Please use this identifier to cite or link to this item: https://hdl.handle.net/10316/100506
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dc.contributor.authorMantas, Vasco-
dc.contributor.authorFonseca, Luís-
dc.contributor.authorBaltazar, Elsa-
dc.contributor.authorCanhoto, Jorge-
dc.contributor.authorAbrantes, Isabel-
dc.date.accessioned2022-06-27T10:50:37Z-
dc.date.available2022-06-27T10:50:37Z-
dc.date.issued2022-
dc.identifier.issn2072-4292-
dc.identifier.urihttps://hdl.handle.net/10316/100506-
dc.description.abstractModerate-resolution satellite imagery is essential to detect conifer tree decline on a regional scale and address the threat caused by pinewood nematode (PWN), (Bursaphelenchus xylophilus. This is a quarantine organism responsible for pine wilt disease (PWD), which has caused substantial ecological and economic losses in the maritime pine (Pinus pinaster) forests of Portugal. This study describes the first instance of a pre-operational algorithm applied to Sentinel-2 imagery to detect PWD-compatible decline in maritime pine. The Random Forest model relied on a pre-wilting and an in-season image, calibrated with data from a 24-month long field campaign enhanced withWorldview- 3 data and the analysis of biological samples (hyperspectral reflectance, pigment quantification in needles, and PWN identification). Independent validation results attested to the good performance of the model with an overall accuracy of 95%, particularly when decline affects more than 30% of the 100 m2 pixel of Sentinel-2. Spectral angle mapper applied to hyperspectral measurements suggested that PWN infection cannot be separated from other drivers of decline in the visible-near infrared domain. Our algorithm can be employed to detect regional decline trends and inform subsequent aerial and field surveys, to further investigate decline hotspots.pt
dc.description.sponsorshipHorizon 2020 Grant Agreement 776026; ‘Monitorizar para Decidir e Valorizar’, funded by Programa PROMOVE of BPI/Fundação La Caixapt
dc.language.isoengpt
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB/04004/2020pt
dc.relationCentro-01-0145-FEDER-000007pt
dc.relationPTDC/ASP-SIL/31999/2017pt
dc.rightsopenAccesspt
dc.subjectmachine-learningpt
dc.subjectpinewood nematodept
dc.subjectpine wilt diseasept
dc.subjectremote sensingpt
dc.subjectSentinel-2pt
dc.subjecttree declinept
dc.titleDetection of Tree Decline (Pinus pinaster Aiton) in European Forests Using Sentinel-2 Datapt
dc.typearticlept
degois.publication.firstPage2028pt
degois.publication.issue9pt
degois.publication.titleRemote Sensingpt
dc.peerreviewedyespt
dc.identifier.doi10.3390/rs14092028-
degois.publication.volume14pt
dc.date.embargo2022-01-01*
uc.date.periodoEmbargo0pt
item.fulltextCom Texto completo-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextopen-
item.languageiso639-1en-
item.openairetypearticle-
item.cerifentitytypePublications-
crisitem.author.researchunitMARE - Marine and Environmental Sciences Centre-
crisitem.author.researchunitCFE - Centre for Functional Ecology - Science for People & the Planet-
crisitem.author.researchunitCFE - Centre for Functional Ecology - Science for People & the Planet-
crisitem.author.orcid0000-0002-6109-4958-
crisitem.author.orcid0000-0001-7405-8916-
crisitem.author.orcid0000-0003-2299-298X-
crisitem.author.orcid0000-0002-8761-2151-
crisitem.project.grantnoCentre for Functional Ecology - Science for People & the Planet-
Appears in Collections:I&D CITEUC - Artigos em Revistas Internacionais
I&D CFE - Artigos em Revistas Internacionais
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