The influence of different data and method on estimating the surface urban heat island intensity. (June 2018)

• Record Type: Journal Article
• Authors: Yao, Rui; Wang, Lunche; Huang, Xin; Niu, Ying; Chen, Yisen; Niu, Zigeng
• Is Part Of: Ecological indicators. Volume 89(2018)
• Page Start: 45
• Page End: 55
• Publication Date: 2018-06
• Subjects: Surface urban heat island -- Different data and method -- Spatiotemporal characteristics -- China; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2018.01.044
• Languages: English
• ISSNs: 1470-160X

Abstract

Graphical abstract: The influences of different data and methods on spatial variations of surface urban heat island intensity (SUHII) in summer days (SDs). A dot represented a city. Highlights: Influences of different data and methods on estimating SUHII were analyzed. Using nearby suburban will underestimate the variation trends of SUHII. Ignoring the influences of water body and elevation will influences the SUHII. The SUHII in SDs monitored by Aqua satellite were higher than Terra satellite. Abstract: Few studies have examined the influence of different data and method on estimating the SUHIs. This study aims at analyzing the impact of different method (to define rural area) and different data (MODIS Terra and Aqua satellite data) on estimating the SUHI intensity (SUHII, LST in urban minus rural reference) in 31 cities of China. The major findings include: (1) For SUHII, ignoring the influence of elevation and water body will overestimate the SUHII by 1.68 °C (averaged for 31 cities, hereafter) and 0.28 °C, respectively, in summer days (SDs). Using nearby suburban area as reference will underestimate the SUHII by 1.48 °C in SDs. Different data and method have smaller impact on estimating the SUHII in summer nights (SNs) than in SDs. (2) For spatial variation of SUHII, ignoring the influence of elevation will influence the spatial variation of SUHII in SDs (r = 0.3, p > .05), but other methods have little impact on estimating the spatial variation of SUHII. (3) For interannual variation of SUHII, using nearby suburban area will underestimate the increasing rate of SUHII (SD: 0.106 °C/year, SN: 0.012 °C/year), whereas ignoring the influence of elevation and water body have little influence on the changing rate of SUHII. The changing rates of SUHII in SDs monitored by Terra satellite were 0.025 °C/year lower than Aqua satellite. In all, the present study can enhance our understanding of the influence of different data and method on estimating the SUHII, and provide a useful reference to study the SUHII.