Análise espacial da turbidez no compartimento aquático São Francisco verdadeiro, reservatório de Itaipu, PR

• Ano de defesa: 2020
• Autor(a) principal: Dezordi, Rafael
• Orientador(a): Pereira Filho, Waterloo
• Banca de defesa: Miola, Alessandro Carvalho, Prado, Diego de Almeida
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Santa Maria; Centro de Ciências Naturais e Exatas
• Programa de Pós-Graduação: Programa de Pós-Graduação em Geografia
• Departamento: Geografia
• País: Brasil
• Palavras-chave em Português: Sensoriamento remoto; Correlação; Modelo matemático
• Palavras-chave em Inglês: Remote sensing; Correlation; Mathematical model
• Área do conhecimento CNPq: CNPQ::CIENCIAS HUMANAS::GEOGRAFIA
• Link de acesso: http://repositorio.ufsm.br/handle/1/21204

Turbidity is a variable of fundamental importance for optimized water management of reservoirs, which can directly affect the photosynthetic process and the trophic chain of the ecosystem. In view of the impact caused by turbidity in reservoirs, the present work analyzed turbidity comprehensively over the area covered by the São Francisco Verdadeiro River (SFV) aquatic compartment, which is a tributary of the ITAIPU hydroelectric reservoir. In this sense, a model was identified based on the correlation and regression of the remote sensing data with the turbidity behavior over the SFV. For this, data were collected at 3 levels: First level, from the field obtained through in situ sampling and analyzed in the laboratory (nephelometry). Second level, field radiometry performed using the field spectroradiometer (ASD model FieldSpec) that operates in the spectral range from 400 to 900 nm. Third level, data from the Operational Land Imager (OLI) sensor of the Landsat 8 orbital platform obtained from the online platform of the United States of America Geological Survey (USGS) on a date concurrent with the survey carried out in the field on 08/11/2016, 08/27/2016 and 06/11/2017 in 13 sample points distributed equidistantly. PH, electrical conductivity and rainfall data were used in addition to the turbidity analysis. The mathematical model generated was subjected to statistical tests and showed that turbidity is significantly related to remote sensing data. In general, turbidity was higher in the high and low course sectors of SFV, always related to rainfall conditions in periods prior to field sampling. The present work sought to deepen the understanding of the dynamics presented by the turbidity in the reservoir in order to optimize the management of the reservoir.