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文章摘要

基于多源数据的昆明市主城区地表热环境影响因素研究

Research on Influencing Factors of Surface Thermal Environment in the Main Urban Area of Kunming Based on Multi source Data

DOI：

中文关键词: 地表热环境影响因素多源数据主成分分析昆明市

英文关键词: Surface thermal environment Influencing factors Multi source data Principal component analysis Kunming

基金项目:国家自然科学基金资助项目（41761081）；云南省社科规划办重点基金资助项目（ZD202218）

作者	单位
陈孜谚	昆明理工大学国土资源工程学院
黄义忠	昆明理工大学国土资源工程学院

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中文摘要:

基于2023年4月16日的Landsat 8 OLI遥感影像反演昆明市主城区地表温度，通过多源数据提取地表热环境影响因子，采用相关性分析、主成分分析对因子进行筛选并得出主成分因子，采用标准化回归模型定量分析各主成分不同量纲指标间的作用程度并得出因子贡献率。结果表明：除植被、水体、海拔＞2 400 m的区域外，各行政区地表温度等级皆以高温像元为主，低温像元区为南部滇池水域及东部、北部、西部海拔＞2 400 m的山区和植被覆盖区；各因子均通过相关性检验，且在001水平显著，主成分分析得出前3个主成分的特征值＞1，累计方差贡献率为76341%；标准化回归模型结果显示，人文和自然9个影响因子每变化1个单位，将使温度变化1884和-1299个单位，人类活动因素作用强于自然因素，两者共同作用下地表升温0585个单位；自然因素中，降温贡献率植被为3778%，水体为2850%，人文因素中，升温贡献率裸地与建筑指数为2844%，不透水面指数为2161%。

英文摘要:

This study inverted the surface temperature in the main urban area of Kunming based on the Landsat 8 OLI remote sensing image on April 16th， 2023. It extracted the influencing factors of the surface thermal environment through multi source data， applied correlation analysis and principal component analysis to screen out principal component factors， adopted a standardized regression model to quantitatively analyze the interactions among indicators of different dimensions for each principal component， and calculated the factor contribution rates. The results showed that except for vegetation， water bodies and areas with an elevation above 2 400 m， the surface temperature grades in each administrative region were dominated by high temperature pixels. The low temperature pixel zones were located in the southern Dianchi water body， and the mountainous and vegetated areas with an elevation above 2 400 m in the east， north and west. All factors passed the correlation test and were significant at the 001 level. Principal component analysis revealed that the eigenvalues of the first three principal components were greater than 1， with the cumulative variance contribution rate of 76341%. The results by standardized regression model indicated that for every one unit change in the nine human and natural influencing factors， the temperature would 〖GK!87mm〗change by 1884 and -1299 units respectively. Human activity factors had 〖JP2〗a stronger impact than natural ones， and their joint effect caused the surface temperature to rise by 0585 units. Among natural factors， vegetation contributed 3778% to cooling， while water bodies accounted for 2850%. Among human fac〖JP3〗tors，〖HK〗bare 〖HK〗land and building index contributed 2844% to warming， while impervious surface index accounted for 2161%.

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