Urban green space, comprising parks, fields, woodlands, and other semi-natural areas, is a fundamental component of urban ecosystems. The determination of the relationship between urban green space and urban sprawl is necessary to understand urbanization and the provision of urban ecosystem services. It has been hypothesized that the center of urban (i.e., population and economic) areas in fast-growing cities would migrate toward urban green space over time. To test this hypothesis, urban expansion and urban green space expansion were examined in five cities in China and five cities in the U.S. that were experiencing high rates of growth. Landsat images of those cities from 2000 to 2017 were combined with annual population and economic data and used to quantify the extent and migration of the urban green space. These data were analyzed using the center of gravity method by Grether and Mathys and circular statistics were used to determine the relationship between urban green space and urban expansion. Eight out of the ten cities showed a divergent pattern, i.e., the population and economic centers moved in a different direction to that of the urban green space. The movement of the mean centers of the urban green spaces in the U.S. cities was more consistent than that of the Chinese cities. Over 18 years, the movement of urban green space and urban expansion in the 10 cities showed a synchronous growth trend; however, the proportion of urban green space in the cities decreased. The urban expansion rate exceeded the population growth rate, which led to problems with an unreasonable urban sprawl that is likely to deplete the provision of ecosystem services in the future. In conclusion, the centrifugal forces of urban green space that lead to the movement of population and economic centers away from green spaces play a larger role in urban change than the centripetal forces that pull these centers toward urban green space.

With the acceleration of urbanization, the disturbance to urban landscape patterns causes changes to urban surface runoff and increases the risk of urban waterlogging. We studied the response relationship between landscape pattern change and surface runoff in the Chang–Zhu–Tan metropolitan area for the period from 2000 to 2020, analyzing the driving factors that affected surface runoff. The influence of landscape pattern evolution on surface runoff was studied using the SCS-CN flow generation model, the moving window method, and Pearson’s analysis. The analysis showed that between 2000 and 2020, the forest area decreased, while the impermeable land area increased substantially. At the same time, the landscape spread degree (CONTAG) decreased, whereas the landscape fragmentation degree (DIVISION) increased, and the Shannon diversity index (SHDI) and landscape shape index (LSI) increased. The surface runoff in the main urban areas of Chang–Zhu–Tan increased substantially. The results showed that surface runoff is negatively correlated with SHDI, LSI, and DIVISION but displays a positive relationship to CONTAG. Soil texture and precipitation had the greatest impact on surface runoff. This study emphasizes the importance of landscape pattern evolution to surface runoff in rapidly developing metropolitan areas in terms of reducing surface runoff, alleviating urban waterlogging and preventing regional floods. Our research additionally seeks to optimize the landscape pattern of the Chang–Zhu–Tan metropolitan area.

In the face of rapid urbanization and global climate change, understanding the trade-offs and synergies of wetland city ecosystem services is vital for mitigating regional ecological and environmental risks, and enhancing human well-being. The Dongting Lake Basin is an ecologically fragile area of global significance. Uncontrolled resource utilization and intensive human activities have severely damaged the ecological environment, including in Miluo. Thus, it is of paramount research importance to uncover the trade-offs and synergies of ecosystem services and their driving mechanisms in Miluo. To achieve this, we classified Miluo’s land use data over the past two decades using a random forest model and Landsat imagery. We quantified the major ecosystem services in Miluo by employing ecological process models such as InVEST, RUSLE, and CASA. Additionally, we examined the trade-offs and synergies between ecosystem services at different scales and identified the driving mechanisms using multi-source remote sensing data. The results revealed that forests exhibited the highest level of ecosystem services, while urban ecosystem services experienced a significant decline. Over the past two decades, Miluo displayed notable trade-offs and synergies between ecosystem services, with synergies prevailing as the dominant pattern, particularly at the county scale. Furthermore, human activities emerged as the primary driver of changes in Miluo’s ecosystem services during the 20-year period. Therefore, it is imperative for scientists, policymakers, and civil society to develop effective and scientifically sound strategies to mitigate the ecological risks resulting from rapid urbanization and climate change in the future.

Modifications in land use patterns exert profound influences on the configuration, arrangement, and functioning of terrestrial ecosystems, thereby inducing fluctuations in carbon sequestration. Consequently, precise ecological decision-making and an in-depth exploration of the interplay between land use alterations and carbon storage dynamics assume paramount importance in the pursuit of optimal regional land use configurations. In this investigation, we employed the InVEST model to analyze the spatiotemporal variations in land utilization and carbon storage in Hunan Province, based on comprehensive land use data spanning the period from 2000 to 2020. Additionally, the PLUS model was utilized to project the future spatial distribution of carbon storage in Hunan Province until 2040, encompassing diverse development scenarios. The findings of our study are as follows: (1) Land use changes instantaneously impact carbon storage within the study area. From 2000 to 2020, urban construction land witnessed an expansion of 3542 km², which accounted for an increase from 1.13% to 2.78% of the total land area. Consequently, there was a decline in arable land, woodlands, and grasslands, resulting in a reduction of 3430.25 tons of carbon storage in Hunan Province. (2) The ecological protection scenario is projected to yield the most substantial increase in carbon storage, with an estimated magnitude of 7.02 × 10⁶ tons by the year 2040. According to the natural evolution scenario, the total amount of carbon storage is anticipated to remain similar to that of 2020, with a marginal increase of 2.81 × 10⁵ tons. Under the arable land protection scenario, carbon storage is predicted to decrease by 1.060 × 10⁷ tons. Conversely, the urban development scenario is expected to result in the most substantial reduction of 2.243 × 10⁷ tons of carbon storage. These findings underscore the efficacy of adopting ecological protection and natural development policies in curbing the decline in carbon storage. (3) The geographic distribution of carbon storage areas exhibits a strong correspondence with that of land use. Regions characterized by elevated carbon storage levels exhibit minimal urban construction land, an abundance of compact and contiguous ecological land, and a higher frequency of such land parcels. To enhance regional carbon storage levels and achieve sustainable development goals, future endeavors should prioritize the implementation of ecological protection and natural development policies.

Mastering the relationship between urban landmarks and urban space morphology in urban planning, landscape planning, and architectural design helps maintain the intelligibility of compact urban districts. The objective of the present study was to numerically determine the structural salience of various landmarks in an urban environment and use it to interpret the intelligibility of the city. Combining the measurement method of 3D visibility and the related principles of space syntax, this study develops a new 3D Node–Landmark Grid Analysis Model (3D NL GAM) for structural salience computation of urban landmarks. In this study, a numerical approach is used to construct a 3D simulation model. Firstly, the visibility of each decision node to landmarks in an urban environment, using a 3D digital model, is measured using the 3D isovist component of Rhinoceros and Grasshopper software. Secondly, links among wayfinding decision nodes and landmarks are established to form a 3D NL GAM. The normalized angular integration of decision nodes and the normalized angular choice of landmarks are computed using the principle of space syntax. Thirdly, the structural salience of landmarks is determined with a function of landmark visibility, spatial properties of landmarks, and wayfinding decision nodes. Finally, a case study was carried out by using a 3D NL GAM to analyze three types of urban areas located in Changsha. The results indicated that large-scale natural landscapes have a higher structural salience among the types of landmarks. The structural salience of architectural landmarks in the combined spatial form of combining tall and low building groups has a clear advantage over the form dominated by high-rise building groups. Raising the height of landmark buildings can modify the structure of the grid analysis model and improve the people aggregation of urban space. The 3D NL GAM can quantify the spatial properties and landmark structural salience of a city and can effectively assist in the evaluation of the intelligibility of built or future urban environments.

The study of spatial layout in China is changing from land use/land cover to territorial spatial layout and is mostly carried out at the administrative division scale. However, the driving factors affecting the evolution of territorial spatial layout are not all influenced by administrative boundaries. As cities are mostly centered on lakes and water systems, the evolution of territorial spatial layout in lake-type basins must have its own special characteristics. Taking the Dongting Lake Basin (DLB), a representative lake-type basin in China, as an example, this study identifies its territorial spatial layout elements and couples the system dynamics model with the patch-generating land-use simulation model based on multi-layer perceptron artificial neural networks. This study identified the best combination of driving factors and research step size affecting the evolution of territorial spatial layout. An improved quantitative spatial coupling model was used to simulate the territorial spatial layout of the DLB from 2000 to 2050 and identify its evolutionary characteristics and trends at both the elemental level and for three types of space. The simulation and prediction of territorial spatial layout from the lake-type basin hydrology scale can provide a reference for the formulation of regional territorial spatial planning policies.