Spatiotemporal Variation of Surface Urban Heat Islands in Relation to Land Cover Composition and Configuration: A Multi-Scale Case Study of Xi’an, China

Linlin Lu; Qihao Weng; Da Xiao; Huadong Guo; Qingting Li; Wenhua Hui

doi:10.3390/rs12172713

Remote Sensing (Aug 2020)

Spatiotemporal Variation of Surface Urban Heat Islands in Relation to Land Cover Composition and Configuration: A Multi-Scale Case Study of Xi’an, China

Linlin Lu,
Qihao Weng,
Da Xiao,
Huadong Guo,
Qingting Li,
Wenhua Hui

Affiliations

Linlin Lu: Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Qihao Weng: Center for Urban and Environmental Change, Department of Earth and Environmental Systems, Indiana State University, Terre Haute, IN 47809, USA
Da Xiao: Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Huadong Guo: Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Qingting Li: Airborne Remote Sensing Center, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Wenhua Hui: College of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, China

DOI: https://doi.org/10.3390/rs12172713
Journal volume & issue: Vol. 12, no. 17
p. 2713

Abstract

Read online

Urban heat islands (UHI) can lead to multiple adverse impacts, including increased air pollution, morbidity, and energy consumption. The association between UHI effects and land cover characteristics has been extensively studied but is insufficiently understood in inland cities due to their unique urban environments. This study sought to investigate the spatiotemporal variations of the thermal environment and their relationships with land cover composition and configuration in Xi’an, the largest city in northwestern China. Land cover maps were classified and land surface temperature (LST) was estimated using Landsat imagery in six time periods from 1995 to 2020. The variations of surface heat island were captured using multi-temporal LST data and a surface urban heat island intensity (SUHII) indicator. The relationship between land cover features and land surface temperature was analyzed through multi-resolution grids and correlation analysis. The results showed that mean SUHII in the study area increased from 0.683 °C in 1995 to 2.759 °C in 2020. The densities of impervious surfaces had a stronger impact on LST than green space, with Pearson’s correlation coefficient r ranging from 0.59 to 0.97. The correlation between normalized difference impervious surface index and LST was enhanced with the enlargement of the grid cell size. The correlations between normalized difference vegetation index and LST reached maxima and stabilized at grid cell sizes of 210 and 240 m. Increasing the total area and aggregation level of urban green space alleviated the negative impacts of UHI in the study area. Our results also highlight the necessity of multi-scale analysis for examining the relationships between landscape configuration metrics and LST. These findings improved our understanding of the spatiotemporal variation of the surface urban heat island effect and its relationship with land cover features in a major inland city of China.

Published in Remote Sensing

ISSN: 2072-4292 (Online)
Publisher: MDPI AG
Country of publisher: Switzerland
LCC subjects: Science
Website: http://www.mdpi.com/journal/remotesensing/

About the journal

Abstract

Keywords