OPEN ACCESS
The urban thermal environment deteriorates with increasing frequency of extreme heat events in cities. Conventionally, the Urban Heat Island (UHI) effect only reflects the temperature difference between the city and its rural surroundings. This scale of analysis is often too broad to help devise mitigation strategies, which are typically implemented at a more local scale within the sphere of urban planning and design. In this research, the city of Wuhan, China, is taken as an example. Through quantitative measurements, a workflow is proposed to mitigate the surface UHI of Wuhan, locally. Also, the satellite images of the MODerate-resolution Imaging Spectroradiometer and Landsat-7 ETM+ are used for technical purposes, and the K-means clustering is applied to classify the Local Climate Zone (LCZ). Further, the Local Scale Urban Heat Island (LSUHI) is captured through morphological parameters, such as Multi-Scale Shape Index (MSSI) based upon the latent Land Surface Temperature (LST) pattern. The mitigation process is organized hierarchically and prioritized by the combination of LCZ and LSUHI. Based on this, Wuhan is divided into seven LCZs and the LSUHI, in the mean time, can be detected by morphological parameters. We present the corresponding quantitative planning advice for places with higher heat threats in the city. This research is conducted on urban microclimate and urban planning on at least two levels: (1) the reduced study scale of urban thermal environment and (2) a planning-driven workflow of urban thermal environment optimization.
climate zone, heat island, hotspot, land surface temperature, local scale, morphology
[1] Stone, B., Vargo, J. & Habeeb, D., Managing climate change in cities: will climate action plans work? Landscape and Urban Planning, 107(3), pp. 263–271, 2012. https://doi.org/10.1016/j.landurbplan.2012.05.014
[2] Brian Stone, J. & Michael, O.R, Urban form and thermal efficiency: how the design of cities influences the Urban Heat Island effect. Journal of the American Planning Association, 67(2), pp. 186–198, 2001. https://doi.org/10.1080/01944360108976228
[3] Debbage, N. & Shepherd, J.M., The urban heat island effect and city contiguity. Computers, Environment and Urban Systems, 54, pp. 181–194, 2015. https://doi.org/10.1016/j.compenvurbsys.2015.08.002
[4] Wheeler, S., State and municipal climate change plans: the first generation. Journal of the American Planning Association, 74(4), pp. 481–496, 2008. https://doi.org/10.1080/01944360802377973
[5] Stewart, I.D., A systematic review and scientific critique of methodology in modern urban heat island literature. International Journal of Climatology, 31(2), pp. 200–217, 2011. https://doi.org/10.1002/joc.2141
[6] Arnfield, A.J., Two decades of urban climate research: a review of turbulence, exchanges of energy and water, and the urban heat island. International Journal of Climatology, 23(1), pp. 1–26, 2003. https://doi.org/10.1002/joc.859
[7] Stewart, I.D. & Oke, T.R., Local climate zones for urban temperature studies. Bulletin of the American Meteorological Society, 93(12), pp. 1879–1900, 2012. https://doi.org/10.1175/BAMS-D-11-00019.1
[8] Yuan, F. & Bauer, M.E., Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Remote Sensing of Environment, 106(3), pp. 375–386, 2007. https://doi.org/10.1016/j.rse.2006.09.003
[9] Song, J., Du, S., Feng, X. & Guo, L., The relationships between landscape compositions and land surface temperature: Quantifying their resolution sensitivity with spatial regression models. Landscape and Urban Planning, 123, pp. 145–157, 2014. https://doi.org/10.1016/j.landurbplan.2013.11.014
[10] Wang, J., Qingming, Z., Guo, H. & Jin, Z., Characterizing the spatial dynamics of land surface temperature–impervious surface fraction relationship. International Journal of Applied Earth Observation and Geoinformation, 45, pp. 55–65, 2016.
https://doi.org/10.1016/j.jag.2015.11.006
[11] Myneni, R.B., Maggion, S., Iaquinta, J., Privette, J.L., Gobron, N., Pinty, B., Kimes, D.S., Verstraete, M.M. & Williams, D.L., Optical remote-sensing of vegetation - modeling, caveats, and algorithms. Remote Sensing of Environment, 51(1), pp. 169–188, 1995. https://doi.org/ 10.1016/0034-4257(94)00073-V
[12] Schaaf, C.B., Gao, F., Strahler, A.H., Lucht, W., Li, X., Tsang, T., Strugnell, N.C., Zhang, X., Jin, Y., Muller, J.P. & Lewis, P., First operational BRDF, albedo nadir reflectance products from MODIS. Remote Sensing of Environment, 83(1–2), pp. 135–148, 2002. https://doi.org/10.1016/S0034-4257(02)00091-3
[13] Larsen, L., Urban climate and adaptation strategies. Frontiers in Ecology and the Environment, 13(9), pp. 486–492, 2015. https://doi.org/10.1890/150103
[14] Chudnovsky, A., Ben-Dor, E. & Saaroni, H., Diurnal thermal behavior of selected urban objects using remote sensing measurements. Energy and Buildings, 36(11), pp. 1063–1074, 2004. https://doi.org/10.1016/j.enbuild.2004.01.052
[15] Unger, J., Intra-urban relationship between surface geometry and urban heat island: review and new approach. Climate Research, 27(3), pp. 253–264, 2004. https://doi.org/10.3354/cr027253
[16] Giannopoulou, K., et al., The impact of canyon geometry on intra urban and urban: suburban night temperature differences under warm weather conditions. Pure and Applied Geophysics, 167(11), pp. 1433–1449, 2010. https://doi.org/10.1007/s00024-010-0099-8
[17] Weng, Q.H., Lu, D.S. & Schubring, J., Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment, 89(4), pp. 467–483, 2004. https://doi.org/10.1016/j.rse.2003.11.005
[18] Wu, H., Ye, L.P., Shi, W.Z. & Clarke, K.C., Assessing the effects of land use spatial structure on urban heat islands using HJ-1B remote sensing imagery in Wuhan, China. International Journal of Applied Earth Observation and Geoinformation, 32, pp. 67–78, 2014. https://doi.org/10.1016/j.jag.2014.03.019
[19] Norton, B.A., Coutts, A.M., Livesley, S.J., Harris, R.J., Hunter, A.M. & Williams, N.S., Planning for cooler cities: a framework to prioritise green infrastructure to mitigate high temperatures in urban landscapes. Landscape and Urban Planning, 134, pp. 127–138, 2015. https://doi.org/10.1016/j.landurbplan.2014.10.018
[20] Schwarz, N., Schlink, U., Franck, U. & Großmann, K., Relationship of land surface and air temperatures and its implications for quantifying urban heat island indicators—an application for the city of Leipzig (Germany). Ecological Indicators, 18, pp. 693–704, 2012. https://doi.org/10.1016/j.ecolind.2012.01.001
[21] Stewart, I.D., Oke, T.R. & Krayenhoff, E.S., Evaluation of the ‘local climate zone’scheme using temperature observations and model simulations. International Journal of Climatology, 34(4), pp. 1062–1080, 2014. https://doi.org/10.1002/joc.3746
[22] Lelovics, E., Unger, J. & Gál, T., Design of an urban monitoring network based on local climate zone mapping and temperature pattern modelling. Climate Research, 60(1), pp. 51–62, 2014. https://doi.org/10.3354/cr01220
[23] Voogt, J.A. & Oke, T.R., Thermal remote sensing of urban climates. Remote Sensing of Environment, 86(3), pp. 370–384, 2003. https://doi.org/10.1016/S0034-4257(03)00079-8
[24] Streutker, D.R., A remote sensing study of the urban heat island of Houston, Texas. International Journal of Remote Sensing, 23(13), pp. 2595–2608, 2002. https://doi.org/10.1080/01431160110115023
[25] Rajasekar, U. & Weng, Q.H., Urban heat island monitoring and analysis using a nonparametric model: A case study of Indianapolis. Isprs Journal of Photogrammetry and Remote Sensing, 64(1), pp. 86–96, 2009. https://doi.org/10.1016/j.isprsjprs.2008.05.002
[26] Rasmussen, C.E. & Nickisch, H., Gaussian Processes for Machine Learning (GPML) Toolbox. Journal of Machine Learning Research, 11, pp. 3011–3015, 2010.
[27] Gál, T., Lindberg, F. & Unger, J., Computing continuous sky view factors using 3D urban raster and vector databases: comparison and application to urban climate. Theoretical and Applied Climatology, 95(1–2), pp. 111–123, 2008. https://doi.org/10.1007/s00704-007-0362-9
[28] Zhan, Q., Meng, F. & Xiao, Y., Exploring the relationships of between land surface temperature, ground coverage ratio and building volume density in an urbanized environment. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-7/W3, pp. 255–260, 2015. https://doi.org/10.5194/isprsarchives-XL-7-W3-255-2015
[29] Chen, X.-L., Zhao, H.M., Li, P.X. & Yin, Z.Y., Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sensing of Environment, 104(2), pp. 133–146, 2006. https://doi.org/10.1016/j.rse.2005.11.016
[30] Koenderink, J.J. & Vandoorn, A.J., Surface shape and curvature scales. Image and Vision Computing, 10(8), pp. 565–565, 1992. https://doi.org/10.1016/0262-8856(92)90076-F
[31] Lindeberg, T., Feature detection with automatic scale selection. International Journal Of Computer Vision, 30(2), pp. 79–116, 1998. https://doi.org/10.1023/A:1008045108935
[32] Wang, J., Zhan, Q. & Guo, H., The morphology, dynamics and potential hotspots of land surface temperature at a local scale in urban areas. Remote Sensing, 8(1), p. 18, 2015. https://doi.org/10.3390/rs8010018