Mapping Local Climate Zones and ExplorMapping Local Climate Zones and Exploring Temperature Dynamics in the Metropolitan Area of Florenceing Temperature Dynamics in the Metropolitan Area of Florence

Authors

  • Mattia Niccoli Dipartimento di Scienze e Tecnologie Agrarie, Alimentari, Ambientali e Forestali, Università di Firenze, Italy
  • Costanza Borghi Dipartimento di Scienze e Tecnologie Agrarie, Alimentari, Ambientali e Forestali, Università di Firenze, Italy
  • Gherardo Chirici Dipartimento di Scienze e Tecnologie Agrarie, Alimentari, Ambientali e Forestali, Università di Firenze, Italy

DOI:

https://doi.org/10.36253/bsgi-7621

Keywords:

Local Climate Zones, Urban Heat Island, Land Surface Temperature, Citizen Weather Stations

Abstract

This paper investigates the temperature dynamics within the Metropolitan Area of Florence, and particularly in the “Piana Fiorentina”. It focuses on the Urban Heat Island (UHI) effect by utilising Local Climate Zones (LCZs), Land Surface Temperature (LST), and crowdsourced air temperature data. The study aims to map LCZs and explore temperature variations across urban and peri-urban landscapes. A supervised classification combining remote sensing data from Sentinel-2 and geospatial urban morphology parameters was employed to generate an LCZ map of the Florence-Prato-Pistoia area. LST data were used to analyse temperature differences across LCZs, highlighting seasonal and spatial variations. Additionally, the study incorporated air temperature measurements from Citizen Weather Stations (CWS) to assess intra-urban temperature dynamics, representing the first use of such data in this context. The results revealed significant thermal contrasts between urban and natural LCZs, demonstrating the UHI effect, its seasonal variability, and the diurnal-to-nocturnal cycle. This work contributes to the understanding of urban climate behaviour in and around Florence and highlights the usefulness of LCZs mapping and CWS data for urban climate studies.

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References

Aslam, A., Rana, I. A. (2022). The use of local climate zones in the urban environment: A systematic review of data sources, methods, and themes. Urban Climate, 42, 101120. DOI: https://doi.org/10.1016/j.uclim.2022.101120 DOI: https://doi.org/10.1016/j.uclim.2022.101120

Balchin, W. G. V., Pye, N. (1947). A micro‐climatological investigation of bath and the surrounding district. Quarterly Journal of the Royal Meteorological Society, 73 (317-318), 297-323. DOI: https://doi.org/10.1002/qj.49707331706 DOI: https://doi.org/10.1002/qj.49707331706

Banti, N., Ciacci, C., Bazzocchi, F., Di Naso, V. (2025). Potential Heat Island Phenomenon in Florence: Microclimatic Assessment Through Digital Urban Modelling. In He, B., Piselli, C., Karunathilake, H., Cheshmehzangi, A., Attia, S., Darko, A. (Eds.). Towards the Framework of Livable and Resilient Cities. Cham, Springer. DOI: https://doi.org/10.1007/978-3-031-97849-4_24 DOI: https://doi.org/10.1007/978-3-031-97849-4_24

Bell, S., Cornford, D., Bastin, L. (2015). How good are citizen weather stations? Addressing a biased opinion. Weather, 70 (3), 75-84. DOI: https://doi.org/10.1002/wea.2316 DOI: https://doi.org/10.1002/wea.2316

Breiman, L. (2001). Random Forests. Machine Learning, 45, 5-32. DOI: https://doi.org/10.1023/A:1010933404324 DOI: https://doi.org/10.1023/A:1010933404324

Cai, M., Ren, C., Xu, Y., Lau, K. K.-L., Wang, R. (2018). Investigating the relationship between local climate zone and land surface temperature using an improved WUDAPT methodology – A case study of Yangtze River Delta, China. Urban Climate, 24, 485-502. DOI: https://doi.org/10.1016/j.uclim.2017.05.010 DOI: https://doi.org/10.1016/j.uclim.2017.05.010

Chapman, S., Thatcher, M., Salazar, A., Watson, J. E. M., McAlpine, C. A. (2019). The impact of climate change and urban growth on urban climate and heat stress in a subtropical city. International Journal of Climatology, 39, 3013-3030. DOI: https://doi.org/10.1002/joc.5998 DOI: https://doi.org/10.1002/joc.5998

Cohen, J. (1977). Statistical Power Analysis for the Behavioral Sciences. New York, Routledge. DOI: https://doi.org/10.4324/9780203771587 DOI: https://doi.org/10.4324/9780203771587

Consorzio LaMMA (n.d.). Il meteo in Toscana. https://www.lamma.toscana.it/

Copernicus Browser. (n.d.). Copernicus Browser. https://browser.dataspace.copernicus.eu/

Copernicus Land Monitoring Services. (n.d.). CLMS Portfolio. https://land.copernicus.eu/en/products

Correa, J., Dorta, P., López-Díez, A., Díaz-Pacheco, J. (2024). Analysis of tropical nights in Spain (1970-2023): Minimum temperatures as an indicator of climate change. International Journal of Climatology, 44 (9), 3006-3027. DOI: https://doi.org/10.1002/joc.8510 DOI: https://doi.org/10.1002/joc.8510

Creutzig, F., Agoston, P., Minx, J. C., Canadell, J. G., Andrew, R. M., Le Quéré, C., Peters, G. P., Sharifi, A., Yamagata, Y., Dhakal, S. (2016). Urban infrastructure choices structure climate solutions. Nature Climate Change, 6, 1054-1056. DOI: https://doi.org/10.1038/nclimate3169 DOI: https://doi.org/10.1038/nclimate3169

Demuzere, M., Kittner, J., Bechtel, B. (2021). The LCZ Generator: A Web Application to Create Local Climate Zone Maps. Frontiers in Environmental Science, 9. DOI: https://doi.org/10.3389/fenvs.2021.637455 DOI: https://doi.org/10.3389/fenvs.2021.637455

ENVI-met. (n.d.). The Future of Sustainable Urban Planning with Climate Adaptation. https://envi-met.com/

European Commission. (n.d.). LIFE 3.0. https://webgate.ec.europa.eu/life/publicWebsite/project/LIFE23-CCA-IT-LIFE-ESCAPOS-101157553/environment-energy-for-strategic-capillary-urban-policies

Evola, G., Gagliano, A., Fichera, A., Marletta, L., Martinico, F., Nocera, F., Pagano, A. (2017). UHI effects and strategies to improve outdoor thermal comfort in dense and old neighbourhoods. Energy Procedia, 134, 692-701. DOI: https://doi.org/10.1016/j.egypro.2017.09.589 DOI: https://doi.org/10.1016/j.egypro.2017.09.589

Feichtinger, M., de Wit, R., Goldenits, G., Kolejka, T., Hollósi, B., Žuvela-Aloise, M., Feigl, J. (2020). Case-study of neighborhood-scale summertime urban air temperature for the City of Vienna using crowd-sourced data. Urban Climate, 32, 100597. DOI: https://doi.org/10.1016/j.uclim.2020.100597 DOI: https://doi.org/10.1016/j.uclim.2020.100597

Fenner, D., Bechtel, B., Demuzere, M., Kittner, J., Meier, F. (2021). CrowdQC+―A Quality-Control for Crowdsourced Air-Temperature Observations Enabling World-Wide Urban Climate Applications. Frontiers in Environmental Science, 9. DOI: https://doi.org/10.3389/fenvs.2021.720747 DOI: https://doi.org/10.3389/fenvs.2021.720747

Geletič, J., Lehnert, M., Dobrovolný, P. (2016). Land Surface Temperature Differences within Local Climate Zones, Based on Two Central European Cities. Remote Sensing, 8 (10), 788. DOI: https://doi.org/10.3390/rs8100788 DOI: https://doi.org/10.3390/rs8100788

GEOscopio - Regione Toscana. (n.d.). Geoscopio. https://www.regione.toscana.it/-/geoscopio

Good, E. J. (2016). An in situ-based analysis of the relationship between land surface “skin” and screen-level air temperatures. Journal of Geophysical Research: Atmospheres, 121 (15), 8801-8819. DOI: https://doi.org/10.1002/2016JD025318 DOI: https://doi.org/10.1002/2016JD025318

Guerri, G., Crisci, A., Congedo, L., Munafò, M., Morabito, M. (2022). A functional seasonal thermal hot-spot classification: Focus on industrial sites. Science of The Total Environment, 806 (4), 151383. DOI: https://doi.org/10.1016/j.scitotenv.2021.151383 DOI: https://doi.org/10.1016/j.scitotenv.2021.151383

Guerri, G., Crisci, A., Messeri, A., Congedo, L., Munafò, M., Morabito, M. (2021). Thermal Summer Diurnal Hot-Spot Analysis: The Role of Local Urban Features Layers. Remote Sensing, 13 (3), 538. DOI: https://doi.org/10.3390/rs13030538 DOI: https://doi.org/10.3390/rs13030538

Guerri, G., Crisci, A., Morabito, M. (2023). Urban microclimate simulations based on GIS data to mitigate thermal hot-spots: Tree design scenarios in an industrial area of Florence. Building and Environment, 245, 110854. DOI: https://doi.org/10.1016/j.buildenv.2023.110854 DOI: https://doi.org/10.1016/j.buildenv.2023.110854

Gurney, K. R., Romero-Lankao, P., Seto, K. C., Hutyra, L. R., Duren, R., Kennedy, C., Grimm, N. B., Ehleringer, J. R., Marcotullio, P., Hughes, S., Pincetl, S., Chester, M. V., Runfola, D. M., Feddema, J. J., Sperling, J. (2015). Climate change: Track urban emissions on a human scale. Nature, 525, 179-181. DOI: https://doi.org/10.1038/525179a DOI: https://doi.org/10.1038/525179a

Hassani, A., Santos, G. S., Schneider, P., Castell, N. (2023). Interpolation, Satellite-Based Machine Learning, or Meteorological Simulation? A Comparison Analysis for Spatio-temporal Mapping of Mesoscale Urban Air Temperature. Environmental Modeling & Assessment, 29, 291-306. DOI: https://doi.org/10.1007/s10666-023-09943-9 DOI: https://doi.org/10.1007/s10666-023-09943-9

Ho, H. C., Knudby, A., Xu, Y., Hodul, M., Aminipouri, M. (2016). A comparison of urban heat islands mapped using skin temperature, air temperature, and apparent temperature (Humidex), for the greater Vancouver area. Science of the Total Environment, 544, 929-938. DOI: https://doi.org/10.1016/j.scitotenv.2015.12.021 DOI: https://doi.org/10.1016/j.scitotenv.2015.12.021

Huang, F., Jiang, S., Zhan, W., Bechtel, B., Liu, Z., Demuzere, M., Huang, Y., Xu, Y., Ma, L., Xia, W., Quan, J., Jiang, L., Lai, J., Wang, C., Kong, F., Du, H., Miao, S., Chen, Y., Chen, J. (2023). Mapping local climate zones for cities: A large review. Remote Sensing of Environment, 292, 113573. DOI: https://doi.org/10.1016/j.rse.2023.113573 DOI: https://doi.org/10.1016/j.rse.2023.113573

Istat. (2024). Resident population by age, sex and marital status on January 1st. https://demo.istat.it/app/?i=POS&l=en

Kattel, D. B., Yao, T., Yang, K., Tian, L., Yang, G., Joswiak, D. (2013). Temperature lapse rate in complex mountain terrain on the southern slope of the central Himalayas. Theoretical and Applied Climatology, 113, 671-682. DOI: https://doi.org/10.1007/s00704-012-0816-6 DOI: https://doi.org/10.1007/s00704-012-0816-6

Konijnendijk, C. C. (2023). Evidence-based guidelines for greener, healthier, more resilient neighbourhoods: Introducing the 3-30-300 rule. Journal of Forestry Research, 34, 821-830. DOI: https://doi.org/10.1007/s11676-022-01523-z DOI: https://doi.org/10.1007/s11676-022-01523-z

Kotharkar, R., Ghosh, A., Kapoor, S., Reddy, D. G. K. (2022). Approach to local climate zone based energy consumption assessment in an Indian city. Energy and Buildings, 259, 111835. DOI: https://doi.org/10.1016/j.enbuild.2022.111835 DOI: https://doi.org/10.1016/j.enbuild.2022.111835

Lang, N., Jetz, W., Schindler, K., Wegner, J. D. (2023). A high-resolution canopy height model of the Earth. Nature Ecology & Evolution, 7, 1778-1789. DOI: https://doi.org/10.1038/s41559-023-02206-6 DOI: https://doi.org/10.1038/s41559-023-02206-6

Meier, F., Fenner, D., Grassmann, T., Otto, M., Scherer, D. (2017). Crowdsourcing air temperature from citizen weather stations for urban climate research. Urban Climate, 19, 170-191. DOI: https://doi.org/10.1016/j.uclim.2017.01.006 DOI: https://doi.org/10.1016/j.uclim.2017.01.006

Morabito, M., Crisci, A., Guerri, G., Messeri, A., Congedo, L., Munafò, M. (2021). Surface urban heat islands in Italian metropolitan cities: Tree cover and impervious surface influences. Science of The Total Environment, 751, 142334. DOI: https://doi.org/10.1016/j.scitotenv.2020.142334 DOI: https://doi.org/10.1016/j.scitotenv.2020.142334

Muller, C. L., Chapman, L., Johnston, S., Kidd, C., Illingworth, S., Foody, G., Overeem, A., Leigh, R. R. (2015). Crowdsourcing for climate and atmospheric sciences: current status and future potential. International Journal of Climatology, 35 (11), 3185-3203. DOI: https://doi.org/10.1002/joc.4210 DOI: https://doi.org/10.1002/joc.4210

Napoly, A., Grassmann, T., Meier, F., Fenner, D. (2018). Development and Application of a Statistically-Based Quality Control for Crowdsourced Air Temperature Data. Frontiers in Earth Science, 6. DOI: https://doi.org/10.3389/feart.2018.00118 DOI: https://doi.org/10.3389/feart.2018.00118

Oke, T. R., Mills, G., Christen, A., Voogt, J. A. (2017). Urban Climates. Cambridge, Cambridge University Press. DOI: https://doi.org/10.1017/9781139016476 DOI: https://doi.org/10.1017/9781139016476

Oxoli, D., Cedeno Jimenez, J. R., Capizzi, E., Brovelli, M. A., Siciliani de Cumis, M., Sacco, P., Tapete, D. (2023). QGIS and Open Data Cube Applications for Local Climate Zones Analysis Leveraging PRISMA Hyperspectral Satellite Data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVIII-1/W2-2023, 111-116. DOI: https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-111-2023 DOI: https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-111-2023

Petralli, M., Massetti, L., Orlandini, S. (2011). Five years of thermal intra-urban monitoring in Florence (Italy) and application of climatological indices. Theoretical and Applied Climatology, 104, 349-356. DOI: https://doi.org/10.1007/s00704-010-0349-9 DOI: https://doi.org/10.1007/s00704-010-0349-9

Puche, M., Vavassori, A., Brovelli, M. A. (2023). Insights into the Effect of Urban Morphology and Land Cover on Land Surface and Air Temperatures in the Metropolitan City of Milan (Italy) Using Satellite Imagery and In Situ Measurements. Remote Sensing, 15 (3), 733. DOI: https://doi.org/10.3390/rs15030733 DOI: https://doi.org/10.3390/rs15030733

Romano, R., Bologna, R., Hasanaj, G., Arnetoli, M. V. (2020). Adaptive Design to Mitigate the Effects of UHI: The Case Study of Piazza Togliatti in the Municipality of Scandicci. In Littlewood, J., Howlett, R., Capozzoli, A., Jain, L. (Eds.). Sustainability in Energy and Buildings. Smart Innovation, Systems and Technologies, 163, Singapore, Springer. DOI: https://doi.org/10.1007/978-981-32-9868-2_45 DOI: https://doi.org/10.1007/978-981-32-9868-2_45

Romano, R., Gallo, P., Donato, A. (2022). Evaluation of Mitigation Strategies of The Urban Heat Island Effect in Mediterranean Area. The Case Study of Largo Annigoni in Florence (Italy). In Alberti, F., Amer, M., Mahgoub, Y., Gallo, P., Galderisi, A., Strauss, E. (Eds.). Urban and Transit Planning. Advances in Science, Technology & Innovation, Cham, Springer. DOI: https://doi.org/10.1007/978-3-030-97046-8_6 DOI: https://doi.org/10.1007/978-3-030-97046-8_6

Rubel, F., Kottek, M. (2010). Observed and projected climate shifts 1901-2100 depicted by world maps of the Köppen-Geiger climate classification. Meteorologische Zeitschrift, 19 (2), 135-141. DOI: https://doi.org/10.1127/0941-2948/2010/0430 DOI: https://doi.org/10.1127/0941-2948/2010/0430

SNPA (2020). Consumo di suolo, dinamiche territoriali e servizi ecosistemici. Edizione 2020. https://www.snpambiente.it/pubblicazioni/report-snpa/consumo-di-suolo-dinamiche-territoriali-e-servizi-ecosistemici-edizione-2020/

Stewart, I. D., Oke, T. R. (2012). Local Climate Zones for Urban Temperature Studies. Bulletin of the American Meteorological Society, 1879-1900. DOI: https://doi.org/10.1175/BAMS-D-11-00019.1 DOI: https://doi.org/10.1175/BAMS-D-11-00019.1

Unal Cilek, M., Cilek, A. (2021). Analyses of land surface temperature (LST) variability among local climate zones (LCZs) comparing Landsat-8 and ENVI-met model data. Sustainable Cities and Society, 69, 102877. DOI: https://doi.org/10.1016/j.scs.2021.102877 DOI: https://doi.org/10.1016/j.scs.2021.102877

United Nations Global Compact. (2019). The Nature-Based Solutions for Climate Manifesto. https://d306pr3pise04h.cloudfront.net/docs/publications%2FNature-Based-Solutions-for-Climate-Manifesto.pdf

Vasić, M., Dunjić, J., Savić, S., Dočkal, O. (2025). Applications of local climate zone classification in European cities: A review of in situ and mobile monitoring methods in urban climate studies. Open Geosciences, 17, 20250878. DOI: https://doi.org/10.1515/geo-2025-0878 DOI: https://doi.org/10.1515/geo-2025-0878

Vavassori, A., Giuliani, G., Brovelli, M. A. (2023). Mapping Local Climate Zones in Lausanne (Switzerland) with Sentinel-2 and PRISMA Imagery: comparison of classification performance using different band combinations and building height data. International Journal of Digital Earth, 16, 4790-4810. DOI: https://doi.org/10.1080/17538947.2023.2283485 DOI: https://doi.org/10.1080/17538947.2023.2283485

Vavassori, A., Oxoli, D., Venuti, G., Brovelli, M. A., Siciliani de Cumis, M., Sacco, P., Tapete, D. (2024). A combined Remote Sensing and GIS-based method for Local Climate Zone mapping using PRISMA and Sentinel-2 imagery. International Journal of Applied Earth Observation and Geoinformation, 131, 103944. DOI: https://doi.org/10.1016/j.jag.2024.103944 DOI: https://doi.org/10.1016/j.jag.2024.103944

Venter, Z. S., Brousse, O., Esau, I., Meier, F. (2020). Hyperlocal mapping of urban air temperature using remote sensing and crowdsourced weather data. Remote Sensing of Environment, 242, 111791. DOI: https://doi.org/10.1016/j.rse.2020.111791 DOI: https://doi.org/10.1016/j.rse.2020.111791

Verdonck, M.-L., Demuzere, M., Hooyberghs, H., Beck, C., Cyrys, J., Schneider, A., Dewulf, R., Van Coillie, F. (2018). The potential of local climate zones maps as a heat stress assessment tool, supported by simulated air temperature data. Landscape and Urban Planning, 178, 183-197. DOI: https://doi.org/10.1016/j.landurbplan.2018.06.004 DOI: https://doi.org/10.1016/j.landurbplan.2018.06.004

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 DOI: https://doi.org/10.1016/S0034-4257(03)00079-8

Wang, Y., Wang, L., Li, X., Chen, D. (2018). Temporal and spatial changes in estimated near-surface air temperature lapse rates on Tibetan Plateau. International Journal of Climatology, 38 (7), 2907-2921. DOI: https://doi.org/10.1002/joc.5471 DOI: https://doi.org/10.1002/joc.5471

Yang, M., Ren, C., Wang, H., Wang, J., Feng, Z., Kumar, P., Haghighat, F., Cao, S.- J. (2024). Mitigating urban heat island through neighboring rural land cover. Nature Cities, 1, 522-532. DOI: https://doi.org/10.1038/s44284-024-00091-z DOI: https://doi.org/10.1038/s44284-024-00091-z

Zumwald, M., Knüsel, B., Bresch, D. N., Knutti, R. (2021). Mapping urban temperature using crowd-sensing data and machine learning. Urban Climate, 35, 100739. DOI: https://doi.org/10.1016/j.uclim.2020.100739 DOI: https://doi.org/10.1016/j.uclim.2020.100739

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2026-05-17

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Niccoli, M., Borghi, C., & Chirici, G. (2026). Mapping Local Climate Zones and ExplorMapping Local Climate Zones and Exploring Temperature Dynamics in the Metropolitan Area of Florenceing Temperature Dynamics in the Metropolitan Area of Florence. Bollettino Della Società Geografica Italiana, 9(1), 85–104. https://doi.org/10.36253/bsgi-7621