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Spectral unmixing of coastal dune plant species from very high resolution satellite imagery

2025-12-10Artigo científico Acesso aberto
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datacite.subject.sdg15:Proteger a Vida Terrestre
datacite.subject.sdg13:Ação Climática
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
dc.contributor.authorKombiadou, Katerina
dc.contributor.authorCostas, Susana
dc.contributor.authorGallego-Fernández, Juan Bautista
dc.contributor.authorYang, Zhicheng
dc.contributor.authorSerrão Bon de Sousa, Maria Luísa
dc.contributor.authorSilvestri, Sonia
dc.date.accessioned2026-01-15T10:48:12Z
dc.date.available2026-01-15T10:48:12Z
dc.date.issued2025-12-10
dc.description.abstractWhile improvements in the spectral and spatial resolution of satellite imagery have opened up new prospects for large-scale environmental monitoring, this potential has remained largely unrealised in dune ecogeomorphology. This is especially true for Mediterranean coastal dunes, where the highly mixed and sparse vegetation requires high resolution satellites and spectral unmixing techniques. To achieve this aim, we employed random forest regressors to predict the fractional cover of dune plant species in two of the sandy barriers of Ria Formosa (S. Portugal) from WorldView-2 imagery (June 2024). The algorithm, tested with spatially upscaled multispectral drone data and satellite imagery, detected the fractional cover of major species (most abundant classes and bushy vegetation) with reasonable to very good accuracy (coefficient of determination, CoD: 0.4 to 0.8) for the former and reasonable to good accuracy (CoD: 0.4 to 0.6) for the latter. Additional tests showed that (a) including the distance to the shoreline can increase model accuracy (CoD by ~0.1); (b) the grouping of species resulted in an insignificant increase in model skill; and (c) testing over independent dune plots showed generalisation beyond the training set and low risk of overfitting or noise. Overall, the approach showed promising results for large-scale observations in highly mixed coastal dunes.eng
dc.description.sponsorshipCEECINST/00146/2018/CP1493/CT0011; CEECINSTLA/00018/2022/CP2967/CT0003
dc.identifier.doi10.3390/rs17243991
dc.identifier.issn2072-4292
dc.identifier.urihttp://hdl.handle.net/10400.1/28102
dc.language.isoeng
dc.peerreviewedyes
dc.publisherMDPI
dc.relationCentre for Marine and Environmental Research
dc.relationAquatic Research Infrastructure Network
dc.relation.ispartofRemote Sensing
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectDune species
dc.subjectSubpixel remote sensing
dc.subjectWorldView-2
dc.subjectMultispectral data
dc.subjectFractional cover
dc.subjectRandom forest regressor
dc.titleSpectral unmixing of coastal dune plant species from very high resolution satellite imageryeng
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleCentre for Marine and Environmental Research
oaire.awardTitleAquatic Research Infrastructure Network
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FMAR%2F00350%2F2013/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0069%2F2020/PT
oaire.citation.issue24
oaire.citation.startPage3991
oaire.citation.titleRemote Sensing
oaire.citation.volume17
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameKombiadou
person.familyNameCostas
person.familyNameSerrão Bon de Sousa
person.givenNameKaterina
person.givenNameSusana
person.givenNameMaria Luísa
person.identifier1448818
person.identifier.ciencia-id1813-F159-070B
person.identifier.ciencia-idAF19-9EEE-7550
person.identifier.ciencia-idE218-3C8E-DF78
person.identifier.orcid0000-0003-1199-1236
person.identifier.orcid0000-0002-4005-077X
person.identifier.orcid0000-0002-8583-8771
person.identifier.ridM-7458-2017
person.identifier.scopus-author-id16029005200
person.identifier.scopus-author-id9043656500
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
relation.isAuthorOfPublication97b4019d-d2a2-480b-a04b-c1c5e4e6fc33
relation.isAuthorOfPublication62d73183-10ba-42ca-80a7-458c42d1375b
relation.isAuthorOfPublicationf241a9af-4e28-4af5-a20f-e141c0e0c7fa
relation.isAuthorOfPublication.latestForDiscovery97b4019d-d2a2-480b-a04b-c1c5e4e6fc33
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