A New Approach to Investigate the Spatially Heterogeneous in the Cooling Effects of Landscape Pattern

Shuang Liu, Xuefei Li, Long Chen, Qing Zhao, Chaohui Zhao, Xisheng Hu and Jian Li
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Shuang Liu: College of Forestry, Fujian Agriculture and Forestry University, Fuzhou 350002, China
Xuefei Li: College of Forestry, Fujian Agriculture and Forestry University, Fuzhou 350002, China
Long Chen: College of Forestry, Fujian Agriculture and Forestry University, Fuzhou 350002, China
Qing Zhao: College of Forestry, Fujian Agriculture and Forestry University, Fuzhou 350002, China
Chaohui Zhao: Fuzhou Highway Development Center, Fuzhou 350002, China
Xisheng Hu: College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
Jian Li: College of Forestry, Fujian Agriculture and Forestry University, Fuzhou 350002, China

Land, 2022, vol. 11, issue 2, 1-21

Abstract: Although many prior studies have found that landscape pattern significantly affects urban heat environment globally, the spatially heterogeneous in the cooling effects of landscape pattern remains poorly understood. In addition, most previous studies have only employed a single landscape metric separately, without holistic consideration of the composition and configuration of different landscapes. Taking one of the new “stove” cities in China-Fuzhou City, Fujian Province, as an example, we employed the principal component analysis (PCA) to synthesize a landscape pattern comprehensive index (LPCI) composed of the four common landscape metrics (i.e., aggregation index, AI; mean patch area, Area mn; largest patch index, LPI; and percentage of landscape, PLAND) of the three major land surfaces (i.e., water, vegetation, and impervious surface). Then, the local model (geographically weighted regression, GWR) was proposed to explore the spatially heterogeneous in the cooling effects of urban landscape. The results revealed that: (1) from 2000 to 2016, the land surface temperature (LST) increased by 4.262 °C, and the proportion of the urban heat island region showed an upward trend, while the urban-heat-island ratio index (URI) increased from 0.328 to 0.457; (2) the cooling effect of different land surfaces ranked from high to low was: water (29.69 °C), vegetation (38.56 °C), and impervious surface (41.82 °C); (3) compared with vegetation patches, water patches had a more obvious cooling effect on the surrounding environment, with the cooling distance within 60–90 m for the vegetation, while reaching 120–150 m for water body; (4) the proposed LPCI could explain more than 80% of the information for all of the landscape metrics for all of the landscape types, and presented a patchy distribution in the study area; (5) the GWR results revealed that the cooling effect of the landscape pattern varied spatially across the study area, indicating that the configuration of landscapes is more important in an urban center in alleviating urban heat environment than in an urban fringe area. The proposed approach provides a new understanding of the interaction between the landscape patterns and urban heat environments, providing a strong basis for landscape planning strategies for specific local sites.

Keywords: urban heat island effect; land surface temperature; landscape pattern comprehensive index; principal component analysis; geographically weighted regression (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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