Search

Cities and urban areas are an important part of global sustainable development, and the health and well-being of urban residents are closely related to the quality, quantity, and diversity of urban ecosystem services. Although the rapid urbanization process has changed the structure and function of urban ecosystems, which is notably different from natural ecosystems, the affected ecosystem services and their interactions—the trade-off impact of urbanization intensity on ecosystem services—remain to be discussed. Using land use/land cover and impervious surface area remote sensing datasets, and InVEST and RUSLE-related ecosystem services models to evaluate seven typical ecosystem services in Shenzhen, this study explored the evolution of multiple ecosystem service trade-offs and synergies during the transition from a natural ecosystem to an urban ecosystem, and how they are affected by urbanization intensity through correlation analysis and a discrete time-step simulation model. The results show that: (1) from 1978 to 2018, in the process of ecosystem transformation, grain production dropped from 228,795 tons to 11,733 tons, fruit production peaked in 1990 at 271,508 tons, and service capacity of both showed obvious degradation. Conversely, the cultural service capacity was remarkably enhanced. (2) With the increase in urbanization level, the trade-off and synergy of ecosystem services gradually transition from linear to nonlinear. The rapid urbanization process drives the nonlinear degradation of ecosystem services and the nonlinear enhancement of synergy. (3) Over the past four decades, ecosystem service bundles within the same kilometer grid have shown a quadratic curve-like decrease with increasing impervious surface area, slowly in the early stages and faster in the later stages. This study concludes that urbanization intensity has a significant impact on ecosystem service trade-offs, which can provide support for the formulation of ecological protection and restoration strategies in territorial space based on ecosystem services.

Urbanization has changed the environmental conditions of vegetation growth, such as the heat island effect, which has an indirect impact on vegetation growth. However, the extent to which the direct and indirect effects of the thermal environment changes caused by urbanization on vegetation growth are unclear. In this study, taking the example of the Guangdong–Hong Kong–Macao Greater Bay Area, a fast-growing national urban agglomeration in China, the relationship between vegetation growth and warming conditions during the period from 2001 to 2020 were explored by the net primary productivity (NPP) and land surface temperature (LST), based on the vegetation growth theory, in urban environments. The results show that there is a significant exponential relationship between the warming and the growth of large-scale vegetation. This relationship is mainly attributable to thermal environmental factors, since their multi-year average contribution rate on the interannual scale is 95.02%. The contribution rate varies on the seasonal scale, according to which the contribution rate is the largest in autumn and the smallest in winter. This research is of great significance for predicting the potential response of vegetation growth to future climate warming and improving vegetation growth in urban areas.

Studying the spatiotemporal changes of the northern limit of winter wheat (NLWW) in China is important to ensure regional food security and deal with the effects of climate change. Previous studies mainly used climate indicators to analyze the variation of the potential NLWW in different historical periods, while little attention has been paid to the actual migrations and changes of the NLWW. The objectives of the present study were three-fold: (i) to map the spatial distribution of winter wheat in northern China in 2001, 2007, 2014 and 2019; (ii) to extract the actual NLWW; and (iii) to quantitatively explore the dynamics of the NLWW. First, we adopted the “combining variations before and after estimated heading dates” method to map the winter wheat in northern China based on time series MODIS EVI2 data. Second, we used the kernel density estimation algorithm to extract the actual NLWW in four historical periods. Finally, the fishnet method was utilized to quantitatively analyze the direction and distance of the spatiotemporal changes of the NLWW. The results demonstrated that the NLWW has exhibited a marked fluctuating trend of migration southward, with a 37-km shift in latitude over the past 20 years. The elevation limit of winter wheat planting was around 1600 m; however, the centroid of winter wheat planting has shifted slowly to lower elevations. There was a gap between the actual NLWW and the potential NLWW. The reason for this gap was that the actual NLWW moved southward under the interacting effects of human activities and climate change, while the potential NLWW moved northward due to climate change. The results of this study are of great scientific value in the formulation of winter wheat planting strategies in climate-sensitive areas to respond to climate change and ensure food security.