Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Open Geosciences

formerly Central European Journal of Geosciences

Editor-in-Chief: Jankowski, Piotr


IMPACT FACTOR 2018: 0.788
5-year IMPACT FACTOR: 0.899

CiteScore 2018: 1.02

SCImago Journal Rank (SJR) 2018: 0.295
Source Normalized Impact per Paper (SNIP) 2018: 0.612

Open Access
Online
ISSN
2391-5447
See all formats and pricing
Online
Open Access
*Prices in US$ apply to orders placed in the Americas only. Prices in GBP apply to orders placed in Great Britain only. Prices in € represent the retail prices valid in Germany (unless otherwise indicated). Prices are subject to change without notice. Prices do not include postage and handling if applicable. RRP: Recommended Retail Price.
More options …

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

Open Access

Download PDF

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

References

  • [1] Benali, A.; Carvalho, A.C.; Nunes, J.P.; Carvalhais, N.; Santos, A. (2012). Estimating air surface temperature in Portugal using MODIS LST data. Remote Sensing of Environment, 124, 108- 121. CrossrefGoogle Scholar

  • [2] Bristow, K.L.; Campbell, G.S. (1984). On the relationship between incoming solar radiation and daily maximum and minimum temperature. Agricultural and Forest Meteorology, 31, 159-166. CrossrefGoogle Scholar

  • [3] Changnon, S.A.; Kunkel, K.E.; Reinke, B.C. (1996). Impairs and responses to the 1995 heatwave: A call to action. Bull. Am. Meteorol. Soc., 77, 1497-1506. Google Scholar

  • [4] Chavez, P.S. (1988). An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sensing of Environment, 24, 459-479. CrossrefGoogle Scholar

  • [5] Chen, X.; Zhao, H.; Li, P.; Yin, Z. (2006). Remote sensing imagebased analysis of the relationship between urban heat island and land use/cover changes. Remote Sensing of Environment, 104, 133-146. CrossrefGoogle Scholar

  • [6] City of Saskatoon. (2010). Neighbourhood profiles, 10th edition. Planning and Development branch. Department of Community Services, City of Saskatoon. (http://www.saskatoon.ca/DEPARTMENTS/ Community{%}20Services/PlanningDevelopment/FutureGrowth/DemographicAndHousingData/Pages/ NeighbourhoodProfiles.aspx (Accessed on June 15, 2013). Google Scholar

  • [7] Dash, P.; Göttsche, F.-M.;Olesen, F.-S.; Fischer, H. (2002). Land surface temperature and emissivity estimation from passive sensor data: Theory and practice-current trends. International Journal of Remote Sensing, 23, 2563-2594. CrossrefGoogle Scholar

  • [8] Doick, K.; Hutchings, T. (2013). Air temperature regulation by urban trees and green infrastructure. Forest research, FCRN012, 1-10. Google Scholar

  • [9] Environment Canada. (2013). National Climate Data and Information Archive. Climate data online. (http://climate. weatheroflce.gc.ca/climateData/canad_e.html) (Accessed on June 2, 2013) Google Scholar

  • [10] Fabrizi, R.; Bonafoni, S.; Biondi, R. (2010). Satellite and Ground-Based Sensors for the Urban Heat Island Analysis in the City of Rome. Remote Sens., 2, 1400-1415. Google Scholar

  • [11] Hamdi, R. (2010). Estimating Urban Heat Island Effects on the Temperature Series of Uccle (Brussels, Belgium) Using Remote Sensing Data and a Land Surface Scheme. Remote Sens., 2, 2773-2784. Google Scholar

  • [12] Hansen, J.; Johnson, D.; Laeis, A.; Lebedeff, S.; Lee, P.; Rind, D.; Russel, G. (19891). Climatic impact of increasing atmospheric carbon dioxide. Science, 213, 957-966. Google Scholar

  • [13] Huang, L.; Li, J.; Zhao, D.; Zhu, J. (2008). A fieldwork study on the diurnal changes of urban microclimate in four types of ground cover and urban heat island of Nanjing, China. Building and Environment, 43, 7-17. Google Scholar

  • [14] Hung,T.; Uchihama,D.; Ochi,S.; Yasuoka,Y. (2006). Assessment with satellite data of the urban heat island effects in Asian mega cities. International Journal of Applied Earth Observation and Geoinformation, 8, 34-48. CrossrefGoogle Scholar

  • [15] Jacobson, M. (2000). Fundamentals of atmospheric modeling. Cambridge University Press. Google Scholar

  • [16] Jensen, J.R. (2007). Remote sensing of the environment: an Earth resource perspective, 2nd ed.; Pearson Prentice Hall: Upper Saddle River, NJ. USA. Google Scholar

  • [17] Jin, M.; Dickinson, R. E. (2010). Land surface skin temperature climatology: benefitting from the strengths of satellite observations. Environmental Research Letters, 5, 44004. CrossrefGoogle Scholar

  • [18] Jin, M.S.; Kessomkiat, W.; Pereira, G. (2011). Satellite- Observed Urbanization Characters in Shanghai, China: Aerosols, Urban Heat Island Effect, and Land–Atmosphere Interactions. Remote Sens., 3, 83-99. Google Scholar

  • [19] Jones, P.I. (1995). Land surface temperatures: is the network good enough? Clim. Change, 31, 545-558. Google Scholar

  • [20] Karl, T.R.; Derr, V.E.; Easterling, D.R.; Folland, C.K.; Hofmann, D.J.; Levitus, S.; Nicholls, N.; Parker, D.E.; Withee, G.W. (1995). Critical issues for long-term climate monitoring. Clim. Change, 31, 185-221. Google Scholar

  • [21] Köppen, W. (1936). Das geographisca System der Klimate, In Handbuch der Klimatologie, Köppen, W. and Geiger, R. Eds.; Verlag von Gebrüder Borntraeger: Berlin, Germany. Google Scholar

  • [22] Kustas, W.P.; Norman, J.M. (2000). Evaluating the effects of subpixel heterogeneity on pixel average fluxes. Remote Sensing of Environment, 74, 327-342. CrossrefGoogle Scholar

  • [23] Kwarteng, A.; Small, C. (2010). Remote sensing of urban environmental convictions. In Remote sensing of urban and suburban areas; Rashed, T., Jürgens, C., Eds.; Springer: New York, USA. Google Scholar

  • [24] Landsat 7 science data users’ handbook. (2013). Goddard Space Flight Center, NASA, Washington, DC. (http://landsathandbook.gsfc.nasa.gov/pdfs/Landsat7_ Handbook.pdf.) (accessed on June 1, 2013) Google Scholar

  • [25] Li, F.; Jackson, T.J.; Kustas, W.P.; Schmugge, T.J.; French, A.N.; Cosh, M.H.; Bindlish, R. (2004). Deriving land surface temperature from Landsat 5 and 7 during SMEX02/SMACEX. Remote Sensing of Environment, 92, 521-534. CrossrefGoogle Scholar

  • [26] Li, X.; Zhou, W.; Ouyang, Z. (2013). Relationship between land surface temperature and spatial pattern of greenspace: what are the effects of spatial resolution? Landscape and Urban Planning, 114, 1-8. CrossrefGoogle Scholar

  • [27] Li, Y.-Y., Zhang,H., Kainz,W. (2012). Monitoring patterns of urban heat islands of the fast-growing Shanghai metropolis, China using time-series of Landsat TM/ETM+ data. International Journal of Applied Earth Observation and Geoinfomation, 19, 127-138. Google Scholar

  • [28] Liao, J.G.; McGee, D. (2003). Adjusted coeflcients of determination for logistic regression. The American Statistician, 57, 161-165. CrossrefGoogle Scholar

  • [29] Liu, L.; Zhang, Y. (2011). Urban Heat Island Analysis Using the Landsat TM Data and ASTER Data: A Case Study in Hong Kong. Remote Sens., 3, 1535-1552. Google Scholar

  • [30] Madden, R.A.; Shea, D.J.; Branstator, G.W.; Tribbia, J, J.; Weber, R.O. (1993). The effects of imperfect spatial surface temperature derived from satellite observations with ground truth during FIFE. International Journal of Remote Sensing, 14, 1659- 1676. Google Scholar

  • [31] Mostovoy, G.V.; King, R.L.; Reddy, K.R.; Kakani, V.G.; Filippova, M.G. (2006). Statistical estimation of daily maximum and minimum air temperatures from MODIS LST data over the state of Mississippi. GIScience and Remote Sensing, 43, 78-110 Google Scholar

  • [32] NASA (National Aeronautics and Space Administration). (2013a). Technical information. (http://landsat.gsfc.nasa. gov/about/technical.html) (accessed on June 2, 2013) Google Scholar

  • [33] NASA (National Aeronautics and Space Administration). (2013b) MODIS Web. (http://modis.gsfc.nasa.gov/data/) (accessed on June 2, 2013) Google Scholar

  • [34] Nicole, J.E. (1996). High-resolution surface temperature patterns related to urban morphology in a tropical city: a satellitebased study. Journal of Applied Meteorology, 35, 135-146. CrossrefGoogle Scholar

  • [35] Peel, M.C.; Finlayson, B.L.; McMahon, T.A. (2007). Updated worldmap of the Köppen-Geiger climate classification. Hydrol. Earth Syst. Sci., 11, 1633-1644. Google Scholar

  • [36] Prihodko, L.; Goward, S.N. (1997). Estimation of air temperature from remotely sensed surface observations. Remote Sensing of Environment, 60, 335-346. CrossrefGoogle Scholar

  • [37] Proudfoot, S. (2011). Saskatoon the Fastest-Growing City in Canada. (http://news.nationalpost.com/2011/07/20/ saskatoon-the-fastest-growing-city-in-canada/) (Accessed on June 15, 2012). Google Scholar

  • [38] Qin, Z.; Karnieli, A.; Berliner, P. (2001). A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel–Egypt border region. International Journal of Remote Sensing, 22, 3719-3746. CrossrefGoogle Scholar

  • [39] Rinner, C.; Hussain, M. (2011). Toronto’s Urban Heat Island- Exploring the Relationship between Land Use and Surface Temperature. Remote Sens., 3, 1251-1265. Google Scholar

  • [40] Rouse, J.W.; Haas, R.H.; Schell, J.A.; Deering, D.W. (1974). Monitoring vegetation systems in the Great Plains with ERTS, In Proc.ERTS-1 Symp., NASA SP-351, Greenbelt, MD, 1974. NASA, Washington DC, USA. Google Scholar

  • [41] Seto, K.C.; Christensen, P. (2013). Remote sensing science to inform urban climate change mitigation strategies. Urban Climate, 3, 1-6. Google Scholar

  • [42] Shen, L.; He, Y.; Guo, X. (2013a). Exploration of Loggerhead Shrike Habitats in Grassland National Park of Canada Based on in SituMeasurements and Satellite-Derived Adjusted Transformed Soil-Adjusted Vegetation Index (ATSAVI). Remote Sens., 5, 432-453. Google Scholar

  • [43] Shen L.; He, Y.; Guo, X. (2013b). Suitability of the normalized difference vegetation index and the adjusted transformed soiladjusted vegetation index for spatially characterizing Loggerhead Shrike habitats in North American mixed prairie. Journal of Applied Remote Sensing, 7(073574), 1-17. Google Scholar

  • [44] Shen, L.; Kyllo, J.M.; Guo, X. (2013c). A potential integrated model based on a hierarchical multi-indices system for monitoring and evaluating urban sustainability. Sustainability, 5, 524-559. Google Scholar

  • [45] Strahler, A.; Archibold, B. (2011). Air temperature. In Physical Geography, 5nd ed.; Burke, R., Rancourt, L., Brown, G., Fenandoe, C., Eds.; John Wiley& Sons Ltd.: Mississauga, Canada. Google Scholar

  • [46] Sobrino, J.A.; Jiménez-Muñoz, J.C.; Sòria, G.; Romaguera, M.; Guanter, L., Moreno,J., Plaza, A.; Martínez, P. (2008). Land Surface Emissivity retrieval from different VNIR and TIR sensors. IEEE Transactions on Geoscience and Remote Sensing, 46, 316-327. CrossrefGoogle Scholar

  • [47] USGS. (2013). EarthExplorer. (http://earthexplorer.usgs.gov/) (accessed on June 2, 2013) Google Scholar

  • [48] Vogt, J.; Viau, A.A.; Paquet, F. (1997).Mapping regional air temperature fields using satellite derived surface skin temperatures. International Journal of Climatology, 17, 1559-1579. CrossrefGoogle Scholar

  • [49] Vukovich, F.M. (1983). An analysis of the ground temperature and reflectivity pattern about St. Louis, Missouri, using HCMM satellite data. Journal of Applied Meteorology, 22, 560-571. CrossrefGoogle Scholar

  • [50] Weng, Q.; Lu, D.; Schubring, J. (2004). Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment, 89, 467- 483. CrossrefGoogle Scholar

  • [51] Zhang, C.; Guo, X.; Wilmshurst, J.; Crump, S. (2008). Monitoring temporal heterogeneity in a protected mixed prairie ecosystem using 10-day NDVI composite. The Prairie Forum, 33, 145-166. Google Scholar

  • [52] Zhu Z.; Woodcock, C.E.; Rogan, J.; Kellndorfer, J. (2012). Assessment of spectral, polarimetric, temporal, and spatial dimensions for urban and peri-urban land cover classification using Landsat and SAR data. Remote Sensing of Environment, 117, 72-82. CrossrefGoogle Scholar

About the article

Received: 2014-02-03

Accepted: 2014-06-23

Published Online: 2014-11-10

Citation Information: Open Geosciences, Volume 7, Issue 1, ISSN (Online) 2391-5447, DOI: https://doi.org/10.1515/geo-2015-0005.

Export Citation

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

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

[1]
Marcin Spyra, Luis Inostroza, Adam Hamerla, and Jan Bondaruk
Urban Ecosystems, 2018
[2]
Maria Mira, Miquel Ninyerola, Meritxell Batalla, Lluís Pesquer, and Xavier Pons
Remote Sensing, 2017, Volume 9, Number 12, Page 1313

Comments (0)

Please log in or register to comment.
Log in