Comparison of surface and canopy urban heat islands within megacities of eastern China

Hu, Yonghong; Hou, Meiting; Jia, Gensuo; Zhao, Chuanyan; Zhen, Xiaoju; Xu, Yanhua

doi:10.1016/j.isprsjprs.2019.08.012

Cited by 108 publications

(36 citation statements)

References 47 publications

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“…Another limitation is the comparison of SUHIIr and SUHIIc with UHII based on station‐measured air temperature. The difference between SUHI and UHI has been studied using the urban radiometric surface temperature and the air temperature observed within the urban canopy (Sun et al ., 2015; Hu et al ., 2019), which documented that the land cover and urban climate affect the difference between SUHIIr and UHII. Zhou et al .…”

Section: Discussionmentioning

confidence: 99%

“…Another limitation is the comparison of SUHIIr and SUHIIc with UHII based on station-measured air temperature. The difference between SUHI and UHI has been studied using the urban radiometric surface temperature and the air temperature observed within the urban canopy (Sun et al, 2015;Hu et al, 2019), which documented that the land cover and urban climate affect the difference between SUHIIr and UHII. Zhou et al (2019) analysed the rural-urban temperature variability in Israel based on different temperatures which are measured air temperature near surface, satellite-observed temperature and simulated canopy air temperature and results showed that different temperatures might lead to contrasting results, with a radiometric surface temperature being dominated by the emittance of horizontal facets.…”

Section: T a B L E 3 Linear Regression Equations Relating Suhiic And mentioning

confidence: 99%

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Assessing the impact of urban geometry on surface urban heat island using complete and nadir temperatures

Yang

Menenti

et al. 2020

Intl Journal of Climatology

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The surface urban heat island intensity (SUHII) when using nadir-viewing radiometric and complete surface temperature (T r and T c) was evaluated. The urban areas of the Kowloon peninsula and Hong Kong Island were selected and four daytime Landsat TM images and two nighttime ASTER images were collected to retrieve T r and then model T c based on a semi-empirical model. SUHIIs were estimated using both the retrieved T r and modelled T c. High spatial resolution (HR) airborne thermal images (0.2 m) observed at 12:10 noon on Oct 24, 2017 were used to retrieve T c directly. Results based on HR data and satellite data were consistent and indicated that the geometry of the built-up space had a larger impact on SUHII when using T c (SUHIIc) than T r (SUHIIr). During daytime SUHIIc decreased while SUHIIr showed a very slight increase with building density. Both SUHIIc and SUHIIr decreased with higher building height but the rate of decrease of SUHIIc was higher than SUHIIr. Both SUHIIc and SUHIIr decreased with increasing building height variance and increased with increasing sky view factor (SVF). The rate of decrease with building height variance for SUHIIc was larger than SUHIIr. The rate of increase of SUHIIc with SVF was higher than SUHIIr. During nighttime, geometry effects on SUHIIc and SUHIIr were different from daytime. Both SUHIIc and SUHIIr increased with building density, while the rate of increase of SUHIIc with building density, as well as with building height, was much higher than SUHIIr. Both SUHIIc and SUHIIr decreased with SVF, but the rate of decrease of SUHIIc was higher than SUHIIr. Both SUHIIc and SUHIIr initially increased with building height variance and then remained Abbreviations: SUHIIc, The difference between the complete surface temperature of reference rural areas and the complete surface temperature of urban areas; SUHIIr, The difference between the radiometric surface temperature of reference rural areas and the radiometric surface temperature of urban areas; UHII, The difference between air temperature of reference rural station and the air temperature of urban stations.

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Section: Discussionmentioning

confidence: 99%

Section: T a B L E 3 Linear Regression Equations Relating Suhiic And mentioning

confidence: 99%

Assessing the impact of urban geometry on surface urban heat island using complete and nadir temperatures

Yang

Menenti

et al. 2020

Intl Journal of Climatology

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“…Meanwhile, surface temperature from large area of urban and rural surface are adopted in quantifying SUHI, which maintain more information than single stations. (Hu et al 2019). Thus, we think it is better for typical urban and rural station selection by considering spatial coverage information from remote sensing and temporal change information by DTW algorithm.…”

Section: Comparison Of Uhi From New Station Groups and Traditional Stmentioning

confidence: 99%

Urban heat island estimation from improved selection of urban and rural stations by DTW algorithm

Jia

et al. 2021

Preprint

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Typical urban and rural temperature records are essential for the estimation and comparison of urban heat island effects in different regions, and the key issues are how to identify the typical urban and rural stations. This study tried to analyze the similarity of air temperature sequences by using dynamic time warping algorithm (DTW) to improve the selection of typical stations. We examined the similarity of temperature sequences of 20 stations in Beijing and validated by remote sensing, and the results indicated that DTW algorithm could identify the difference of temperature sequence, and clearly divide them into different groups according to their probability distribution information. The analysis for station pairs with high similarity could provide appropriate classification for typical urban stations (FT, SY, HD, TZ, CY, CP, MTG, BJ, SJS, DX, FS) and typical rural stations (ZT, SDZ, XYL) in Beijing. We also found that some traditional rural stations can’t represent temperature variation in rural surface because of their surrounding environments highly modified by urbanization process in last decades, and they may underestimate the urban climate effect by 1.24℃. DTW algorithm is simple in analysis and application for temperature sequences, and has good potentials in improving urban heat island estimation in regional or global scale by selecting more appropriate temperature records.

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“…The growing number of outdoor public activities in cities has also increased the risk of exposure to high temperatures. Summer UHI in megacities, such as Tianjin, Beijing and Chongqing in China, is extremely severe [12,13]. Data from the Chinese Center for Disease Control and Prevention show an increase in health problems caused by high temperatures [14], which seriously affects residents' comfort, respiratory and circulatory systems, and mental state.…”

Section: State Of the Artmentioning

confidence: 99%

Spatial Evolution of the Effects of Urban Heat Island on Residents' Health

et al. 2020

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Rising summer temperatures caused by the urban heat island (UHI) has considerable effects on the physical and mental health of urban residents globally. To categorize residents' health risk areas and evaluate the characteristics of urban spatial evolution, based on data analysis methods, such as ArcGIS, ENVI software, and geostatistical analysis, data from meteorological stations, satellite images, and electronic maps were used to investigate spatial evolution and the process by which UHI affects the respiratory, circulatory, and cardiovascular systems and emotional health of the residents of Tianjin. Results show the UHI significantly increases respiratory, circulatory, and cardiovascular diseases. The emotional health of residents is also significantly affected with the affected level moving from level 1 to level 2-4. Highly concentrated areas in the urban center and patches with high health risks are found to be scattered and fragmented, as indicated by the phased pattern of spatially deteriorating hotspots. Hotspots expansion occurs unidirectionally to the south, surrounding the city center, while shrinking from the inside to the outside. The study identifies urban health space risks and provides theoretical guidance for urban space optimization and healthy urban planning.

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Comparison of surface and canopy urban heat islands within megacities of eastern China

Cited by 108 publications

References 47 publications

Assessing the impact of urban geometry on surface urban heat island using complete and nadir temperatures

Assessing the impact of urban geometry on surface urban heat island using complete and nadir temperatures

Urban heat island estimation from improved selection of urban and rural stations by DTW algorithm

Spatial Evolution of the Effects of Urban Heat Island on Residents' Health

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