Located in an important biodiversity conservation area in the Yangtze River Delta, the habitats of many species have been severely eroded because of human activities such as tourism development. There is no relevant species conservation plan in place in the region, and scientific guidance on ecosystem change and corridor construction is urgently needed. In this study, we first assess ecosystem service functions based on the InVEST model; then, we assess ecological sensitivity and identify landscape resistance surfaces by constructing ecosystem sensitivity indicators; finally, we construct ecological security patterns by combining landscape resistance surfaces and circuit theory identification. The main results are as follows: (1) The high value area of ecosystem services is located in the southwest, while the northeast part of the study area has lower ecosystem services, and there is a trade-off between the ecosystem services in the study area. (2) There are 38 ecological sources in southern Anhui, with a total area of more than 5742.79 km², that are the basic guarantees of ecological security, mainly located in the northeast of the study area, and woodland and grassland are the most important components, accounting for 18.4% of the total study area. (3) The ecological security pattern in the study area consists of 63 ecological sources, 37 important corridors, and 26 potential corridors, of which there are 28 pinch point areas and 6 barrier point patches in the study area, mainly located within Huangshan City and Xuancheng City. We recommend that when implementing restoration and rehabilitation measures in the future, policy makers should give priority to pinch points and barrier areas.

Exploring the constraint relationship between physical geographic features and urbanization on ecosystem services is important for managing and optimizing regional ecosystem services. Taking Anhui Province as an example, we assessed the spatial and temporal evolution characteristics of five types of ecosystem services (habitat support, water production services, soil conservation, NPP, and carbon fixation) and five types of urbanization levels (population, economic, social, ecological, and spatial) in 2000, 2010, and 2020, and integrated the constraint line method, bivariate spatial autocorrelation model, and spatial regression model to measure the relationship between ecosystem services. The spatial constraints between ecosystem services and urbanization level and natural topography in Anhui Province were measured using the constraint line method, bivariate spatial autocorrelation model and the spatial regression model. The results show that: (1) the spatial distribution of the five types of ecosystem services in Anhui Province is characterized as “low in the north and high in the south”. At the provincial level, the five ecosystem services in southern and central Anhui Province are synergistic, while the five ecosystem services in northern Anhui Province show a trade-off; (2) topography has different effects on the five ecosystem services with “exponential” effects on water production services and NPP, “positive convex” effects on habitat support, and “positive convex” effects on habitat support”; (3) the bivariate global autocorrelation Moran’s I index between ecosystem services and urbanization level in Anhui Province is significant, confirming that ecosystem services and urbanization are spatially related, where the development of population urbanization, spatial urbanization, economic urbanization, and social urbanization leads to the decrease in ecosystem services, and ecological urbanization promotes the increase in ecosystem services. In the spatial regression model, the Spatial Lag Model passed the significance test, indicating that there is a spatial spillover effect between ecosystem services and urbanization. That is, changes in ecosystem services are influenced not only by their own urbanization elements, but also by urbanization elements in neighboring units or more distant units. Exploring the constraints of ecosystem services and identifying their interaction with urbanization can provide a scientific basis for land-use optimization, adjusting management measures and achieving regional sustainability.

The spatial pattern of regional green space is an important dimension to describe and quantitatively express the characteristics of regional green spaces outside the built-up area of a city. With the expansion of urban and rural construction land, regional green space has been continuously encroached upon. This leads to a decline in regional ecological well-being and the loss of biodiversity. Based on the remote sensing data of Shanghai city from 2000 to 2020, we quantitatively studied the spatial morphological change characteristics of regional green space outside the built-up area of Shanghai city. Firstly, with the help of the GEE platform, the optimal decoding accuracy classification method was selected through machine learning (random forest, support vector machine, classification regression tree); then, based on the biophysical component (BCI) and CA binarization, the built-up area ranges for up to five time nodes were obtained; finally, through GIS spatial data analysis and processing technology, the regional green space dynamic data of Shanghai for five time nodes were extracted. Based on the above data, an analysis index system was constructed to quantitatively analyze the spatial morphology characteristics of the regional green space outside the built-up area of Shanghai. The results show that (1) the area of regional green space outside the built-up area of Shanghai had a fluctuating growth pattern of “decreasing and then increasing”. The arable land and water areas in Shanghai decreased, and the woodland area increased steadily, while the wetland and grassland areas showed a trend of first decreasing and then increasing. (2) The regional green patch fragmentation shows a fluctuating development trend of increasing, decreasing, and increasing. (3) The change in the spatial center of gravity of the regional green space in Shanghai had a high degree of consistency with the overall green space change. The center of gravity of the grasslands in the regional green space moved substantially to the northwest, while the center of gravity of the other types remained basically unchanged. This study reveals the spatial morphology characteristics of regional green spaces and provides a research method to study the dynamic changes in regional ecological resources. The results of this study can provide a scientific basis for the identification, protection, and development of regional ecological resources.

Non-built-up green areas are essential for preserving the ecological functions of cities and fostering sustainable growth. Focusing on Shanghai, we developed a comprehensive framework of driving forces that integrates socioeconomic, natural, policy, and financial indicators. To assess the spatial-temporal changes in regional green space configurations and their underlying mechanisms between 2000 and 2020, we utilized stepwise regression alongside Geographically Weighted Regression (GWR) techniques. The results show that regional green space exhibited a clear stage-dependent evolution, with the total area decreasing from 580.56 km² in 2000 to 506.43 km² in 2005 and then increasing continuously to 905.70 km² in 2020. Forest land consistently expanded and became the dominant land type, while wetland showed a “decrease–increase” pattern and grassland experienced an early decline followed by partial recovery. The primary elements driving these changes underwent substantial transformations over the study period. During the initial phase, socioeconomic variables, particularly real estate investments (β = −0.296), demonstrated pronounced adverse impacts. Conversely, post-2005, financial allocations for landscaping and policy interventions emerged as the main favorable drivers (β = 0.598). Furthermore, environmental aspects like NDVI and waterway density provided a continuous positive influence on green space enlargement. Certain socioeconomic indicators, notably population density, transitioned from exerting adverse impacts to having beneficial effects during the latter periods. The primary drivers demonstrated considerable spatial variation; socioeconomic impacts were largely localized in regions undergoing urban growth, whereas environmental and policy variables exerted broader and more consistent influences. Overall, these outcomes highlight a shift from a socioeconomic-dominated evolutionary process to one governed by a synergy of multiple factors. This offers a theoretical foundation for refining urban ecological strategies and harmonizing city expansion with ecological conservation.