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@MastersThesis{Dutra:2019:MaMoCo,
               author = "Dutra, Andeise Cerqueira",
                title = "Mapeamento e monitoramento da cobertura vegetal do Estado da Bahia 
                         utilizando dados multitemporais de sensores {\'o}pticos 
                         orbitais",
               school = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                 year = "2019",
              address = "S{\~a}o Jos{\'e} dos Campos",
                month = "2019-03-29",
             keywords = "Modelo linear de mistura espectral, moderate resolution imaging 
                         spectroradiometer, random forest, Nordeste, Bahia, linear spectral 
                         mixture model, moderate resolution imaging spectroradiometer, 
                         random forest, Northeast, Bahia state.",
             abstract = "Conhecer a extens{\~a}o e as taxas de degrada{\c{c}}{\~a}o da 
                         terra para proteger os ecossistemas terrestres de um maior 
                         esgotamento, tem sido uma das quest{\~o}es mais importantes do 
                         nosso tempo. Entretanto, o Nordeste brasileiro tem sido 
                         negligenciado e pobremente estudado tanto em termos de programas 
                         de conserva{\c{c}}{\~a}o quanto de investiga{\c{c}}{\~a}o 
                         cient{\'{\i}}fica. Os produtos de Sensoriamento Remoto 
                         tornaram-se uma importante fonte de informa{\c{c}}{\~o}es para 
                         monitorar as mudan{\c{c}}as de cobertura da terra, no entanto, 
                         ainda {\'e} escasso o n{\'u}mero de estudos para detectar e 
                         monitorar a vegeta{\c{c}}{\~a}o de clima semi{\'a}rido. Nesse 
                         contexto, o estado da Bahia foi selecionado para a 
                         realiza{\c{c}}{\~a}o desta pesquisa por possuir diversas 
                         forma{\c{c}}{\~o}es vegetais e apresentar significativa 
                         mudan{\c{c}}a no uso e cobertura da terra. Devido {\`a} alta 
                         incid{\^e}ncia de nuvens no estado, os produtos provenientes do 
                         sensor MODIS foram utilizados por apresentarem alta 
                         resolu{\c{c}}{\~a}o temporal. Assim, esta pesquisa teve por 
                         objetivo propor uma abordagem de mapeamento e monitoramento do uso 
                         e cobertura da terra para o estado da Bahia utilizando dados 
                         multitemporais provenientes do sensor MODIS, abrangendo o 
                         per{\'{\i}}odo entre 2000 e 2017. Os objetivos 
                         espec{\'{\i}}ficos foram: a) estimar endmembers (pixels puros) a 
                         partir de imagens de melhor resolu{\c{c}}{\~a}o espacial (30 m) 
                         do sensor OLI/Landsat 8 para posterior aplica{\c{c}}{\~a}o do 
                         Modelo Linear de Mistura Espectral (MLME) nos produtos 
                         provenientes do sensor MODIS (250 m) ; b) gerar uma s{\'e}rie 
                         temporal de imagens fra{\c{c}}{\~a}o derivadas do MLME entre 
                         2000 e 2017 utilizando os endmembers estimados na fase anterior; 
                         c) gerar mosaicos das imagens fra{\c{c}}{\~a}o calculando o 
                         valor m{\'a}ximo anual das propor{\c{c}}{\~o}es para os anos de 
                         2000 e 2017; d) gerar dois mapas tem{\'a}ticos base de uso e 
                         cobertura da terra (LULC Land Use and Land Cover) no estado da 
                         Bahia para os anos 2000 e 2017 utilizando os mosaicos gerados na 
                         fase anterior, aplicando o classificador Random Forest; e) avaliar 
                         as acur{\'a}cias dos mapas de LULC obtidos; e f) analisar 
                         qualitativamente as s{\'e}ries temporais das imagens 
                         fra{\c{c}}{\~a}o obtidas para todo o per{\'{\i}}odo analisado. 
                         Os resultados obtidos foram: 1) os endmembers estimados permitiram 
                         a melhoria dos resultados no que se refere ao erro, a 
                         variabilidade e a identifica{\c{c}}{\~a}o das 
                         propor{\c{c}}{\~o}es dos componentes existentes nas imagens, 
                         visto a dificuldade na determina{\c{c}}{\~a}o dos endmembers e 
                         que a escolha indevida de pixels considerados como puros em 
                         produtos de baixa e moderada resolu{\c{c}}{\~a}o espacial pode 
                         afetar a qualidade das imagens fra{\c{c}}{\~a}o para uso 
                         operacional; 2) a abordagem utilizando as imagens 
                         fra{\c{c}}{\~a}o contendo a propor{\c{c}}{\~a}o m{\'a}xima 
                         anual dos componentes reduziu o volume de dados, ao mesmo tempo em 
                         que permitiu a separa{\c{c}}{\~a}o das classes de LULC em 
                         fun{\c{c}}{\~a}o da associa{\c{c}}{\~a}o entre as 
                         propor{\c{c}}{\~o}es de vegeta{\c{c}}{\~a}o, solo e sombra, 
                         extraindo as caracter{\'{\i}}sticas relacionadas aos 
                         padr{\~o}es anuais das classes de LULC; 3) Os mapas de LULC para 
                         os anos de 2000 e 2017 obtiveram acur{\'a}cias totais de 0,77 e 
                         0,67, respectivamente, gerando a hip{\'o}tese de que a seca 
                         severa que atingiu o Nordeste entre 2012 e 2017 influenciou o pior 
                         desempenho do classificador utilizado; 4) o uso de s{\'e}ries 
                         temporais das imagens fra{\c{c}}{\~a}o permitiu o monitoramento 
                         das mudan{\c{c}}as ocorridas na vegeta{\c{c}}{\~a}o e 
                         tamb{\'e}m os impactos que podem estar associados aos eventos de 
                         seca. Assim, a abordagem aqui apresentada demonstra a 
                         potencialidade das imagens fra{\c{c}}{\~a}o para a 
                         classifica{\c{c}}{\~a}o e monitoramento semiautom{\'a}tico da 
                         cobertura vegetal a n{\'{\i}}vel global e regional. ABSTRACT: 
                         Knowing the extent and rates of land degradation to protect 
                         terrestrial ecosystems from further depletion has been one of the 
                         most important issues of our time. However, the Brazilian 
                         Northeast has been neglected and poorly studied both in terms of 
                         conservation programs and scientific research. Remote Sensing 
                         products have become an important source of information for 
                         monitoring changes in land cover, however, there are still few 
                         studies to detect and monitor semi-arid vegetation. In this 
                         context, the state of Bahia was selected to carry out this 
                         research because it has diverse vegetation formations and presents 
                         a significant change in land use and land cover (LULC). Due to the 
                         high incidence of clouds in the Bahia state, the products from 
                         MODIS sensor were used because it presents a high temporal 
                         resolution. The purpose of this research was to propose a mapping 
                         and monitoring approach to land use and land cover for the state 
                         of Bahia using multitemporal data from MODIS sensor, covering the 
                         period between 2000 and 2017. The specific objectives were: a) to 
                         estimate endmembers (pure pixels) from images of higher spatial 
                         resolution (30 m) of OLI / Landsat 8 sensor for later application 
                         of the Linear Spectral Mixture Model (LSMM) in products from MODIS 
                         sensor (250 m spatial resolution) ; b) to generate a time series 
                         of fraction images derived from the MLME between 2000 and 2017 
                         using the estimated endmembers in the previous phase; c) to 
                         generate mosaics of the fraction images by calculating the maximum 
                         annual value of the proportions for the years 2000 and 2017; d) to 
                         generate two LULC thematic maps in the state of Bahia for the 
                         years 2000 and 2017 using the mosaics generated in the previous 
                         phase, applying the Random Forest classifier; e) to evaluate the 
                         accuracy of the LULC maps obtained; and f) qualitatively analyse 
                         the time series of the fraction images obtained for the entire 
                         analysed period. The results obtained were: 1) the estimated 
                         endmembers allowed the improvement of the results regarding error, 
                         variability and identification of the components proportions in 
                         the images, due to the difficulty in determining the endmembers 
                         and that the undue choice of considered pixels as pure in low and 
                         moderate spatial resolution products can affect the quality of the 
                         fraction images for operational use; 2) the approach using the 
                         fraction images containing the maximum annual proportion of the 
                         components reduced the data volume, while allowing the separation 
                         of LULC classes due to the association between vegetation, soil 
                         and shade proportions, extracting the characteristics related to 
                         the annual LULC class patterns; 3) The LULC maps for the years 
                         2000 and 2017 obtained a global accuracy of 0.77 and 0.67, 
                         respectively, generating the hypothesis that the severe drought 
                         that reached the Northeast between 2012 and 2017 influenced the 
                         worse performance of the classifier; 4) the use of time series of 
                         the fraction images allowed to monitoring the changes occurred in 
                         the vegetation and also the impacts that may be associated to the 
                         drought events. Thus, the approach presented here demonstrates the 
                         potentiality of the fraction images for the semiautomatic 
                         classification and monitoring of vegetation cover at global and 
                         regional level.",
            committee = "K{\"o}rting, Thales Sehn (presidente) and Shimabukuro, Yosio 
                         Edemir (orientador) and Arai, Egidio (orientador) and Sampaio, 
                         Cl{\'a}udia Bloisi Vaz",
         englishtitle = "Mapping and monitoring of vegetation cover in the Bahia state 
                         using multitemporal data from optical orbital sensors",
             language = "pt",
                pages = "139",
                  ibi = "8JMKD3MGP3W34R/3T298T8",
                  url = "http://urlib.net/rep/8JMKD3MGP3W34R/3T298T8",
           targetfile = "publicacao.pdf",
        urlaccessdate = "23 nov. 2020"
}


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