| Under the background of global climate change,the process of urbanization is developing rapidly,and more and more people are pouring into cities.Under the combined effects of climate change and urbanization,the urban heat island phenomenon is becoming more and more common and gradually becoming normalized,and its negative impact on urban green and sustainable development is becoming increasingly prominent.In order to reduce the impact of the urban heat island effect,it is particularly important to improve the living environment quality of urban residents and promote the high-quality development of the city.Therefore,large-scale and efficient research on urban thermal comfort and its influencing factors is of great significance for improving urban living environment and subsequent sustainable and coordinated urban development.This study takes Xi’an,the capital city of Shaanxi Province,as the research area,uses the Google Earth Engine cloud remote sensing processing platform to obtain Landsat 8series remote sensing satellite data,and uses Pearson correlation analysis,geographic detectors and other analysis methods to analyze the 2015,The temporal and spatial changes and influencing factors of urban thermal comfort in 2017,2019 and 2021 were analyzed.First,based on the Google Earth Engine platform,the Landsat 8 remote sensing image data is used to retrieve the surface temperature and the normalized water vapor index,and calculate the thermal comfort index of Xi’an in four years in different seasons.Classification of comfort levels reveals the spatial-temporal pattern change characteristics of different thermal comfort levels through visualization methods,and then combines the transfer matrix to analyze the dynamic conversion and change characteristics of areas of different comfort levels in different seasons,and uses the Slope trend analysis method for trend prediction.Finally,Pearson correlation analysis and geographic detector method were used to explore the driving factors and influence degree of thermal comfort.The main research conclusions are as follows:(1)The thermal comfort of the four seasons in Xi’an is relatively high overall.Among the pixels of the thermal comfort level of the four seasons,the proportion of "more comfortable" pixels is relatively small,and the comfort range is mainly concentrated in "comfortable" and "comfortable".The summer comfort in the area north of the northern slope of the Qinling Mountains in Xi’an was lower than that of the other three seasons.The spatial distribution of thermal comfort differs significantly between the north and the south.The comfort of the southern Qinling Mountains is lower than that of the northern plains in spring,autumn,and winter;the comfort of the southern Qinling Mountains is higher than that of the northern plains in summer.In general,summer and winter are less comfortable in Xi’an,and spring and autumn are more suitable for outdoor activities.(2)It can be seen from the land transfer change data of different thermal comfort levels in Xi’an in four seasons that the distribution of thermal comfort levels is normally distributed,and the comfort levels are mainly concentrated in comfortable,relatively comfortable,and less comfortable levels,and are less comfortable and uncomfortable in four years A large number of graded areas have been transferred out,indicating that the thermal comfort of Xi’an is gradually improving.(3)In spring,autumn,and winter in Xi’an,the proportion of pixels with a decreasing trend in the mean value of the Modified Temperature-humidity Index(MTHI)is more than that of an increasing trend,and the proportion of pixels with a decreasing trend is the largest in autumn.was 95.71%;the proportion of pixels with an increasing trend in the average value of the corrected temperature and humidity index in summer was more than that of a decreasing trend,and the proportion of increasing pixels in summer was 62.23%.The pixels showing an increasing trend in spring are mainly distributed in the northern plains such as the main urban area of Xi’an City,Huyi District,and Zhouzhi County;the pixels showing a decreasing trend in summer are scattered in the plains;the pixels showing an increasing trend in autumn are less;The trend pixels are mainly distributed in the central part of the plain area and parts of the northeast.(4)Through Pearson correlation analysis,thermal comfort spatial distribution is positively correlated with air temperature,relative humidity,wind speed,population,and night light,and the correlation coefficients are 0.86,0.44,0.33,0.27,and 0.58;it is negatively correlated with NDVI,and the correlation coefficient is-0.65.Among them,the spatial distribution of thermal comfort is highly correlated with air temperature,and the relationship is the most closely related.(5)In exploring the factors affecting the spatial distribution of thermal comfort in Xi’an,the explanatory power of the factors is ranked as follows: air temperature(0.700)>population(0.606)> night light(0.588)> NDVI(0.505)> humidity(0.198)> wind speed(0.111).The main type of factor interaction detection results is the two-factor enhancement,in which temperature plays a very obvious role in the factor interaction,and the two-factor enhancement effect with NDVI is the most significant.Ecological detection further indicated that population,night light and NDVI had a great influence on the spatial distribution of thermal comfort in the study area. |
PDF Full Text Request