Exploring the Relationships between Land Surface Temperature and Its Influencing Factors Using Multisource Spatial Big Data: A Case Study in Beijing, China

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....

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Bibliographic Details
Published inRemote sensing (Basel, Switzerland) Vol. 15; no. 7; p. 1783
Main Authors Wang, Xiaoxi, Zhang, Yaojun, Yu, Danlin
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.04.2023
Subjects
Analysis
Autocorrelation
Autoregressive models
Big Data
Case studies
Climate change
Correlation analysis
High temperature
human daily activities
Influence
Land surface temperature
Land use
Megacities
MODIS
Remote sensing
Spatial analysis
spatial autoregressive model
spatial big data
Spatial data
Sustainable development
Thermal environments
Urban areas
Urban environments
Urban heat islands
Weibo Check-in
China
Beijing China
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Summary: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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs15071783