Rural revitalization places higher demands on the productive–living–ecological (P-L-E) spaces of towns and cities. It is necessary, therefore, to identify, evaluate, and optimize P-L-E spaces to better guide spatial planning. Existing studies typically evaluate a single space, lacking a comprehensive consideration of whole-area integration. This study, therefore, developed a coupled spatial/developmental suitability evaluation system for Feixi County, Anhui Province, China, combining spatial quality evaluation, a coupled coordination model, and future land-use simulation (FLUS) model. The spatial quality of Feixi County in 2010, 2015, and 2020 was obtained by applying the evaluation system to the spatial development pattern. The results were analyzed and verified using the landscape pattern index and development suitability evaluation. The results showed the following: (1) The coupling coordination degree of the region increased from 0.131 to 0.372, changing from low to moderate coordination. (2) Based on the FLUS model to better capture the uncertainty and stochastic basis of the development in the study area. The kappa coefficient and Figure of Merit (FoM) index of the land-use simulation accuracy verification index were 0.7647 and 0.0508, respectively, and the logistic regression ROC values were above 0.75, thus meeting accuracy requirements. This demonstrated that the simulation model—based on a factor library of the evaluation of resource and environmental carrying capacity and suitability for development and construction—could better reflect future land-use changes. (3) The simulation showed that under the baseline development scenario, the area’s spatial layout is too concentrated in terms of construction land, ignoring P-L-E coordination. Under the ecological optimization scenario, high-quality ecological space is ensured, but other types of spaces are lacking. Under the comprehensive guidance scenario, lagging ecological space is optimized and P-L-E spatial development is enhanced through aggregation, clustering, concentration and integration. This way, the spatial quantity structure and distribution form can meet P-L-E spatial development needs in Feixi County. In this study, on the basis of scientific assessment of the current P-L-E space, the FLUS model was applied to carry out a scenario simulation according to different objectives. Moreover, based on the construction of the coupling system of human–nature system, the driving factors were improved to enhance the prediction accuracy of the FLUS model. This study’s findings can help improve the scientificity, flexibility and management efficiency of Feixi County’s P-L-E spatial layout, thereby supporting its sustainable development.

Taking Binhu New Area (BNA) as a case study, based on multi-period (2005, 2012, 2020) Landsat remote sensing images, the landscape pattern index and grey relational analysis (GRA) were chosen to quantitatively examine the dynamic change process and driving mechanism of green space landscape patterns. The results showed: (1) From 2005 to 2020, the green space area in BNA showed increases at first and decreases later with the highest degree of dynamic change found in regional green space. (2) Urban green space showed increased diversity and good connectivity, but its green patch was simple with a decreased degree of sprawl. The green patch also showed a tendency towards homogenization, aggregation, and simplification. The patch shapes of park green space, attached green space, and protective green space are relatively simple and there are blind spots in the distribution. (3) Population growth and industrial structure changes are the main driving forces of the changes in green space landscape patterns in BNA. Besides, urban planning and policies also play a big part in these changes. The research results can provide a reference for green space planning and construction in BNA.

Accurately analyzing the spatio-temporal pattern of impervious surface areas (ISA) and its effect on thermal environments is crucial in urban development planning. This research was aimed to explore the spatio-temporal distribution characteristics and correlation of ISA and land surface temperature (LST) in the built-up areas of Hefei from 2002 to 2017. LST and ISA data were retrieved using radiative transfer equations and the Biophysical Composition Index (BCI), respectively, and their relationship was quantitatively analyzed using multiple model regression. We found that from 2002 to 2017, the total ISA increased by 181.32 km2 and expanded from the central to the surrounding area. The Shushan and Baohe districts had the largest expansion area and intensity, respectively. Although the mean value, extreme value, and standard deviation of LST tended to decrease, the proportion of sub-high and high-temperature areas increased considerably. The ISA and LST showed a strong quadratic function relationship, with R2>0.68. LST increased with increasing ISA BCI value, and the average increase in LST was >1ºC for every 0.1 increase in the BCI value of ISA. Our findings can be valuable for urban planning and construction and promote the sustainable development of urban ecology.