Signal classification of submerged aquatic vegetation based on the hemispherical-conical reflectance factor spectrum shape in the yellow and red regions

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dc.contributor.authorWatanabe, Fernanda Sayuri Yoshino [UNESP]
dc.contributor.authorImai, Nilton Nobuhiro [UNESP]
dc.contributor.authorAlcântara, Enner Herenio [UNESP]
dc.contributor.authorDa Silva Rotta, Luiz Henrique [UNESP]
dc.contributor.authorUtsumi, Alex Garcez [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2014-05-27T11:28:49Z
dc.date.available2014-05-27T11:28:49Z
dc.date.issued2013-04-01
dc.description.abstractThe water column overlying the submerged aquatic vegetation (SAV) canopy presents difficulties when using remote sensing images for mapping such vegetation. Inherent and apparent water optical properties and its optically active components, which are commonly present in natural waters, in addition to the water column height over the canopy, and plant characteristics are some of the factors that affect the signal from SAV mainly due to its strong energy absorption in the near-infrared. By considering these interferences, a hypothesis was developed that the vegetation signal is better conserved and less absorbed by the water column in certain intervals of the visible region of the spectrum; as a consequence, it is possible to distinguish the SAV signal. To distinguish the signal from SAV, two types of classification approaches were selected. Both of these methods consider the hemispherical-conical reflectance factor (HCRF) spectrum shape, although one type was supervised and the other one was not. The first method adopts cluster analysis and uses the parameters of the band (absorption, asymmetry, height and width) obtained by continuum removal as the input of the classification. The spectral angle mapper (SAM) was adopted as the supervised classification approach. Both approaches tested different wavelength intervals in the visible and near-infrared spectra. It was demonstrated that the 585 to 685-nm interval, corresponding to the green, yellow and red wavelength bands, offered the best results in both classification approaches. However, SAM classification showed better results relative to cluster analysis and correctly separated all spectral curves with or without SAV. Based on this research, it can be concluded that it is possible to discriminate areas with and without SAV using remote sensing. © 2013 by the authors; licensee MDPI, Basel, Switzerland.en
dc.description.affiliationCollege of Science and Technology Sao Paulo State University (UNESP), Rua Roberto Simonsen, 305, Presidente Prudente, SP 19060
dc.description.affiliationDepartment of Cartography College of Science and Technology Sao Paulo State University (UNESP), Centro Educacional, Rua Roberto Simonsen, 305, Presidente Prudente, SP 19060
dc.description.affiliationUnespCollege of Science and Technology Sao Paulo State University (UNESP), Rua Roberto Simonsen, 305, Presidente Prudente, SP 19060
dc.description.affiliationUnespDepartment of Cartography College of Science and Technology Sao Paulo State University (UNESP), Centro Educacional, Rua Roberto Simonsen, 305, Presidente Prudente, SP 19060
dc.format.extent1856-1874
dc.identifierhttp://dx.doi.org/10.3390/rs5041856
dc.identifier.citationRemote Sensing, v. 5, n. 4, p. 1856-1874, 2013.
dc.identifier.doi10.3390/rs5041856
dc.identifier.file2-s2.0-84880448206.pdf
dc.identifier.issn2072-4292
dc.identifier.lattes2985771102505330
dc.identifier.lattes6691310394410490
dc.identifier.orcid0000-0003-0516-0567
dc.identifier.orcid0000-0002-8077-2865
dc.identifier.scopus2-s2.0-84880448206
dc.identifier.urihttp://hdl.handle.net/11449/75031
dc.identifier.wosWOS:000318020600017
dc.language.isoeng
dc.relation.ispartofRemote Sensing
dc.relation.ispartofjcr3.406
dc.relation.ispartofsjr1,386
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.subjectCluster analysis
dc.subjectContinuum removal
dc.subjectHyperspectral
dc.subjectSpectral angle mapper
dc.subjectSubmerged aquatic vegetation
dc.subjectClassification approach
dc.subjectHyperSpectral
dc.subjectSpectral angle mappers
dc.subjectSubmerged aquatic vegetations
dc.subjectSupervised classification
dc.subjectVisible and near infrared
dc.subjectWater optical properties
dc.subjectImage reconstruction
dc.subjectInfrared devices
dc.subjectReflection
dc.subjectVegetation
dc.titleSignal classification of submerged aquatic vegetation based on the hemispherical-conical reflectance factor spectrum shape in the yellow and red regionsen
dc.typeArtigo
dcterms.licensehttp://www.mdpi.com/about/openaccess
unesp.author.lattes2985771102505330[2]
unesp.author.lattes6691310394410490[1]
unesp.author.orcid0000-0002-8077-2865[1]
unesp.author.orcid0000-0003-0516-0567[2]
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Ciências e Tecnologia, Presidente Prudentept

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