The Urban Heat Island (UHI) phenomenon can be harmful during the summer season jeopardizing the safety of some vulnerable population classes. Typically, the study of the UHI requires long data acquisitions using weather stations widely distributed within the cities. To this aim, recent research proposed multicriteria approaches aimed at quantifying the hazard of the summer absolute maximum Urban Heat Island Intensity (UHII), in urban districts. On the contrary, these approaches are time consuming and involve a large number of parameters. This article proposes a simplified multiparametric approach based on the three principal parameters involved in the UHI (albedo, greenery, and anthropogenic heat) obtained by a refined remote sensing data acquisition. In comparison with other approaches, the proposed method is simpler and quick to apply while maintaining good precision. Moreover, a calibration is achieved by exploiting the real absolute max UHII of a set of 41 European urban districts and a validation is obtained by a comparison with another multiparametric approach already validated in literature. Both the approaches are applied to 96 urban districts of Berlin. The results show that the simplified procedure keep an average error less than 1°C but improving in applicability.
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References
1.
Al-GhussainL. (2019). Global warming: Review on driving forces and mitigation. Environ. Prog. Sustain Energy, 38, 13.
2.
AllenR.G., TasumiM., TrezzaR., WatersR., and BastiaanssenW. (2002). SEBAL Surface Energy Balance Algorithms for Land—Advanced Training and Users Manual—Idaho Implementation (Version 1.0). Boise, ID: The Idaho Department of Water Resources.
3.
AnchangJ.Y., AnangaE.O., and PuR. (2016). An efficient unsupervised index based approach for mapping urban vegetation from IKONOS imagery. Int. J. Appl. Earth Obs. Geoinf. 50, 211.
4.
ApollonioC., BalaccoG., NovelliA., TarantinoE., and PiccinniA.F. (2016). Land use change impact on flooding areas: The case study of Cervaro Basin (Italy). Sustainability, 8, 996.
5.
BocciaL., CapolupoA., RigilloM., and RussoV.T. (2020). Abandonment hazards in a Mediterranean cultural landscape. J. Hazard Toxic Radioact. Waste, 24, 04019034.
6.
BusatoF., LazzarinR.M., and NoroM. (2014). Three years of study of the Urban Heat Island in Padua: Experimental results. Sustain Cities Soc. 10, 251.
7.
CantelliA. M, ontiP., and LeuzziG. (2015). Numerical study of the urban geometrical representation impact in a surface energy budget model. Environ. Fluid Mech. 1525, 1.
8.
CapolupoA., KooistraL., and BocciaL. (2018). A novel approach for detecting agricultural terraced landscapes from historical and contemporaneous photogrammetric aerial photos. Int. J. Appl. Earth Obs. Geoinf. 73, 800.
9.
CapolupoA., MonterisiC., CaporussoG., and TarantinoE. (2020a). Extracting land cover data using GEE: A review of the classification indices. In International Conference on Computational Science and Its Applications. Springer, Cham, p. 782–796.
10.
CapolupoA., MonterisiC., SaponaroM., and TarantinoE. (2020b). Multi-temporal analysis of land cover changes using Landsat data through Google Earth Engine platform. In Eighth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2020), Vol. 11524. Bellingham, Washington: International Society for Optics and Photonics, p. 1152419.
11.
CapolupoA., MonterisiC., and TarantinoE. (2020c). Landsat Images Classification Algorithm (LICA) to automatically extract land cover information in google earth engine environment. Remote Sens. 12, 1201.
12.
CaprioliM., and TarantinoE. (2001). Accuracy assessment of per-field classification integrating very fine spatial resolution satellite imagery with topographic data. J. Geospat. Eng. 3, 127.
13.
ChenX.L., ZhaoH.M., LiP.X., and YinZ.Y. (2006). Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sens. Environ. 104, 133.
14.
ChevalS., DumitrescuA., and BellA. (2009). The urban heat island of Bucharest during the extreme high temperatures of July 2007. Theor. Appl. Climatol. 97, 391.
FortuniakK., KłysikK., and WibigJ. (2006). Urban–rural contrasts of meteorological parameters in Łódź. Theor. Appl. Climatol. 84, 91.
17.
GiovanniniL., ZardiD., and De FranceschiM. (2011). Analysis of the urban thermal fingerprint of the city of Trento in the Alps. J. Appl. Meteorol. Climatol. 50, 1145.
18.
GorelickN., HancherM., DixonM., IlyushchenkoS., ThauD., and MooreR. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18.
19.
GuattariC., EvangelistiL., and BalarasC.A. (2018). On the assessment of urban heat island phenomenon and its effects on building energy performance: A case study of Rome (Italy). Energy Build. 158, 605.
20.
HeavisideC., VardoulakisS., and CaiX.M. (2016). Attribution of mortality to the urban heat island during heatwaves in the West Midlands, UK. Environ. Health, 15, 49.
21.
KolokotroniM., and GiridharanR. (2008). Urban heat island intensity in London: An investigation of the impact of physical characteristics on changes in outdoor air temperature during summer. Sol. Energy. 82, 986.
22.
KrügerE., and EmmanuelR. (2013). Accounting for atmospheric stability conditions in urban heat island studies: The case of Glasgow, UK. Landsc. Urban Plan. 117, 112.
23.
KumarL., and MutangaO. (2018). Google Earth Engine applications since inception: Usage, trends, and potential. Remote Sens. 10, 1509.
24.
LasdonL.S., FoxR.L., and RatnerM.W. (1974). Nonlinear optimization using the generalized reduced gradient method. Revue française d'automatique, informatique, recherche opérationnelle. Rev Fr Inform Rech O. 8(V3), 73.
25.
LemonsuA., and MassonV. (2002). Simulation of a summer urban breeze over Paris. Boundary Layer Meteorol. 104, 463.
26.
MarandoF., SalvatoriE., SebastianiA., FusaroL., and ManesF. (2019). Regulating ecosystem services and green infrastructure: Assessment of urban heat island effect mitigation in the municipality of Rome, Italy. Ecol. Modell. 392, 92.
27.
Mateo-GarcíaG., Gómez-ChovaL., Amorós-LópezJ., Muñoz-MaríJ., and Camps-VallsG. (2018). Multitemporal cloud masking in the Google Earth Engine. Remote Sens. 10, p.1079.
28.
McMichaelA.J., WoodruffR.E., and HalesS. (2006). Climate change and human health: Present and future risks. Lancet, 367, 859.
29.
MenbergK., BayerP., ZossederK., RumohrS., and BlumP. (2013). Subsurface urban heat islands in German cities. Sci. Total Environ. 442, 123.
30.
MerkinR. (2004). The Urban Heat Island's Effect on the Diurnal Temperature Range. Doctoral Dissertation, Massachusetts Institute of Technology.
31.
MilelliM. (2016). Urban heat island effects over Torino. COSMO Newsl. 16, 1.
32.
MirzaeiM., VerrelstJ., ArbabiM., ShaklabadiZ., and LotfizadehM. (2020). Urban heat island monitoring and impacts on Citizen's General Health Status in Isfahan Metropolis: A remote sensing and field survey approach. Remote Sens. 12, 1350.
33.
OkeT.R. (1991). Climate of cities. In Climate in Human Perspective. Dordrecht: Springer, pp. 61–75. [Epub ahead of print]; DOI: 10.1007/978-94-011-3320-3_6
34.
OkeT.R., MillsG., ChristenA., and VoogtJ.A. (2017). Urban climates. Cambridge, UK: Cambridge University Press.
35.
PantavouK., TheoharatosG., MavrakisA., and SantamourisM. (2011). Evaluating thermal comfort conditions and health responses during an extremely hot summer in Athens. Build. Environ. 46, 339.
36.
PetralliM., ProkoppA., MorabitoM., BartoliniG., TorrigianiT., and OrlandiniS. (2006). Ruolo delle aree verdi nella mitigazione dell'isola di calore urbana: Uno studio nella città di Firenze. Riv. Ital. Agrometeorol. 1, 51.
37.
SangiorgioV., FioritoF., and SantamourisM. (2020). Development of a holistic urban heat island evaluation methodology. Sci. Rep. 10, 1.
38.
SangiorgioV., and ParisiF. (2020). A multicriteria approach for risk assessment of Covid-19 in urban district lockdown. Saf. Sci. 104862.
39.
SangiorgioV., UvaG., FatigusoF., and AdamJ.M. (2019). A new index to evaluate exposure and potential damage to RC building structures in coastal areas. Eng. Fail. Anal. 100, 439.
40.
SantamourisM. (2015). Analyzing the heat island magnitude and characteristics in one hundred Asian and Australian cities and regions. Sci. Total Environ. 512, 582.
41.
SantamourisM. (2016). Innovating to zero the building sector in Europe: Minimising the energy consumption, eradication of the energy poverty and mitigating the local climate change. Sol. Energy. 128, 61.
42.
SantamourisM. (2020). Recent progress on urban overheating and heat island research. Integrated assessment of the energy, environmental, vulnerability and health impact. Synergies with the global climate change. Energy Build., 207, 109482.
43.
SarzanaT., MalteseA., CapolupoA., and TarantinoE. (2020). July. Post-processing of pixel and object-based land cover classifications of very high spatial resolution images. In International Conference on Computational Science and Its Applications. Cham: Springer, p. 797.
44.
SinghA., and PurohitB.M. (2014). Public health impacts of global warming and climate change. Peace Rev. 26, 112.
45.
Sociedad Limitada. Tutiempo Network (2020). Climate data: Europe. Available at: https://www.tutiempo.net (accessed March30, 2020).
46.
TarantinoE., and FigoritoB. (2012). Mapping rural areas with widespread plastic covered vineyards using true color aerial data. Remote Sens. 4, 1913.
47.
TayancM., and TorosH. (1997). Urbanization effects on regional climate change in the case of four large cities of Turkey. Clim. Change, 35, 501.
48.
Van HoveL.W.A., SteeneveldG.J., JacobsC.M.J., HeusinkveldB.G., ElbersJ.A., MoorsE.J., and HoltslagA.A.M. (2011). Exploring the urban heat island intensity of Dutch cities: assessment based on a literature review, recent meteorological observation and datasets provide by hobby meteorologists (No. 2170). Alterra.
49.
VarentsovM., WoutersH., PlatonovV., and KonstantinovP. (2018). Megacity-induced mesoclimatic effects in the lower atmosphere: A modeling study for multiple summers over Moscow, Russia. Atmosphere, 9, 50.
50.
WangG., JiangW., and WeiM. (2008). An Assessment of Urban Heat Island Effect using Remote Sensing Data. Mar. Sci. Bull. 10, 14.
51.
WengQ., LuD., and SchubringJ. (2004). Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sens. Environ. 89, 467.
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