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A Hybrid Modelling Approach for Detecting Seasonal Variations in Inland Green-Blue Ecosystems [poster]

2024-07-03Póster em conferência Acesso aberto
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dc.contributor.authorAlmeida, Bruna
dc.contributor.authorCabral, Pedro
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.date.accessioned2024-10-08T22:10:21Z
dc.date.available2024-10-08T22:10:21Z
dc.date.issued2024-07-03
dc.descriptionAlmeida, B., & Cabral, P. (2024). A Hybrid Modelling Approach for Detecting Seasonal Variations in Inland Green-Blue Ecosystems [poster]. 1. Poster session presented at Encontro Ciência 2024, Porto, Portugal. --- This study was supported by the research project MaSOT – Mapping Ecosystem Services from Earth Observations, funded by the Portuguese Science Foundation – FCT [EXPL/CTA-AMB/0165/2021], and by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 (DOI: 10.54499/UIDB/04152/2020) - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS)
dc.description.abstractDeforestation, environmental pollution, and the overexploitation of resources, in addition to the Earth's natural cycles, are scaling up the impacts of climate change in the provision of Ecosystem Services (ES). Green-Blue Ecosystems (GBE) are impacted by climatic conditions, topography, and water presence. In the context of climate change, Portugal is recognized as a hotspot among the most vulnerable European countries. Recent studies have shown evidence of climatic changes, such as the long periods of drought recorded in 1990, 2004/2005 and2012. The more frequent occurrence of these events is increasing the severity of seasonality effects on GBE and compromising the provision of services such as freshwater supply, and consequently crop and wood production, and carbon storage and sequestration.en
dc.description.versionpublishersversion
dc.description.versionunpublished
dc.format.extent1
dc.format.extent1282575
dc.identifier.otherPURE: 100883326
dc.identifier.otherPURE UUID: 6fca3e5d-0553-4d7d-8ea3-bc3e7625e77f
dc.identifier.otherORCID: /0000-0002-3349-1470/work/170199348
dc.identifier.otherORCID: /0000-0001-8622-6008/work/170201413
dc.identifier.urihttp://hdl.handle.net/10362/173177
dc.identifier.urlhttps://app.encontrociencia.pt/exhibitor/22838
dc.language.isoeng
dc.peerreviewedno
dc.relationinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/EXPL%2FCTA-AMB%2F0165%2F2021/PT
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT
dc.relationInformation Management Research Center
dc.subjectRemote Sensing
dc.subjectMachine Learning
dc.subjectGeographic Information Systems
dc.subjectAquatic Ecosystems
dc.subjectTerrestrial Ecosystems
dc.subjectClimate Change
dc.subjectSDG 6 - Clean Water and Sanitation
dc.subjectSDG 11 - Sustainable Cities and Communities
dc.subjectSDG 12 - Responsible Consumption and Production
dc.subjectSDG 13 - Climate Action
dc.subjectSDG 15 - Life on Land
dc.titleA Hybrid Modelling Approach for Detecting Seasonal Variations in Inland Green-Blue Ecosystems [poster]en
dc.typeconference poster
degois.publication.firstPage
degois.publication.issue13
degois.publication.lastPage
degois.publication.titleEncontro Ciência 2024
dspace.entity.typePublication
oaire.awardNumberEXPL/CTA-AMB/0165/2021
oaire.awardNumberUIDB/04152/2020
oaire.awardTitleInformation Management Research Center
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/EXPL%2FCTA-AMB%2F0165%2F2021/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT
oaire.fundingStream3599-PPCDT
oaire.fundingStream6817 - DCRRNI ID
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsrestrictedAccess
relation.isProjectOfPublication552e8c8e-f234-401d-ad95-e438d980226a
relation.isProjectOfPublication3274bdb3-4dd3-4bbe-8f74-d34190081f87
relation.isProjectOfPublication.latestForDiscovery3274bdb3-4dd3-4bbe-8f74-d34190081f87

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