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Use of sub-pixel imagery classification to assess salt marsh plants’ adaptation

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datacite.subject.sdg15:Proteger a Vida Terrestre
datacite.subject.sdg14:Proteger a Vida Marinha
datacite.subject.sdg13:Ação Climática
dc.contributor.authorCarrasco, Rita
dc.contributor.authorAstori, Alexandra
dc.contributor.authorKombiadou, Katerina
dc.date.accessioned2026-07-23T14:43:55Z
dc.date.available2026-07-23T14:43:55Z
dc.date.issued2026
dc.description.abstractThe inherent heterogeneity of coastal wetlands and the small size of halophytic plants present challenges in accurately sensing plant species, even with very high-resolution satellite imagery. This study used sub-pixel imagery classification methods on high spectral and spatial resolution imagery from Worldview-3 to predict plant species distribution in a mesotidal coastal wetland system. The predicted sub-pixel fractional abundance of plant species is discussed for three targeted wetland categories in the Ria Formosa lagoon: naturally evolving patches, patches modified by human activities, and patches affected by coastal squeeze. The Random Forest Regression algorithm was proven to be highly effective in unmixing the spectral signal of halophytic vegetation, enabling the retrieval of plant species distribution (7 plant species). To train the algorithm, field observations were used to classify satellite images. Differences in band feature importance for key species and bare soil were observed across the various sites. The comparison of species distribution between sites suggests that, in addition to biotic factors, other environmental influences likely affect ecological succession; therefore, large-scale mapping approaches based on remote sensing should be undertaken with caution. The results are important for understanding the diverse ecological behavior of marsh plants within the same system and highlight the variability in plant reflectance and the need for ground truthing when sensing plant cover from satellite data.eng
dc.description.sponsorshipCEECINST/00052/2021/CP2792/CT0007; CEECINST/00146/2018/CP1493/CT0011
dc.identifier.doi10.1007/978-3-032-15473-6_59
dc.identifier.eissn2211-0585
dc.identifier.isbn978-3-032-15472-9
dc.identifier.isbn978-3-032-15473-6
dc.identifier.issn2211-0577
dc.identifier.urihttp://hdl.handle.net/10400.1/29302
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer
dc.relationAquatic Research Infrastructure Network
dc.relation.ispartofCoastal Research Library
dc.relation.ispartofCoastal Dynamics 2025
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectSalt marsh
dc.subjectSatellite imagery
dc.subjectSpecies fractional cover
dc.subjectReflectance
dc.subjectEnvironmental pressures
dc.titleUse of sub-pixel imagery classification to assess salt marsh plants’ adaptationeng
dc.typebook part
dspace.entity.typePublication
oaire.awardNumberLA/P/0069/2020
oaire.awardTitleAquatic Research Infrastructure Network
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0069%2F2020/PT
oaire.citation.endPage387
oaire.citation.startPage381
oaire.citation.titleCoastal Dynamics 2025
oaire.citation.volume1
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameCarrasco
person.familyNameKombiadou
person.givenNameRita
person.givenNameKaterina
person.identifier545203
person.identifier.ciencia-idA31E-1703-4030
person.identifier.ciencia-id1813-F159-070B
person.identifier.orcid0000-0002-8980-0068
person.identifier.orcid0000-0003-1199-1236
person.identifier.ridD-2235-2016
person.identifier.ridM-7458-2017
person.identifier.scopus-author-id15724386100
person.identifier.scopus-author-id16029005200
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
relation.isAuthorOfPublicationdbfe713e-6ca3-4946-be23-c63049394986
relation.isAuthorOfPublication97b4019d-d2a2-480b-a04b-c1c5e4e6fc33
relation.isAuthorOfPublication.latestForDiscoverydbfe713e-6ca3-4946-be23-c63049394986
relation.isProjectOfPublication5af011f9-3888-449a-a18c-d08b59e87091
relation.isProjectOfPublication.latestForDiscovery5af011f9-3888-449a-a18c-d08b59e87091

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