Environmental and anthropogenic drivers of surface urban heat island intensity

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

Wang, Zian (Chinese Academy of Sciences; University of Chinese Academy of Sciences; Sanya Institute of Remote Sensing)
Meng, Qingyan (Chinese Academy of Sciences; Sanya Institute of Remote Sensing)
Allam, Mona (Chinese Academy of Sciences; National Water Research Center, Egypt)
Hu, Die (Chinese Academy of Sciences; Sanya Institute of Remote Sensing)
Zhang, Linlin (Chinese Academy of Sciences; Sanya Institute of Remote Sensing)
Menenti, M. (TU Delft Optical and Laser Remote Sensing; Chinese Academy of Sciences)

2021

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.

Boosted regression trees
Land surface temperature
Surface urban heat island
Yangtze River Delta

http://resolver.tudelft.nl/uuid:67d15412-58c7-4a5e-9ee3-369297fd7e7d

https://doi.org/10.1016/j.ecolind.2021.107845

1470-160X

Ecological Indicators, 128

Institutional Repository

journal article

© 2021 Zian Wang, Qingyan Meng, Mona Allam, Die Hu, Linlin Zhang, M. Menenti