Remote Sensing and Social Sensing Data Reveal Scale-Dependent and System-Specific Strengths of Urban Heat Island Determinants

Luan, Xia-li; Yu, Zhaowu; Zhang, Yuting; Wei, Sheng; Miao, Xinyu; Huang, Zheng; Teng, Shuqing N.; Xu, Chi

doi:10.3390/rs12030391

Cited by 36 publications

(23 citation statements)

References 49 publications

(97 reference statements)

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“…This finding indicates that the scale and extent have a certain effect on the factors that influence the RHI. This result is similar to that of Luan et al 43 . Therefore, it is necessary to control the expansion of construction land as much as possible, original sand and gravel surfaces should not be covered and the area of the impervious surface should be controlled.…”

Section: Discussionsupporting

confidence: 93%

Trends of the contributions of biophysical (climate) and socioeconomic elements to regional heat islands

Chen

Liu

et al. 2021

Sci Rep

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The development of urban accumulations in recent decades has led to the transformation of urban heat islands (UHI) into regional heat islands (RHI). The contributions of the biophysical, climate, and socioeconomic factors to RHI in urban agglomeration remain poorly understood. Here Yangtze River Delta urban agglomeration (YRDUA) in eastern China has been selected as a case area to explore the influences trends, of the influencing factors to RHI by using MODIS data from 2003 to 2017. Results showed that, in summer, the area fraction of daytime RHI in YRDUA has increased from 21.74 to 31.03% in 2003 and 2017, respectively. As compared to 2003, the annual nighttime RHI area in 2017 has increased from 7510 to 20,097 km2. The dominant factors of surface RHI intensity (SRHII) showed seasonal variation. Enhanced vegetation index (EVI) (interpretation of 33.27%) was the dominant factor of daytime SRHII in spring. The most important factor was normalized difference build-up density (NDBI) (37.28% and 26.83%, respectively) in summer and autumn. In winter, precipitation (26.16%) was the most influential. At night, Modified Normalized Difference Water Index (MNDWI) had a dominant effect on SRHII in spring (54.12%), autumn (52.62%), and winter (24.19%). The dominant factor of nighttime SRHII in summer was EVI (42%). Moreover, water bodies harm RHI during the day while having a positive effect at night. These findings can provide a theoretical basis for regional environment improvement and regional sustainable development.

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Section: Discussionsupporting

confidence: 93%

Trends of the contributions of biophysical (climate) and socioeconomic elements to regional heat islands

Chen

Liu

et al. 2021

Sci Rep

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“…Previous studies have found that the LST retrieved by the radiative transfer equation algorithm can obtain the highest accuracy in the high atmospheric water vapor environments [7,10,53]. Therefore, this study chose the radiative transfer equation algorithm proposed by Jiménez-Muñoz et al [54] to calculate LST.…”

Section: Lst and Relative Lst Calculationmentioning

confidence: 99%

“…In the next step, it is necessary to identify the heat corridor through constructing the resistance surface [60], methods like circuit theory and the leastcost method can be employed. Second, the MSPA model is sensitive to the scale effect (i.e., the resolution of the raster image) [41], as well as the UHI determinants [10]. Hence, scale effects (grain size and spatial extent) must be considered in future related research.…”

Section: Limitations and Further Studymentioning

confidence: 99%

“…Generally, UHI can be classified into two categories: atmospheric UHI (AUHI) and surface UHI (SUHI) [1]. However, owing to the feasibility of consistent and repeatable observations of the remote-sensing-based LST, UHI effects can be measured from an extensive spatial perspective [9][10][11]. Therefore, LST is widely used to investigate the spatiotemporal patterns of the SUHI and the associated thermal environment consequences, which are also a focus of this study.…”

Section: Introductionmentioning

confidence: 99%

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Reverse Thinking: A New Method from the Graph Perspective for Evaluating and Mitigating Regional Surface Heat Islands

Zhang

Yang

et al. 2021

Remote Sensing

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Accurately locating key nodes and corridors of an urban heat island (UHI) is the basis for effectively mitigating a regional surface UHI. However, we still lack appropriate methods to describe it, especially considering the interaction between UHIs and the role of connectivity (network). Specifically, previous studies paid much attention to the raster and vector perspective—based on standard landscape configuration metrics that only provide an overall statistic over the entire study area without further indicating locations where different types of pattern and fragmentation occur. Therefore, by reverse thinking, here we attempt to propose a new method from the graph perspective which integrates morphological spatial pattern analysis (MSPA)—which is used to characterize binary patterns with emphasis on connections between their parts as measured at varying analysis scales, and habitat availability indices to evaluate and mitigate regional surface UHI. We selected the Pearl River Delta Metropolitan Region (PRDR), one of the most rapidly urbanized regions in the world as the case study (1995–2015). The results of the case study showed: (1) the core (UHI) type accounts for the vast majority of the MSPA model, with the relative land surface temperature (LST) rises, the proportion of the core type will increase, and it could influence the edge (UHI) type significantly; (2) the branch, bridge, and islet (UHI) types have similar results to the lower temperature (4 < Relative LST ≤ 6) area and account for the majority, indicating that these types are more susceptible to their surrounding environment; (3) the importance and extreme importance area (node) from 1995 to 2015 have increased significantly and mainly distributed in the urbanized areas, which means cooling measures need to be implemented in these areas in order of priority. Shifting the research logic of UHI evaluation and mitigation from “patch” to “network”, we hold the point that the method (reverse thinking) has significant theoretical and practical implications for mitigating regional UHI and urban climate-resilience.

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“…For instance, paved surfaces, building walls, roofs and other impervious surfaces efficiently absorb solar radiation and have a greater heat capacity and thermal conductivity in comparison to natural surfaces (grass, trees, etc.) (Akbari & Kolokotsa, 2016;Luan et al, 2020). Urban surfaces re-emit this heat, acting as heat-sources, while forests and water act as heat-sinks (Yu et al, 2020).…”

Section: Urban Heat Processesmentioning

confidence: 99%

Swimming through the urban heat island: Can thermal mitigation practices reduce the stress?

Timm

Ouellet

Daniels

2020

River Research & Apps

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Green infrastructure (GI) and other stormwater management practices are commonly designed to reduce stormflow volume and pollutant loads by using infiltration, retention, and evapotranspiration to capture stormwater. Although these methods are be designed to reduce impacts of stormwater volumes and pollutant loads, they may not be designed to mitigate thermal load from stormwater runoff in urban areas. This review of literature identifies key drivers of stream temperature in urban streams, including heat transference from stormflow and effects of pipe networks and evapotranspiration on baseflow. Recent simulation studies indicate the need for more than bioretention, increased infiltration, and tree canopy mitigation practices to reduce heat stress in urban streams. These studies show greatest reductions in thermal load by applying cool surfaces as a single thermal mitigation practice (TMP) and comprehensive applications of TMPs to all available areas at the watershed scale. This review of available literature suggests that incorporating TMPs into current and future GI designs will help maintain water resources, water quality, aquatic ecosystems, and coldwater stream species across the landscape. Further research is needed on the most effective ways to implement TMPs as part of our current and future GI designs for stormwater and how to best incorporate these measures into urban design concepts at larger spatial scales. K E Y W O R D S bioretention, coldwater fish, cool surfaces, effective impervious, forest canopy, thermal load, urban stormwater 1 | INTRODUCTION Urbanization is known to impact stream fish communities, with previous research relating percent impervious surface in watersheds to changes in fish species diversity and tolerance to pollution (Roy et al., 2005). Specific stormwater hydrology processes related to impervious surfaces that drive temperature and thermal habitat characteristics for coldwater fish have not been explored extensively by the literature. Fish require specific ranges of temperatures to survive, successfully spawn, and recruit all life stages into the next generation, and thermal habitat connectivity throughout stream networks needs to be maintained (Al-Chokhachy, Wenger, Isaak, & Kershner, 2013). While stream water temperature increases have been documented throughout the United States, rates of increase are the greatest for urban streams (Kaushal et al., 2010). With urban land use in the United States projected to increase by 79% from 39.5 million hectares in 1997 to 70.5 million hectares by 2025, it is vital to understand the magnitude and mechanisms of thermal impacts to urban streams (Alig, Kline, & Lichtenstein, 2004) both from traditional and "green" urban infrastructure. Green infrastructure (GI) to treat stormwater is commonly designed to reduce stormflow volume and pollutant loads by using infiltration, retention, and evapotranspiration to capture stormwater. Examples of stormwater, infiltration-based GI include bioretention

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Remote Sensing and Social Sensing Data Reveal Scale-Dependent and System-Specific Strengths of Urban Heat Island Determinants

Cited by 36 publications

References 49 publications

Trends of the contributions of biophysical (climate) and socioeconomic elements to regional heat islands

Trends of the contributions of biophysical (climate) and socioeconomic elements to regional heat islands

Reverse Thinking: A New Method from the Graph Perspective for Evaluating and Mitigating Regional Surface Heat Islands

Swimming through the urban heat island: Can thermal mitigation practices reduce the stress?

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