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K d(PAR) and a depth based model to estimate the height of submerged aquatic vegetation in an oligotrophic reservoir: A case study at Nova Avanhandava

dc.contributor.authorRotta, Luiz Henrique [UNESP]
dc.contributor.authorMishra, Deepak R.
dc.contributor.authorAlcântara, Enner [UNESP]
dc.contributor.authorImai, Nilton [UNESP]
dc.contributor.authorWatanabe, Fernanda [UNESP]
dc.contributor.authorRodrigues, Thanan
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniversity of Georgia (UGA)
dc.contributor.institutionScience and Technology of Pará State (IFPA)
dc.date.accessioned2019-10-06T16:17:08Z
dc.date.available2019-10-06T16:17:08Z
dc.date.issued2019-02-01
dc.description.abstractSubmerged aquatic vegetation (SAV) carry out important biological functions in freshwater systems, however, uncontrolled growth can lead to many negative ecologic and economic impacts. Radiation availability is the primary limiting factor for SAV and it is a function of water transparency measured by K d(PAR) (downwelling attenuation coefficient of Photosynthetically Active Radiation) and depth. The aim of this study was to develop a K d(PAR) and depth based model to estimate the height of submerged aquatic vegetation in a tropical oligotrophic reservoir. This work proposed a new graphical model to represent the SAV height in relation to K d(PAR) and depth. Based on the visual analysis of the model, it was possible to establish a set of Boolean rules to classify the SAV height and identify regions where SAV can grow with greater or lesser vigor. K d(PAR) was estimated using a model based on satellite data. Overall, the occurrence and height of SAV were directly influenced by the K d(PAR) , depending on the depth. This study highlights the importance of optical parameters in examining the occurrence and status of SAV in Brazilian Reservoirs. It was concluded that the digital model and its graphical representation overcomes the limitations found by other researchers to understand the SAV behavior in relation to those independent variables: depth and K d(PAR) .en
dc.description.affiliationDepartment of Cartography-Presidente Prudente São Paulo State University (UNESP)
dc.description.affiliationDepartment of Geography University of Georgia (UGA)
dc.description.affiliationDepartment of Environmental Engineering-São José dos Campos São Paulo State University (UNESP)
dc.description.affiliationFederal Institute of Education Science and Technology of Pará State (IFPA)
dc.description.affiliationUnespDepartment of Cartography-Presidente Prudente São Paulo State University (UNESP)
dc.description.affiliationUnespDepartment of Environmental Engineering-São José dos Campos São Paulo State University (UNESP)
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipIdFAPESP: 2012/19821-1
dc.identifierhttp://dx.doi.org/10.3390/rs11030317
dc.identifier.citationRemote Sensing, v. 11, n. 3, 2019.
dc.identifier.doi10.3390/rs11030317
dc.identifier.issn2072-4292
dc.identifier.lattes6691310394410490
dc.identifier.orcid0000-0002-8077-2865
dc.identifier.scopus2-s2.0-85061400786
dc.identifier.urihttp://hdl.handle.net/11449/188721
dc.language.isoeng
dc.relation.ispartofRemote Sensing
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.subjectBoolean classification
dc.subjectEchosounder data
dc.subjectInland waters
dc.subjectRemote sensing
dc.subjectWater quality
dc.titleK d(PAR) and a depth based model to estimate the height of submerged aquatic vegetation in an oligotrophic reservoir: A case study at Nova Avanhandavaen
dc.typeArtigo
dspace.entity.typePublication
unesp.author.lattes6691310394410490[5]
unesp.author.orcid0000-0002-8077-2865[5]
unesp.departmentCartografia - FCTpt

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