Abstract
Urbanization processes greatly change urban landscape patterns and the urban thermal environment. Significant multi-scale correlation exists between the land surface temperature (LST) and landscape pattern. Compared with traditional linear regression methods, the regression model based on random forest has the advantages of higher accuracy and better learning ability, and can remove the linear correlation between regression features. Taking Beijing’s metropolitan area as an example, this paper conducted multi-scale relationship analysis between 3D landscape patterns and LST using Pearson Correlation Coefficient (PCC), Multiple Linear Regression and Random Forest Regression (RFR). The results indicated that LST was relatively high in the central area of Beijing, and decreased from the center to the surrounding areas. The interpretation effect of 3D landscape metrics on LST was more obvious than that of the 2D landscape metrics, and 3D landscape diversity and evenness played more important roles than the other metrics in the change of LST. The multi-scale relationship between LST and the landscape pattern was discovered in the fourth ring road of Beijing, the effect of the extent of change on the landscape pattern is greater than that of the grain size change, and the interpretation effect and correlation of landscape metrics on LST increase with the increase in the rectangle size. Impervious surfaces significantly increased the LST, while the impervious surfaces located at low building areas were more likely to increase LST than those located at tall building areas. It seems that increasing the distance between buildings to improve the rate of energy exchange between urban and rural areas can effectively decrease LST. Vegetation and water can effectively reduce LST, but large, clustered and irregularly shaped patches have a better effect on land surface cooling than small and discrete patches. The Coefficients of Rectangle Variation (CORV) power function fitting results of landscape metrics showed that the optimal rectangle size for studying the relationship between the 3D landscape pattern and LST is about 700 m. Our study is useful for future urban planning and provides references to mitigate the daytime urban heat island (UHI) effect.
Highlights
The results indicate that, in the area within the second ring road, the impervious surface positively contributed to the land surface temperature (LST) more significantly than it did in the area within the fourth ring road
The results showed that the interpretation effect of the 3D landscape metrics on LST was better than that of the
The results showed that the interpretation effect of the 3D landscape metrics on LST was better than sion accuracy was higher in the second ring road
Summary
Urbanization has become one of the most important human activities since the 21st century, with people’s living environments requiring continuous improvement [1]. Heat accumulates in urban areas due to urban buildings and human activities, and makes the urban temperature. Zhao et al used the Kriging method with monitoring summer data and proved that high temperatures in the center of Beijing can deteriorate air quality [14]. He et al demonstrated that synergistic interactions between UHI and heatwaves presents heat-related risks for urban society and residents using observation and a numerical model [15]. Cui et al indicated that UHI led to the increase of the urban heating and cooling load by statistical analysis of the 50-year
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