Abstract
The relationship between urban landscape pattern and land surface temperature (LST) is one of the core issues in urban thermal environment research. Although previous studies have shown a significant correlation between LST and landscape pattern, most were conducted at a single scale and rarely involve multi-scale effects of the landscape pattern. Wavelet coherence can relate the correlation between LST and landscape pattern to spatial scale and location, which is an effective multi-scale correlation method. In this paper, we applied wavelet coherence and Pearson correlation coefficient to analyze the multi-scale correlations between landscape pattern and LST, and analyzed the spatial pattern of the urban thermal environment during the urbanization of Beijing from 2004 to 2017 by distribution index of high-temperature center (HTC). The results indicated that the HTC of Beijing gradually expands from the main urban zone and urban function extended zone to the new urban development zone and far suburb zone, and develops from monocentric to polycentric spatial pattern. Land cover types, such as impervious surfaces and bare land, have a positive contribution to LST, while water and vegetation play a role in mitigating LST. The wavelet coherence and Pearson correlation coefficients showed that landscape composition and spatial configuration have significant effects on LST, but landscape composition has a greater effect on LST in Beijing metropolitan area. Landscape composition indexes (NDBI and NDVI) showed significant multi-scale characteristics with LST, especially at larger scales, which has a strong correlation on the whole transect. There was no significant correlation between the spatial configuration indexes (CONTAG, DIVISION, and LSI) and LST at smaller scales, only at larger scales near the urban area has a significant correlation. With the increase of the scale, Pearson correlation coefficient calculated by spatial rectangle sampling and wavelet coherence coefficient have the same trend, although it had some fluctuations in several locations. However, the wavelet coherence coefficient diagram was smoother and less affected by position and rectangle size, which more conducive to describe the correlation between landscape pattern index and LST at different scales and locations. In general, wavelet coherence provides a multi-scale method to analyze the relationship between landscape pattern and LST, helping to understand urban planning and land management to mitigate the factors affecting urban thermal environment.
Highlights
Rapid urbanization has tremendously changed the urban landscape pattern, which is one of the most remarkable human land surface activities since the 20th century [1,2,3]
By 2017, there was still an obvious high-temperature area in the main urban zone and urban functional extended zone, and the high-temperature centers (HTC) had spread to the far suburb zone and new urban development zone, which was related to the process of urban expansion, and showed a significant polycentric spatial pattern
In 2017, the HTC gradually expanded to the new urban development zone and far suburb zone, with a spatial pattern of polycentric distribution, and gradually connected to the main urban zone and urban function extended zone, which has an adverse effect on urban Land surface temperature (LST)
Summary
Rapid urbanization has tremendously changed the urban landscape pattern, which is one of the most remarkable human land surface activities since the 20th century [1,2,3]. With the acceleration of urbanization, the changes of urban land cover types have intensified, and the effects of landscape space configuration on LST has received increasing attention, especially the identification of relationship between the fragmentation, aggregation, and spatial morphology of landscape patches and LST [13,21,22,23]. The major models to quantify the relationship between landscape pattern and LST include correlation coefficient, regression analysis, principal component regression analysis, and spatial analysis [10,24,25] Most of these models analyzed the effects of landscape pattern metrics on LST at a single scale, and the hierarchical structure and scale dependence of landscape patterns were usually ignored [26,27]. Data and Preprocessing Two remote sensing images obtained in late summer (September) with highly clear atmospheric conditions (cloud amount less than 5%) were used in this study (Table 1), and both the two remote
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