Abstract
The purpose of this study is to spatiotemporallyexplore the characteristics of urban temperaturesbased on multi-temporal satellite data and historical insitu measurements. As one of the most rapidly urbanizedcities in Canada, Saskatoon (SK) was selected as our studyarea. Surface brightness retrieving, Pearson correlation,linear regression modeling, and buffer analysis were appliedto different satellite datasets. The results indicatethat both Landsat and MODIS data can yield pronouncedestimations of daily air temperature with a significantlyadjusted R2 of 0.803 and 0.518 at the spatial scales of 120mand 1000 m, respectively. MODIS monthly LST data ishighly suitable for monitoring the trend of monthly urbanair temperature throughout summer (June, July, and August)due to a high average R2 of 0.8 (P<0.05), especiallyfor the warmest month (July). Our findings also reveal thatboth the Saskatchewan River and urban green spaces havestatistically significant cooling effects on the surroundingurban surface temperatures within 500 m and 200 m, respectively.In addition, a multiple linear regression modelwith four influential factors as independent variables canbe developed to estimate urban surface temperatures witha highest adjusted R2 of 0.649 and a lowest standard errorof 0.076.
References
[1] Benali, A.; Carvalho, A.C.; Nunes, J.P.; Carvalhais, N.; Santos,A. (2012). Estimating air surface temperature in Portugal usingMODIS LST data. Remote Sensing of Environment, 124, 108-121.10.1016/j.rse.2012.04.024Search in Google Scholar
[2] Bristow, K.L.; Campbell, G.S. (1984). On the relationship betweenincoming solar radiation and daily maximum and minimumtemperature. Agricultural and Forest Meteorology, 31,159-166.10.1016/0168-1923(84)90017-0Search in Google Scholar
[3] Changnon, S.A.; Kunkel, K.E.; Reinke, B.C. (1996). Impairs andresponses to the 1995 heatwave: A call to action. Bull. Am. Meteorol.Soc., 77, 1497-1506.10.1175/1520-0477(1996)077<1497:IARTTH>2.0.CO;2Search in Google Scholar
[4] Chavez, P.S. (1988). An improved dark-object subtraction techniquefor atmospheric scattering correction of multispectraldata. Remote Sensing of Environment, 24, 459-479.10.1016/0034-4257(88)90019-3Search in Google Scholar
[5] Chen, X.; Zhao, H.; Li, P.; Yin, Z. (2006). Remote sensing imagebasedanalysis of the relationship between urban heat islandand land use/cover changes. Remote Sensing of Environment,104, 133-146.10.1016/j.rse.2005.11.016Search in Google 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).Search in Google Scholar
[7] Dash, P.; Göttsche, F.-M.;Olesen, F.-S.; Fischer, H. (2002). Landsurface temperature and emissivity estimation from passivesensor data: Theory and practice-current trends. InternationalJournal of Remote Sensing, 23, 2563-2594.10.1080/01431160110115041Search in Google Scholar
[8] Doick, K.; Hutchings, T. (2013). Air temperature regulationby urban trees and green infrastructure. Forest research,FCRN012, 1-10.Search in Google Scholar
[9] Environment Canada. (2013). National Climate Data andInformation Archive. Climate data online. (http://climate.weatheroflce.gc.ca/climateData/canad_e.html) (Accessed onJune 2, 2013)Search in Google Scholar
[10] Fabrizi, R.; Bonafoni, S.; Biondi, R. (2010). Satellite andGround-Based Sensors for the Urban Heat Island Analysis inthe City of Rome. Remote Sens., 2, 1400-1415.10.3390/rs2051400Search in Google Scholar
[11] Hamdi, R. (2010). Estimating Urban Heat Island Effects on theTemperature Series of Uccle (Brussels, Belgium) Using RemoteSensing Data and a Land Surface Scheme. Remote Sens., 2,2773-2784.10.3390/rs2122773Search in Google Scholar
[12] Hansen, J.; Johnson, D.; Laeis, A.; Lebedeff, S.; Lee, P.; Rind, D.;Russel, G. (19891). Climatic impact of increasing atmosphericcarbon dioxide. Science, 213, 957-966.10.1126/science.213.4511.957Search in Google Scholar
[13] Huang, L.; Li, J.; Zhao, D.; Zhu, J. (2008). A fieldwork studyon the diurnal changes of urban microclimate in four types ofground cover and urban heat island of Nanjing, China. Buildingand Environment, 43, 7-17.10.1016/j.buildenv.2006.11.025Search in Google Scholar
[14] Hung,T.; Uchihama,D.; Ochi,S.; Yasuoka,Y. (2006). Assessmentwith satellite data of the urban heat island effects inAsian mega cities. International Journal of Applied Earth Observationand Geoinformation, 8, 34-48.Search in Google Scholar
[15] Jacobson, M. (2000). Fundamentals of atmospheric modeling.Cambridge University Press.Search in Google Scholar
[16] Jensen, J.R. (2007). Remote sensing of the environment: anEarth resource perspective, 2nd ed.; Pearson Prentice Hall: UpperSaddle River, NJ. USA.Search in Google Scholar
[17] Jin, M.; Dickinson, R. E. (2010). Land surface skin temperatureclimatology: benefitting from the strengths of satellite observations.Environmental Research Letters, 5, 44004.10.1088/1748-9326/5/4/044004Search in Google Scholar
[18] Jin, M.S.; Kessomkiat, W.; Pereira, G. (2011). Satellite-Observed Urbanization Characters in Shanghai, China:Aerosols, Urban Heat Island Effect, and Land–AtmosphereInteractions. Remote Sens., 3, 83-99.10.3390/rs3010083Search in Google Scholar
[19] Jones, P.I. (1995). Land surface temperatures: is the networkgood enough? Clim. Change, 31, 545-558.Search in 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, 185-221.10.1007/BF01095146Search in Google Scholar
[21] Köppen, W. (1936). Das geographisca System der Klimate, InHandbuch der Klimatologie, Köppen, W. and Geiger, R. Eds.;Verlag von Gebrüder Borntraeger: Berlin, Germany.Search in Google Scholar
[22] Kustas, W.P.; Norman, J.M. (2000). Evaluating the effects ofsubpixel heterogeneity on pixel average fluxes. Remote Sensingof Environment, 74, 327-342.10.1016/S0034-4257(99)00081-4Search in Google Scholar
[23] Kwarteng, A.; Small, C. (2010). Remote sensing of urban environmentalconvictions. In Remote sensing of urban and suburbanareas; Rashed, T., Jürgens, C., Eds.; Springer: New York,USA.Search in Google Scholar
[24] Landsat 7 science data users’ handbook. (2013). GoddardSpace Flight Center, NASA, Washington, DC.(http://landsathandbook.gsfc.nasa.gov/pdfs/Landsat7_Handbook.pdf.) (accessed on June 1, 2013)Search in 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 temperaturefrom Landsat 5 and 7 during SMEX02/SMACEX. RemoteSensing of Environment, 92, 521-534.Search in Google Scholar
[26] Li, X.; Zhou, W.; Ouyang, Z. (2013). Relationship between landsurface temperature and spatial pattern of greenspace: whatare the effects of spatial resolution? Landscape and UrbanPlanning, 114, 1-8.10.1016/j.landurbplan.2013.02.005Search in Google Scholar
[27] Li, Y.-Y., Zhang,H., Kainz,W. (2012). Monitoring patterns of urbanheat islands of the fast-growing Shanghai metropolis,China using time-series of Landsat TM/ETM+ data. InternationalJournal of Applied Earth Observation and Geoinfomation,19, 127-138.10.1016/j.jag.2012.05.001Search in Google Scholar
[28] Liao, J.G.; McGee, D. (2003). Adjusted coeflcients of determinationfor logistic regression. The American Statistician, 57,161-165.10.1198/0003130031964Search in Google Scholar
[29] Liu, L.; Zhang, Y. (2011). Urban Heat Island Analysis Using theLandsat TM Data and ASTER Data: A Case Study in Hong Kong.Remote Sens., 3, 1535-1552.10.3390/rs3071535Search in 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 temperaturederived from satellite observations with ground truthduring FIFE. International Journal of Remote Sensing, 14, 1659-1676.Search in 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 minimumair temperatures from MODIS LST data over the state ofMississippi. GIScience and Remote Sensing, 43, 78-11010.2747/1548-1603.43.1.78Search in 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)Search in Google Scholar
[33] NASA (National Aeronautics and Space Administration).(2013b) MODIS Web. (http://modis.gsfc.nasa.gov/data/)(accessed on June 2, 2013)Search in Google Scholar
[34] Nicole, J.E. (1996). High-resolution surface temperature patternsrelated to urban morphology in a tropical city: a satellitebasedstudy. Journal of Applied Meteorology, 35, 135-146.10.1175/1520-0450(1996)035<0135:HRSTPR>2.0.CO;2Search in Google Scholar
[35] Peel, M.C.; Finlayson, B.L.; McMahon, T.A. (2007). Updatedworldmap of the Köppen-Geiger climate classification. Hydrol.Earth Syst. Sci., 11, 1633-1644.10.5194/hess-11-1633-2007Search in Google Scholar
[36] Prihodko, L.; Goward, S.N. (1997). Estimation of air temperaturefrom remotely sensed surface observations. Remote Sensingof Environment, 60, 335-346.10.1016/S0034-4257(96)00216-7Search in Google Scholar
[37] Proudfoot, S. (2011). Saskatoon the Fastest-Growing Cityin Canada. (http://news.nationalpost.com/2011/07/20/saskatoon-the-fastest-growing-city-in-canada/) (Accessedon June 15, 2012).Search in Google Scholar
[38] Qin, Z.; Karnieli, A.; Berliner, P. (2001). A mono-window algorithmfor retrieving land surface temperature from Landsat TMdata and its application to the Israel–Egypt border region. InternationalJournal of Remote Sensing, 22, 3719-3746.10.1080/01431160010006971Search in Google Scholar
[39] Rinner, C.; Hussain, M. (2011). Toronto’s Urban Heat Island-Exploring the Relationship between Land Use and SurfaceTemperature. Remote Sens., 3, 1251-1265.10.3390/rs3061251Search in Google Scholar
[40] Rouse, J.W.; Haas, R.H.; Schell, J.A.; Deering, D.W. (1974). Monitoringvegetation systems in the Great Plains with ERTS, In Proc.ERTS-1 Symp., NASA SP-351, Greenbelt, MD, 1974. NASA,Washington DC, USA.Search in Google Scholar
[41] Seto, K.C.; Christensen, P. (2013). Remote sensing science toinform urban climate change mitigation strategies. Urban Climate,3, 1-6.Search in Google Scholar
[42] Shen, L.; He, Y.; Guo, X. (2013a). Exploration of LoggerheadShrike Habitats in Grassland National Park of CanadaBased on in SituMeasurements and Satellite-Derived AdjustedTransformed Soil-Adjusted Vegetation Index (ATSAVI). RemoteSens., 5, 432-453.10.3390/rs5010432Search in Google Scholar
[43] Shen L.; He, Y.; Guo, X. (2013b). Suitability of the normalizeddifference vegetation index and the adjusted transformed soiladjustedvegetation index for spatially characterizing LoggerheadShrike habitats in North American mixed prairie. Journalof Applied Remote Sensing, 7(073574), 1-17.10.1117/1.JRS.7.073574Search in Google Scholar
[44] Shen, L.; Kyllo, J.M.; Guo, X. (2013c). A potential integratedmodel based on a hierarchical multi-indices system for monitoringand evaluating urban sustainability. Sustainability, 5,524-559.10.3390/su5020524Search in Google Scholar
[45] Strahler, A.; Archibold, B. (2011). Air temperature. In PhysicalGeography, 5nd ed.; Burke, R., Rancourt, L., Brown, G., Fenandoe,C., Eds.; John Wiley& Sons Ltd.: Mississauga, Canada.Search in Google Scholar
[46] Sobrino, J.A.; Jiménez-Muñoz, J.C.; Sòria, G.; Romaguera, M.;Guanter, L., Moreno,J., Plaza, A.; Martínez, P. (2008). LandSurface Emissivity retrieval from different VNIR and TIR sensors.IEEE Transactions on Geoscience and Remote Sensing,46, 316-327.10.1109/TGRS.2007.904834Search in Google Scholar
[47] USGS. (2013). EarthExplorer. (http://earthexplorer.usgs.gov/)(accessed on June 2, 2013)Search in Google Scholar
[48] Vogt, J.; Viau, A.A.; Paquet, F. (1997).Mapping regional air temperaturefields using satellite derived surface skin temperatures.International Journal of Climatology, 17, 1559-1579.10.1002/(SICI)1097-0088(19971130)17:14<1559::AID-JOC211>3.0.CO;2-5Search in Google Scholar
[49] Vukovich, F.M. (1983). An analysis of the ground temperatureand reflectivity pattern about St. Louis, Missouri, using HCMMsatellite data. Journal of Applied Meteorology, 22, 560-571.10.1175/1520-0450(1983)022<0560:AAOTGT>2.0.CO;2Search in Google Scholar
[50] Weng, Q.; Lu, D.; Schubring, J. (2004). Estimation of land surfacetemperature-vegetation abundance relationship for urbanheat island studies. Remote Sensing of Environment, 89, 467-483.10.1016/j.rse.2003.11.005Search in Google Scholar
[51] Zhang, C.; Guo, X.; Wilmshurst, J.; Crump, S. (2008). Monitoringtemporal heterogeneity in a protected mixed prairieecosystem using 10-day NDVI composite. The Prairie Forum,33, 145-166.Search in Google Scholar
[52] Zhu Z.; Woodcock, C.E.; Rogan, J.; Kellndorfer, J. (2012). Assessmentof spectral, polarimetric, temporal, and spatial dimensionsfor urban and peri-urban land cover classificationusing Landsat and SAR data. Remote Sensing of Environment,117, 72-82.10.1016/j.rse.2011.07.020Search in Google Scholar
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