Depth Retrieval from A Reservoir Using A Conditional-Based Model

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversidade Federal de Santa Catarina (UFSC)-
Autor(es): dc.creatorNunes, Melina Brunelli [UNESP]-
Autor(es): dc.creatorPoz, Aluir Porfirio Dal [UNESP]-
Autor(es): dc.creatorAlcantara, Enner [UNESP]-
Autor(es): dc.creatorCurtarelli, Marcelo-
Data de aceite: dc.date.accessioned2022-08-04T22:09:00Z-
Data de disponibilização: dc.date.available2022-08-04T22:09:00Z-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2020-03-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/LAGIRS48042.2020.9165636-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/221571-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/221571-
Descrição: dc.descriptionWater depth is an important measure for nautical charts. Accurate methods to provide water depth information are expensive and time costing. For this reason, since late 70's, it started to be estimate by multispectral sensors with empirical models. In the literature there is no investigation using empirical models partitioned in depth intervals, for this reason, we evaluated the accuracy of partitioned and single bathymetric models. The results have shown that to retrieve depth in from 0 to 15 m the single model provided an RMSE of 3.57 m, with a bias of about -0.83 m; while the RMSE for the partitioned model was 2.29 m with a bias of 0.41 m. For updating nautical charts using multispectral sensors it was concluded that the partitioned model can provide a better result than using a single model.-
Descrição: dc.descriptionSão Paulo State University-
Descrição: dc.descriptionFederal University of Santa Catarina-
Descrição: dc.descriptionSão Paulo State University-
Formato: dc.format121-125-
Idioma: dc.languageen-
Relação: dc.relation2020 IEEE Latin American GRSS and ISPRS Remote Sensing Conference, LAGIRS 2020 - Proceedings-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectaccuracy-
Palavras-chave: dc.subjectAmazonian region-
Palavras-chave: dc.subjectbathymetry-
Palavras-chave: dc.subjectdam-
Palavras-chave: dc.subjectLandsat-8-
Palavras-chave: dc.subjectLyzega-
Palavras-chave: dc.subjectmultispectral sensor-
Título: dc.titleDepth Retrieval from A Reservoir Using A Conditional-Based Model-
Aparece nas coleções:Repositório Institucional - Unesp

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