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
Extreme heat events occur frequently in urban areas, seriously affecting human well-being and productivity. Therefore, this article aimed to quantify the impact of and regional differences in the urban heat island (UHI) effect within the broader context of achieving sustainable development goals. To...
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IEEE
2024-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10460303/ |
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| author | Huisheng Yu Dongqi Sun |
| author_facet | Huisheng Yu Dongqi Sun |
| author_sort | Huisheng Yu |
| collection | DOAJ |
| description | Extreme heat events occur frequently in urban areas, seriously affecting human well-being and productivity. Therefore, this article aimed to quantify the impact of and regional differences in the urban heat island (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 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. In addition, 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 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 article elucidates the changes in and influencing mechanisms of the UHI intensity and footprint and provides an important reference for mitigating the UHI effect and rationally planning urban land use. |
| format | Article |
| id | doaj-art-2d1ea78991a948fb976e5e7d18c144b8 |
| institution | DOAJ |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-2d1ea78991a948fb976e5e7d18c144b82025-08-20T02:55:53ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352024-01-01176910691910.1109/JSTARS.2024.337340910460303Quantification of Urban Heat Island Effect and Differences in Regional Influence Based on Footprint Analysis: A Case Study of the Beijing–Tianjin–Hebei Urban AgglomerationHuisheng Yu0https://orcid.org/0000-0001-5482-6041Dongqi Sun1https://orcid.org/0000-0003-4301-2071School of Management Engineering, Qingdao University of Technology, Qingdao, ChinaKey Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing, ChinaExtreme heat events occur frequently in urban areas, seriously affecting human well-being and productivity. Therefore, this article aimed to quantify the impact of and regional differences in the urban heat island (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 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. In addition, 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 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 article elucidates the changes in and influencing mechanisms of the UHI intensity and footprint and provides an important reference for mitigating the UHI effect and rationally planning urban land use.https://ieeexplore.ieee.org/document/10460303/Driving factorsland surface temperature (LST)thermal environmenturban agglomerationurban heat island (UHI) footprint |
| spellingShingle | Huisheng Yu Dongqi Sun 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 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Driving factors land surface temperature (LST) thermal environment urban agglomeration urban heat island (UHI) footprint |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | quantification of urban heat island effect and differences in regional influence based on footprint analysis a case study of the beijing x2013 tianjin x2013 hebei urban agglomeration |
| topic | Driving factors land surface temperature (LST) thermal environment urban agglomeration urban heat island (UHI) footprint |
| url | https://ieeexplore.ieee.org/document/10460303/ |
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