation and the decrease of heterogeneity. Urban green space is gradually marginalized and concentrated in local areas. The blue space has an obvious blocking effect on the expansion of the built-up area, and its spatial pattern tends to be stable gradually. In addition, the coupling degree between urban blue-green space and urban built area presents an inverted U-shape, and the coordinated development level among the three has entered a stage of moderate imbalance. It is urgent to strengthen the protection of blue-green space, curb the disorderly expansion of cities, and improve the quality of urban development.

Lake reclamation for urban construction has caused serious damage to lakes in cities undergoing rapid urbanization. This process affects urban ecological environment and leads to inconsistent urban expansion, population surge, and uneven distribution of urban lakes. This study measured the fairness of urban lakes’ distribution and explored the spatial matching relationship between service supply and user group demand. The interpretation and analysis of Wuhan’s remote sensing images, population, administrative area, traffic network, and other data in 2018 were used as the basis. Specifically, the spatial distribution pattern and fairness of lakes’ distribution in Wuhan urban development zone were investigated. This study establishes a geographic weighted regression (GWR) model of land cover types and population data based on a spatialization method of population data based on land use, and uses population spatial data and network accessibility analysis results to evaluate lake service levels in the study area. Macroscopically, the correlation analysis of sequence variables and Gini coefficient analysis method are used to measure the fairness of the Wuhan lake distribution problem and equilibrium degree, and the location entropy analysis is used to quantitatively analyze the fairness of lakes and Wuhan streets from the perspective of supply and demand location entropy. Levels improve the accuracy of the research. Results showed that (1) the area covered by lakes in Wuhan urban development zone is 1007.96 km² within 60-min of walking, accounting for 30.6% of the total area of the study area. This area can house 5,050,275 people, accounting for 60.8% of Wuhan’s total population. (2) The lakes in the central city area are less fair than the lakes outside the Third Ring Road. (3) The service level of North Lake is the highest among all the lakes in the study area, and that of Hou Lake is the lowest. (4) The spatial layout of the fairness of the lakes’ distribution is roughly distributed in circles. The fairness level collapses toward the city center, indicating that the closer to the city center, the lower the fairness level.

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.

The urban texture is the physical manifestation of the urban form’s evolution. In the rapid process of urbanization, protecting and reshaping the urban texture has become an essential means to sustain the overall form and vitality of cities. Previous studies in this field have primarily relied on image analysis or typological methods, lacking a quantitative approach to identify and analyze the urban texture on a large scale. Moreover, the hierarchical structure and networked organization of the urban texture are gradually being elucidated and emphasized. This study takes a complex network perspective and applies percolation theory to identify and analyze the urban texture. The hierarchical evolutionary characteristics of the urban fabric and the underlying mechanisms driving the scale-dependent differences are analyzed, and the toughness of the urban texture is evaluated based on hierarchical connections. The research findings reveal the key scale in the cross-scale evolution of urban textures, with variations in scale dependence and hierarchical evolution characteristics among different types of urban texture. The traditional urban texture displays sensitivity to scale changes, maintaining its integrity and internal connectivity at small scales. On the other hand, the texture characteristics of modern and contemporary urban areas are only discernible at larger scales. The emergence of large-scale texture clusters at specific scale levels can be explained using multiple index systems. This study has reference significance for the preservation and renewal of the urban fabric in urban renewal processes.