Examining the relationship between the landscape pattern index and thermal comfort at the small town level to optimize the landscape pattern: take the central area of Shanghang County as an example.

The global "high-temperature heat wave" is becoming increasingly severe, and the in-depth advancement of new urbanization construction has put the construction of small towns in full swing. Solving the desire of small-town residents for an ecological and livable environment from the perspective of thermal comfort can promote the high-quality development and construction of small towns. This study takes the central area of ​​Shanghang County, Fujian Province, as the research object. Based on GF-2 image data, an object-oriented classification method is used to extract the underlying landscape of small towns. The landscape pattern is analyzed at the type level and the landscape level. The spatial correlation analysis method was used to find the spatial correlation laws between the landscape pattern and the thermal comfort simulation results. The simulation results at 8:00, 12:00, 14:00, and 18:00 are divided into five levels: comfort zone, warm zone, hot zone, very hot zone, and extremely hot zone. It was found that the thermal comfort situation was the worst at 14:00, with very hot areas accounting for 54.29% and extremely hot areas accounting for 23.18%. The correlation between PLAND, LPI, and UTCI is most significant at the small-town scale. The strong correlation indicators of vegetation are PLAND, LPI, AREA_MN, and ED; the strong correlation indicators of water are PLAND, LPI, and AREA_MN; the strong correlation indicators of asphalt pavement are LPI; the strong correlation indicators of cement pavement are PLAND, AREA_MN, and LPI. ENVI-met was used to simulate the thermal comfort of the study area, classify levels, and summarize spatiotemporal patterns. Then, the landscape pattern optimization principles, layout plans, and strategies were proposed, and finally, the results were compared to quantitatively evaluate the thermal comfort improvement benefits. After optimization, the minimum, maximum, and average values ​​of UTCI all decreased, with the average decreasing by 11.39℃. The thermal comfort level has been significantly improved, and the extremely hot area has been reduced by 16.98%, which provides theoretical support and a basis for the scientific development of regulating urban thermal comfort.