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@MastersThesis{Fragal:2015:ReHiMu,
               author = "Fragal, Everton Hafemann",
                title = "Reconstru{\c{c}}{\~a}o hist{\'o}rica de mudan{\c{c}}as na 
                         cobertura florestal em v{\'a}rzeas do Baixo Amazonas utilizando o 
                         algoritmo LandTrendr",
               school = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                 year = "2015",
              address = "S{\~a}o Jos{\'e} dos Campos",
                month = "2015-04-09",
             keywords = "Amaz{\^o}nia, floresta inund{\'a}vel, altera{\c{c}}{\~a}o da 
                         floresta, monitoramento, Amazon, wetland forests, forest change, 
                         monitoring.",
             abstract = "As florestas de v{\'a}rzea s{\~a}o importantes para a 
                         manuten{\c{c}}{\~a}o da biodiversidade e para o provimento de 
                         servi{\c{c}}os ecossist{\^e}micos. Entretanto, atividades 
                         antr{\'o}picas t{\^e}m levado {\`a} redu{\c{c}}{\~a}o da 
                         cobertura de florestas de v{\'a}rzea no decorrer do tempo. A 
                         perda florestal impacta tanto a popula{\c{c}}{\~a}o humana local 
                         e regional, quanto o ecossistema de v{\'a}rzea, enquanto o 
                         desenvolvimento de nova cobertura florestal promove um novo ciclo 
                         de servi{\c{c}}os ecossist{\^e}micos fornecidos pelas florestas. 
                         Diversos trabalhos buscaram quantificar a perda florestal e 
                         identificar seu agente causador a partir de s{\'e}ries temporais 
                         de imagens de sat{\'e}lite. No entanto, abordagens de mapeamento 
                         manuais limitam o n{\'u}mero de imagens que podem ser avaliadas. 
                         Algoritmos semi-autom{\'a}ticos apresentam-se como alternativa 
                         {\`a} an{\'a}lise manual, maximizando a quantidade de 
                         informa{\c{c}}{\~o}es sobre mudan{\c{c}}as na cobertura 
                         florestal. Nesta pesquisa foi avaliada aplicabilidade do algoritmo 
                         \emph{Landsat-based Detection of Trends in Disturbance and 
                         Recovery} (LandTrendr) para reconstru{\c{c}}{\~a}o 
                         hist{\'o}rica das mudan{\c{c}}as na cobertura florestal de 
                         v{\'a}rzea em um trecho do Baixo Amazonas, no per{\'{\i}}odo de 
                         1984 a 2009. Para tal, foram definidos os seguintes objetivos 
                         espec{\'{\i}}ficos: 1) Avaliar qual informa{\c{c}}{\~a}o 
                         espectral {\'e} mais eficiente para detectar mudan{\c{c}}as na 
                         cobertura florestal; 2) Examinar o conjunto {\'o}timo de 
                         par{\^a}metros do algoritmo LandTrendr para ajuste de 
                         trajet{\'o}rias espectro-temporais em florestas de v{\'a}rzea; 
                         3) Avaliar a confiabilidade dos atributos gerados pelo algoritmo 
                         para caracterizar mudan{\c{c}}as da cobertura florestal; e 4) 
                         Analisar a exatid{\~a}o na discrimina{\c{c}}{\~a}o entre 
                         agentes antr{\'o}picos e naturais causadores de mudan{\c{c}}as 
                         na cobertura florestal, a partir dos atributos providos pelo 
                         algoritmo. Foi utilizada uma s{\'e}rie temporal de 37 imagens 
                         Landsat TM e EMT+, adquirida entre setembro e novembro para o 
                         per{\'{\i}}odo de 1984 a 2009. O {\'{\i}}ndice de 
                         vegeta{\c{c}}{\~a}o NDVI mostrou-se mais eficiente para detectar 
                         mudan{\c{c}}as na cobertura florestal, mas 37\% da perda e 31\% 
                         do desenvolvimento da cobertura florestal na {\'a}rea estudada 
                         n{\~a}o foi detectada pelo algoritmo. Os valores {\'o}timos dos 
                         par{\^a}metros foram kernel size=3x3; pval=0,05; e max 
                         segments=6, maximizando a detec{\c{c}}{\~a}o dos eventos de 
                         mudan{\c{c}}a e minimizando falsos eventos. As trajet{\'o}rias 
                         espectro-temporais refletiram eventos ocorridos na cobertura 
                         florestal, e o n{\'{\i}}vel de confiabilidade dos atributos que 
                         caracterizam a perda e desenvolvimento da cobertura florestal foi 
                         mais alto ao longo do rio Amazonas, em rela{\c{c}}{\~a}o ao 
                         interior da v{\'a}rzea. Estimou-se uma maior incid{\^e}ncia de 
                         perdas de origem antr{\'o}pica (1.071 ha) do que de origem 
                         natural (884 ha), com Exatid{\~a}o Global M{\'e}dia de 94\%. 
                         Contudo, houve dificuldade na discrimina{\c{c}}{\~a}o entre 
                         causas naturais e antr{\'o}picas de perda florestal para o 
                         {\'u}ltimo ano da s{\'e}rie temporal. Conclui-se que algoritmo 
                         LandTrendr foi eficiente na detec{\c{c}}{\~a}o e 
                         caracteriza{\c{c}}{\~a}o dos eventos de perda e desenvolvimento 
                         da cobertura florestal, especialmente em {\'a}reas ao longo do 
                         canal do rio Amazonas, podendo ser {\'u}til para avaliar 
                         espa{\c{c}}o-temporalmente a ocorr{\^e}ncia de eventos de 
                         mudan{\c{c}}a ao longo de toda a calha do rio Amazonas. ABSTRACT: 
                         Floodplain forests are important for maintaining biodiversity and 
                         providing ecosystem services. However, anthropogenic activities 
                         have brought a reduction of floodplain forest cover over time. 
                         Forest loss impacts local and regional human populations, as well 
                         as the floodplain ecosystem, while forest cover growth promotes a 
                         new cycle of ecosystem services provision by forests. Several 
                         studies have attempted to quantify forest loss and its agents of 
                         causation, based on time series of satellite images. However, 
                         manual mapping approaches limit the number of images that can be 
                         assessed. Semi-automatic algorithms can be considered as an 
                         alternative to manual analysis, maximizing the amount of 
                         information that can be obtained on forest cover change. We 
                         investigated the applicability of the Landsat-based Detection of 
                         Trends in Disturbance and Recovery (LandTrendr) algorithm for 
                         historical reconstruction of changes in floodplain forest cover, 
                         in a portion of the Lower Amazon River floodplain, from 1984 to 
                         2009. We defined the following specific objectives: 1) Evaluate 
                         which spectral information is more efficient to detect changes in 
                         forest cover; 2) Examine the optimal set of LandTrendr parameters 
                         for fitting spectral-temporal trajectories in v{\'a}rzea forests; 
                         3) Evaluate how reliable are the attributes generated by the 
                         algorithm to characterize changes in forest cover; 4) Evaluate the 
                         attainable accuracy for the discrimination between natural and 
                         anthropogenic causes of change in forest cover, based on the 
                         attributes provided by the algorithm. A time series of 37 Landsat 
                         TM and ETM+ images were acquired between September and November 
                         for the period extending from 1984 to 2009. NDVI was the most 
                         efficient spectral information to detect changes in forest cover, 
                         but 37\% of mapped forest loss and 31\% of mapped forest growth 
                         in the study area were not identified by the algorithm. The 
                         optimal set of parameters were kernel size=3x3; pval=0,05; and max 
                         segments=6, which maximized the detection of change events and 
                         minimized false events. The spectral-temporal trajectories 
                         reflected actual events in forest cover, and the reliability level 
                         of attributes characterizing the loss and growth of forest cover 
                         was highest along the Amazon River margins, when compared to the 
                         floodplain interior. We estimated a higher incidence of forest 
                         loss with anthropogenic origin (1,071 ha) versus natural origins 
                         (884 ha), with an Average Global Accuracy of 94\%. However, it 
                         was difficult to discriminate between natural and anthropogenic 
                         causes of forest loss for the latter years of the time series. We 
                         conclude that the LandTrendr algorithm was efficient in detecting 
                         and characterizing forest cover loss and growth events, especially 
                         in areas along the Amazon River margins. The algorithm can 
                         therefore be applied to evaluate spatial and temporal forest 
                         change events along the entire Amazon River floodplain.",
            committee = "Novo, Evlyn M{\'a}rcia Le{\~a}o de Moraes 
                         (presidente/orientadora) and Silva, Thiago Sanna Freire 
                         (orientador) and Santos, Jo{\~a}o Roberto dos and Arag{\~a}o, 
                         Luiz Eduardo Oliveira e Cruz de and Lima, Andr{\'e} de and 
                         Sch{\"o}gart, Jochen",
           copyholder = "SID/SCD",
         englishtitle = "Historical reconstruction of forest cover changes in v{\'a}rzeas 
                         of Lower Amazon using the algorithm LandTrendr",
             language = "pt",
                pages = "124",
                  ibi = "8JMKD3MGP3W34P/3J83FGH",
                  url = "http://urlib.net/rep/8JMKD3MGP3W34P/3J83FGH",
           targetfile = "publicacao.pdf",
        urlaccessdate = "24 nov. 2020"
}


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