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Open Geosciences

formerly Central European Journal of Geosciences

Editor-in-Chief: Jankowski, Piotr

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2391-5447
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Spatiotemporally characterizing urban temperatures based on remote sensing and GIS analysis: a case study in the city of Saskatoon (SK, Canada)

Li Shen
  • Corresponding author
  • School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, Hubei Province, 430074, Chinam
  • Department of Geography and Planning, University of Saskatchewan, Saskatoon, SK S7N5C8, Canada
  • Sustainable Cities International, West Hastings Street, Vancouver, V6B1G8, Canada
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Xulin Guo
  • Department of Geography and Planning, University of Saskatchewan, Saskatoon, SK S7N5C8, Canada
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Kang Xiao
Published Online: 2014-11-10 | DOI: https://doi.org/10.1515/geo-2015-0005

Abstract

The purpose of this study is to spatiotemporally explore the characteristics of urban temperatures based on multi-temporal satellite data and historical in situ measurements. As one of the most rapidly urbanized cities in Canada, Saskatoon (SK) was selected as our study area. Surface brightness retrieving, Pearson correlation, linear regression modeling, and buffer analysis were applied to different satellite datasets. The results indicate that both Landsat and MODIS data can yield pronounced estimations of daily air temperature with a significantly adjusted R2 of 0.803 and 0.518 at the spatial scales of 120m and 1000 m, respectively. MODIS monthly LST data is highly suitable for monitoring the trend of monthly urban air temperature throughout summer (June, July, and August) due to a high average R2 of 0.8 (P<0.05), especially for the warmest month (July). Our findings also reveal that both the Saskatchewan River and urban green spaces have statistically significant cooling effects on the surrounding urban surface temperatures within 500 m and 200 m, respectively. In addition, a multiple linear regression model with four influential factors as independent variables can be developed to estimate urban surface temperatures with a highest adjusted R2 of 0.649 and a lowest standard error of 0.076.

Keywords : air temperature; surface brightness temperature; Landsat; MODIS; regression modeling; buffer analysis

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About the article

Received: 2014-02-03

Accepted: 2014-06-23

Published Online: 2014-11-10


Citation Information: Open Geosciences, ISSN (Online) 2391-5447, DOI: https://doi.org/10.1515/geo-2015-0005.

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© 2015 Li Shen et al.. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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