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@PhDThesis{Pereira:2017:EsCaAP,
               author = "Pereira, Francisca Rocha de Souza",
                title = "Sensoriamento remoto LiDAR e {\'o}ptico aplicados {\`a} 
                         estimativa de biomassa a{\'e}rea de manguezais: estudo de caso na 
                         APA de Guapimirim, RJ",
               school = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                 year = "2017",
              address = "S{\~a}o Jos{\'e} dos Campos",
                month = "2016-11-18",
             keywords = "LiDAR, imagem {\'o}ptica de alta resolu{\c{c}}{\~a}o, 
                         an{\'a}lise textural, estimativa de biomassa a{\'e}rea, 
                         manguezal, high resolution optical image, textural analysis, 
                         biomass estimation, mangrove.",
             abstract = "Os manguezais s{\~a}o ecossistemas costeiros que ocorrem na 
                         interface entre a terra e o mar tipicamente em regi{\~o}es 
                         tropicais, apresentando esp{\'e}cies adaptadas {\`a} salinidade 
                         e inunda{\c{c}}{\~o}es pelas mar{\'e}s. Os manguezais realizam 
                         fun{\c{c}}{\~o}es ecol{\'o}gicas essenciais para a 
                         manuten{\c{c}}{\~a}o da vida terrestre e marinha e para o 
                         sustento de comunidades costeiras. S{\~a}o importantes 
                         transformadores de nutrientes em mat{\'e}ria org{\^a}nica e 
                         geradores de bens e servi{\c{c}}os como a 
                         estabiliza{\c{c}}{\~a}o e prote{\c{c}}{\~a}o da linha de 
                         costa, controle da polui{\c{c}}{\~a}o, sequestro de carbono 
                         atmosf{\'e}rico e regula{\c{c}}{\~a}o do clima. Seu atual 
                         desflorestamento {\'e} preocupante tanto ambientalmente como 
                         socioeconomicamente e sua restaura{\c{c}}{\~a}o e 
                         conserva{\c{c}}{\~a}o s{\~a}o importantes n{\~a}o s{\'o} para 
                         a regula{\c{c}}{\~a}o dos fluxos de carbono e controle das 
                         mudan{\c{c}}as clim{\'a}ticas, mas tamb{\'e}m para a 
                         manuten{\c{c}}{\~a}o de seus valiosos servi{\c{c}}os prestados 
                         {\`a} zona costeira. No presente trabalho uma {\'a}rea 
                         relativamente extensa (\$\sim\$58,2 km\$^{2}\$) de manguezal 
                         inserida na APA de Guapimirim na Ba{\'{\i}}a de Guanabara, RJ 
                         foi estudada. O objetivo geral do estudo {\'e} averiguar o 
                         potencial uso de dados LiDAR aerotransportado de retorno discreto 
                         para estimar a biomassa acima do solo (AGB) do manguezal com 
                         distintos graus de altera{\c{c}}{\~a}o, e comparativamente, 
                         investigar o potencial uso de {\'{\i}}ndices texturais derivados 
                         de imagem {\'o}ptica de alta resolu{\c{c}}{\~a}o WordView-2 
                         para estimar a AGB e distinguir tipos de cobertura do manguezal. 
                         Foram extra{\'{\i}}das 26 m{\'e}tricas descritivas da altura 
                         normalizada da nuvem de pontos LiDAR e os {\'{\i}}ndices 
                         texturais \emph{Fourier-based textural ordination} (FOTO) e 
                         Grey-\emph{Level Co-occurrence Matrix} (GLCM) da imagem 
                         {\'o}ptica pancrom{\'a}tica. Foram testados os m{\'e}todos de 
                         an{\'a}lise de regress{\~a}o Random Forest, AutoPLS e PLS para 
                         estimativa da AGB. Foi demonstrado que o uso de dados LiDAR para 
                         estimativa de AGB de manguezal com distintos graus de 
                         altera{\c{c}}{\~a}o foi efetivo e superior aos resultados 
                         obtidos com uso dos {\'{\i}}ndices texturais extra{\'{\i}}dos 
                         da imagem {\'o}ptica. O modelo preditivo mais preciso da AGB 
                         utilizando dados LiDAR (M2a) apresentou R\$^{2}\$(CAL)=0,89, 
                         R\$^{2}\$(LOO)=0,80, RMSE(CAL)=11,20 t/ha, RMSE (LOO)= 14,80 
                         t/ha e SER\% = 8,90. As vari{\'a}veis preditoras que mais 
                         contribu{\'{\i}}ram na modelagem foram avg, min, max, d02, d03, 
                         d04, d05 e d08 demonstrando que informa{\c{c}}{\~o}es de 
                         densidade de pontos relativos aos estratos estruturais da floresta 
                         s{\~a}o importantes vari{\'a}veis para a estimativa de AGB de 
                         bosques de mangue com distintos graus de altera{\c{c}}{\~a}o, 
                         bem como para detec{\c{c}}{\~a}o de {\'a}reas mais alteradas ou 
                         mais preservadas. O padr{\~a}o de variabilidade textural 
                         associado {\`a}s caracter{\'{\i}}sticas dos doss{\'e}is 
                         florestais com distintos graus de altera{\c{c}}{\~a}o mensuradas 
                         pelos {\'{\i}}ndices FOTO e GLCM n{\~a}o apresentou forte 
                         rela{\c{c}}{\~a}o com os valores de AGB. Por{\'e}m, a 
                         classifica{\c{c}}{\~a}o Random Forest baseada nos 
                         {\'{\i}}ndices texturais apresentou bons resultados na 
                         discrimina{\c{c}}{\~a}o de tipos de cobertura como {\'a}reas de 
                         n{\~a}o mangue, mangue alterado e mangue mais preservado. A 
                         presente tese demonstra a efic{\'a}cia do uso de t{\'e}cnicas de 
                         sensoriamento remoto, em especial de dados LiDAR de retorno 
                         discreto para estimar e mapear a AGB com boa acur{\'a}cia e para 
                         discriminar tipos de cobertura no manguezal. Os resultados aqui 
                         apresentados podem contribuir com as an{\'a}lises e 
                         caracteriza{\c{c}}{\~a}o estrutural do manguezal, 
                         quantifica{\c{c}}{\~a}o e qualifica{\c{c}}{\~a}o da AGB e 
                         estoques de carbono, bem como, contribuir com o monitoramento, 
                         formula{\c{c}}{\~a}o de pol{\'{\i}}ticas p{\'u}blicas de 
                         conserva{\c{c}}{\~a}o e prote{\c{c}}{\~a}o deste ecossistema, 
                         auxiliando a sua gest{\~a}o. ABSTRACT: Mangroves form important 
                         intertidal ecosystems that link terrestrial and marine systems 
                         typically in tropical and subtropical regions, presenting 
                         physiological and morphological adaptations to environmental 
                         stresses of high salinity and flooding by tides. Mangroves perform 
                         essential ecological functions for the maintenance of terrestrial 
                         and marine life and the livelihoods of coastal communities. They 
                         provide valuable ecological and economical ecosystem goods and 
                         services transforming nutrients in organic matter, contributing to 
                         coastal erosion protection, pollution control, atmospheric carbon 
                         sequestration and climate regulation, among many other factors. 
                         Nevertheless, mangroves have experienced a dramatic decline in 
                         area caused by overexploitation and conversion to other uses. 
                         Their restoration and conservation are important not only for the 
                         regulation of carbon fluxes and climate change control, but also 
                         to maintain their valuable services for the coastal zone. Remote 
                         sensing techniques offer a useful tool of estimating forest 
                         biomass contributing with the monitoring of land use and land 
                         cover dynamics and the effectiveness of environmental policies. In 
                         the present work a relatively large area (\$\sim\$58.2 
                         km\$^{2}\$) of mangroves inserted in the Environmental 
                         Protection Area of Guapimirim, Guanabara Bay, RJ was studied. The 
                         main goal of this study is to investigate the potential use of 
                         discrete return LiDAR data to estimate the aboveground biomass 
                         (AGB) of a mangrove forest with different degrees of disturbance, 
                         and comparatively investigate the potential use of textural 
                         indices derived from a high resolution WorldView-2 image to 
                         estimate AGB and to distinguish types of mangrove coverage. 
                         Twenty-six descriptive LiDAR metrics were extracted from the 
                         normalized height of the LiDAR point cloud data together with the 
                         Fourier-based textural ordination (FOTO) and Grey-Level 
                         Co-occurrence Matrix (GLCM) textural indices from the panchromatic 
                         optical image. Random Forest, AutoPLS and PLS regression methods 
                         were tested to estimate AGB. The results obtained using LiDAR data 
                         for estimating AGB were effective and superior to the results 
                         obtained using the textural indices. The most accurate predictive 
                         model of AGB using LiDAR data (M2a) presented R\$^{2}\$(CAL) = 
                         0.89, R\$^{2}\$(LOO) = 0.80, RMSE(CAL) = 11.20 t/ha, RMSE(LOO) = 
                         14.80 t/ha and RSE\% = 8.90\%. The most important predictor 
                         variables for the M2a model were avg, min, max, d02, d03, d04, d05 
                         and d08 demonstrating that point density relative to the forest 
                         structural strata are important variables for the AGB estimation 
                         in mangrove forests with different degrees of disturbance as well 
                         as for detecting more altered or preserved areas. The textural 
                         variability pattern associated with the canopy characteristics 
                         with different degrees of disturbance measured by FOTO and GLCM 
                         indices showed weak relationships with AGB values. However, the 
                         Random Forest classification based on the textural indices showed 
                         good results on the discrimination of different types of coverage 
                         such as non-mangrove, altered and preserved mangroves. This thesis 
                         demonstrates the effectiveness use of remote sensing techniques, 
                         particularly discrete return LiDAR data to accurately estimate and 
                         map the AGB and to discriminate types of mangrove coverage. The 
                         results presented here can contribute to the analysis and 
                         structural characterization of mangroves, its AGB and carbon 
                         stocks quantification and qualification, also contributing with 
                         the monitoring and formulation of public policies for the 
                         conservation and protection of this ecosystem.",
            committee = "Kampel, Silvana Amaral (presidente) and Kampel, Milton 
                         (orientador) and Arag{\~a}o, Luiz Eduardo Oliveira e Cruz de and 
                         Valeriano, Dalton de Morisson and Bentz, Cristina Maria and Longo, 
                         Marcos",
           copyholder = "SID/SCD",
         englishtitle = "LiDAR and optic remote sensing applied to mangrove aboveground 
                         biomass estimates: study case APA de Guapimirim, RJ.",
             language = "pt",
                pages = "211",
                  ibi = "8JMKD3MGP3W34P/3MM3DAP",
                  url = "http://urlib.net/rep/8JMKD3MGP3W34P/3MM3DAP",
           targetfile = "publicacao.pdf",
        urlaccessdate = "24 nov. 2020"
}


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