Spatial expansion paths of urban heat islands in Chinese cities: Analysis from a dynamic topological perspective for the improvement of climate resilience

https://doi.org/10.1016/j.resconrec.2022.106680Get rights and content

Highlights

  • The urban heat island expansion index was developed to identify the new UHI patches.

  • The new UHI patches are reclassified as infilling, edge-expanding and leapfrogging.

  • The new UHI patches have lower UHII but higher contributions than pre-existing patches.

  • The new UHI patches are predominantly edge-expanding with the maximum UHII.

  • Croplands, rural residential areas, and forests were the main sources for new UHI patches.

Abstract

Urban ecological resilience enhancement is important for the mitigation of climate change risks; however, urban heat islands (UHIs) have negative impacts on urban resilience. A UHI expansion index (UHIEI) was developed to identify new UHI patches produced by including infilling, edge expansion, and leapfrogging. In this study, we used a simplified urban extent (SUE) algorithm to estimate new UHI patches and their associated UHI intensity (UHII) values in 371 cities in China during 2005–2020. Then, we analyzed the sources and sinks of the UHI patches according to land use data. The study sought to comprehensively determine the spatial expansion path of the new UHI patches at the national scale. The results showed mean UHII values of 2.11 °C ± 0.63 °C and 1.06 °C ± 0.54 °C during the day and night, respectively, for the 371 cities in the summer of 2020; these were slightly higher than the corresponding values in 2005. The UHII values of the new UHI patches were 0.57 °C and 0.29 °C lower during the day and night, respectively, compared with pre-existing UHI patches in summer. New UHI patches were predominantly formed through edge expansion, with maximum UHII values of 1.74 °C ± 0.80 °C and 0.88 °C ± 0.42 °C during the day and night, respectively; these are lower than the values of pre-existing UHI patches, but they represent greater contributions to the land surface temperature (LST) of the city. Croplands, rural residential areas, and forests were the main sources for new UHI patches. The results of this study will allow better identification and comparison of the temporal and spatial characteristics of pre-existing and new UHI patches; they will also facilitate the design of targeted measures to mitigate their ecological impacts according to expansion type, thereby improving the cities’ ecological resilience characteristics.

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.

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