The Liangzi Lake Basin (LLB) is an important ecological buffer for Wuhan’s urban agglomeration. It involves the ecological security of the middle reaches of the Yangtze River. Historical land misuse has altered the topography and impacted the ecosystem services value (ESV). Amid urbanization, it is vital to highlight changing land use methods and their effects on ESV valuation, understanding the underlying drivers comprehensively. The research is centered on the LLB as its designated study region, and utilizes remote sensing satellite data spanning from 2000 to 2020. This data is combined with a value equivalence table to quantify ESV. The GeoDetector method is employed to investigate the driving factors behind ESV fluctuations. The findings indicate a substantial shift in land use patterns within the LLB between 2000 and 2020. Notably, arable land decreased by 6.28% and water bodies decreased by 0.92%, while built-up areas expanded by 5.14% and forest land expanded by 2.05%. During this period, the LLB’s ecosystem services value decreased by approximately 2.035 billion yuan. This drop was mainly due to reduced water areas resulting from urbanization, negatively impacting the ecological regulatory services provided by these water bodies. Based on the geoprobe model, possible drivers of changes in ESV in the LLB were identified, with human activity intensity and NDVI detection results being the most obvious. The research emphasized protecting and restoring key ecological areas, like water bodies and forests, to maintain a delicate balance between the environment and socio-economic development. Additionally, they exemplify the effectiveness of ecological policies, including initiatives such as “Returning Farmland to Forest or Pasture” (RFFP), and the prohibition of lake and field reclamation.

The natural environment has been widely recognized as an important way of alleviating mental disorders. While a growing number of studies have discussed the restorative effect of the natural environment, the role of sounds in the natural environment (i.e., soundscape) on promoting mental restoration is under limited exploration. Therefore, we examined the restorative effect of visual stimuli and the combination of audio and visual factors in the natural environment. A total of 149 college students were divided into five groups, and exposed to different combinations of photos and sounds (with ‘silence’ as a control group) collected along the East Lake Greenway—the longest urban greenway in China. Structural equation modeling (SEM) was used to examine the correlation between characteristics of the natural environment and volunteers’ mental restoration, with place attachment as a mediator. Results showed that: (1) sound can alter the effect of visual factors on mental restoration; (2) birdsong can significantly enhance the restorative benefit of urban greenways; (3) an environment that is covered in greenery and consists of water sounds will contribute to a higher level of mental restoration; (4) human noise will have a negative impact on mental restoration; (5) place attachment is a mediator between the natural environment and mental restoration. This study highlights the effectiveness of sounds in the natural environment in promoting mental restoration, and broadens our understanding of the link between the natural environment and mental health. We propose that not only visual factors, but also audio-visual combinations, should be considered when creating restorative environments.

Under the continuous disturbance of ecosystems driven by urbanization, landscape fragmentation and the disruption of ecological processes and functions are key challenges in optimizing ecological networks (EN). This study aims to examine the spatiotemporal evolution of topological patterns, ecological processes, and ecosystem services (ES) in Wuhan from the “pattern–process–function” perspective. To overcome the lag in research concerning the coupling of ecological processes, functions, and spatial patterns, we explore the long-term dynamic evolution of ecosystem structure, process, and function by integrating multi-source data, including remote sensing, enabling comprehensive spatiotemporal analysis from 2000 to 2020. Addressing limitations in current EN optimization approaches, we integrate morphological spatial pattern analysis (MSPA), use circuit theory to identify EN components, and conduct spatial optimization accurately. We further assess the effectiveness of two scenario types: “pattern–function” and “pattern–process”. The results reveal a distinct “increase-then-decrease” trend in EN structural attributes: from 2000 to 2020, source areas declined from 39 (900 km²) to 37 (725 km²), while corridor numbers fluctuated before stabilizing at 89. Ecological processes and functions exhibited phased fluctuations. Among water-related indicators, water conservation (as a core function), and modified normalized difference water index (MNDWI, as a key process) predominantly drive positive correlations under the “pattern–function” and “pattern–process” scenarios, respectively. The “pattern–function” scenario strengthens core area connectivity (24% and 4% slower degradation under targeted/random attacks, respectively), enhancing resistance to general disturbances, whereas the “pattern–process” scenario increases redundancy in edge transition zones (21% slower degradation under targeted attacks), improving resilience to targeted disruptions. This complementary design results in a gradient EN structure characterized by core stability and peripheral resilience. This study pioneers an EN optimization framework that systematically integrates identification, assessment, optimization, and validation into a closed-loop workflow. Notably, it establishes a quantifiable, multi-objective decision basis for EN optimization, offering transferable guidance for green infrastructure planning and ecological restoration from a pattern–process–function perspective.

Urban roads are the lifeline of urban transportation and satisfy the commuting and travel needs of citizens. Following the acceleration of urbanization and the frequent extreme weather in recent years, urban waterlogging is occurring more than usual in summer and has negative effects on the urban traffic networks. Extracting flooded roads is a critical procedure for improving the resistance ability of roads after urban waterlogging occurs. This paper proposes a flooded road extraction method to extract the flooding degree and the time at which roads become flooded in large urban areas by using global positioning system (GPS) trajectory points with driving status information and the high position accuracy of vector road data with semantic information. This method uses partition statistics to create density grids (grid layer) and uses map matching to construct a time-series of GPS trajectory point density for each road (vector layer). Finally, the fusion of grids and vector layers obtains a more accurate result. The experiment uses a dataset of GPS trajectory points and vector road data in the Wuchang district, which proves that the extraction result has a high similarity with respect to the flooded roads reported in the news. Additionally, extracted flooded roads that were not reported in the news were also found. Compared with the traditional methods for extracting flooded roads and areas, such as rainfall simulation and SAR image-based classification in urban areas, the proposed method discovers hidden flooding information from geospatial big data, uploaded at no cost by urban taxis and remaining stable for a long period of time.

Changing the water permeability ratio of urban underlying surface helps alleviate urban flood. This paper designs the swale identification experiment to modify the flood-submerging simulation experiment based on the SCS-CN model and proves that the results generated by the modified experiment better reflect the realities. The modified flood-submerging simulation experiment is then applied to downtown Wuhan to obtain the quantitative data. The data are used to quantify the catchment capacities of the lots. Based on the rainfall collection capacities, the maximum surface rainfall runoff volume that would not cause flood is arrived at using the rainfall runoff formula. The maximum runoff volume represents the rainwater storage capacities of the lot based on the proportion of the green space that is identified within the study area. The results suggest that this rainwater storage capacity evaluation model works efficiently to identify the urban areas with flood risks and provides the rainwater runoff thresholds for different areas. Adjustments in the spatial patterns and proportions of the green space help ensure that the rainwater runoff volume is below the thresholds, thus contributing to the prevention and control of the urban flood risks.

Establishing a resilient bird habitat network (BHN) and identifying ecological strategic areas for protection are critical for conserving biodiversity and maintaining ecosystem stability in wetland cities. However, existing ecological network studies often overlook dynamic resilience that incorporates explicit species information, and their scenario-based assessments lack systematic evaluation metrics. This study, using Wuhan—an international wetland city—as a case study, integrates Maximum Entropy (MaxEnt), remote sensing ecological index (RSEI) and circuit theory to identify a high-quality BHN. A comprehensive resilience assessment and optimization framework is developed, grounded in structure–function–quality indicators and informed by resilience and complex network theory. Key findings include: (1) The network comprises 147 habitat patches and 284 ecological corridors, demonstrating marked spatial heterogeneity. Habitats are predominantly located in the southern and southwestern regions of Wuhan, concentrated in contiguous green spaces. In contrast, habitats in the urban core are fragmented and small. Corridors are mainly distributed in the southwestern and central metropolitan areas. (2) Under deliberate attack, considering resilience centrality, the network’s resilience declined more slowly than in scenarios based on traditional centrality measures. Across combined node and corridor attack simulations, two critical resilience thresholds were identified at 30% and 50%. (3) The ecological strategic space is primarily composed of key habitat patches (58, 108, 117, and 27) and corridors (119–128, 9–12, 122–147, 128–138, 76–85, and 20–29), mainly located in the southern region of Wuhan, particularly around Liangzi Lake and Anshan National Wetland Park. This study advances a dynamic framework for BHN resilience assessment, planning, and restoration, providing scientific guidance for enhancing ecological security and biodiversity conservation in urban wetland environments.