Thermal Summer Diurnal Hot-Spot Analysis: The Role of Local Urban Features Layers

Giulia Guerri; Alfonso Crisci; Alessandro Messeri; Luca Congedo; Michele Munafò; Marco Morabito

doi:10.3390/rs13030538

journal article Open Access Feb 02, 2021

Thermal Summer Diurnal Hot-Spot Analysis: The Role of Local Urban Features Layers

Giulia Guerri

Alfonso Crisci Alessandro Messeri

Luca Congedo

Michele Munafò

Marco Morabito

Remote Sensing Vol. 13 No. 3 pp. 538 · MDPI AG

View at Publisher Save 10.3390/rs13030538

Abstract

This study was focused on the metropolitan area of Florence in Tuscany (Italy) with the aim of mapping and evaluating thermal summer diurnal hot- and cool-spots in relation to the features of greening, urban surfaces, and city morphology. The work was driven by Landsat 8 land surface temperature (LST) data related to 2015–2019 summer daytime periods. Hot-spot analysis was performed adopting Getis-Ord Gi* spatial statistics applied on mean summer LST datasets to obtain location and boundaries of hot- and cool-spot areas. Each hot- and cool-spot was classified by using three significance threshold levels: 90% (LEVEL-1), 95% (LEVEL-2), and 99% (LEVEL-3). A set of open data urban elements directly or indirectly related to LST at local scale were calculated for each hot- and cool-spot area: (1) Normalized Difference Vegetation Index (NDVI), (2) tree cover (TC), (3) water bodies (WB), (4) impervious areas (IA), (5) mean spatial albedo (ALB), (6) surface areas (SA), (7) Shape index (SI), (8) Sky View Factor (SVF), (9) theoretical solar radiation (RJ), and (10) mean population density (PD). A General Dominance Analysis (GDA) framework was adopted to investigate the relative importance of urban factors affecting thermal hot- and cool-spot areas. The results showed that 11.5% of the studied area is affected by cool-spots and 6.5% by hot-spots. The average LST variation between hot- and cold-spot areas was about 10 °C and it was 15 °C among the extreme hot- and cool-spot levels (LEVEL-3). Hot-spot detection was magnified by the role of vegetation (NDVI and TC) combined with the significant contribution of other urban elements. In particular, TC, NDVI and ALB were identified as the most significant predictors (p-values < 0.001) of the most extreme cool-spot level (LEVEL-3). NDVI, PD, ALB, and SVF were selected as the most significant predictors (p-values < 0.05 for PD and SVF; p-values < 0.001 for NDVI and ALB) of the hot-spot LEVEL-3. In this study, a reproducible methodology was developed applicable to any urban context by using available open data sources.

Topics

No keywords indexed for this article. Browse by subject →

References

132

[1]

Population Division, Department of Economic and Social Affairs, United Nations (2019). World Urbanization Prospects: The 2018 Revision, Population Division, Department of Economic and Social Affairs, United Nations.

[2]

European Commission (2012). Guidelines on Best Practice to Limit, Mitigate or Compensate Soil Sealing, Publications Office of the European Union.

[3]

(2020, December 01). Eurostat LUCAS Primary Data 2015. Available online: https://ec.europa.eu/eurostat/web/lucas.

[4]

Munafò, M. (2020). Land Consumption, Land Cover Changes, and Ecosystem Services, Report SNPA 15/20.

[5]

U.S. Environmental Protection Agency (2020, December 01). Reducing Urban Heat Islands: Compendium, Available online: https://www.epa.gov/heat-islands/heat-island-compendium.

[6]

Voogt "Thermal Remote Sensing of Urban Climates" Remote Sens. Environ. (2003) 10.1016/s0034-4257(03)00079-8

[7]

Chakraborty "A Simplified Urban-Extent Algorithm to Characterize Surface Urban Heat Islands on a Global Scale and Examine Vegetation Control on Their Spatiotemporal Variability" Int. J. Appl. Earth Obs. Geoinf. (2019)

[8]

Guo "Impact of Urban Morphology and Landscape Characteristics on Spatiotemporal Heterogeneity of Land Surface Temperature" Sustain. Cities Soc. (2020) 10.1016/j.scs.2020.102443

[9]

Zhou "Surface Urban Heat Island in China’s 32 Major Cities: Spatial Patterns and Drivers" Remote Sens. Environ. (2014) 10.1016/j.rse.2014.05.017

[10]

Wang "Spatiotemporal Variation in Surface Urban Heat Island Intensity and Associated Determinants across Major Chinese Cities" Remote Sens. (2015) 10.3390/rs70403670

[11]

Morabito "Surface Urban Heat Islands in Italian Metropolitan Cities: Tree Cover and Impervious Surface Influences" Sci. Total Environ. (2021) 10.1016/j.scitotenv.2020.142334

[12]

Du "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. (2016) 10.1016/j.scitotenv.2016.07.012

[13]

Oke, T.R. (1978). Boundary Layer Climates, Wiley and Sons. [2nd ed.].

[14]

Ferrini, F., Fini, A., Mori, J., and Gori, A. (2020). Role of Vegetation as a Mitigating Factor in the Urban Context. Sustainability, 22. 10.3390/su12104247

[15]

Osborne "Quantifying How Landscape Composition and Configuration Affect Urban Land Surface Temperatures Using Machine Learning and Neutral Landscapes" Comput. Environ. Urban Syst. (2019) 10.1016/j.compenvurbsys.2019.04.003

[16]

Manteghi "Water Bodies an Urban Microclimate: A Review" Mod. Appl. Sci. (2015) 10.5539/mas.v9n6p1

[17]

Trlica "Albedo, Landcover, and Daytime Surface Temperature Variation across an Urbanized Landscape" Earths Future (2017) 10.1002/2017ef000569

[18]

Jamei "Review on the Impact of Urban Geometry and Pedestrian Level Greening on Outdoor Thermal Comfort" Renew. Sustain. Energy Rev. (2016) 10.1016/j.rser.2015.10.104

[19]

Musco, F. (2016). Counteracting Urban Heat Island Effects in a Global Climate Change Scenario, Springer International Publishing. 10.1007/978-3-319-10425-6

[20]

Logan "Night and Day: The Influence and Relative Importance of Urban Characteristics on Remotely Sensed Land Surface Temperature" Remote Sens. Environ. (2020) 10.1016/j.rse.2020.111861

[21]

Goddard "Scaling up from Gardens: Biodiversity Conservation in Urban Environments" Trends Ecol. Evol. (2010) 10.1016/j.tree.2009.07.016

[22]

Kabisch "Nature-Based Solutions to Climate Change Mitigation and Adaptation in Urban Areas: Perspectives on Indicators, Knowledge Gaps, Barriers, and Opportunities for Action" Ecol. Soc. (2016) 10.5751/es-08373-210239

[23]

Kabisch, N., Korn, H., Stadler, J., and Bonn, A. (2017). Nature-Based Solutions to Climate Change Adaptation in Urban Areas: Linkages between Science, Policy and Practice, Springer International Publishing. 10.1007/978-3-319-56091-5

[24]

Anselin "Local Indicators of Spatial Association-LISA" Geogr. Anal. (1995) 10.1111/j.1538-4632.1995.tb00338.x

[25]

Ord "Local Spatial Autocorrelation Statistics: Distributional Issues and an Application" Geogr. Anal. (1995) 10.1111/j.1538-4632.1995.tb00912.x

[26]

Getis, A., Lacambra, J., Mur, J., and Zoller, H.G. (2004). Spatial Econometrics and Spatial Statistics, Palgrave Macmillan.

[27]

Wolf "The Development of a Heat Wave Vulnerability Index for London, United Kingdom" Weather Clim. Extrem. (2013) 10.1016/j.wace.2013.07.004

[28]

Feyisa "Locally Optimized Separability Enhancement Indices for Urban Land Cover Mapping: Exploring Thermal Environmental Consequences of Rapid Urbanization in Addis Ababa, Ethiopia" Remote Sens. Environ. (2016) 10.1016/j.rse.2015.12.026

[29]

Ren "Quantifying the Influences of Various Ecological Factors on Land Surface Temperature of Urban Forests" Environ. Pollut. (2016) 10.1016/j.envpol.2016.06.004

[30]

Sismanidis, P., Keramitsoglou, I., and Kiranoudis, C.T. (2017, January 6–8). Identifying and Characterizing the Diurnal Evolution of Urban Land Surface Temperature Patterns. Proceedings of the 2017 Joint Urban Remote Sensing Event (JURSE), Dubai, United Arab Emirates. 10.1109/jurse.2017.7924598

[31]

Tran "Characterizing the Relationship between Land Use Land Cover Change and Land Surface Temperature" ISPRS J. Photogramm. Remote Sens. (2017) 10.1016/j.isprsjprs.2017.01.001

[32]

"Spatial Hotspot Analysis of Bucharest’s Urban Heat Island (UHI) Using Modis Data" Ann. Valahia Univ. Targoviste Geogr. Ser. (2018) 10.2478/avutgs-2018-0002

[33]

Mavrakou, T., Polydoros, A., Cartalis, C., and Santamouris, M. (2018). Recognition of Thermal Hot and Cold Spots in Urban Areas in Support of Mitigation Plans to Counteract Overheating: Application for Athens. Climate, 6. 10.3390/cli6010016

[34]

Ranagalage, M., Estoque, R., Zhang, X., and Murayama, Y. (2018). Spatial Changes of Urban Heat Island Formation in the Colombo District, Sri Lanka: Implications for Sustainability Planning. Sustainability, 10. 10.3390/su10051367

[35]

Jamei "(Chayn) Spatial Structure of Surface Urban Heat Island and Its Relationship with Vegetation and Built-up Areas in Melbourne, Australia" Sci. Total Environ. (2019) 10.1016/j.scitotenv.2018.12.308

[36]

Guha "Analytical Study of Land Surface Temperature with NDVI and NDBI Using Landsat 8 OLI and TIRS Data in Florence and Naples City, Italy" Eur. J. Remote Sens. (2018) 10.1080/22797254.2018.1474494

[37]

Kaplan, G., Avdan, U., and Avdan, Z.Y. (2018). Urban Heat Island Analysis Using the Landsat 8 Satellite Data: A Case Study in Skopje, Macedonia. Proceedings, 2. 10.3390/ecrs-2-05171

[38]

Zhou, D., Xiao, J., Bonafoni, S., Berger, C., Deilami, K., Zhou, Y., Frolking, S., Yao, R., Qiao, Z., and Sobrino, J.A. (2019). Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives. Remote Sens., 11. 10.3390/rs11010048

[39]

Morabito, M., Crisci, A., Gioli, B., Gualtieri, G., Toscano, P., Stefano, V.D., Orlandini, S., and Gensini, G.F. (2015). Urban-Hazard Risk Analysis: Mapping of Heat-Related Risks in the Elderly in Major Italian Cities. PLoS ONE, 10. 10.1371/journal.pone.0127277

[40]

Morabito "The Impact of Built-up Surfaces on Land Surface Temperatures in Italian Urban Areas" Sci. Total Environ. (2016) 10.1016/j.scitotenv.2016.02.029

[41]

Dominance analysis: A new approach to the problem of relative importance of predictors in multiple regression.

David V. Budescu

Psychological Bulletin 1993 10.1037/0033-2909.114.3.542

[42]

Rubel "Observed and Projected Climate Shifts 1901-2100 Depicted by World Maps of the Köppen-Geiger Climate Classification" Meteorol. Z. (2010) 10.1127/0941-2948/2010/0430

[43]

Directorate-General of the Government of the Territory of Tuscany Region Territorial, Italian Ministry of Cultural Heritage and Activities and Tourism (2020, December 01). “Piano di Indirizzo Territoriale con valenza di Piano Paesaggistico” of Tuscany Region, PIT-PPR, Available online: https://www.regione.toscana.it/piano-di-indirizzo-territoriale-con-valenza-di-piano-paesaggistico.

[44]

Zhou, J., Liang, S., Cheng, J., Wang, Y., and Ma, J. (2018). The GLASS Land Surface Temperature Product. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 493–507. 10.1109/jstars.2018.2870130

[45]

Duan, S.-B., Han, X.-J., Huang, C., Li, Z.-L., Wu, H., Qian, Y., Gao, M., and Leng, P. (2020). Land Surface Temperature Retrieval from Passive Microwave Satellite Observations: State-of-the-Art and Future Directions. Remote Sens., 12. 10.3390/rs12162573

[46]

(2020, December 01). U.S. Geological Survey Landsat 8 (L8). Data Users Handbook. Version 5.0, Available online: https://www.usgs.gov/media/files/landsat-8-data-users-handbook.

[47]

R Core Team (2020). R version 3.6.3, The R Foundation for Statistical Computing. Available online: https://cran.r-project.org/bin/windows/base/old/3.6.3/.

[48]

Land surface temperature retrieval from LANDSAT TM 5

José A. Sobrino, Juan C. Jiménez-Muñoz, Leonardo Paolini

Remote Sensing of Environment 2004 10.1016/j.rse.2004.02.003

[49]

Congedo, L. Semi-Automatic Classification Plugin Documentation, Release 6.4.0.2.

[50]

Sekertekin, A., and Bonafoni, S. (2020). Land Surface Temperature Retrieval from Landsat 5, 7, and 8 over Rural Areas: Assessment of Different Retrieval Algorithms and Emissivity Models and Toolbox Implementation. Remote Sens., 12. 10.3390/rs12020294

Showing 50 of 132 references

Cited By

67

Unraveling urban thermal comfort dynamics: Multi-scale interactions with urban building and green space landscapes in Beijing

Ying Xu, Lei Yao · 2025

Environmental and Sustainability In...

The Mitigation Effect of Park Landscape on Thermal Environment in Shanghai City Based on Remote Sensing Retrieval Method

Tian Wang, Hui Tu · 2022

International Journal of Environmen...

Integrated Land Use and Urban Function Impacts on Land Surface Temperature: Implications on Urban Heat Mitigation in Berlin with Eight-Type Spaces

Dagmar Haase, Salman Qureshi · 2022

Sustainable Cities and Society

Metrics

67

Citations

132

References

Details

Published: Feb 02, 2021
Vol/Issue: 13(3)
Pages: 538
License: View

Authors

G

Giulia Guerri

Institute of Bioeconomy (IBE), National Research Council, 50019 Florence, Italy

A

Alfonso Crisci

Institute of Bioeconomy (IBE), National Research Council, 50019 Florence, Italy

A

Alessandro Messeri

Institute of Bioeconomy (IBE), National Research Council, 50019 Florence, Italy

L

Luca Congedo

Italian National Institute for Environmental Protection and Research (ISPRA), 00144 Rome, Italy

M

Michele Munafò

Italian National Institute for Environmental Protection and Research (ISPRA), 00144 Rome, Italy

M

Marco Morabito

Institute of Bioeconomy (IBE), National Research Council, 50019 Florence, Italy; Centre of Bioclimatology (CIBIC), Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, 50144 Florence, Italy

Cite This Article

Giulia Guerri, Alfonso Crisci, Alessandro Messeri, et al. (2021). Thermal Summer Diurnal Hot-Spot Analysis: The Role of Local Urban Features Layers. Remote Sensing, 13(3), 538. https://doi.org/10.3390/rs13030538

Thermal Summer Diurnal Hot-Spot Analysis: The Role of Local Urban Features Layers

You May Also Like