Background climate modulates the impact of land cover on urban surface temperature.


Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
14 09 2022
Historique:
received: 21 06 2022
accepted: 29 08 2022
entrez: 14 9 2022
pubmed: 15 9 2022
medline: 17 9 2022
Statut: epublish

Résumé

Cities with different background climates experience different thermal environments. Many studies have investigated land cover effects on surface urban heat in individual cities. However, a quantitative understanding of how background climates modify the thermal impact of urban land covers remains elusive. Here, we characterise land cover and their impacts on land surface temperature (LST) for 54 highly populated cities using Landsat-8 imagery. Results show that urban surface characteristics and their thermal response are distinctly different across various climate regimes, with the largest difference for cities in arid climates. Cold cities show the largest seasonal variability, with the least seasonality in tropical and arid cities. In tropical, temperate, and cold climates, normalised difference built-up index (NDBI) is the strongest contributor to LST variability during warm months followed by normalised difference vegetation index (NDVI), while normalised difference bareness index (NDBaI) is the most important factor in arid climates. These findings provide a climate-sensitive basis for future land cover planning oriented at mitigating local surface warming.

Identifiants

pubmed: 36104404
doi: 10.1038/s41598-022-19431-x
pii: 10.1038/s41598-022-19431-x
pmc: PMC9474840
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

15433

Informations de copyright

© 2022. The Author(s).

Références

Oke, T. R., Mills, G., Christen, A. & Voogt, J. A. Urban heat island. Urban climates. (Cambridge University Press, 2017).
Aflaki, A. et al. Urban heat island mitigation strategies: A state-of-the-art review on Kuala Lumpur Singapore and Hong Kong. Cities 62, 131–145 (2017).
doi: 10.1016/j.cities.2016.09.003
Méndez-Lázaro, P., Muller-Karger, F. E., Otis, D., McCarthy, M. J. & Rodríguez, E. A heat vulnerability index to improve urban public health management in San Juan Puerto Rico. Int. J. Biometeorol. 62, 709–722 (2018).
pubmed: 28210860 doi: 10.1007/s00484-017-1319-z
Tan, J. et al. The urban heat island and its impact on heat waves and human health in Shanghai. Int. J. Biometeorol. 54, 75–84 (2010).
pubmed: 19727842 doi: 10.1007/s00484-009-0256-x
O’Malley, C., Piroozfar, P., Farr, E. R. & Pomponi, F. Urban Heat Island (UHI) mitigating strategies: A case-based comparative analysis. Sustain. Cities Soc. 19, 222–235 (2015).
doi: 10.1016/j.scs.2015.05.009
Radhi, H., Sharples, S. & Assem, E. Impact of urban heat islands on the thermal comfort and cooling energy demand of artificial islands—A case study of AMWAJ Islands in Bahrain. Sustain. Cities Soc. 19, 310–318 (2015).
doi: 10.1016/j.scs.2015.07.017
Oke, T. The heat island of the urban boundary layer: characteristics, causes and effects. Wind Clim. Cities 81–107 (Springer, 1995).
Voogt, J. A. & Oke, T. R. Thermal remote sensing of urban climates. Remote Sens. Environ. 86, 370–384 (2003).
doi: 10.1016/S0034-4257(03)00079-8
Martilli, A., Krayenhoff, E. S. & Nazarian, N. Is the urban heat island intensity relevant for heat mitigation studies?. Urban Clim. 31, 100541 (2020).
doi: 10.1016/j.uclim.2019.100541
Deilami, K., Kamruzzaman, M. & Liu, Y. Urban heat island effect: A systematic review of spatio-temporal factors, data, methods, and mitigation measures. Int. J. Appl. Earth Obs. Geoinf. 67, 30–42 (2018).
Gartland, L. M. Heat islands: understanding and mitigating heat in urban areas. (Routledge, 2012).
Shahmohamadi, P., Che-Ani, A., Maulud, K., Tawil, N. & Abdullah, N. The impact of anthropogenic heat on formation of urban heat island and energy consumption balance. Urban Stud. Res. 2011 (2011).
Zhou, D. et al. Satellite remote sensing of surface urban heat islands: Progress, challenges, and perspectives. Rem. Sens. 11, 48 (2019).
doi: 10.3390/rs11010048
Duan, S.-B., Li, Z.-L. & Leng, P. A framework for the retrieval of all-weather land surface temperature at a high spatial resolution from polar-orbiting thermal infrared and passive microwave data. Remote Sens. Environ. 195, 107–117 (2017).
doi: 10.1016/j.rse.2017.04.008
Manoli, G. et al. Magnitude of urban heat islands largely explained by climate and population. Nature 573, 55–60 (2019).
pubmed: 31485056 doi: 10.1038/s41586-019-1512-9
Miles, V. & Esau, I. Seasonal and spatial characteristics of urban heat islands (UHIs) in northern West Siberian cities. Remote Sens. 9, 989 (2017).
doi: 10.3390/rs9100989
Yang, Q., Huang, X. & Li, J. Assessing the relationship between surface urban heat islands and landscape patterns across climatic zones in China. Sci. Rep. 7, 1–11 (2017).
Imhoff, M. L., Zhang, P., Wolfe, R. E. & Bounoua, L. Remote sensing of the urban heat island effect across biomes in the continental USA. Remote Sens. Environ. 114, 504–513 (2010).
doi: 10.1016/j.rse.2009.10.008
Zhou, D., Zhang, L., Li, D., Huang, D. & Zhu, C. Climate–vegetation control on the diurnal and seasonal variations of surface urban heat islands in China. Environ. Res. Lett. 11, 074009 (2016).
doi: 10.1088/1748-9326/11/7/074009
Zhou, D., Zhao, S., Liu, S., Zhang, L. & Zhu, C. Surface urban heat island in China’s 32 major cities: Spatial patterns and drivers. Remote Sens. Environ. 152, 51–61 (2014).
doi: 10.1016/j.rse.2014.05.017
Heinl, M., Hammerle, A., Tappeiner, U. & Leitinger, G. Determinants of urban–rural land surface temperature differences—A landscape scale perspective. Landsc. Urban Plan. 134, 33–42 (2015).
doi: 10.1016/j.landurbplan.2014.10.003
Peng, S. et al. Surface urban heat island across 419 global big cities. Environ. Sci. Technol. 46, 696–703 (2012).
pubmed: 22142232 doi: 10.1021/es2030438
Su, Y. et al. Phenology acts as a primary control of urban vegetation cooling and warming: A synthetic analysis of global site observations. Agric. For. Meteorol. 280, 107765 (2020).
doi: 10.1016/j.agrformet.2019.107765
Zeng, Z. et al. Climate mitigation from vegetation biophysical feedbacks during the past three decades. Nat. Clim. Chang. 7, 432–436 (2017).
doi: 10.1038/nclimate3299
Su, Y. et al. Quantifying the biophysical effects of forests on local air temperature using a novel three-layered land surface energy balance model. Environ. Int. 132, 105080 (2019).
pubmed: 31465951 doi: 10.1016/j.envint.2019.105080
Wang, C., Li, Y., Myint, S. W., Zhao, Q. & Wentz, E. A. Impacts of spatial clustering of urban land cover on land surface temperature across Köppen climate zones in the contiguous United States. Landsc. Urban Plan. 192, 103668 (2019).
doi: 10.1016/j.landurbplan.2019.103668
Guo, G. et al. Impacts of urban biophysical composition on land surface temperature in urban heat island clusters. Landsc. Urban Plan. 135, 1–10 (2015).
doi: 10.1016/j.landurbplan.2014.11.007
Naserikia, M., Asadi Shamsabadi, E., Rafieian, M. & Leal Filho, W. The urban heat island in an urban context: A case study of Mashhad Iran. Int. J. Environ. Res. Public Health 16, 313 (2019).
pmcid: 6388183 doi: 10.3390/ijerph16030313
Mathew, A., Khandelwal, S. & Kaul, N. Spatio-temporal variations of surface temperatures of Ahmedabad city and its relationship with vegetation and urbanization parameters as indicators of surface temperatures. Remote Sens. Appl. Soc. Environ. 11, 119–139 (2018).
Marzban, F., Sodoudi, S. & Preusker, R. The influence of land-cover type on the relationship between NDVI–LST and LST-T air. Int. J. Remote Sens. 39, 1377–1398 (2018).
doi: 10.1080/01431161.2017.1402386
Zhou, W., Qian, Y., Li, X., Li, W. & Han, L. Relationships between land cover and the surface urban heat island: Seasonal variability and effects of spatial and thematic resolution of land cover data on predicting land surface temperatures. Landsc. Ecol. 29, 153–167 (2014).
doi: 10.1007/s10980-013-9950-5
Ali, J. M., Marsh, S. H. & Smith, M. J. A comparison between London and Baghdad surface urban heat islands and possible engineering mitigation solutions. Sustain. Cities Soc. 29, 159–168 (2017).
doi: 10.1016/j.scs.2016.12.010
Beck, H. E. et al. Present and future Köppen-Geiger climate classification maps at 1-km resolution. Sci. Data 5, 1–12 (2018).
doi: 10.1038/sdata.2018.214
Van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9 (2008).
Pedregosa, F. et al. Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
Sandholt, I., Rasmussen, K. & Andersen, J. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sens. Environ. 79, 213–224 (2002).
doi: 10.1016/S0034-4257(01)00274-7
Lambin, E. F. & Ehrlich, D. The surface temperature-vegetation index space for land cover and land-cover change analysis. Int. J. Remote Sens. 17, 463–487 (1996).
doi: 10.1080/01431169608949021
Goetz, S. Multi-sensor analysis of NDVI, surface temperature and biophysical variables at a mixed grassland site. Int. J. Remote Sens. 18, 71–94 (1997).
doi: 10.1080/014311697219286
Nemani, R., Pierce, L., Running, S. & Goward, S. Developing satellite-derived estimates of surface moisture status. J. Appl. Meteorol. Climatol. 32, 548–557 (1993).
doi: 10.1175/1520-0450(1993)032<0548:DSDEOS>2.0.CO;2
Qiao, Z., Tian, G. & Xiao, L. Diurnal and seasonal impacts of urbanization on the urban thermal environment: A case study of Beijing using MODIS data. ISPRS J. Photogramm. Remote. Sens. 85, 93–101 (2013).
doi: 10.1016/j.isprsjprs.2013.08.010
Venter, Z. S., Chakraborty, T. & Lee, X. Crowdsourced air temperatures contrast satellite measures of the urban heat island and its mechanisms. Sci. Adv. 7, eabb9569 (2021).
Hastie, T., Tibshirani, R., Friedman, J. H. & Friedman, J. H. The elements of statistical learning: data mining, inference, and prediction. Vol. 2 (Springer, 2009).
Di, B. et al. Assessing susceptibility of debris flow in southwest China using gradient boosting machine. Sci. Rep. 9, 1–12 (2019).
doi: 10.1038/s41598-019-48986-5
Rasul, A. et al. Applying built-up and bare-soil indices from Landsat 8 to cities in dry climates. Land 7, 81 (2018).
doi: 10.3390/land7030081
Oke, T. R. The energetic basis of the urban heat island. Q. J. R. Meteorol. Soc. 108, 1–24 (1982).
Gunawardena, K. R., Wells, M. J. & Kershaw, T. Utilising green and bluespace to mitigate urban heat island intensity. Sci. Total Environ. 584, 1040–1055 (2017).
pubmed: 28161043 doi: 10.1016/j.scitotenv.2017.01.158
Moran, M., Clarke, T., Inoue, Y. & Vidal, A. Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index. Remote Sens. Environ. 49, 246–263 (1994).
doi: 10.1016/0034-4257(94)90020-5
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 Sens. Environ. 106, 375–386 (2007).
doi: 10.1016/j.rse.2006.09.003
Du, H. et al. Influences of land cover types, meteorological conditions, anthropogenic heat and urban area on surface urban heat island in the Yangtze River Delta Urban Agglomeration. Sci. Total Environ. 571, 461–470 (2016).
pubmed: 27424113 doi: 10.1016/j.scitotenv.2016.07.012
Huang, Q. & Lu, Y. Urban heat island research from 1991 to 2015: A bibliometric analysis. Theoret. Appl. Climatol. 131, 1055–1067 (2018).
doi: 10.1007/s00704-016-2025-1
Yang, X. & Li, Y. The impact of building density and building height heterogeneity on average urban albedo and street surface temperature. Build. Environ. 90, 146–156 (2015).
doi: 10.1016/j.buildenv.2015.03.037
Zhou, X. & Wang, Y. C. Dynamics of land surface temperature in response to land‐use/cover change. Geograph. Res. 49, 23–36 (2011).
Zhao, H. & Chen, X. Use of normalized difference bareness index in quickly mapping bare areas from TM/ETM+. Int. Geosci. Remote Sens. Symp. 1666 (2005).
Tran, H., Uchihama, D., Ochi, S. & Yasuoka, Y. Assessment with satellite data of the urban heat island effects in Asian mega cities. Int. J. Appl. Earth Obs. Geoinf. 8, 34–48 (2006).
Kafy, A.-A. et al. Assessing and predicting land use/land cover, land surface temperature and urban thermal field variance index using Landsat imagery for Dhaka Metropolitan area. Environ. Challenges 4, 100192 (2021).
doi: 10.1016/j.envc.2021.100192
Desa, U. World urbanization prospects: the 2014 revision, CD-ROM edition. ST/ESA/SER. A/366) (2014).
Gorelick, N. et al. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).
doi: 10.1016/j.rse.2017.06.031
Duguay-Tetzlaff, A. et al. Meteosat land surface temperature climate data record: Achievable accuracy and potential uncertainties. Remote Sens. 7, 13139–13156 (2015).
doi: 10.3390/rs71013139
Ermida, S. L., Soares, P., Mantas, V., Göttsche, F.-M. & Trigo, I. F. Google earth engine open-source code for land surface temperature estimation from the landsat series. Remote Sensing 12, 1471 (2020).
doi: 10.3390/rs12091471
Tucker, C. J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8, 127–150 (1979).
doi: 10.1016/0034-4257(79)90013-0
McFeeters, S. K. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. Int. J. Remote Sens. 17, 1425–1432 (1996).
doi: 10.1080/01431169608948714
Zha, Y., Gao, J. & Ni, S. Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. Int. J. Remote Sens. 24, 583–594 (2003).
doi: 10.1080/01431160304987
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 Sens. Environ. 104, 133–146 (2006).
doi: 10.1016/j.rse.2005.11.016
Liu, H. & Weng, Q. Scaling effect on the relationship between landscape pattern and land surface temperature. Photogramm. Eng. Remote. Sens. 75, 291–304 (2009).
doi: 10.14358/PERS.75.3.291
Li, J. et al. Impacts of landscape structure on surface urban heat islands: A case study of Shanghai China. Rem. Sens. Environ. 115, 3249–3263 (2011).
doi: 10.1016/j.rse.2011.07.008
Kong, F., Yin, H., James, P., Hutyra, L. R. & He, H. S. Effects of spatial pattern of greenspace on urban cooling in a large metropolitan area of eastern China. Landsc. Urban Plan. 128, 35–47 (2014).
doi: 10.1016/j.landurbplan.2014.04.018
Deng, Y. et al. Relationship among land surface temperature and LUCC, NDVI in typical karst area. Sci. Rep. 8, 1–12 (2018).
doi: 10.1038/s41598-017-19088-x
Wilson, J. S., Clay, M., Martin, E., Stuckey, D. & Vedder-Risch, K. Evaluating environmental influences of zoning in urban ecosystems with remote sensing. Remote Sens. Environ. 86, 303–321 (2003).
doi: 10.1016/S0034-4257(03)00084-1
Xu, H. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int. J. Remote Sens. 27, 3025–3033 (2006).
doi: 10.1080/01431160600589179
Feyisa, G. L., Meilby, H., Fensholt, R. & Proud, S. R. Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery. Remote Sens. Environ. 140, 23–35 (2014).
doi: 10.1016/j.rse.2013.08.029
Liang, S. Narrowband to broadband conversions of land surface albedo I: Algorithms. Remote Sens. Environ. 76, 213–238 (2001).
doi: 10.1016/S0034-4257(00)00205-4
Smith, R. The heat budget of the earth’s surface deduced from space (Yale University Center for Earth Observation, 2010).
Nasa, J. Nasadem Merged DEM Global 1 arc second V001. NASA EOSDIS Land Processes DAAC. Last accessed September (2020).
Wattenberg, M., Viégas, F. & Johnson, I. How to use t-SNE effectively. Distill 1, e2 (2016).
doi: 10.23915/distill.00002
Marshall, J. D. et al. Continuous whole-body 3D kinematic recordings across the rodent behavioral repertoire. Neuron 109, 420–437. e428 (2021).
Friedman, J. H. Greedy function approximation: a gradient boosting machine. Ann. Stat. 1189–1232 (2001).
Reid, C. E. et al. Spatiotemporal prediction of fine particulate matter during the 2008 Northern California wildfires using machine learning. Environ. Sci. Technol. 49, 3887–3896 (2015).
pubmed: 25648639 doi: 10.1021/es505846r
Georganos, S. et al. Very high resolution object-based land use–land cover urban classification using extreme gradient boosting. IEEE Geosci. Remote Sens. Lett. 15, 607–611 (2018).
doi: 10.1109/LGRS.2018.2803259
Dormann, C. F. et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46 (2013).
doi: 10.1111/j.1600-0587.2012.07348.x

Auteurs

Marzie Naserikia (M)

Australian Research Council Centre of Excellence for Climate Extremes, University of New South Wales, Sydney, Australia. m.naserikia@unsw.edu.au.

Melissa A Hart (MA)

Australian Research Council Centre of Excellence for Climate Extremes, University of New South Wales, Sydney, Australia.

Negin Nazarian (N)

Australian Research Council Centre of Excellence for Climate Extremes, University of New South Wales, Sydney, Australia.
School of Built Environment, University of New South Wales, Sydney, Australia.
City Futures Research Centre, University of New South Wales, Sydney, Australia.

Benjamin Bechtel (B)

Department of Geography, Ruhr-University Bochum, Bochum, Germany.

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