Abstract
Given the rapid urbanization worldwide, Urban Heat Island (UHI) effect has been a severe issue limiting urban sustainability in both large and small cities. In order to study the spatial pattern of Surface urban heat island (SUHI) in China’s Meihekou City, a combination method of Monte Carlo and Random Forest Regression (MC-RFR) is developed to construct the relationship between landscape pattern indices and Land Surface Temperature (LST). In this method, Monte Carlo acceptance-rejection sampling was added to the bootstrap layer of RFR to ensure the sensitivity of RFR to outliners of SUHI effect. The SHUI in 2030 was predicted by using this MC-RFR and the modeled future landscape pattern by Cellular Automata and Markov combination model (CA-Markov). Results reveal that forestland can greatly alleviate the impact of SUHI effect, while reasonable construction of urban land can also slow down the rising trend of SUHI. MC-RFR performs better for characterizing the relationship between landscape pattern and LST than single RFR or Linear Regression model. By 2030, the overall SUHI effect of Meihekou will be greatly enhanced, and the center of urban development will gradually shift to the central and western regions of the city. We suggest that urban designer and managers should concentrate vegetation and disperse built-up land to weaken the SUHI in the construction of new urban areas for its sustainability.
Similar content being viewed by others
References
Ashtiani A, Mirzaei P A, Haghighat F, 2014. Indoor thermal condition in urban heat island: comparison of the artificial neural network and regression methods prediction. Energy and Buildings, 76: 597–604. doi: https://doi.org/10.1016/j.enbuild.2014.03.018
Baatz M, Schape A, 2000. Multi-resolution segmentation: an optimization approach for high quality multi-scale image segmentation. Heidelberg: Angewandte Geographische Informationsverarbeitung.
Breiman L, 1998. Arcing classifier (with discussion and a rejoinder by the author). The Annals of Statistics, 26(3): 801–849. doi: https://doi.org/10.1214/aos/1024691079
Breiman L, 2001. Random forests. Machine Learning, 45(1): 5–32. doi: https://doi.org/10.1023/A:1010933404324
Casella G, Robert C P, Wells M T, 2004. Generalized accept-reject sampling schemes: a festschrift for Herman Rubin. Institute of Mathematical Statistics, 342–347. doi: https://doi.org/10.1214/lnms/1196285403
Cheng X Y, Wei B S, Chen G J et al., 2015. Influence of park size and its surrounding urban landscape patterns on the park cooling effect. Journal of Urban Planning and Development, 141(3): A4014002. doi: https://doi.org/10.1061/(ASCE)UP.1943-5444.0000256
Connors J P, Galletti C S, Chow W T L, 2013. Landscape configuration and urban heat island effects: assessing the relationship between landscape characteristics and land surface temperature in Phoenix, Arizona. Landscape Ecology, 28(2): 271–283. doi: https://doi.org/10.1007/s10980-012-9833-1
Du D Y, Li A H, Zhang L L, 2014. Survey on the applications of big data in Chinese real estate enterprise. Procedia Computer Science, 30: 24–33. doi: https://doi.org/10.1016/j.procs.2014.05.377
Estoque R C, Murayama Y, Myint S W, 2017. Effects of landscape composition and pattern on land surface temperature: an urban heat island study in the megacities of Southeast Asia. Science of the Total Environment, 577: 349–359. doi: https://doi.org/10.1016/j.scitotenv.2016.10.195
Ferguson B, Fisher K, Golden J et al., 2008. Reducing Urban Heat Islands: Compendium of Strategies-cool Pavements. Washington, DC, United States: Environmental Protection Agency.
Firozjaei M K, Fathololoumi S, Kiavarz M et al., 2020. Modelling surface heat island intensity according to differences of biophysical characteristics: a case study of Amol City. Ecological Indicators, 109: 105816. doi: https://doi.org/10.1016/j.ecolind.2019.105816
Firozjaei M K, Kiavarz M, Alavipanah S K et al., 2018. Monitoring and forecasting heat island intensity through multi-temporal image analysis and cellular automata-Markov chain modelling: a case of Babol city. Ecological Indicators, 91: 155–170. doi: https://doi.org/10.1016/j.ecolind.2018.03.052
Grömping U, 2009. Variable importance assessment in regression: linear regression versus random forest. The American Statistician, 63(4): 308–319. doi: https://doi.org/10.1198/tast.2009.08199
Hoag H, 2015. How cities can beat the heat: rising temperatures are threatening urban areas, but efforts to cool them may not work as planned. Nature, 524(7566): 402–405. doi: https://doi.org/10.1038/524402a
Hoverter S P, 2012. Adapting to urban heat: a tool kit for local governments. Georgetown Climate Center, August 2012.
Kalkstein L S, Greene J S, 1997. An evaluation of climate/mortality relationships in large US cities and the possible impacts of a climate change. Environmental Health Perspectives, 105(1): 84–93. doi: https://doi.org/10.1289/ehp.9710584
Maimaitiyiming M, Ghulam A, Tiyip T et al., 2014. Effects of green space spatial pattern on land surface temperature: implications for sustainable urban planning and climate change adaptation. ISPRS Journal of Photogrammetry and Remote Sensing, 89: 59–66. doi: https://doi.org/10.1016/j.isprsjprs.2013.12.010
Mao D H, Wang Z M, Wu J G et al., 2018. China’s wetlands loss to urban expansion. Land Degradation & Development, 29(8): 2644–2657. doi: https://doi.org/10.1002/ldr.2939
Mao D H, He X Y, Wang Z M et al., 2019. Diverse policies leading to contrasting impacts on land cover and ecosystem services in Northeast China. Journal of Cleaner Production, 240: 117961. doi: https://doi.org/10.1016/j.jclepro.2019.117961
Myint S W, Brazel A, Okin G et al., 2010. Combined effects of impervious surface and vegetation cover on air temperature variations in a rapidly expanding desert city. GIScience & Remote Sensing, 47(3): 301–320. doi: https://doi.org/10.2747/1548-1603.47.3.301
Patz J A, Campbell-Lendrum D, Holloway T et al., 2005. Impact of regional climate change on human health. Nature, 438(7066): 310–317. doi: https://doi.org/10.1038/nature04188
Peng J, Xie P, Liu Y X et al., 2016. Urban thermal environment dynamics and associated landscape pattern factors: a case study in the Beijing metropolitan region. Remote Sensing of Environment, 173: 145–155. doi: https://doi.org/10.1016/j.rse.2015.11.027
Qin Y H, 2015. A review on the development of cool pavements to mitigate urban heat island effect. Renewable and Sustainable Energy Reviews, 52: 445–459. doi: https://doi.org/10.1016/j.rser.2015.07.177
Qin Z H, Karnieli A, Berliner P A, 2001. 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(18): 3719–3746. (in Chinese). doi: https://doi.org/10.1080/01431160010006971
Rajagopalan P, Lim K C, Jamei E, 2014. Urban heat island and wind flow characteristics of a tropical city. Solar Energy, 107: 159–170. doi: https://doi.org/10.1016/j.solener.2014.05.042
Ren Limin, Wang Gang, Guo Lihong, 2009. Discussion on climate change in Meihekou city in recent 50 years and its influence on agricultural production. Changchun, Jilin, China: Collection of papers on climate change of the 26th annual meeting of China Meteorological Society. (in Chinese)
Segal M R, 2004. Machine learning benchmarks and random forest regression. Center for Bioinformatics and Molecular Biostatistics. April 14, 2003.
Sharma A, Conry P, Fernando H J S et al., 2016. Green and cool roofs to mitigate urban heat island effects in the Chicago metropolitan area: evaluation with a regional climate model. Environmental Research Letters, 11(6): 064004. doi: https://doi.org/10.1088/17489326/11/6/064004
Song J, Du S H, Feng X et al., 2014. The relationships between landscape compositions and land surface temperature: quantifying their resolution sensitivity with spatial regression models. Landscape and Urban Planning, 123: 145–157. doi: https://doi.org/10.1016/j.landurbplan.2013.11.014
Sun Q Q, Wu Z F, Tan J J, 2012. The relationship between land surface temperature and land use/land cover in Guangzhou, China. Environmental Earth Sciences, 65(6): 1687–1694. doi: https://doi.org/10.1007/s12665-011-1145-2
Taha H, 1997. Urban climates and heat islands: albedo, evapotranspiration, and anthropogenic heat. Energy and Buildings, 25(2): 99–103. doi: https://doi.org/10.1016/S0378-7788(96)00999-1
Voogt J A, Oke T R, 2003. Thermal remote sensing of urban climates. Remote Sensing of Environment, 86(3): 370–384. doi: https://doi.org/10.1016/S0034-4257(03)00079-8
Weng Q H, Lu D S, Schubring J, 2004. Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment, 89(4): 467–483. doi: https://doi.org/10.1016/j.rse.2003.11.005
Weng Q, 2001. A remote sensing-GIS evaluation of urban expansion and its impact on surface temperature in the Zhujiang Delta, China. International Journal of Remote Sensing, 22(10): 1999–2014. doi: https://doi.org/10.1080/713860788
Weng Q H, 2009. Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends. ISPRS Journal of Photogrammetry and Remote Sensing, 64(4): 335–344. doi: https://doi.org/10.1016/j.isprsjprs.2009.03.007
WHO, 2010. The world health report: health systems financing: the path to universal coverage: executive summary. World Health Organization, 2010.
Xiao R B, Ouyang Z Y, Zheng H et al., 2007. Spatial pattern of impervious surfaces and their impacts on land surface temperature in Beijing, China. Journal of Environmental Sciences, 19(2): 250–256. doi: https://doi.org/10.1016/S1001-0742(07)60041-2
Zhou D C, Zhang L X, Hao L et al., 2016. Spatiotemporal trends of urban heat island effect along the urban development intensity gradient in China. Science of the Total Environment, 544: 617–626. doi: https://doi.org/10.1016/j.scitotenv.2015.11.168
Zhou W Q, Huang G L, Cadenasso M L, 2011. Does spatial configuration matter: understanding the effects of land cover pattern on land surface temperature in urban landscapes. Landscape and Urban Planning, 102(1): 54–63. doi: https://doi.org/10.1016/j.landurbplan.2011.03.009
Author information
Authors and Affiliations
Corresponding author
Additional information
Foundation item
Under the auspices of National Natural Science Foundation of China (No. 41977411, 41771383), Technology Research Project of the Education Department of Jilin Province (No. JJKH20210445KJ)
Rights and permissions
About this article
Cite this article
Zhang, Y., Liu, J. & Wen, Z. Predicting Surface Urban Heat Island in Meihekou City, China: A Combination Method of Monte Carlo and Random Forest. Chin. Geogr. Sci. 31, 659–670 (2021). https://doi.org/10.1007/s11769-021-1215-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11769-021-1215-7