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

Detalhes bibliográficos
Autor(a) principal: Watanabe, Fernanda Sayuri Yoshino [UNESP]
Data de Publicação: 2013
Outros Autores: Imai, Nilton Nobuhiro [UNESP], Alcântara, Enner Herenio [UNESP], Da Silva Rotta, Luiz Henrique [UNESP], Utsumi, Alex Garcez [UNESP]
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.3390/rs5041856
http://hdl.handle.net/11449/75031
Resumo: The 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.
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spelling Signal classification of submerged aquatic vegetation based on the hemispherical-conical reflectance factor spectrum shape in the yellow and red regionsCluster analysisContinuum removalHyperspectralSpectral angle mapperSubmerged aquatic vegetationClassification approachHyperSpectralSpectral angle mappersSubmerged aquatic vegetationsSupervised classificationVisible and near infraredWater optical propertiesImage reconstructionInfrared devicesReflectionVegetationThe 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.College of Science and Technology Sao Paulo State University (UNESP), Rua Roberto Simonsen, 305, Presidente Prudente, SP 19060Department of Cartography College of Science and Technology Sao Paulo State University (UNESP), Centro Educacional, Rua Roberto Simonsen, 305, Presidente Prudente, SP 19060College of Science and Technology Sao Paulo State University (UNESP), Rua Roberto Simonsen, 305, Presidente Prudente, SP 19060Department of Cartography College of Science and Technology Sao Paulo State University (UNESP), Centro Educacional, Rua Roberto Simonsen, 305, Presidente Prudente, SP 19060Universidade Estadual Paulista (Unesp)Watanabe, Fernanda Sayuri Yoshino [UNESP]Imai, Nilton Nobuhiro [UNESP]Alcântara, Enner Herenio [UNESP]Da Silva Rotta, Luiz Henrique [UNESP]Utsumi, Alex Garcez [UNESP]2014-05-27T11:28:49Z2014-05-27T11:28:49Z2013-04-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article1856-1874application/pdfhttp://dx.doi.org/10.3390/rs5041856Remote Sensing, v. 5, n. 4, p. 1856-1874, 2013.2072-4292http://hdl.handle.net/11449/7503110.3390/rs5041856WOS:0003180206000172-s2.0-848804482062-s2.0-84880448206.pdf298577110250533066913103944104900000-0003-0516-05670000-0002-8077-2865Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengRemote Sensing3.4061,386info:eu-repo/semantics/openAccess2024-06-18T18:18:15Zoai:repositorio.unesp.br:11449/75031Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T20:46:00.411134Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Signal classification of submerged aquatic vegetation based on the hemispherical-conical reflectance factor spectrum shape in the yellow and red regions
title Signal classification of submerged aquatic vegetation based on the hemispherical-conical reflectance factor spectrum shape in the yellow and red regions
spellingShingle Signal classification of submerged aquatic vegetation based on the hemispherical-conical reflectance factor spectrum shape in the yellow and red regions
Watanabe, Fernanda Sayuri Yoshino [UNESP]
Cluster analysis
Continuum removal
Hyperspectral
Spectral angle mapper
Submerged aquatic vegetation
Classification approach
HyperSpectral
Spectral angle mappers
Submerged aquatic vegetations
Supervised classification
Visible and near infrared
Water optical properties
Image reconstruction
Infrared devices
Reflection
Vegetation
title_short Signal classification of submerged aquatic vegetation based on the hemispherical-conical reflectance factor spectrum shape in the yellow and red regions
title_full Signal classification of submerged aquatic vegetation based on the hemispherical-conical reflectance factor spectrum shape in the yellow and red regions
title_fullStr Signal classification of submerged aquatic vegetation based on the hemispherical-conical reflectance factor spectrum shape in the yellow and red regions
title_full_unstemmed Signal classification of submerged aquatic vegetation based on the hemispherical-conical reflectance factor spectrum shape in the yellow and red regions
title_sort Signal classification of submerged aquatic vegetation based on the hemispherical-conical reflectance factor spectrum shape in the yellow and red regions
author Watanabe, Fernanda Sayuri Yoshino [UNESP]
author_facet Watanabe, Fernanda Sayuri Yoshino [UNESP]
Imai, Nilton Nobuhiro [UNESP]
Alcântara, Enner Herenio [UNESP]
Da Silva Rotta, Luiz Henrique [UNESP]
Utsumi, Alex Garcez [UNESP]
author_role author
author2 Imai, Nilton Nobuhiro [UNESP]
Alcântara, Enner Herenio [UNESP]
Da Silva Rotta, Luiz Henrique [UNESP]
Utsumi, Alex Garcez [UNESP]
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Watanabe, Fernanda Sayuri Yoshino [UNESP]
Imai, Nilton Nobuhiro [UNESP]
Alcântara, Enner Herenio [UNESP]
Da Silva Rotta, Luiz Henrique [UNESP]
Utsumi, Alex Garcez [UNESP]
dc.subject.por.fl_str_mv Cluster analysis
Continuum removal
Hyperspectral
Spectral angle mapper
Submerged aquatic vegetation
Classification approach
HyperSpectral
Spectral angle mappers
Submerged aquatic vegetations
Supervised classification
Visible and near infrared
Water optical properties
Image reconstruction
Infrared devices
Reflection
Vegetation
topic Cluster analysis
Continuum removal
Hyperspectral
Spectral angle mapper
Submerged aquatic vegetation
Classification approach
HyperSpectral
Spectral angle mappers
Submerged aquatic vegetations
Supervised classification
Visible and near infrared
Water optical properties
Image reconstruction
Infrared devices
Reflection
Vegetation
description The 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.
publishDate 2013
dc.date.none.fl_str_mv 2013-04-01
2014-05-27T11:28:49Z
2014-05-27T11:28:49Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.3390/rs5041856
Remote Sensing, v. 5, n. 4, p. 1856-1874, 2013.
2072-4292
http://hdl.handle.net/11449/75031
10.3390/rs5041856
WOS:000318020600017
2-s2.0-84880448206
2-s2.0-84880448206.pdf
2985771102505330
6691310394410490
0000-0003-0516-0567
0000-0002-8077-2865
url http://dx.doi.org/10.3390/rs5041856
http://hdl.handle.net/11449/75031
identifier_str_mv Remote Sensing, v. 5, n. 4, p. 1856-1874, 2013.
2072-4292
10.3390/rs5041856
WOS:000318020600017
2-s2.0-84880448206
2-s2.0-84880448206.pdf
2985771102505330
6691310394410490
0000-0003-0516-0567
0000-0002-8077-2865
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Remote Sensing
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1,386
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 1856-1874
application/pdf
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
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