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Water hyacinth (Eichhornia crassipes) detection using coarse and high resolution multispectral data

2022Journal article Open access
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dc.contributor.authorPádua, Luís
dc.contributor.authorGeraldes, Ana Maria
dc.contributor.authorSousa, Joaquim J.
dc.contributor.authorRodrigues, M.A.
dc.contributor.authorOliveira, Verónica
dc.contributor.authorSantos, Daniela
dc.contributor.authorMiguens, Maria Filomena
dc.contributor.authorCastro, João Paulo
dc.date.accessioned2022-03-03T12:02:10Z
dc.date.available2022-03-03T12:02:10Z
dc.date.issued2022
dc.description.abstractEfficient detection and monitoring procedures of invasive plant species are required. It is of crucial importance to deal with such plants in aquatic ecosystems, since they can affect biodiversity and, ultimately, ecosystem function and services. In this study, it is intended to detect water hyacinth (Eichhornia crassipes) using multispectral data with different spatial resolutions. For this purpose, high-resolution data (<0.1 m) acquired from an unmanned aerial vehicle (UAV) and coarse-resolution data (10 m) from Sentinel-2 MSI were used. Three areas with a high incidence of water hyacinth located in the Lower Mondego region (Portugal) were surveyed. Different classifiers were used to perform a pixel-based detection of this invasive species in both datasets. From the different classifiers used, the results were achieved by the random forest classifiers stand-out (overall accuracy (OA): 0.94). On the other hand, support vector machine performed worst (OA: 0.87), followed by Gaussian naive Bayes (OA: 0.88), k-nearest neighbours (OA: 0.90), and artificial neural networks (OA: 0.91). The higher spatial resolution from UAV-based data enabled us to detect small amounts of water hyacinth, which could not be detected in Sentinel-2 data. However, and despite the coarser resolution, satellite data analysis enabled us to identify water hyacinth coverage, compared well with a UAV-based survey. Combining both datasets and even considering the different resolutions, it was possible to observe the temporal and spatial evolution of water hyacinth. This approach proved to be an effective way to assess the effects of the mitigation/control measures taken in the study areas. Thus, this approach can be applied to detect invasive species in aquatic environments and to monitor their changes over time.pt_PT
dc.description.sponsorshipThis research activity was funded by POCI-FEDER as part of the project “BioComp_2.0— Produção de compostos orgânicos biológicos para o controlo do jacinto de água e para a valorização de subprodutos agropecuários, florestais e agroindustriais” (POCI-01-0247-FEDER-070123) and by national funds through FCT (Portuguese Foundation for Science and Technology) under the projects UIDB/04033/2020 and UIDB/00690/2020.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationPádua, Luís; Geraldes, Ana M.; Sousa, Joaquim J.; Rodrigues, M.A.; Oliveira, Verónica; Santos, Daniela; Miguens, Maria Filomena P.; Castro, João Paulo (2022). Water hyacinth (Eichhornia crassipes) detection using coarse and high resolution multispectral data. Drones. ISSN 2504-446X. 6:2, p. 1-14pt_PT
dc.identifier.doi10.3390/drones6020047pt_PT
dc.identifier.issn2504-446X
dc.identifier.urihttp://hdl.handle.net/10198/25141
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relationCentre for the Research and Technology of Agro-Environmental and Biological Sciences
dc.relationMountain Research Center
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectInvasive speciespt_PT
dc.subjectUnmanned aerial vehiclespt_PT
dc.subjectSentinel-2pt_PT
dc.subjectMachine learningpt_PT
dc.subjectMultitemporal analysispt_PT
dc.titleWater hyacinth (Eichhornia crassipes) detection using coarse and high resolution multispectral datapt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleCentre for the Research and Technology of Agro-Environmental and Biological Sciences
oaire.awardTitleMountain Research Center
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04033%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00690%2F2020/PT
oaire.citation.issue2pt_PT
oaire.citation.startPage47pt_PT
oaire.citation.titleDronespt_PT
oaire.citation.volume6pt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
person.familyNameGeraldes
person.familyNameRodrigues
person.familyNameCastro
person.givenNameAna Maria
person.givenNameManuel Ângelo
person.givenNameJoão Paulo
person.identifier.ciencia-id7D13-2CED-D6C5
person.identifier.ciencia-id371D-DF0D-8D68
person.identifier.ciencia-id8D19-DBCC-8EF5
person.identifier.orcid0000-0003-4966-2227
person.identifier.orcid0000-0002-5367-1129
person.identifier.orcid0000-0003-0647-8892
person.identifier.ridJ-8566-2014
person.identifier.ridO-1721-2016
person.identifier.ridA-8581-2014
person.identifier.scopus-author-id57204313331
person.identifier.scopus-author-id35270106800
person.identifier.scopus-author-id21233448700
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.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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relation.isAuthorOfPublication43621353-fa11-4559-9b24-27eba5ad3de0
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