Spatial expansion paths of urban heat islands in Chinese cities: Analysis from a dynamic topological perspective for the improvement of climate resilience
Introduction
Urbanization is associated with population growth and land use changes; it can have serious societal, economic, and environmental impacts (Martilli et al., 2020; Santamouris, 2020; Sun et al., 2015). According to United Nations population statistics, more than half of the global population is concentrated in urban areas (United Nations Population Division, 2018). Overpopulation in cities exerts pressure on ecosystems, including overexploitation of forest and water resources, industrial pollution of air and soil, and urban heat island (UHI) effects (He et al., 2021; Manoli et al., 2019; Ulpiani, 2021). UHI effects include considerable temperature differences between urban and suburban areas (Voogt and Oke, 2003), which have negative impacts on biodiversity, vegetation phenology, and air and water quality; they are associated with increased human morbidity and mortality. UHI effects threaten the sustainable development of cities and human well-being; they are currently increasing in frequency (Ceplova et al., 2017; Li et al., 2019, 2020b; Zhou et al., 2019).
Resilient cities are able to withstand external disturbances while maintaining their original characteristics, structures, and key functions (Alliance, 2007). Cities can be evaluated in terms of their organizational, economic, social, and ecological resilience (Gao, 2019). In particular, urban thermal environmental risks must be considered by urban builders and decision makers when they are building cities (Yue et al., 2019). The spatial expansion of UHI patches reflects the spatiotemporal characteristics of the UHI, as well as changes in human activities, land use, and land cover. Therefore, clarification of the sources and expansion characteristics of new UHI patches is important for the effective control of UHI expansion to maintain urban ecological resilience. The enhancement of urban resilience will allow cities to more effectively respond to environmental hazards and disasters. Future urban planning will focus on the design of prosperous, inclusive, and green cities, as well as the mitigation of climate change through the enhancement of resilient urban systems (Qiao et al., 2020; Wei, 2020; Shi et al., 2022). Therefore, a comprehensive understanding of UHI spatial expansion patterns is urgently needed, particularly under diverse climatic conditions.
Analyses of the spatiotemporal patterns of UHI patches can provide important insights concerning the conditions that lead to UHIs; such analyses can also reduce risks associated with urban thermal environments (Xu et al., 2015; Oke et al., 2017). Thus far, studies of UHIs have concentrated primarily on their spatial and temporal dynamics, driving forces, prediction modeling, ecological impact assessment, and human health risks (Guan et al., 2019; Huang and Lu, 2018; Peng et al., 2020, 2018; Wang and Upreti, 2019). To analyze the spatial patterns of UHIs, early studies mapped urban–rural land surface temperature (LST) profiles on various structures, such as standard ellipses or Gaussian surfaces (Liang and Weng, 2018; Qiao et al., 2019; Xiong and Zhang, 2021; Yang et al., 2019). Multi-source spatial statistical analyses have been performed to analyze the spatial characteristics of UHIs in terms of the impervious layer, nighttime light, and urban landscape pattern index (Estoque et al., 2017; Fan et al., 2019; Masoudi and Tan, 2019); these analyses reveal additional information about the mechanisms of UHI formation. Some researchers have performed morphological spatial pattern analysis to define the high temperature core region (Clay et al., 2016; Hu et al., 2022; Wang and Pei, 2020; Xiao et al., 2020). Spatial clustering and geographic autocorrelation approaches were used to investigate the spatial clustering patterns of UHI patches; for example, K-means clustering was performed to classify UHI intensity (UHII) into low-, medium-, and high-intensity patches (Li et al., 2021). The local Moran's I index was used to determine spatial associations among UHI patches (Fan and Wang, 2020; Niu et al., 2022). Thermal maps, centroid migration methods, and source–sink models have been used to evaluate spatiotemporal processes that form the UHI landscape, thereby enhancing knowledge concerning the migration and evolution of UHI patches at different scales (Ma et al., 2020; Qiao et al., 2019; Qiao and Tian, 2015; Zhao et al., 2018). However, few studies have examined the development of new UHI patches and their interactions with pre-existing UHI patches.
The objectives of the present study were to examine the spatial expansion patterns of new UHI patches, then determine the contributions of new UHI patches to UHII. To achieve these objectives, we developed the UHI expansion index (UHIEI) to quantify the spatial topological relationship between new and pre-existing UHI patches in 371 cities in China during 2005–2020, based on land use changes over time. The results of this study will provide a new perspective for exploring the spatiotemporal processes and causal mechanisms of UHI development, thus supplying theoretical and experimental insights for the construction of resilient cities.
Section snippets
Study area
China has undergone rapid urbanization in recent decades, leading to dramatic changes in its urban thermal environment. Therefore, China is an ideal setting for the investigation of UHI spatial expansion patterns (Du et al., 2022). We selected 371 cities throughout China based on data availability and validity.
Data sources and preprocessing
LST data were derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day synthetic LST product (MYD11A2) for summer 2005 and 2020 (//lpdaac.usgs.gov/products/myd11a2v061/
Identification of UHI patches
Because summer UHIs and heat waves synergistically exacerbate human health risks (Keellings and Moradkhani, 2020), we focused on new UHI patches that occur in summer. We calculated day and night LST means for June, July, and August in 2005 and 2020, representing the highest and lowest temperatures in summer, to examine differences in UHIs between day and night.
The simplified urban extent (SUE) algorithm was used to extract UHI patches for 371 cities and calculate UHII values using the global
Changes in area and UHII of UHI patches between 2005 and 2020
In most cities, UHIs significantly increased in area during the 15-year study period. In 2020, the area of UHIs in China increased to 112,055 and 108,060 km2 in the daytime and nighttime, respectively, in summer; these represented increases of 73,438 and 68,943 km2, respectively, compared with 2005. The largest daytime increases in summer were observed in Beijing (2064 km2), Suzhou (1766 km2), Tianjin (1445 km2), and Shanghai (1445 km2); the largest nighttime increases were observed in Beijing
Conclusion
The accurate identification of UHI patches and spatial expansion types contribute to a more thorough understanding of spatiotemporal changes in UHIs; the analysis of UHI patch expansion based on land use changes enables active mitigation of the risks associated with the urban thermal environment. In this study, we designed a UHIEI to identify the spatial expansion patterns of leapfrogging, edge expansion, and infilling among new UHI patches in 371 cities in China in summer during 2005–2020. By
CRediT authorship contribution statement
Zhi Qiao: Conceptualization, Methodology, Writing – original draft, Writing – review & editing. Yingshuang Lu: Software, Data curation, Formal analysis, Writing – original draft. Tong He: Visualization, Investigation. Feng Wu: Supervision, Writing – review & editing, Funding acquisition. Xinliang Xu: Software, Resources, Funding acquisition. Luo Liu: Software, Validation. Fang Wang: Visualization. Zongyao Sun: Validation. Dongrui Han: Validation.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China (52270187, 41971389, and 41971233), in part by the Natural Science Foundation of Tianjin City (21JCYBJC00390), and in part by the Major Projects of High-Resolution Earth Observation Systems of National Science and Technology under Grant 05-Y30B01-9001-19/20-4.
References (54)
- et al.
Effects of settlement size, urban heat island and habitat type on urban plant biodiversity
Landsc. Urban Plan.
(2017) - et al.
A simplified urban-extent algorithm to characterize surface urban heat islands on a global scale and examine vegetation control on their spatiotemporal variability
Int. J. Appl. Earth Obs. Geoinf.
(2019) - et al.
Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes
Remote Sens. Environ.
(2006) - et al.
Effects of landscape composition and pattern on land surface temperature: an urban heat island study in the megacities of Southeast Asia
Sci. Total Environ.
(2017) - et al.
How to cool hot-humid (Asian) cities with urban trees? An optimal landscape size perspective
Agric. For. Meteorol.
(2019) - et al.
A long-term and comprehensive assessment of the urbanization-induced impacts on vegetation net primary productivity
Sci. Total Environ.
(2019) - et al.
Urban heat island impacts on building energy consumption: a review of approaches and findings
Energy
(2019) - et al.
Multi-year comparison of the effects of spatial pattern of urban green spaces on urban land surface temperature
Landsc. Urban Plan.
(2019) - et al.
Seasonal contrast of the dominant factors for spatial distribution of land surface temperature in urban areas
Remote Sens. Environ.
(2018) - et al.
Diurnal and seasonal impacts of urbanization on the urban thermal environment: a case study of Beijing using MODIS data
ISPRS J. Photogramm. Remote Sens.
(2013)
How climate change is going to affect urban livability in China
Clim. Serv.
Thermal remote sensing of urban climates
Remote Sens. Environ.
A scenario analysis of thermal environmental changes induced by urban growth in Colorado River Basin, USA
Landsc. Urban Plan.
Polycentric urban development and urban thermal environment: a case of Hangzhou, China
Landsc. Urban Plan.
Urban Resilience Research Prospectus
National Assessment of the Fragmentation Levels and Fragmentation-Class Transitions of the Forests in Mexico for 2002, 2008 and 2013
Forests
Dynamic Expansion of Urban Land in China's Coastal Zone since 2000
Remote Sens.
Spatiotemporal characterization of land cover impacts on urban warming: a spatial autocorrelation approach
Remote Sens.
A study on the idea of resilient city and its application in planning and practice in China
IOP Conf. Ser.
Localized synergies between heat waves and urban heat islands: implications on human thermal comfort and urban heat management
Environ. Res.
How do urban morphological blocks shape spatial patterns of land surface temperature over different seasons? A multifactorial driving analysis of Beijing, China
Int. J. Appl. Earth Observ. Geoinform.
Long-term trend of urban heat island intensity and climatological affecting mechanism in Beijing City
Scientia Geograph. Sinica
Spatiotemporal evolution of heat wave severity and coverage across the United States
Geophys. Res. Lett.
Comparative Analysis of Variations and Patterns between Surface Urban Heat Island Intensity and Frequency across 305 Chinese Cities
Remote Sens.
Mapping global urban boundaries from the global artificial impervious area (GAIA) data
Environ. Res. Lett.
On the influence of density and morphology on the Urban Heat Island intensity
Nat. Commun.
Characterizing urban landscape by using fractal-based texture information
Photogramm. Eng. Remote Sens.
Cited by (21)
Spatiotemporal heterogeneity in global urban surface warming
2024, Remote Sensing of EnvironmentAssessing and mapping urban ecological resilience using the loss-gain approach: A case study of Tehran, Iran
2024, Sustainable Cities and SocietyQuantifying morphology evolutions of urban heat islands and assessing their heat exposure in a metropolis
2024, Sustainable Cities and SocietySpatial and temporal inequity of urban land use efficiency in China: A perspective of dynamic expansion
2024, Environmental Impact Assessment ReviewHow do 2D/3D urban landscapes impact diurnal land surface temperature: Insights from block scale and machine learning algorithms
2023, Sustainable Cities and Society