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Master's Dissertation
DOI
Document
Author
Full name
Tainá Thomassim Guimarães
Institute/School/College
Knowledge Area
Date of Defense
Published
São Carlos, 2019
Supervisor
Committee
Mauad, Frederico Fabio (President)
Larocca, Ana Paula Camargo
Veronez, Mauricio Roberto
Title in Portuguese
Utilização de imagens de satélite para predição de clorofila-a e sólidos suspensos em corpos d'água: estudo de caso da Represa do Lobo/SP
Keywords in Portuguese
Clorofila
RNA
Sensoriamento remoto
Sentinel
Sólidos suspensos
Abstract in Portuguese
Medidas complementares ao monitoramento in situ da qualidade da água podem ser obtidas por meio de sensoriamento remoto, sendo clorofila-a e sólidos suspensos alguns dos parâmetros que podem ser estimados. Este trabalho teve como objetivo explorar técnicas de processamento de imagens, análises estatísticas e de inteligência artificial com o objetivo de predizer e modelar as concentrações de clorofila-a e sólidos suspensos totais na Represa do Lobo/SP. Metodologicamente, foram realizadas coletas em campo, em três diferentes datas, para amostragem de água e posterior análise laboratorial. Os resultados limnológicos foram analisados, modelados e comparados com imagens processadas do satélite Sentinel-2. Análises de regressão e redes neurais artificiais (RNA) foram exploradas para gerar modelos de predição para a área de estudo. Os resultados indicam que métodos de regressão podem não ser adequados para capturar as relações lineares e/ou não-lineares entre os compostos de interesse e as respostas espectrais da água recebidas pelo satélite, indicando a capacidade das redes neurais em modelar relações mais complexas. Através da integração da resposta que o sensor MSI do satélite Sentinel-2 coletou nas regiões do visível ao infravermelho médio e de RNAs foi possível modelar a concentração de clorofila-a, com valores de R² superiores a 0,65 e de RMSE inferiores a 2,5 μg/L, e gerar mapas que permitam seu monitoramento temporal e análise espacial na área de estudo. Os resultados para SST não foram satisfatórios devido à complexidade óptica do ambiente analisado, bem como as baixas concentrações de SST na represa. Portanto, a integração de dados de sensoriamento remoto no mapeamento de corpos d'água com a aplicação de redes neurais na análise de dados é uma abordagem promissora para prever clorofila-a e sólidos suspensos, bem como suas variações temporais e espaciais.
Title in English
Use of satellite images to predict chlorophyll-a and suspended solids in water bodies: a study case of the Lobo Reservoir/SP
Keywords in English
ANN
Chlorophyll
Remote Sensing
Sentinel
Suspended solids
Abstract in English
Complementary measures to in situ monitoring of water quality can be obtained through remote sensing, with chlorophyll-a and suspended solids being some of the parameters that can be estimated. The objective of this work was to explore techniques for image processing, statistical analysis and artificial intelligence with the objective of predicting and modeling the concentrations of chlorophyll-a and total suspended solids in the Lobo Reservoir/SP. Methodologically, field samples were collected in three different dates for water sampling and laboratory analysis. The limnological results were analyzed, modeled and compared with processed images of the Sentinel-2 satellite. Regression analysis and artificial neural networks (ANNs) were explored to generate prediction models for the study area. The results indicate that regression methods may not be adequate to capture linear and/or nonlinear relationships between the compounds of interest and the spectral responses of water received by the satellite, indicating the ability of neural networks to model more complex relationships. Through of the integration of response wich the MSI sensor of Sentinel satellite collected in the visible and near-infrared regions and of the ANN analysis was possible modeling the chlorophyll-a concentration, wich R² values highers of 0.65 and RMSE less 2.5, and create predict maps wich allow your temporal monitoring and spatial analysis in the study area. The TSS results were unsatisfactory because of the optic complexity of analysed ambient, as well as your small TSS concentrations in the Lobo Reservoir. Therefore, the integration of remote sensing data in the mapping of water bodies with the application of neural networks in the data analysis is a promising approach to predict chlorophyll-a and suspended solids as well as their temporal and spatial variations.
 
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Publishing Date
2019-09-09
 
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