Urban greenspace is an effective strategy to mitigate the urban heat island (UHI) effect. While its cooling effects are well-established, uncertainties remain regarding the combined impact of internal and external landscape patterns, particularly the role of morphological spatial patterns. Taking 40 urban greenspaces in Wuhan as the sample, this study quantified cooling effects from maximum and accumulative perspectives and investigated the impacts of internal and external landscape patterns. First, using land surface temperature (LST) data, four cooling indexes—greenspace cooling area (GCA), cooling efficiency (GCE), cooling intensity (GCI), and cooling gradient (GCG)—were quantified. Then, the relationships between these indexes and landscape patterns, including scale and landscape composition, morphological spatial pattern, and surrounding environmental characteristics, were investigated by correlation analysis and multiple stepwise regression. The results showed that the cooling effects of greenspace varied across different perspectives. Both greenspace area and perimeter exerted non-linear impacts on cooling effects, and morphological spatial pattern significantly influenced cooling effects. Core proportion was positively correlated with cooling effects, with an optimal threshold of 55%, whereas bridge and branch proportions had negative impacts. External landscape patterns, particularly the proportion of impervious surfaces and building coverage, also affected cooling effects. Additionally, cluster analysis using Ward’s system clustering method revealed five cooling bundles, indicating that urban greenspaces with diverse cooling needs exhibited different cooling effects. This study offers valuable insights for optimizing urban greenspace design to enhance cooling effects and mitigate UHI.

Land fragmentation hinders sustainable development in rural areas by reducing the efficiency of land use, and it could be mitigated by optimum allocation of land resources. However, most land use allocation models address micro-scale interaction, which is not conducive to the arrangement of the specific implementation plan. Facing such issues, this study proposed a village classification method (LUEOVC) that can provide specific optimization strategies for each village according to different optimization objectives. Specifically, we used a multi-objective particle swarm optimization algorithm to find the best land use adjustment strategies under different land use optimization objectives, and the pros and cons of these strategies are based on land use efficiency evaluation. The proposed village classification method can reflect the impact of the optimal allocation of different types of land resources on the land use efficiency of each village. The results of experiments conducted in Xinxing County, Guangdong Province showed that the village-based land use optimization strategy provided in this method can improve the land use efficiency of the cultivated land with the most serious fragmentation in the study area by 0.9%. The method also enables planners to compare the costs and gains under different objections, so as to better help decision-makers in formulating land use optimization strategies for different villages.