Spatiotemporal Patterns and Drivers of the Surface Urban Heat Island in 36 Major Cities in China: A Comparison of Two Different Methods for Delineating Rural Areas

Abstract

Urban heat islands (UHIs) are an important issue in urban sustainability, and the standardized calculation of surface urban heat island (SUHI) intensity has been a common concern of researchers in the past. In this study, we used the administrative borders (AB) method and an optimized simplified urban-extent (OSUE) algorithm to calculate the surface urban heat island intensity from 2001 to 2017 for 36 major cities in mainland China by using Moderate Resolution Imaging Spectroradiometer (MODIS) images. The spatiotemporal differences between these two methods were analyzed from the perspectives of the regional and national patterns and the daily, monthly, and annual trends. Regardless of the spatial or temporal scale, the calculation results of these two methods showed extremely similar patterns, especially for the daytime. However, when the calculated SUHI intensities were investigated through a regression analysis with multiple driving factors, we found that, although natural conditions were the main drivers for both methods, the anthropogenic factors obtained from statistical data (population and gross domestic product) were more correlated with the SUHI intensity from the AB method. This trend was probably caused by the spatial extent of the statistical data, which aligned more closely with the rural extent in the AB method. This study not only explores the standardization of the calculation of urban heat intensity but also provides insights into the relationship between urban development and the SUHI.

Analysis of the Thermal Environment in Pedestrian Space Using 3D Thermal Imaging

“…Although the thermal environment in pedestrian space is primarily controlled by the amount of solar radiation, vegetation, paving materials etc., the methods for selecting appropriate evaluation indicators for studying the spatial distribution of thermal comfort remain largely unclear. Few previous studies have focused on the differences in spatial distribution, with most of the comparison generally focused on the spatiotemporal distribution [55]. However, the most important purpose to study thermal comfort in pedestrian spaces is to explore the factors that cause differences in spatial distribution and to propose suggestions for further mitigation.…”

Opposite Spatiotemporal Patterns for Surface Urban Heat Island of Two “Stove Cities” in China: Wuhan and Nanchang

“…Secondly, the definition of SUHII was not uniform in the literature, which resulted in different SUHII across different papers, even though the same study area was used. The discussion of SUHII defined by two different rural areas also confirmed that a large difference existed between different SUHII definitions [37].…”

Identifying Surface Urban Heat Island Drivers and Their Spatial Heterogeneity in China’s 281 Cities: An Empirical Study Based on Multiscale Geographically Weighted Regression

“…A large amount of uncertainty in selecting rural references in the SUHI intensity calculations makes it challenging to compare the conclusions of related literature [38,64]. In contrast, taking administrative boundaries (AB) as a rural reference is considered an appropriate SUHI standardized calculation scheme and has been widely used in SUHI studies around the world, including China and the United States [19,65].…”

“…Previous empirical research models for SUHI driver analysis can generally be divided into two main categories. The first type includes global-scale analysis methods, such as correlation coefficients [28,35,36], ordinary least square (OLS) [37][38][39], generalized additive model (GAM) [40], and various models for machine learning [41][42][43]. The biggest problem with these methods is that they cannot adequately analyze the spatial variations of SUHI drivers, making them feasible for small regional studies but leading to obvious bias at large spatial scales, such as in China.…”