Influence of Canopy Cover on Surface Temperature

Vitor Vieira Vasconcelos, Helenice Maria Sacht


Trees affect the microclimate, which influences thermal comfort and ecosystem processes. This study investigated the influence of the canopy cover on daily maximum and minimum temperatures. The data are from a collaborative database, and each measurement consists of the minimum and maximum temperatures under the canopy and in an open adjacent area over a 24-hour period. Paired sample t-tests indicated that the canopy decreased the maximum and minimum daily temperatures and narrowed the daily temperature range. Multiple regression showed that the canopy cover percentage decreased the maximum daily temperatures, and this effect was greater in rural areas than in urbanized areas. Another multiple regression indicated that the canopy cover percentage and the distance to the edge of the canopy decreased the daily temperature range. An independent sample t-test also indicated that the effect of the canopy on the daily temperature range was higher in rural areas when analysed by parametric and non-parametric tests but not when measured by a robust test. Other independent sample t-tests indicated that the distance from a light source also decreased the canopy effect on the minimum daily temperature and the daily temperature range. The main plausible underlying processes include the canopy shade and wind insulation, litter insulation of the ground surface, heat pumps through evapotranspiration and lateral heat fluxes from light bulbs and other anthropogenic sources, especially in urbanized areas. These results provide a greater understanding of the effects of arborization in rural and urban ecosystems, as well as their respective benefits to human communities.


Microclimatology, arborisation, temperature, urbanization, canopy, citizen science

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Alkama, R., Cescatti, A. 2016. Biophysical climate impacts of recent changes in global forest cover. Science, 351(6273): 600-604.

Bonan, G. B., Pollard, D., Thompson, S. L. 1992. Effects of boreal forest vegetation on global climate. Nature, 359(6397):716.

Breusch, T. S., Pagan, A. R. 1979. A simple test for heteroscedasticity and random coefficient variation. Econometrica, 47:1287-1294.

Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M., West, G. B. 2004. Toward a metabolic theory of ecology. Ecology, 85(7):1771-1789.

Carlson, T. N., Boland, F.E. 1978. Analysis of Urban-Rural Canopy Using a Surface Heat Flux/Temperature Model. J Appl Meteorology, 17(7):998-1013.<0998:AOURCU>2.0.CO;2

Cook, R. D., Weisber, S. 1982. Residuals and influence in regression. Chapman & Hall, New York.

Cragg, J. G., Uhler, R. S. 1970. The demand for automobiles. The Canadian J Economics, 3:386-406.

Davies, S., Gowing, D. 2016. S369 Practical Task Guide. The Open University, Milton Keynes.

Durbin, J., Watson, G. S. 1971. Testing for serial correlation in least squares regression. III. Biometrika, 58(1):1-19.

Elmes, A., Rogan, J., Williams, C., Ratick, S., Nowak, D., Martin, D. 2017. Effects of urban tree canopy loss on land surface temperature magnitude and timing. ISPRS J Photogrammetry Remote Sens, 128:338-353.

Godinho, S., Gil, A., Guiomar, N., Costa, M. J., Neves, N., 2016. Assessing the role of Mediterranean evergreen oaks canopy cover in land surface albedo and temperature using a remote sensing-based approach. Appl Geography, 74:84-94.

Gowing, D. J. G., Davies, S. J. M., Denny, H., Edwards, N. R., Gauci, V., Gillman, M. P., Halliday, T. R., Stevens, C. J., 2008. Ecosystems. The Open University, Milton Keynes.

Greene, C. S., Millward, A. A. 2017. Getting closure: The role of urban forest canopy density in moderating summer surface temperatures in a large city. Urban Ecosyst 20(1):141-156.

Hair, J. F., Anderson, R. E., Tatham, R. L., Black, W. C. 2018. Multivariate Data Analysis (8th ed.). Cengage Learning EMEA, Hampshire.

Henson, R. K., Smith, A. D. 2000. State of the art in statistical significance and effect size reporting: A review of the APA Task Force report and current trends. J Res Development Education, 33(4):285-296.

Holland, P., Welsch, R. E. 1977. Robust regression using iteratively reweighted least-squares. Comm Statist Theory Meth, 6:813-888.

King, G., Roberts, M. E. 2014. How robust standard errors expose methodological problems they do not fix, and what to do about it. Political Analysis, 23(2):159-179.

Koenker, R., Bassett, G., 1978. Regression Quantiles. Econometrica, 46(1):33–50.

Koller, M., Stahel, W.A., 2011. Sharpening Wald-type inference in robust regression for small samples. Computational Statistics Data Analysis, 55(8):2504–2515.

Levene, H., 1960. Robust test for equality of variances. In: Olkin I, Ghurye SG, Hoeffding W, Madow WG, Mann HB (eds.) Contributions to Probability and Statistics: Essays in Honour of Harold Hotelling. Stanford University Press, Stanford, pp 278–292.

Lewis, T., 1998. The effect of deforestation on ground surface temperatures. Glob Planet Change, 18(1-2):1-13.

Li, Y., Zhao, M., Motesharrei, S., Mu, Q., Kalnay, E., Li, S., 2015. Local cooling and warming effects of forests based on satellite observations. Nat communications, 6:6603.

Mascaró, L., Mascaró, J. J. 2009. Ambiência Urbana – Urban environment. Masquatro Editora, Porto Alegre.

Nagelkerke, N. J. D. 1991. A note on a general definition of the coefficient of determination. Biometrika,78:691-692.

Nichol, J. E. 1996. High-resolution surface temperature patterns related to urban morphology in a tropical city: A satellite-based study. J Appl Meteorology, 35(1):135-146.<0135:HRSTPR>2.0.CO;2

Pituch, K. A., Stevens, J., 2015. Applied multivariate statistics for the social sciences (6th ed.). Routledge.

Renaud, O., Victoria-Feser, M. P. 2010. A robust coefficient of determination for regression, J Statistical Plan Inference, 140, 1852-1862.

Ricklefs, R. E., Relyea, R. 2019. Ecology: The economy of nature (8th ed.). Freeman, W. H. & Company.

Robinette, G. O., 1972. Plants, people, and environmental quality: A study of plants and their environmental functions (1st ed.). U.S. Dept. of the Interior, National Park Service, Washington DC.

Salmond, J. A., Tadaki, M., Vardoulakis, S., Arbuthnott, K., Coutts, A, Demuzere, M., Dirks, K. M., McInnes, R. N., Wheeler, B. W., 2016. Health and climate related ecosystem services provided by street trees in the urban environment. Environmental Health, 15(1)S36:95-107.

Shapiro, S. S., Wilk, M. B., 1965. An analysis of variance test for normality (complete samples). Biometrika, 52(3/4):591-611.

Shashua-Bar, L., Hoffman, M. E. 2003. Geometry and orientation aspects in passive cooling of canyon streets with trees. Energy and Buildings, 35(1):61–68.

Smith, D. M., Jarvis, P. G., 1998. Physiological and environmental control of transpiration by trees in windbreaks. For Ecology Management, 105(1):159-173.

Tyrväinen, L., Pauleit, S., Seeland, K., Vries, S., 2005. Benefits and uses of urban forests and trees. In Schipperijn J (ed), Urban Forests and Trees. Springer-Verlag, Netherlands, Chapter 4, pp 81-114.

Welch, B. L., 1938. The significance of the difference between two means when the population variances are unequal. Biometrika, 29, 350-362.

White, H., 1980. A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4):817–838.

Wilcoxon, F., 1945. Individual comparisons by ranking methods. Biometrics, 1:80-83.

Yohai, V. J., Zamar, R. H., 1988. High breakdown-point and high efficiency estimates for regression. Journal of the American Statistical Association, 83(402):406-413.

Yuen, K. K., 1974. The two-sample trimmed t for unequal population variances. Biometrika,(1):165-170.


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Revista Brasileira de Geografia Física - ISSN: 1984-2295

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