Publication
An urban energy balance-guided machine learning approach for synthetic nocturnal surface Urban Heat Island prediction: a heatwave event in Naples
| Summary: | Southern European functional urban areas (FUAs) are increasingly subject to heatwave (HW) events, calling for anticipated climate adaptation measures. In the urban context, such adaptation strategies require a thorough understanding of the built-up response to the incoming solar radiation, i.e., the urban energy balance cycle and its implications for the Urban Heat Island (UHI) effect. Despite readily available, diurnal Land Surface Temperature (LST) data does not provide a meaningful picture of the UHI, in these midlatitudes FUAs. On the contrary, the mid-morning satellite overpass is characterized by the absence of a significant surface UHI (SUHI) signal, corresponding to the period of the day when the urban-rural air temperature difference is typically negative. Conversely, nocturnal high-resolution LST data is rarely available. In this study, an energy balance-based machine learning approach is explored, considering the Local Climate Zones (LCZ), to describe the daily cycle of the heat flux components and predict the nocturnal SUHI, during an HW event. While the urban and rural spatial outlines are not visible in the diurnal thermal image, they become apparent in the latent and storage heat flux maps – built-up infrastructures uptake heat during the day which is released back into the atmosphere, during the night, whereas vegetation land surfaces loose diurnal heat through evapotranspiration. For the LST prediction model, a random forest (RF) approach is implemented. RF results show that the model accurately predicts the LST, ensuring mean square errors inferior to 0.1 K. Both the latent and storage heat flux components, together with LCZ classification, are the most important explanatory variables for the nocturnal LST prediction, supporting the adoption of the energy balance approach. In future research, other locations and time-series data shall be trained and tested, providing an efficient local urban climate monitoring tool, where in-situ air temperature observations are not available. |
|---|---|
| Main Authors: | Oliveira, Ana |
| Other Authors: | Lopes, António; Niza, Samuel; Soares, Amílcar |
| Subject: | Urban climate adaptation Heatwave Urban heat island Land Surface Temperature Local Climate Zones Random Forest Multisensor Data Fusion Satellite Thermal Imagery |
| Year: | 2021 |
| Country: | Portugal |
| Document type: | article |
| Access type: | restricted access |
| Associated institution: | Universidade de Lisboa |
| Language: | English |
| Origin: | Repositório da Universidade de Lisboa |
| _version_ | 1877770993622056960 |
|---|---|
| author | Oliveira, Ana |
| author2 | Lopes, António Niza, Samuel Soares, Amílcar |
| author2_role | author author author |
| author_facet | Oliveira, Ana Lopes, António Niza, Samuel Soares, Amílcar |
| author_role | author |
| contributor_name_str_mv | Repositório Científico de Acesso Aberto da ULisboa |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Oliveira, Ana\"},{\"Person.name\":\"Lopes, António\",\"Person.identifier.cienciaID\":\"1D15-FB93-4687\",\"Person.identifier.orcid\":\"0000-0002-9357-7639\",\"Person.identifier.researcherID\":\"F-3217-2010\",\"Person.identifier.scopusAuthorID\":\"55951850000\"},{\"Person.name\":\"Niza, Samuel\"},{\"Person.name\":\"Soares, Amílcar\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Repositório Científico de Acesso Aberto da ULisboa |
| datacite.creators.creator.creatorName.fl_str_mv | Oliveira, Ana Lopes, António Niza, Samuel Soares, Amílcar |
| datacite.date.Accepted.fl_str_mv | 2021-01-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2021-09-09T09:23:49Z |
| datacite.date.embargoed.fl_str_mv | 2021-09-09T09:23:49Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_16ec |
| datacite.subjects.subject.fl_str_mv | Urban climate adaptation Heatwave Urban heat island Land Surface Temperature Local Climate Zones Random Forest Multisensor Data Fusion Satellite Thermal Imagery |
| datacite.titles.title.fl_str_mv | An urban energy balance-guided machine learning approach for synthetic nocturnal surface Urban Heat Island prediction: a heatwave event in Naples |
| dc.contributor.none.fl_str_mv | Repositório Científico de Acesso Aberto da ULisboa |
| dc.creator.none.fl_str_mv | Oliveira, Ana Lopes, António Niza, Samuel Soares, Amílcar |
| dc.date.Accepted.fl_str_mv | 2021-01-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2021-09-09T09:23:49Z |
| dc.date.embargoed.fl_str_mv | 2021-09-09T09:23:49Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10451/49488 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | Elsevier |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_16ec |
| dc.subject.none.fl_str_mv | Urban climate adaptation Heatwave Urban heat island Land Surface Temperature Local Climate Zones Random Forest Multisensor Data Fusion Satellite Thermal Imagery |
| dc.title.fl_str_mv | An urban energy balance-guided machine learning approach for synthetic nocturnal surface Urban Heat Island prediction: a heatwave event in Naples |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_6501 |
| description | Southern European functional urban areas (FUAs) are increasingly subject to heatwave (HW) events, calling for anticipated climate adaptation measures. In the urban context, such adaptation strategies require a thorough understanding of the built-up response to the incoming solar radiation, i.e., the urban energy balance cycle and its implications for the Urban Heat Island (UHI) effect. Despite readily available, diurnal Land Surface Temperature (LST) data does not provide a meaningful picture of the UHI, in these midlatitudes FUAs. On the contrary, the mid-morning satellite overpass is characterized by the absence of a significant surface UHI (SUHI) signal, corresponding to the period of the day when the urban-rural air temperature difference is typically negative. Conversely, nocturnal high-resolution LST data is rarely available. In this study, an energy balance-based machine learning approach is explored, considering the Local Climate Zones (LCZ), to describe the daily cycle of the heat flux components and predict the nocturnal SUHI, during an HW event. While the urban and rural spatial outlines are not visible in the diurnal thermal image, they become apparent in the latent and storage heat flux maps – built-up infrastructures uptake heat during the day which is released back into the atmosphere, during the night, whereas vegetation land surfaces loose diurnal heat through evapotranspiration. For the LST prediction model, a random forest (RF) approach is implemented. RF results show that the model accurately predicts the LST, ensuring mean square errors inferior to 0.1 K. Both the latent and storage heat flux components, together with LCZ classification, are the most important explanatory variables for the nocturnal LST prediction, supporting the adoption of the energy balance approach. In future research, other locations and time-series data shall be trained and tested, providing an efficient local urban climate monitoring tool, where in-situ air temperature observations are not available. |
| dirty | 0 |
| eu_rights_str_mv | restrictedAccess |
| format | article |
| fulltext.url.fl_str_mv | https://repositorio.ulisboa.pt/bitstreams/3d4e3efe-baa8-442c-baf7-1920cdc97051/download |
| funder_facet_str_mv | FCT{{{_:::_}}}Fundação para a Ciência e a Tecnologia |
| funding.funder.alternateName_str_mv | FCT |
| funding.funder.identifier_str_mv | http://doi.org/10.13039/501100001871 |
| funding.funder.name_str_mv | Fundação para a Ciência e a Tecnologia |
| funding.identifier_str_mv | PD/BD/52304/2013 |
| funding_str_mv | PD/BD/52304/2013 info:eu-repo/grantAgreement/FCT//PD%2FBD%2F52304%2F2013/PT |
| id | ul_ffe8b8e26f28b4a3a13c2e21c1a85599 |
| identifier.url.fl_str_mv | http://hdl.handle.net/10451/49488 |
| inst_facet_str | urn:organizationAcronym:ul{{{_:::_}}}Universidade de Lisboa |
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| institution | Universidade de Lisboa |
| instname_str | Universidade de Lisboa |
| language | eng |
| network_acronym_str | ul |
| network_name_str | Repositório da Universidade de Lisboa |
| oai_identifier_str | oai:repositorio.ulisboa.pt:10451/49488 |
| organization_str_mv | urn:organizationAcronym:ul |
| person_str_mv | Oliveira, Ana Lopes, António Lopes, António https://www.ciencia-id.pt/1D15-FB93-4687 1D15-FB93-4687 http://orcid.org/0000-0002-9357-7639 0000-0002-9357-7639 Niza, Samuel Soares, Amílcar |
| publishDate | 2021 |
| publisher.none.fl_str_mv | Elsevier |
| repo_facet_str | urn:repositoryAcronym:ul{{{_:::_}}}Repositório da Universidade de Lisboa |
| reponame_str | Repositório da Universidade de Lisboa |
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| service_str_mv | urn:repositoryAcronym:ul |
| spelling | engElsevierpt_PTSouthern European functional urban areas (FUAs) are increasingly subject to heatwave (HW) events, calling for anticipated climate adaptation measures. In the urban context, such adaptation strategies require a thorough understanding of the built-up response to the incoming solar radiation, i.e., the urban energy balance cycle and its implications for the Urban Heat Island (UHI) effect. Despite readily available, diurnal Land Surface Temperature (LST) data does not provide a meaningful picture of the UHI, in these midlatitudes FUAs. On the contrary, the mid-morning satellite overpass is characterized by the absence of a significant surface UHI (SUHI) signal, corresponding to the period of the day when the urban-rural air temperature difference is typically negative. Conversely, nocturnal high-resolution LST data is rarely available. In this study, an energy balance-based machine learning approach is explored, considering the Local Climate Zones (LCZ), to describe the daily cycle of the heat flux components and predict the nocturnal SUHI, during an HW event. While the urban and rural spatial outlines are not visible in the diurnal thermal image, they become apparent in the latent and storage heat flux maps – built-up infrastructures uptake heat during the day which is released back into the atmosphere, during the night, whereas vegetation land surfaces loose diurnal heat through evapotranspiration. For the LST prediction model, a random forest (RF) approach is implemented. RF results show that the model accurately predicts the LST, ensuring mean square errors inferior to 0.1 K. Both the latent and storage heat flux components, together with LCZ classification, are the most important explanatory variables for the nocturnal LST prediction, supporting the adoption of the energy balance approach. In future research, other locations and time-series data shall be trained and tested, providing an efficient local urban climate monitoring tool, where in-situ air temperature observations are not available.application/pdfpt_PTAn urban energy balance-guided machine learning approach for synthetic nocturnal surface Urban Heat Island prediction: a heatwave event in NaplesOliveira, AnaPersonalLopes, AntónioDSpacehttp://dspace.org/items/5ec106ce-350f-4b1b-aed6-1acd9f11f7f1DSpacehttp://dspace.org/items/5ec106ce-350f-4b1b-aed6-1acd9f11f7f1LopesAntónioCiência IDhttps://www.ciencia-id.pt1D15-FB93-4687ORCIDhttp://orcid.org0000-0002-9357-7639Researcher IDhttps://www.researcherid.comF-3217-2010Scopus Author IDhttps://www.scopus.com55951850000Niza, SamuelSoares, AmílcarHostingInstitutionOrganizationalRepositório Científico de Acesso Aberto da ULisboae-mailmailto:repositorio@ulisboa.ptrepositorio@ulisboa.ptISSNIsPartOf0048-9697DOIIsPartOf10.1016/j.scitotenv.2021.1501302021-09-09T09:23:49Z20212021-01-01T00:00:00ZHandlehttp://hdl.handle.net/10451/49488http://purl.org/coar/access_right/c_16ecrestricted accessUrban climate adaptationHeatwaveUrban heat islandLand Surface TemperatureLocal Climate ZonesRandom ForestMultisensor Data FusionSatellite Thermal Imagery2698759 bytesFundação para a Ciência e a TecnologiaSUSTAINABLE ENERGY SISTEMSinfo:eu-repo/grantAgreement/FCT//PD%2FBD%2F52304%2F2013/PTPD/BD/52304/2013Crossref Funder IDhttp://doi.org/10.13039/501100001871literaturehttp://purl.org/coar/resource_type/c_6501journal articlehttp://purl.org/coar/access_right/c_16ecapplication/pdffulltexthttps://repositorio.ulisboa.pt/bitstreams/3d4e3efe-baa8-442c-baf7-1920cdc97051/downloadScience of The Total Environment805150130 |
| spellingShingle | An urban energy balance-guided machine learning approach for synthetic nocturnal surface Urban Heat Island prediction: a heatwave event in Naples Oliveira, Ana Urban climate adaptation Heatwave Urban heat island Land Surface Temperature Local Climate Zones Random Forest Multisensor Data Fusion Satellite Thermal Imagery |
| status | SINGLETON |
| subject.fl_str_mv | Urban climate adaptation Heatwave Urban heat island Land Surface Temperature Local Climate Zones Random Forest Multisensor Data Fusion Satellite Thermal Imagery |
| title | An urban energy balance-guided machine learning approach for synthetic nocturnal surface Urban Heat Island prediction: a heatwave event in Naples |
| title_full | An urban energy balance-guided machine learning approach for synthetic nocturnal surface Urban Heat Island prediction: a heatwave event in Naples |
| title_fullStr | An urban energy balance-guided machine learning approach for synthetic nocturnal surface Urban Heat Island prediction: a heatwave event in Naples |
| title_full_unstemmed | An urban energy balance-guided machine learning approach for synthetic nocturnal surface Urban Heat Island prediction: a heatwave event in Naples |
| title_short | An urban energy balance-guided machine learning approach for synthetic nocturnal surface Urban Heat Island prediction: a heatwave event in Naples |
| title_sort | An urban energy balance-guided machine learning approach for synthetic nocturnal surface Urban Heat Island prediction: a heatwave event in Naples |
| topic | Urban climate adaptation Heatwave Urban heat island Land Surface Temperature Local Climate Zones Random Forest Multisensor Data Fusion Satellite Thermal Imagery |
| topic_facet | Urban climate adaptation Heatwave Urban heat island Land Surface Temperature Local Climate Zones Random Forest Multisensor Data Fusion Satellite Thermal Imagery |
| url | http://hdl.handle.net/10451/49488 |
| visible | 1 |