With the advancement of urbanization, the stress on the green infrastructure around the urban agglomeration has intensified, which causes severe ecological problems. The uncertainty of urban growth makes it difficult to achieve effective protection only by setting protection red lines and other rigid measures. It is of practical significance to optimize the resilience of the stressed green infrastructure. To this end, we explore a scenario simulation analysis method for the resilience management of green infrastructure under stress. This research applies artificial neural network cellular automata to simulate the impacts of the Chang-Zhu-Tan urban agglomeration expansion on the green infrastructure in 2030 in three scenarios: no planning control, urban planning control, and ecological protection planning control. Based on the analysis, we identify four green infrastructure areas under stress and formulate resilience management measures, respectively. The results show that: (1) The distribution pattern of green infrastructure under stress is different in three scenarios. Even in the scenario of ecological protection planning and control, urban growth can easily break through the ecological protection boundary; (2) Residential, industrial, and traffic facility land are the main types of urban land causing green infrastructure stress, while forest, shrub, and wetland are the main types of the stressed green infrastructure; (3) Efficient protection of green infrastructure and the management of the urban growth boundary should be promoted by resilient management measures such as urban planning adjustment, regulatory detailed planning, development strength control and setting up the ecological protection facilities for the stressed green infrastructure areas of the planning scenarios and the no-planning control scenarios, for the areas to be occupied by urban land, and for the important ecological corridors. The results of this study provide an empirical foundation for formulating policies and the methods of this study can be applied to urban ecological planning and green infrastructure management practice in other areas as well.

Walking exercise is a prevalent physical activity in urban areas, with streetscapes playing a significant role in shaping preferences. Understanding this influence is essential for creating urban environments conducive to walking exercise and improving residents’ quality of life. In this study, we utilize scenic beauty estimation and deep learning methods, leveraging street view images and walking exercise trajectories to analyze this influence from a human-centric perspective. We begin by generating sampling points along streets covered by trajectories and acquiring street view images. Subsequently, we apply a deep learning model to segment the images, yielding six visual indicators. Additionally, we use scenic beauty estimation to derive the seventh visual indicator. Finally, we match these indicators with trajectory data to implement preference analysis. The main findings are: (1) preferences for walking and running exercises differ on multiple indicators; (2) there are gender distinctions, with males preferring openness and females prioritizing enclosed spaces; (3) age plays a role, with those aged 30–40 preferring openness and those aged 40–50 preferring enclosed spaces; (4) preferences for different indicators vary over time and across different locations. These insights can inform policymakers in tailoring urban planning and design to specific population segments and promoting sustainable residential landscapes.

Exploring the influence of settlement patterns on the landscape fragmentation in woodlands and biological reserves is key to achieving ecologically sustainable development. In this research, we chose the Nanshan National Park in Hunan Province, China, as a case study, to explore the influence mechanisms. First, we identified the biological reserves through the landscape security patterns of biological conservation. Second, we constructed a coupling coordination model to analyze the coupling relationship between the settlement patterns and landscape fragmentation in the woodlands and biological reserves. The analysis showed that, overall, the effect of the settlement area on the landscape fragmentation in the biological reserves was more pronounced, while the effect of the settlement spread and shape on the landscape fragmentation in the woodlands was more obvious. From a type-specific perspective, we analyzed the coupling relationship between the settlement patterns and (1) the landscape fragmentation in different woodlands and (2) the landscape fragmentation in the biological reserves, namely concerning Leiothrix lutea and Emberiza aureola. We found that the effect of the settlement patterns on the landscape fragmentation of the Leiothrix lutea biological reserve was more significant than that of the landscape fragmentation of its main habitat, the evergreen broad-leaved forest. The effect of settlement patterns on the landscape fragmentation of the Emberiza aureola biological reserve was more significant than that of the landscape fragmentation of its other habitats. In addition, the results demonstrated that the habitat protection of the woodlands was not a substitute for the systematic protection of biosecurity patterns. This research could assist in developing more efficient conservation measures for ecologically protected sites with rural settlements.