Effects of spatio-temporal landscape patterns on land surface temperature: a case study of Xi'an city, China.

A city is a mixed ecosystem of nature, economy, and society and is simultaneously transforming natural areas and adapting to nature. Urbanization causes the population to expand rapidly, leading to rapid expansions of scale. Consequently, the proportions of impermeable surfaces (ISs) and greenspaces (GSs) change drastically, which has a considerable influence on the urban thermal environment. The aim of this study was to research the effects of spatio-temporal landscape patterns on land surface temperature (LST) and between GS and IS in the city of Xi'an using the urban-rural gradient, the moving split-window algorithm (MSA), multiple grid resolutions, and landscape metrics based on three-phase Landsat data. The results showed that there was a significantly positively correlated with IS density and significantly negatively correlated with the GS density from the urban center to rural areas. Over the past 25 years, the main urban area of Xi'an has expanded by nearly 6.2 times its initial size. The correlation between IS density and LST increased with increasing grid size, and the correlation between GS density and LST increased with decreasing grid size. Thus, LST is highly sensitive to the ISs and GSs at particular grid sizes. The correlation coefficients of the ISs and GSs with LST increased with decreasing grid size during 1992-2016. Hence, the LST was less sensitive to IS and the GS densities in conjunction with larger grid sizes. The class area (CA) and the landscape shape index (LSI) of the ISs were significantly positively correlated with the LST, whereas the CA and largest patch index (LPI) of the GSs were negatively correlated with the LST. The LST of the ISs in 1992, 2006, and 2016 were 1.6, 1.8, and 3.9 °C higher, respectively, than those of the GSs, indicating that GSs are important to lowering urban LSTs. Therefore, the government and urban planning departments should consider optimizing the spatial patterns of ISs and GSs to fully exploit the cooling effect of optimally configured GSs, which would be conducive to the sustainable development of the urban ecological environment.