Predicting Urban Heat Island Mitigation with Random Forest Regression in Belgian Cities

[en] An abundance of impervious surfaces like building roofs in densely populated cities make green roofs a suitable solution for urban heat island (UHI) mitigation. Therefore, we employ random forest (RF) regression to predict the impact of green roofs on the surface UHI (SUHI) in Liege, Belgium. While there have been several studies identifying the impact of green roofs on UHI, fewer studies utilize a remote-sensing-based approach to measure impact on Land Surface Temperatures (LST) that are used to estimate SUHI. Moreover, the RF algorithm, can provide useful insights. In this study, we use LST obtained from Landsat-8 imagery and relate it to 2D and 3D morphological parameters that influence LST and UHI effects. Additionally, we utilise parameters that influence wind (e.g., frontal area index). We simulate the green roofs by assigning suitable values of normalised difference-vegetation index and built-up index to the buildings with flat roofs. Results suggest that green roofs decrease the average LST.

Disciplines :

Engineering, computing & technology: Multidisciplinary, general & others

Author, co-author :

Joshi, Mitali ; Université de Liège - ULiège > Urban and Environmental Engineering

Aliaga, Daniel G.; Department of Computer Science, Purdue University, West Lafayette, United States

Teller, Jacques ; Université de Liège - ULiège > Département ArGEnCo > LEMA (Local environment management and analysis)

Language :

English

Title :

Predicting Urban Heat Island Mitigation with Random Forest Regression in Belgian Cities

Publication date :

2023

Main work title :

Urban Book Series

Publisher :

Springer Science and Business Media Deutschland GmbH

ISBN/EAN :

978-3-03-131746-0
978-3-03-131745-3

Peer reviewed :

Peer reviewed

Additional URL :

https://link.springer.com/content/pdf/10.1007/978-3-031-31746-0_16

Available on ORBi :

since 07 November 2023

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Scopus citations^®

Scopus citations^®
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Bibliography

Adeyeri OE, Akinsanola AA, Ishola KA (2017) Investigating surface urban heat island characteristics over Abuja, Nigeria: relationship between land surface temperature and multiple vegetation indices. Remote Sens Appl 7:57–68. https://doi.org/10.1016/J.RSASE.2017.06.005
Asadi A, Arefi H, Fathipoor H (2020) Simulation of green roofs and their potential mitigating effects on the urban heat island using an artificial neural network: a case study in Austin, Texas. Adv Space Res 66:1846–1862. https://doi.org/10.1016/J.ASR.2020.06.039
Bartesaghi Koc C, Osmond P, Peters A (2018) Evaluating the cooling effects of green infrastructure: a systematic review of methods, indicators and data sources. Sol Energy 166:486–508. https://doi.org/10.1016/J.SOLENER.2018.03.008
Bechtel B, Alexander PJ, Böhner J, et al (2015) Mapping local climate zones for a worldwide database of the form and function of cities. ISPRS Int J Geo-Inform 4:199–219. https://doi.org/10.3390/IJGI4010199
Berardi U, GhaffarianHoseini AH, GhaffarianHoseini A (2014) State-of-the-art analysis of the environmental benefits of green roofs. Appl Energy 115:411–428. https://doi.org/10.1016/J. APENERGY.2013.10.047
Boccalatte A, Fossa M, Gaillard L, Menezo C (2020) Microclimate and urban morphology effects on building energy demand in different European cities. Energy Build 224:110129. https://doi. org/10.1016/j.enbuild.2020.110129
Bowler DE, Buyung-Ali L, Knight TM, Pullin AS (2010) Urban greening to cool towns and cities: a systematic review of the empirical evidence. Landsc Urban Plan 97:147–155. https://doi.org/10.1016/J.LANDURBPLAN.2010.05.006
Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. CRC Press
Breiman L (1996) Bagging predictors. Mach Learn 24:123–140. https://doi.org/10.1007/BF0005 8655
Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:101093340 4324
Bristow KL, Campbell GS (1984) On the relationship between incoming solar radiation and daily maximum and minimum temperature. Agric for Meteorol 31:159–166. https://doi.org/10.1016/0168-1923(84)90017-0
Busato S, Gordon M, Chaudhari M et al (2023) Compositionality, sparsity, spurious heterogeneity, and other data-driven challenges for machine learning algorithms within plant microbiome studies. Curr Opin Plant Biol 71:102326. https://doi.org/10.1016/J.PBI.2022.102326
Chen X-L, Zhao H-M, Li P-X, Yin Z-Y (2006) Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sens Environ 104:133–146. https://doi.org/10.1016/j.rse.2005.11.016
Christis M, Athanassiadis A, Vercalsteren A (2019) Implementation at a city level of circular economy strategies and climate change mitigation—the case of Brussels. J Clean Prod 218:511– 520. https://doi.org/10.1016/J.JCLEPRO.2019.01.180
Deilami K, Kamruzzaman M, Liu Y (2018) Urban heat island effect: a systematic review of spatiotemporal factors, data, methods, and mitigation measures. Int J Appl Earth Obs Geoinf 67:30–42. https://doi.org/10.1016/J.JAG.2017.12.009
di Giuseppe E, D’Orazio M (2014) Assessment of the effectiveness of cool and green roofs for the mitigation of the Heat Island effect and for the improvement of thermal comfort in Nearly Zero Energy Building. 58:134–143. https://doi.org/10.1080/00038628.2014.966050
Dirksen M, Ronda RJ, Theeuwes NE, Pagani GA (2019) Sky view factor calculations and its application in urban heat island studies. Urban Clim 30:100498. https://doi.org/10.1016/J.UCLIM. 2019.100498
Dong J, Lin M, Zuo J et al (2020) Quantitative study on the cooling effect of green roofs in a high-density urban area—a case study of Xiamen, China. J Clean Prod 255:120152. https://doi. org/10.1016/J.JCLEPRO.2020.120152
El-Zeiny AM, Effat HA (2017) Environmental monitoring of spatiotemporal change in land use/land cover and its impact on land surface temperature in El-Fayoum governorate, Egypt. Remote Sens Appl 8:266–277. https://doi.org/10.1016/J.RSASE.2017.10.003
Ferreira LS, Duarte DHS (2019) Exploring the relationship between urban form, land surface temperature and vegetation indices in a subtropical megacity. Urban Climate 27:105–123. https://doi.org/10.1016/J.UCLIM.2018.11.002
Francis LFM, Jensen MB (2017) Benefits of green roofs: a systematic review of the evidence for three ecosystem services. Urban for Urban Green 28:167–176. https://doi.org/10.1016/J.UFUG. 2017.10.015
Govil H, Guha S, Diwan P et al (2020) Analyzing linear relationships of LST with NDVI and MNDISI using various resolution levels of landsat 8 OLI and TIRS data. Adv Intell Syst Comput 1042:171–184. https://doi.org/10.1007/978-981-32-9949-8_13/TABLES/5
Hereher M, Eissa R, Alqasemi A, el Kenawy AM (2022) Assessment of air pollution at Greater Cairo in relation to the spatial variability of surface urban heat island. Environ Sci Pollut Res 29:21412–21425. https://doi.org/10.1007/S11356-021-17383-9/FIGURES/10
Huynh-Thu VA, Geurts P (2019) Unsupervised gene network inference with decision trees and random forests. Methods Mol Biol 1883:195–215. https://doi.org/10.1007/978-1-4939-8882-2_8/COVER
Izquierdo-Verdiguier E, Zurita-Milla R (2020) An evaluation of guided regularized random forest for classification and regression tasks in remote sensing. Int J Appl Earth Obs Geoinf 88:102051. https://doi.org/10.1016/J.JAG.2020.102051
Jamei E, Chau HW, Seyedmahmoudian M, Stojcevski A (2021) Review on the cooling potential of green roofs in different climates. Sci Total Environ 791:148407. https://doi.org/10.1016/J.SCI TOTENV.2021.148407
Jato-Espino D, Manchado C, Roldán-Valcarce A, Moscardó V (2022) ArcUHI: a GIS add-in for automated modelling of the urban heat Island effect through machine learning. Urban Clim 44:101203. https://doi.org/10.1016/J.UCLIM.2022.101203
Joshi MY, Rodler A, Musy M et al (2022) Identifying urban morphological archetypes for microclimate studies using a clustering approach. Build Environ 224:109574. https://doi.org/10.1016/J. BUILDENV.2022.109574
Joshi MY, Teller J (2021) Urban integration of green roofs: current challenges and perspectives. Sustainability 13:12378. https://doi.org/10.3390/SU132212378
Joshi MY, Selmi W, Binard M, et al (2020) Potential for urban greening with green roofs: a way towards smart cities. https://doi.org/10.5194/isprs-annals-VI-4-W2-2020-87-2020
Kim SW, Brown RD (2021) Urban heat island (UHI) variations within a city boundary: a systematic literature review. Renew Sustain Energy Rev 148:111256. https://doi.org/10.1016/j.rser.2021. 111256
Kleerekoper L, van Esch M, Salcedo TB (2012) How to make a city climate-proof, addressing the urban heat island effect. Resour Conserv Recycl 64:30–38. https://doi.org/10.1016/j.resconrec. 2011.06.004
Kokalj Ž, Somrak M (2019) Why not a single image? Combining visualizations to facilitate fieldwork and on-screen mapping. Remote Sens 11:747. https://doi.org/10.3390/RS11070747
Kontschieder P, Bulò SR, Bischof H, Pelillo M (2011) Structured class-labels in random forests for semantic image labelling. Proc IEEE Int Conf Comp Vis 2190–2197. https://doi.org/10.1109/ICCV.2011.6126496
Li H, Liu Y, Zhang H et al (2021) Urban morphology in China: dataset development and spatial pattern characterization. Sustain Cities Soc 71:102981. https://doi.org/10.1016/j.scs. 2021.102981
Li D, Bou-Zeid E, Oppenheimer M (2014) The effectiveness of cool and green roofs as urban heat island mitigation strategies. Environ Res Lett 9. https://doi.org/10.1088/1748-9326/9/5/055002
Lin M, Dong J, Jones L et al (2021) Modeling green roofs’ cooling effect in high-density urban areas based on law of diminishing marginal utility of the cooling efficiency: a case study of Xiamen Island, China. J Clean Prod 316:128277. https://doi.org/10.1016/J.JCLEPRO.2021.128277
Lyu F, Wang S, Han SY, et al (2022) An integrated cyberGIS and machine learning framework for fine-scale prediction of Urban Heat Island using satellite remote sensing and urban sensor network data. Urban Inform 1:1–15. https://doi.org/10.1007/S44212-022-00002-4
Matsuki K, Kuperman V, van Dyke JA (2016) The Random Forests statistical technique: an examination of its value for the study of reading. Sci Stud Read 20:20–33. https://doi.org/10.1080/10888438.2015.1107073/SUPPL_FILE/HSSR_A_1107073_SM1111.DOCX
Mirzaei PA (2015) Recent challenges in modeling of urban heat island. Sustain Cities Soc 19:200– 206. https://doi.org/10.1016/J.SCS.2015.04.001
Montandon L, Small E (2008) The impact of soil reflectance on the quantification of the green vegetation fraction from NDVI. Remote Sens Environ 112:1835–1845. https://doi.org/10.1016/j.rse.2007.09.007
Razzaghmanesh M, Beecham S, Salemi T (2016) The role of green roofs in mitigating urban heat Island effects in the metropolitan area of Adelaide, South Australia. Urban for Urban Green 15:89–102. https://doi.org/10.1016/J.UFUG.2015.11.013
Rodler A, Leduc T (2019) Local climate zone approach on local and micro scales: dividing the urban open space. Urban Clim 28:100457. https://doi.org/10.1016/j.uclim.2019.100457
Roth M, Oke TR, Emery WJ (1989) Satellite-derived urban heat islands from three coastal cities and the utilization of such data in urban climatology. Int J Remote Sens 10(11):1699–1720
Santamouris M (2013) Energy and climate in the urban built environment. Routledge, London
Stewart ID, Oke TR (2012) Local climate zones for urban temperature studies. Bull Am Meteorol Soc 93:1879–1900. https://doi.org/10.1175/BAMS-D-11-00019.1
USGS (2019) Landsat 8 (L8) Data Users Handbook. USGS, Sioux Falls, South Dakota
Wang X, Li H, Sodoudi S (2022) The effectiveness of cool and green roofs in mitigating urban heat island and improving human thermal comfort. Build Environ 217:109082. https://doi.org/10.1016/J.BUILDENV.2022.109082
Wang B, Geoffroy S, Bonhomme M (2021) Urban form study for wind potential development. 49:76–91. https://doi.org/10.1177/2399808321994449
Wong MS, Nichol JE, To PH, Wang J (2010) A simple method for designation of urban ventilation corridors and its application to urban heat island analysis. Build Environ 45:1880–1889. https://doi.org/10.1016/j.buildenv.2010.02.019
Wu Z, Zhang Y (2018) Spatial variation of urban thermal environment and its relation to green space patterns: implication to sustainable landscape planning. Sustainability 10:2249. https://doi.org/10.3390/SU10072249
Yang J, Bou-Zeid E (2019) Scale dependence of the benefits and efficiency of green and cool roofs. Landsc Urban Plan 185:127–140. https://doi.org/10.1016/J.LANDURBPLAN.2019.02.004
Zakšek K, Oštir K, Kokalj Ž (2011) Sky-view factor as a relief visualization technique. Remote Sensing 3:398–415. https://doi.org/10.3390/RS3020398
Zha Y, Gao J, Ni S (2010) Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. 24:583–594. https://doi.org/10.1080/01431160304987