Environmental and anthropogenic drivers of surface urban heat island intensity: A case-study in the Yangtze River Delta, China

Nowadays urban climate is a global problem and many studies focused on understanding the relation between urban climate the built-up space using radiometric observations of the land surface temperature to estimate and monitor the surface urban heat island intensity (SUHIs). In this study MODIS land...

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Autores principales:	Zian Wang, Qingyan Meng, Mona Allam, Die Hu, Linlin Zhang, Massimo Menenti
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Surface urban heat island Land surface temperature Boosted regression trees Yangtze River Delta Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/f64eac28ea8542898cd1c88749d2eeda
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spelling	oai:doaj.org-article:f64eac28ea8542898cd1c88749d2eeda2021-12-01T04:54:23ZEnvironmental and anthropogenic drivers of surface urban heat island intensity: A case-study in the Yangtze River Delta, China1470-160X10.1016/j.ecolind.2021.107845https://doaj.org/article/f64eac28ea8542898cd1c88749d2eeda2021-09-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X21005100https://doaj.org/toc/1470-160XNowadays urban climate is a global problem and many studies focused on understanding the relation between urban climate the built-up space using radiometric observations of the land surface temperature to estimate and monitor the surface urban heat island intensity (SUHIs). In this study MODIS land surface temperature (LST) data were used. The Yangtze River Delta Urban Agglomeration (YRDUA), eastern China, was selected as an example to study SUHI and multiple influencing factors in 16 big cities. Anthropogenic factors are considered the most important ones in determining SUHI, while natural factors remain influential. By using stratified random sampling (SRS), 78,085 random points were selected within the 16 cities. Nine influencing factors were selected in this study: distance from building (BD), distance from the main roads (RD), distance from water (WD), digital elevation model product (DEM), gross domestic product (GDP), normalized difference vegetation index product (NDVI), nighttime lighting intensity (NTI), population (POP) and impervious surface area data (%ISA). The SUHI intensity was extracted at each random point as well as the values of the influencing factors, NDVI, DEM, ISA, POP, NTI and GDP. For BD, WD and RD, random points were selected from the water, building and main roads using the near tool in ArcGIS to measure these distances. Boosted regression tree (BRT) model was applied to capture the contributions of the above factors to SUHI. We also applied a different procedure to evaluate the relative influence of Land Use and Land Cover (LULC). The relative influence refers to the contribution of each factor to determine SUHI. The influencing factors were ranked on the basis of the relative influence on SUHI. The results showed that (1) higher SUHI intensity was recorded in Shanghai, Jiaxing and Nanjing cities respectively, while Hangzhou recorded the lowest SUHI. (2) Anthropogenic drivers have slightly higher relative influence on SUHI than natural drivers, i.e. 51.29% and 48.71% respectively. The influence of all drivers on SUHI from high to low is NTI (27.62%), ISA (24.38%), NDVI (12.11%), GDP (7.95%), DEM (7.29%), POP (6.37%), BD (5.33%), WD (4.93%), RD (4.02%). (3) The variation in the socioeconomic level lead to different spatial patterns of different influence factors, further indicating that the overall mean SUHI intensity is affected by the development of the city.Zian WangQingyan MengMona AllamDie HuLinlin ZhangMassimo MenentiElsevierarticleSurface urban heat islandLand surface temperatureBoosted regression treesYangtze River DeltaEcologyQH540-549.5ENEcological Indicators, Vol 128, Iss , Pp 107845- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Surface urban heat island Land surface temperature Boosted regression trees Yangtze River Delta Ecology QH540-549.5
spellingShingle	Surface urban heat island Land surface temperature Boosted regression trees Yangtze River Delta Ecology QH540-549.5 Zian Wang Qingyan Meng Mona Allam Die Hu Linlin Zhang Massimo Menenti Environmental and anthropogenic drivers of surface urban heat island intensity: A case-study in the Yangtze River Delta, China
description	Nowadays urban climate is a global problem and many studies focused on understanding the relation between urban climate the built-up space using radiometric observations of the land surface temperature to estimate and monitor the surface urban heat island intensity (SUHIs). In this study MODIS land surface temperature (LST) data were used. The Yangtze River Delta Urban Agglomeration (YRDUA), eastern China, was selected as an example to study SUHI and multiple influencing factors in 16 big cities. Anthropogenic factors are considered the most important ones in determining SUHI, while natural factors remain influential. By using stratified random sampling (SRS), 78,085 random points were selected within the 16 cities. Nine influencing factors were selected in this study: distance from building (BD), distance from the main roads (RD), distance from water (WD), digital elevation model product (DEM), gross domestic product (GDP), normalized difference vegetation index product (NDVI), nighttime lighting intensity (NTI), population (POP) and impervious surface area data (%ISA). The SUHI intensity was extracted at each random point as well as the values of the influencing factors, NDVI, DEM, ISA, POP, NTI and GDP. For BD, WD and RD, random points were selected from the water, building and main roads using the near tool in ArcGIS to measure these distances. Boosted regression tree (BRT) model was applied to capture the contributions of the above factors to SUHI. We also applied a different procedure to evaluate the relative influence of Land Use and Land Cover (LULC). The relative influence refers to the contribution of each factor to determine SUHI. The influencing factors were ranked on the basis of the relative influence on SUHI. The results showed that (1) higher SUHI intensity was recorded in Shanghai, Jiaxing and Nanjing cities respectively, while Hangzhou recorded the lowest SUHI. (2) Anthropogenic drivers have slightly higher relative influence on SUHI than natural drivers, i.e. 51.29% and 48.71% respectively. The influence of all drivers on SUHI from high to low is NTI (27.62%), ISA (24.38%), NDVI (12.11%), GDP (7.95%), DEM (7.29%), POP (6.37%), BD (5.33%), WD (4.93%), RD (4.02%). (3) The variation in the socioeconomic level lead to different spatial patterns of different influence factors, further indicating that the overall mean SUHI intensity is affected by the development of the city.
format	article
author	Zian Wang Qingyan Meng Mona Allam Die Hu Linlin Zhang Massimo Menenti
author_facet	Zian Wang Qingyan Meng Mona Allam Die Hu Linlin Zhang Massimo Menenti
author_sort	Zian Wang
title	Environmental and anthropogenic drivers of surface urban heat island intensity: A case-study in the Yangtze River Delta, China
title_short	Environmental and anthropogenic drivers of surface urban heat island intensity: A case-study in the Yangtze River Delta, China
title_full	Environmental and anthropogenic drivers of surface urban heat island intensity: A case-study in the Yangtze River Delta, China
title_fullStr	Environmental and anthropogenic drivers of surface urban heat island intensity: A case-study in the Yangtze River Delta, China
title_full_unstemmed	Environmental and anthropogenic drivers of surface urban heat island intensity: A case-study in the Yangtze River Delta, China
title_sort	environmental and anthropogenic drivers of surface urban heat island intensity: a case-study in the yangtze river delta, china
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/f64eac28ea8542898cd1c88749d2eeda
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Environmental and anthropogenic drivers of surface urban heat island intensity: A case-study in the Yangtze River Delta, China

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