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Complex mechanisms linking land surface temperature to greenspace spatial patterns: Evidence from four southeastern Chinese cities
- Guo, Guanhua, Wu, Zhifeng, Chen, Yingbiao
- The Science of the total environment 2019 v.674 pp. 77-87
- Landsat, cities, green infrastructure, heat island, landscapes, summer, surface temperature, winter
- Many studies have explored the complex mechanisms of urban heat islands by examining the relationship between land surface temperature (LST) and greenspace spatial patterns. Few, however, have explored the relative contributions of greenspace spatial composition and configuration to LST using comparisons between cities. In this study, the authors sought to identify the relative contributions of greenspace spatial composition and configuration to LST and the stability mechanisms linking LST to greenspace at multiple locations. We looked at four highly-urbanized Chinese cities in a comparative study. Landsat 5/8 images for summer and winter were used to estimate LST and greenspace data were extracted from 0.5-m resolution imagery. The complex relationship between LST and greenspace spatial patterns was quantified and compared using a novel method that combines stepwise regression with hierarchical partitioning analysis concerning statistical size variations. The results indicated that greenspace spatial composition and configuration both consistently affect LST. However, the magnitude and significance of these relationships were very different. The combined contributions of the greenspace landscape metrics played a more critical role in determining LST than their independent contributions, especially in summer. However, the relative importance of spatial composition and spatial configuration was largely dependent on specific variables such as season or selected statistical grid size. The urban heat island (UHI) effect can be reduced not only by increasing the amount of greenspace, but also by optimizing greenspace spatial configuration; the latter is more effective than the former. Although scale dependence continued to be evident in our study, we were not able to confirm a universal “best” scale for analysis. This study extended our understanding of the complex mechanisms of UHI in the region with respect to seasonal and scale factors, and has provided valuable information to support UHI adaptation strategy development by urban planners.