1.School of Civil Engineering and Architecture, Southwest Petroleum University, Chengdu 610500, China 2.State Key Laboratory of Resources and Environmental Information System, Institute of Geographic and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China 3.Agriculture Research Institute, Tibet Academy of Agriculture and Animal Husbandry Sciences, Lhasa 850000, China
Revealing the spatial differentiation characteristics and influencing factors of land surface temperature (LST) in the plateau area is of great significance for the study of local climate change. However, the existing research merely analyzes the relationship between single factor and LST, whereas the study of the spatial differentiation characteristics and the quantitative analysis of influencing factors of LST in the plateau area are relatively insufficient. Taking the Sangzhuzi District of Xigaze City as an example, the authors used Landsat8 remote sensing data to invert the LST of the study area by using radiative transfer equation algorithm and the universal single-channel algorithm. In addition, the factor detector and interaction detector in the geodetector model were used to quantitatively detect the influence of single factor and multiple factors on LST, respectively. The results show that, in the quantifiable factors, LST increases first and then decreases with the increase in the degree of aspect, and there is a significant negative correlation between LST and other factors with a difference in the descent speed. Elevation is the most important factor affecting the spatial distribution and forming the differentiation characteristics of LST in the plateau area, followed by normalized difference vegetation index(NDVI), aspect, normalized difference moisture index(NDMI), soil type, slope, and average annual precipitation; the spatial distribution and the formation of differentiation characteristics of LST in the plateau area are the result of multiple factors, all of which have a synergistic enhancement effect under interactions, such as the interaction of elevation and aspect, elevation and NDMI, and elevation and NDVI with the most significant impact.
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