Spatial patterns and temporal variations of footprint and intensity of surface urban heat island in 141 China cities

Hu, Jia; Yang, Yong; Zhou, Yuyu; Zhang, Tao; Ma, Zhuguo; Meng, Xiangjin

doi:10.1016/j.scs.2021.103585

Cited by 29 publications

(10 citation statements)

References 57 publications

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“…Although fine-resolution mesoscale simulating models can estimate the LST background and SUHI intensity at each grid point over urban agglomerations, complex data collection, data pre-and post-processing, and model validation make it difficult to simulate complex urbanization processes and human activities over urban agglomerations at a high spatial resolution [27,28]. The two-dimensional Gaussian surface model has been widely used to estimate the SUHI intensity and footprint owing to its good performance in quantifying SUHIs [9,17,30]. Applying a two-dimensional Gaussian surface model to quantify the SUHIs in a single city includes two key steps: First, identifying the rural LST pixels in a single city according to the land cover data.…”

Section: Advantages and Limitations Of The Proposed Solution In Quant...mentioning

confidence: 99%

Machine-Learning-Assisted Characterization of Regional Heat Islands with a Spatial Extent Larger than the Urban Size

Du,

Xie,

Zhang

et al. 2024

Remote Sensing

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Surface urban heat islands (SUHIs) can extend beyond the urban boundaries and greatly affect the thermal environment of continuous regions over an agglomeration. Traditional urban-rural dichotomy depending on the built-up and non-urban lands is challenged in characterizing regional SUHIs, such as how to accurately quantify the intensity, spatial pattern, and scales of SUHIs, which are vulnerable to SUHIs, and what the optimal scale for conducting measures to mitigate the SUHIs. We propose a machine-learning-assisted solution to address these problems based on the thermal similarity in the Yangtze River Delta of China. We first identified the regional-level SUHI zone of approximately 42,328 km2 and 38,884 km2 and the areas that have no SUHI effects from the annual cycle of land surface temperatures (LSTs) retrieved from Terra and Aqua satellites. Defining SUHI as an anomaly on background condition, random forest (RF) models were further adopted to fit the LSTs in the areas without the SUHI effects and estimate the LST background and SUHI intensity at each grid point in the SUHI zone. The RF models performed well in fitting rural LSTs with a simulation error of approximately 0.31 °C/0.44 °C for Terra/Aqua satellite data and showed a good generalization ability in estimating the urban LST background. The RF-estimated daytime Aqua/SUHI intensity peaked at approximately 6.20 °C in August, and the Terra/SUHI intensity had two peaks of approximately 3.18 and 3.81 °C in May and August, with summertime RF-estimated SUHIs being more reliable than other SUHI types owing to the smaller simulation error of less than 1.0 °C in July–September. This machine-learning-assisted solution identified an optimal SUHI scale of 30,636 km2 and a zone of approximately 23,631 km2 that is vulnerable to SUHIs, and it provided the SUHI intensity and statistical reliability for each grid point identified as being part of the SUHI. Urban planners and decision-makers can focus on the statistically reliable RF-estimated summertime intensities in SUHI zones that have an LST annual cycle similar to that of large cities in developing effective strategies for mitigating adverse SUHI effects. In addition, the selection of large cities might strongly affect the accuracy of identifying the SUHI zone, which is defined as the areas that have an LST annual cycle similar to large cities. Water bodies might reduce the RF performance in estimating the LST background over urban agglomerations.

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Section: Advantages and Limitations Of The Proposed Solution In Quant...mentioning

confidence: 99%

Machine-Learning-Assisted Characterization of Regional Heat Islands with a Spatial Extent Larger than the Urban Size

Du,

Xie,

Zhang

et al. 2024

Remote Sensing

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“…Many statistical models have been applied in SUHI studies [46,47,63]. The Gaussian surface model (GSM) performs well in quantifying the SUHI, and the spatial distribution of heat islands can be described using a Gaussian surface superimposed on a flat rural background.…”

Section: Statistical Modelsmentioning

confidence: 99%

Spatiotemporal Patterns of the Application of Surface Urban Heat Island Intensity Calculation Methods

Zhang,

Tu,

Shi

2023

Atmosphere

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Using the China National Knowledge Infrastructure (CNKI) and Web of Science (WoS) databases, 487 articles that used remote sensing methods to study the intensity of surface urban heat islands (SUHIs) over the past 20 years were obtained using keyword searches. A multidimensional analysis was conducted on these articles from the perspectives of the research methods used, spatiotemporal distribution characteristics of the research area, research development trends, and main challenges. The research found that (1) the growth trend of the various SUHI research methods over the years was similar to the overall trend in the number of publications, which has rapidly increased since 2009. (2) Among the SUHI research methods, temperature dichotomy is the most widely used worldwide; however, defining urban and rural areas is a main challenge. The Gaussian surface and local climate zoning methods have gradually emerged in recent years; however, owing to the limitations of the different urban development levels and scales, these methods require further improvement. (3) There are certain differences in the application of SUHI research methods between China and other countries.

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“…An in-depth study of the UHI effect found that the associated warming occurs both within the built-up urban area and in the suburbs surrounding the city. The UHI effect affected areas outside the built-up areas are defined as the surface urban heat island footprint (SUHIF) [27], [28]. Additionally, Zhou et al (2015) [29] found that the SUHIF can encompass an area several times the size of the city.…”

Section: Introductionmentioning

confidence: 99%

Quantification of Urban Heat Island Effect and Differences in Regional Influence Based on Footprint Analysis: A Case Study of the Beijing–Tianjin–Hebei Urban Agglomeration

Yu,

Sun

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Extreme heat events occur frequently in urban areas, seriously affecting human well-being and productivity. Therefore, this study aimed to quantify the impact of and regional differences in the UHI effect within the broader context of achieving sustainable development goals. To this end, we combined footprint analysis with principal component analysis and multivariate linear regression analysis to quantify the spatiotemporal distribution of heat island intensity and footprint within the Beijing-Tianjin-Hebei (BTH) urban agglomeration as well as the impact of regional differences. We found that the surface urban heat island intensity (SUHII) value was higher during the daytime than at night. In 2005, 2010, 2015, and 2018, the average daytime values of SUHII were 0.21 °C, 0.03 °C, 0.35 °C, and 0.53 °C higher than those at night, respectively. High daytime values of SUHII mainly occurred in larger cities (e.g., Beijing), and high nighttime values of SUHII mainly occurred at higher latitudes. Additionally, we determined that the maximum values of the SUHIF were concentrated in densely populated areas such as Beijing, Tianjin, and Shijiazhuang. Furthermore, principal component analysis revealed that PM2.5 was negatively correlated with SUHII, whereas population density (PD) and enhanced vegetation index (EVI) were positively correlated with SUHII. In contrast, PM2.5 and EVI were negatively correlated with SUHIF, whereas PD and SUHIF showed a negative correlation. This study elucidates the changes in and influencing mechanisms of the urban heat island intensity and footprint and provides an important reference for mitigating the UHI effect and rationally planning urban land use.

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Spatial patterns and temporal variations of footprint and intensity of surface urban heat island in 141 China cities

Cited by 29 publications

References 57 publications

Machine-Learning-Assisted Characterization of Regional Heat Islands with a Spatial Extent Larger than the Urban Size

Machine-Learning-Assisted Characterization of Regional Heat Islands with a Spatial Extent Larger than the Urban Size

Spatiotemporal Patterns of the Application of Surface Urban Heat Island Intensity Calculation Methods

Quantification of Urban Heat Island Effect and Differences in Regional Influence Based on Footprint Analysis: A Case Study of the Beijing–Tianjin–Hebei Urban Agglomeration

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