Fechar
Metadados

@MastersThesis{FloresJśnior:2019:PaAlEm,
               author = "Flores J{\'u}nior, Rog{\'e}rio",
                title = "Parametriza{\c{c}}{\~a}o de algoritmos emp{\'{\i}}ricos e 
                         algoritmo quasi-anal{\'{\i}}tico QAA para estimativa de 
                         clorofila-a em lagos da v{\'a}rzea do rio Amazonas",
               school = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                 year = "2019",
              address = "S{\~a}o Jos{\'e} dos Campos",
                month = "2019-03-14",
             keywords = "Qualidade da {\'a}gua, sensoriamento remoto da {\'a}gua, 
                         fitoplancton, algoritmos bio-{\'o}pticos, lagos amaz{\^o}nicos, 
                         water quality, water remote sensing, phytoplankton, bio-optical 
                         models, Amazon lakes.",
             abstract = "O monitoramento sistem{\'a}tico, essencial para a 
                         manuten{\c{c}}{\~a}o dos servi{\c{c}}os ecossist{\^e}micos, 
                         visa auxiliar na gest{\~a}o, manuten{\c{c}}{\~a}o e 
                         recupera{\c{c}}{\~a}o de recursos h{\'{\i}}dricos diante das 
                         a{\c{c}}{\~o}es antr{\'o}pica. Suplementando as metodologias 
                         tradicionais, o sensoriamento remoto (SR) de ambientes 
                         aqu{\'a}ticos relaciona as propriedades {\'o}pticas e 
                         biogequ{\'{\i}}micas do meio, possibilitando uma vis{\~a}o 
                         sin{\'o}ptica de seus padr{\~o}es de distribui{\c{c}}{\~a}o no 
                         tempo e espa{\c{c}}o. A determina{\c{c}}{\~a}o do estado 
                         tr{\'o}fico e da produtividade prim{\'a}ria do ambiente, 
                         estimadas por meio da concentra{\c{c}}{\~a}o de clorofila-a 
                         (Chl-a), podem ser utilizados como {\'{\i}}ndice da qualidade do 
                         recurso h{\'{\i}}drico. A quantifica{\c{c}}{\~a}o de Chl-a por 
                         SR embasa-se na aplica{\c{c}}{\~a}o emp{\'{\i}}rica e/ou 
                         semi-anal{\'{\i}}tica de algoritmos, geralmente desenvolvidos 
                         para {\'a}guas oce{\^a}nicas e costeiras, necessitando assim 
                         serem calibrados para sua utiliza{\c{c}}{\~a}o em {\'a}guas 
                         continentais complexas como {\`a}s da plan{\'{\i}}cie de 
                         inunda{\c{c}}{\~a}o amaz{\^o}nica. Assim, este trabalho 
                         objetivou: i) A aplica{\c{c}}{\~a}o e valida{\c{c}}{\~a}o de 
                         algoritmos emp{\'{\i}}ricos em dados in situ e imagens orbitais 
                         simulados para os sensores OLI e MSI, por meio de 
                         simula{\c{c}}{\~a}o de Monte Carlo (MC); ii) 
                         Aplica{\c{c}}{\~a}o do algoritmo quasi anal{\'{\i}}tico QAA da 
                         literatura, calibra{\c{c}}{\~a}o e Parametriza{\c{c}}{\~a}o de 
                         uma vers{\~a}o para {\`a}s {\'a}guas complexas da 
                         plan{\'{\i}}cie amaz{\^o}nica (QAALCG) e aplica{\c{c}}{\~a}o 
                         de {\'{\I}}ndices para a estimativa de Chl-a com os produtos 
                         gerados, utilizando dados in situ (Rrs(\λ)) simulados para o 
                         sensor OLCI. Para isto, foram obtidos dados {\'o}pticos e 
                         limnol{\'o}gicos em lagos da plan{\'{\i}}cie de 
                         inunda{\c{c}}{\~a}o do baixo amazonas para quatro campanhas de 
                         campo entre 2015 e 2017, formando um conjunto de 94 pontos 
                         amostrais. Foram utilizadas as m{\'e}tricas estat{\'{\i}}sticas 
                         R2, MAPE, NRMSE e bias, para a avalia{\c{c}}{\~a}o do desempenho 
                         dos algoritmos. A calibra{\c{c}}{\~a}o dos algoritmos 
                         emp{\'{\i}}ricos esbarra na heterogeneidade dos dados das 
                         campanhas, coletadas em diferentes fases da hidr{\'o}grafa, 
                         apresentando resultados insatisfat{\'o}rios no conjunto com todos 
                         os dados. A calibra{\c{c}}{\~a}o e valida{\c{c}}{\~a}o (MC) 
                         apenas para a campanha de 2017 foi satisfat{\'o}ria e 
                         possibilitou a aplica{\c{c}}{\~a}o do algoritmo {\`a}s imagens 
                         dos sensores OLI e MSI de mesma data. Os algoritmos 
                         emp{\'{\i}}ricos aplicados {\`a}s imagens de sat{\'e}lite 
                         n{\~a}o foram satisfat{\'o}rios para quantifica{\c{c}}{\~a}o 
                         da Chl-a, o que pode ser atribu{\'{\i}}do a alta din{\^a}mica 
                         observada entre a data de aquisi{\c{c}}{\~a}o das imagens e a 
                         aquisi{\c{c}}{\~a}o de medidas in situ, mesmo considerando-se 
                         uma defasagem de apenas 2 dias. A calibra{\c{c}}{\~a}o e 
                         parametriza{\c{c}}{\~a}o das rela{\c{c}}{\~o}es 
                         emp{\'{\i}}ricas do QAALGC foram essenciais para o bom 
                         desempenho do algoritmo. Os coeficientes de absor{\c{c}}{\~a}o 
                         derivados do QAALGC, obtiveram resultados satisfat{\'o}rios para 
                         o conjunto de dados testado, por{\'e}m com tend{\^e}ncia a 
                         subestimar os valores como os demais algoritmos avaliados. A alta 
                         concentra{\c{c}}{\~a}o de part{\'{\i}}culas inorg{\^a}nicas 
                         na campanha independente, utilizada para validar o QAALCG, limitou 
                         a obten{\c{c}}{\~a}o de resultados satisfat{\'o}rios para a 
                         estimativa de Chl-a utilizando os coeficientes de 
                         absor{\c{c}}{\~a}o modelados pelo algoritmo. Os resultados 
                         obtidos pelo QAALCG para as {\'a}guas complexas da 
                         plan{\'{\i}}cie de inunda{\c{c}}{\~a}o amaz{\^o}nica foram 
                         satisfat{\'o}rios. A alta din{\^a}mica sazonal e espacial da 
                         regi{\~a}o de estudo dificulta a modelagem dos constituintes 
                         pressentes no meio aqu{\'a}tico, por{\'e}m os algoritmos 
                         (emp{\'{\i}}ricos e semi-anal{\'{\i}}tico) testados mostraram 
                         potencial para a quantifica{\c{c}}{\~a}o da Chl-a. ABSTRACT: 
                         Systematic monitoring, essential to maintain the ecosystem 
                         services, aim to assist the management, maintenance, and recovery 
                         of the water resources in contrast to anthropic actions. 
                         Supporting the traditional methodologies, the remote sensing (SR) 
                         of aquatic environments associate optical and biogeochemical 
                         properties of the medium, allowing a synoptic view of its 
                         distribution patterns in time and space. The determination of the 
                         environment trophic state and primary productivity, estimated by 
                         the concentration of chlorophyll-a (Chl-a), can be used as quality 
                         index to water resources. The quantification of Chl-a by SR is 
                         based on empirical and/or semi-analytical application of 
                         algorithms, generally developed for oceanic and coastal waters, 
                         needing to be calibrated for use in complex inland waters such as 
                         those on the Amazon floodplains. Thus, this study aimed to: i) 
                         application and validation of empirical algorithms in in situ data 
                         and satellite images simulated to OLI and MSI sensors, through 
                         Monte Carlo simulation (MC); ii) Application of the 
                         quasianalytical algorithm QAA from literature, calibration and 
                         parameterization of a version for the complex waters of the Amazon 
                         floodplain (QAALCG), and application of Indices to estimate Chl-a 
                         with the products generated, using in situ data (Rrs(\λ)) 
                         simulated for the OLCI sensor. So, optical and limnological data 
                         were obtained in lakes from the lower Amazon floodplains in four 
                         field campaigns between 2015 and 2017, composing a dataset of 94 
                         sampling points. The statistical metrics R2, MAPE, NRMSE and bias 
                         were used to evaluate the performance of the algorithms. The 
                         calibration of the empirical algorithms faces the heterogeneity of 
                         the campaigns, collected in different phases of the hydrograph, 
                         presenting unsatisfactory results when calibrated with all data. 
                         However, Calibration and validation (MC) only for the 2017 
                         campaign was satisfactory and allowed the application of the 
                         algorithm to OLI and MSI sensor images of the same date. Yet, the 
                         empirical algorithms applied to the satellite images were not 
                         satisfactory to quantify Chl-a, which can be attributed to high 
                         dynamics observed between the images date of acquisition and in 
                         situ measurements, even only with two days gap. Therefore, 
                         calibration and parameterization of the empirical relations on the 
                         QAALCG were essential for optimizing its performance. The 
                         absorption coefficients derived from the QAALGC obtained 
                         satisfactory results for the dataset tested, but with a tendency 
                         to underestimate it as well as the other algorithms evaluated. The 
                         high concentration of inorganic particles in the independent 
                         campaign, used to validate the QAALCG, limited the achievement of 
                         satisfactory results of Chl-a using the absorption coefficients 
                         modeled by the algorithm. However, the results obtained by QAALCG 
                         for complex waters of the Amazon floodplain lakes were 
                         satisfactory considering that the algorithm was developed for 
                         oceanic waters. The highly seasonal and spatial dynamics of the 
                         study region makes it difficult to model the constituents present 
                         in the water bodies, but the algorithms (empirical and 
                         semi-analytical) tested showed potential for quantification of 
                         Chl-a.",
            committee = "Novo, Evlyn M{\'a}rcia Le{\~a}o de Moraes (presidente) and 
                         Barbosa, Claudio Clemente Faria (orientador) and Lobo, Felipe de 
                         Lucia (orientador) and Watanabe, Fernanda Sayuri Yoshino and 
                         Carvalho, Lino Augusto Sander de",
         englishtitle = "Parametrization of empirical algorithms and quasi-analytical 
                         algorithm QAA to estimate chlorophil-a in lakes of the Amazon 
                         river floodplains",
             language = "pt",
                pages = "157",
                  ibi = "8JMKD3MGP3W34R/3SUQ3U2",
                  url = "http://urlib.net/rep/8JMKD3MGP3W34R/3SUQ3U2",
           targetfile = "publicacao.pdf",
        urlaccessdate = "04 dez. 2020"
}


Fechar