A new method to quantify surface urban heat island intensity

Huidong Li; Yuyu Zhou; Xiaoma Li; Lin Meng; Xun Wang; Sha Wu; Sahar Sodoudi

doi:10.1016/j.scitotenv.2017.11.360

A new method to quantify surface urban heat island intensity

Sci Total Environ. 2018 May 15:624:262-272. doi: 10.1016/j.scitotenv.2017.11.360. Epub 2017 Dec 16.

Authors

Huidong Li¹, Yuyu Zhou², Xiaoma Li², Lin Meng², Xun Wang¹, Sha Wu³, Sahar Sodoudi⁴

Affiliations

¹ Institute of Meteorology, Freie Universität Berlin, Berlin, Germany.
² Department of Geological and Atmospheric Sciences, Iowa State University, Ames, IA, USA.
³ Forestry Experiment Center of North China, Chinese Academy of Forestry, Beijing, China.
⁴ Institute of Meteorology, Freie Universität Berlin, Berlin, Germany. Electronic address: sodoudi@zedat.fu-berlin.de.

PMID: 29253774
DOI: 10.1016/j.scitotenv.2017.11.360

Abstract

Reliable quantification of urban heat island (UHI) can contribute to the effective evaluation of potential heat risk. Traditional methods for the quantification of UHI intensity (UHII) using pairs-measurements are sensitive to the choice of stations or grids. In order to get rid of the limitation of urban/rural divisions, this paper proposes a new approach to quantify surface UHII (SUHII) using the relationship between MODIS land surface temperature (LST) and impervious surface areas (ISA). Given the footprint of LST measurement, the ISA was regionalized to include the information of neighborhood pixels using a Kernel Density Estimation (KDE) method. Considering the footprint improves the LST-ISA relationship. The LST shows highly positive correlation with the KDE regionalized ISA (ISA_KDE). The linear functions of LST are well fitted by the ISA_KDE in both annual and daily scales for the city of Berlin. The slope of the linear function represents the increase in LST from the natural surface in rural regions to the impervious surface in urban regions, and is defined as SUHII in this study. The calculated SUHII show high values in summer and during the day than in winter and at night. The new method is also verified using finer resolution Landset data, and the results further prove its reliability.

Keywords: Footprint of remote sensing observation; Impervious surface area; Kernel density estimation; Land surface temperature; Surface urban heat island.