Document Type
Article
Publication Date
4-1-2023
Journal / Book Title
Remote Sensing
Abstract
A better understanding of the relationship between land surface temperature (LST) and its influencing factors is important to the livable, healthy, and sustainable development of cities. In this study, we focused on the potential effect of human daily activities on LST from a short-term perspective. Beijing was selected as a case city, and Weibo check-in data were employed to measure the intensity of human daily activities. MODIS data were analyzed and used for urban LST measurement. We adopted spatial autocorrelation analysis, Pearson correlation analysis, and spatial autoregressive model to explore the influence mechanism of LST, and the study was performed at both the pixel scale and subdistrict scale. The results show that there is a significant and positive spatial autocorrelation between LSTs, and urban landscape components are strong explainers of LST. A significant and positive effect of human daily activities on LST is captured at night, and this effect can last and accumulate over a few hours. The variables of land use functions and building forms show varying impacts on LST from daytime to nighttime. Moreover, the comparison between results at different scales indicates that the relationships between LST and some explainers are sensitive to the study scale. The current study enriches the literature on LST and offers meaningful and practical suggestions for the monitoring, early warning, and management of urban thermal environment with remote sensing technology and spatial big data sources.
DOI
10.3390/rs15071783
Journal ISSN / Book ISBN
85152770528 (Scopus)
MSU Digital Commons Citation
Wang, Xiaoxi; Zhang, Yaojun; and Yu, Danlin, "Exploring the Relationships between Land Surface Temperature and Its Influencing Factors Using Multisource Spatial Big Data: A Case Study in Beijing, China" (2023). Department of Earth and Environmental Studies Faculty Scholarship and Creative Works. 774.
https://digitalcommons.montclair.edu/earth-environ-studies-facpubs/774
Published Citation
Wang, X., Zhang, Y., & Yu, D. (2023). Exploring the Relationships between Land Surface Temperature and Its Influencing Factors Using Multisource Spatial Big Data: A Case Study in Beijing, China. Remote Sensing, 15(7), 1783. https://doi.org/10.3390/rs15071783
Comments
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).