[Relationship Between Urban Spatial Pattern and Thermal Environment Response in Summer: A Case Study of Hefei City].

With the continuous expansion of cities, the land cover type of the region is transformed, a large number of natural landscapes are replaced by man-made landscapes, and the environmental temperature rises. The study of the response relationship between urban spatial pattern and thermal environment provides some guidance for improving the ecological environment and optimizing the urban spatial layout. Based on the Landsat 8 series remote sensing image data of Hefei City in 2020 and analysis platforms such as ENVI and ARCGIS, Pearson correlation and profile lines were used to reflect the correlation between the two. Then, the three spatial pattern components with the greatest correlation were selected to construct multiple regression functions to investigate the influence of urban spatial pattern on urban thermal environment and its mechanism of action. The results showed that:① the high temperature area of Hefei City increased significantly with the advance of time during 2013-2020. For different seasons, the urban heat island effect showed that summer>autumn>spring>winter. ② In the central urban area, the building occupancy, building height, imperviousness occupancy, and population density were significantly higher than those in the suburbs, whereas fractional vegetation coverage presented a higher suburban than urban area and mainly showed a point distribution in the urban area and an irregular distribution of water bodies. ③ The urban high-temperature zone was mainly distributed in various development zones in urban areas, whereas other places in urban areas were dominated by medium-high temperature and above-temperature zoning, and suburban areas were dominated by medium-low temperature. ④ The Pearson coefficients between the spatial pattern of each element and the thermal environment were positively correlated with the building occupancy (0.395), impervious surface occupancy (0.333), population density (0.481), and building height (0.188) and negatively correlated with fractional vegetation coverage (-0.577) and water occupancy (-0.384). The coefficients of the constructed multiple regression functions, including building occupancy, population density, and fractional vegetation coverage, were 8.372, 0.295, and -5.639 respectively, with a constant of 38.555. The results of this study can provide a reference basis for optimizing urban spatial layouts and improving urban living quality.