Quantifying the cool island effects of urban green spaces using remote sensing Data

Du, Han; Cai, Wenbo; Xu, Yanqing; Wang, Zhibao; Wang, Yuanyuan; Cai, Yongli

doi:10.1016/j.ufug.2017.06.008

Cited by 196 publications

(128 citation statements)

References 31 publications

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“…The effect of urban green spaces on SUHI intensity varies across climatically different regions (i.e., Beijing and Islamabad). The results of this study were found to be consistent with some previous studies on urban green spaces and surface temperature [38,[68][69][70].…”

Section: Discussionsupporting

confidence: 92%

“…These results coincide with the research conclusions of Chang et al and Cao et al [31,72]. VFi show a weak correlation with surface temperature of the green space samples of Beijing, which coincides with the results of Du et al [68] and is different from the results of Kong et al [74]. However, the correlation between VFi and LST of Islamabad is better than Beijing, which coincides with the research conclusions of Kong et al [74].…”

Section: Gci Indicators and Lst Impact Factorssupporting

confidence: 83%

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Studying the Association between Green Space Characteristics and Land Surface Temperature for Sustainable Urban Environments: An Analysis of Beijing and Islamabad

Naeem

Cao

Qazi

et al. 2018

IJGI

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Increasing trends of urbanization lead to vegetation degradation in big cities and affect the urban thermal environment. This study investigated (1) the cooling effect of urban green space spatial patterns on Land Surface Temperature (LST); (2) how the surrounding environment influences the green space cool islands (GCI), and vice versa. The study was conducted in two Asian capitals: Beijing, China and Islamabad, Pakistan by utilizing Gaofen-1 (GF-1) and Landsat-8 satellite imagery. Pearson's correlation and normalized mutual information (NMI) were applied to investigate the relationship between green space characteristics and LST. Landscape metrics of green spaces including Percentage of Landscape (PLAND), Patch Density (PD), Edge Density (ED), and Landscape Shape Index (LSI) were selected to calculate the spatial patterns of green spaces, whereas GCI indicators were defined by Green Space Range (GR), Temperature Difference (TD), and Temperature Gradient (TG). The results indicate that both vegetation composition and configuration influence LST distributions; however, vegetation composition appeared to have a slightly greater effect. The cooling effect can be produced more effectively by increasing green space percentage, planting trees in large patches with equal distribution, and avoiding complex-shaped green spaces. The GCI principle indicates that LST can be decreased by increasing the green space area, increasing the water body fraction, or by decreasing the fraction of impervious surfaces. GCI can also be strengthened by decreasing the fraction of impervious surfaces and increasing the fraction of water body or vegetation in the surrounding environment. The cooling effect of vegetation and water could be explained based on their thermal properties. Beijing has already enacted the green-wedge initiative to increase the vegetation canopy. While designing the future urban layout of Islamabad, the construction of artificial lakes within the urban green spaces would also be beneficial, as is the case with Beijing.

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Section: Discussionsupporting

confidence: 92%

Section: Gci Indicators and Lst Impact Factorssupporting

confidence: 83%

Studying the Association between Green Space Characteristics and Land Surface Temperature for Sustainable Urban Environments: An Analysis of Beijing and Islamabad

Naeem

Cao

Qazi

et al. 2018

IJGI

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“…The results from this study verified that urban parks have cooling island effects (except for during the winter) and that PCI intensity varies considerably by month, similar to the cooling effect of urban green spaces [10,30,43]. The PCI effect was strong during the warm months.…”

Section: The Relationships Between the Pci And Impact Factorssupporting

confidence: 74%

“…In this study, most parks had water bodies that significantly reduced the LST, and this cooling effect may have reduced the effect of park trees. However, if the area of trees and water bodies are considered a single variable, there is a clear negative correlation between this variable and LST [43]. The park soil did not significantly affect LST due to a limited amount of soil land area in the parks studied.…”

Section: Urban Park Lst and Impact Factorsmentioning

confidence: 81%

“…Based on previous studies, the PCI intensity includes three main aspects: the largest PCI (LPCI), the PCI extent (EPCI), and the temperature gradient of PCI (TPCI) [43]. LPCI is the highest LST difference between the parks and their surrounding areas, reported in • C. EPCI is the spatial extent, in which the PCI has significant influence, given in m. TPCI is the rate of temperature increase with distance, in units of • C/m.…”

Section: Park Cool Islandmentioning

confidence: 99%

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The Cooling Effect of Urban Parks and Its Monthly Variations in a Snow Climate City

Yang

et al. 2017

Remote Sensing

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Urban parks have been shown to form park cool islands (PCIs), which can effectively alleviate the negative influences of urban heat islands (UHI). However, few studies have examined the detailed characteristics of PCIs, the effect of urban park features on their individual temperatures, and monthly variation in PCIs. Land surface temperature (LST) retrieved from Landsat 8 TIR images between May and October were used to represent the thermal environment. Urban park characteristics were extracted from high-resolution GF-2 images. Using these datasets, the relationships between urban park characteristics and PCIs were explored in this study using Changchun, which has a snow climate, as a case study. The results showed the following: (1) the urban parks exhibited a cooling island effect, and the PCIs showed significant monthly variations with the highest intensities in the hot months; (2) the effects of composition (e.g., park size and the percentage of water area) on LSTs and PCIs showed significant monthly variability and were stronger than the configuration effects. Furthermore, an unexpected, negative correlation between PCIs and the area of park grass was also found; and (3) larger parks tended to have stronger PCI intensities and extents of influence. For parks larger than 30 ha, the cooling effects extended approximately 480 m from the park edge between June and August. For all of parks during the study duration, the rate of temperature increase was highest within 60 m from the park edge. The PCI we employ specifically in this study is characterized by LST.

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Remote Sensing for Urban Sustainability Research and Sustainable Development Goals

Lehner

Blaschke

2021

Urban Remote Sensing

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Quantifying the cool island effects of urban green spaces using remote sensing Data

Cited by 196 publications

References 31 publications

Studying the Association between Green Space Characteristics and Land Surface Temperature for Sustainable Urban Environments: An Analysis of Beijing and Islamabad

Studying the Association between Green Space Characteristics and Land Surface Temperature for Sustainable Urban Environments: An Analysis of Beijing and Islamabad

The Cooling Effect of Urban Parks and Its Monthly Variations in a Snow Climate City

Remote Sensing for Urban Sustainability Research and Sustainable Development Goals

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