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@PhDThesis{Ferreira:2017:DeEsAr,
               author = "Ferreira, Matheus Pinheiro",
                title = "Detec{\c{c}}{\~a}o de esp{\'e}cies arb{\'o}reas em floresta 
                         estacional semidecidual por sensoriamento remoto hiperespectral e 
                         modelagem de transfer{\^e}ncia radiativa",
               school = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                 year = "2017",
              address = "S{\~a}o Jos{\'e} dos Campos",
                month = "2017-05-19",
             keywords = "sensoriamento remoto hiperespectral, Floresta Atl{\^a}ntica, 
                         WorldView-3, DART. hyperspectral remote sensing, Atlantic Forest, 
                         WorldView-3, DART.",
             abstract = "O mapeamento da distribui{\c{c}}{\~a}o espacial de esp{\'e}cies 
                         arb{\'o}reas em ambientes tropicais fornece 
                         informa{\c{c}}{\~o}es valiosas para ec{\'o}logos e gestores 
                         florestais. Esse procedimento pode reduzir custos com trabalhos de 
                         campo, auxiliar o monitoramento da diversidade flor{\'{\i}}stica 
                         do dossel e auxiliar a localiza{\c{c}}{\~a}o de {\'a}rvores 
                         matrizes para a coleta de sementes em iniciativas de 
                         restaura{\c{c}}{\~a}o florestal. Entretanto, a 
                         detec{\c{c}}{\~a}o de esp{\'e}cies arb{\'o}reas em florestas 
                         tropicais com dados de sensoriamento remoto {\'e} um desafio 
                         devido {\`a} elevada diversidade espectral e flor{\'{\i}}stica. 
                         O objetivo principal desta pesquisa de doutorado foi explorar a 
                         utiliza{\c{c}}{\~a}o de dados reais e simulados de sensoriamento 
                         remoto multi e hiperespectral para mapear e classificar 
                         esp{\'e}cies arb{\'o}reas da Floresta Estacional Semidecidual. 
                         Os objetivos espec{\'{\i}}ficos inclu{\'{\i}}ram: (i) avaliar 
                         o desempenho de m{\'e}todos estat{\'{\i}}sticos de 
                         classifica{\c{c}}{\~a}o na discrimina{\c{c}}{\~a}o de 
                         esp{\'e}cies arb{\'o}reas da {\'a}rea de estudo; (ii) 
                         identificar regi{\~o}es e bandas espectrais, no intervalo de 450 
                         a 2400 nm, prop{\'{\i}}cias {\`a} classifica{\c{c}}{\~a}o das 
                         esp{\'e}cies; (iii) avaliar a utiliza{\c{c}}{\~a}o de 
                         {\'{\i}}ndices de vegeta{\c{c}}{\~a}o de bandas estreitas na 
                         discrimina{\c{c}}{\~a}o das esp{\'e}cies; (iv) quantificar a 
                         variabilidade espectral intra e interespec{\'{\i}}fica, bem como 
                         sua influ{\^e}ncia, na classifica{\c{c}}{\~a}o das 
                         esp{\'e}cies; (v) desenvolver um m{\'e}todo para o mapeamento 
                         autom{\'a}tico das esp{\'e}cies ao n{\'{\i}}vel de 
                         {\'a}rvores individuais; (vi) desenvolver uma abordagem de 
                         simula{\c{c}}{\~a}o da resposta espectral das esp{\'e}cies ao 
                         n{\'{\i}}vel de dossel a partir da utiliza{\c{c}}{\~a}o de 
                         modelagem de transfer{\^e}ncia radiativa em tr{\^e}s 
                         dimens{\~o}es (3D) e (vii) comparar a resposta espectral simulada 
                         e medida das esp{\'e}cies. Tr{\^e}s m{\'e}todos de 
                         classifica{\c{c}}{\~a}o supervisionada foram testados para 
                         discriminar oito esp{\'e}cies arb{\'o}reas: An{\'a}lise 
                         Discriminante Linear (LDA), Support Vector Machines com kernel 
                         linear (L-SVM) e fun{\c{c}}{\~a}o de base radial (RBF-SVM) e 
                         Random Forest (RF). Uma exatid{\~a}o de classifica{\c{c}}{\~a}o 
                         m{\'e}dia de 70\% foi obtida ao se utilizar as bandas do 
                         vis{\'{\i}}vel/infravermelho pr{\'o}ximo (VNIR, 450-919 nm). A 
                         inclus{\~a}o de bandas do infravermelho de ondas curtas (SWIR, 
                         1045-2400 nm) elevaram a exatid{\~a}o para 84\%. 
                         {\'{\I}}ndices de vegeta{\c{c}}{\~a}o de bandas estreitas 
                         tamb{\'e}m foram testados e elevaram a exatid{\~a}o em 5\% 
                         quando combinados a bandas do VNIR. Dados reais e simulados do 
                         sensor WorldView-3 (WV-3) foram utilizados para fins de 
                         classifica{\c{c}}{\~a}o. Enquanto as bandas VNIR simuladas de 
                         sensor forneceram uma exatid{\~a}o de 57,4\%, o conjunto 
                         VNIR+SWIR aumentou a exatid{\~a}o para 74,8\%. Este padr{\~a}o 
                         se manteve na classifica{\c{c}}{\~a}o de dados WV-3 reais 
                         (aumento de 3,2 \% ap{\'o}s a inclus{\~a}o de bandas SWIR). O 
                         grau de sobreposi{\c{c}}{\~a}o das variabilidades intra e 
                         interespec{\'{\i}}fica influenciou diretamente as 
                         classifica{\c{c}}{\~o}es. O m{\'e}todo para mapeamento de 
                         {\'a}rvores desenvolvido produziu mapas fidedignos da 
                         distribui{\c{c}}{\~a}o espacial das esp{\'e}cies e elevou a 
                         exatid{\~a}o em rela{\c{c}}{\~a}o {\`a}s 
                         classifica{\c{c}}{\~o}es ao n{\'{\i}}vel de pixel em at{\'e} 
                         6\%. Um procedimento de sele{\c{c}}{\~a}o de atributos baseado 
                         em regress{\~a}o stepwise identificou bandas localizadas ao redor 
                         do pico do verde (550 nm), na fei{\c{c}}{\~a}o de 
                         absor{\c{c}}{\~a}o do vermelho (650 nm) e no SWIR em 1200, 1700, 
                         2100 e 2300 nm, como {\'u}teis para discriminar as esp{\'e}cies. 
                         A resposta espectral das esp{\'e}cies no VNIR foi acuradamente 
                         simulada pelo modelo Discrete Anisotropic Radiative Transfer 
                         (DART) que opera em tr{\^e}s dimens{\~o}es (3D). Por meio de uma 
                         estrutura simplificada da copa, a resposta espectral das 
                         esp{\'e}cies no topo do dossel foi simulada com at{\'e} 1,5\% 
                         de erro quadr{\'a}tico m{\'e}dio. A invers{\~a}o do modelo na 
                         imagem hiperespectral gerou rela{\c{c}}{\~o}es 
                         estat{\'{\i}}sticas significantes (R\$^{}\$=0,65) entre o 
                         conte{\'u}do de clorofila (C\$_{ab}\$) e o {\'{\i}}ndice 
                         MCARI (Modified Chlorophyll Absorption in Reflectance Index), 
                         sendo C\$_{ab}\$ estatisticamente diferente entre as 
                         esp{\'e}cies. A abordagem de simula{\c{c}}{\~a}o desenvolvida 
                         pode ser utilizada para reproduzir aquisi{\c{c}}{\~o}es 
                         hiperespectrais de florestas tropicais. ABSTRACT: Accurately 
                         mapping the spatial distribution of tree species in tropical 
                         environments provides valuable insights for ecologists and forest 
                         managers. This process may play an important role in reducing 
                         fieldwork costs, monitoring changes in canopy biodiversity, and 
                         locating parent trees to collect seeds for forest restoration 
                         efforts. However, mapping tree species in tropical forests with 
                         remote sensing data is a challenge because of high floristic and 
                         spectral diversity. The main objective of this study was to 
                         explore the use of experimental and simulated multi and 
                         hyperspectral remotely sensed data for tree species discrimination 
                         and mapping in a tropical seasonal semi-deciduous forest. 
                         Specifically we aimed: (i) to evaluate the performance of machine 
                         learning methods in the discrimination of tree species of the 
                         study area; (ii) to identify spectral regions and bands, in 
                         450-2400 nm range, suitable for species classification; (iii) to 
                         evaluate the use of narrow band vegetation indices in species 
                         discrimination; (iv) to quantify within- and among-species 
                         spectral variability as well as its influence on species 
                         classification; (v) develop a method for tree species mapping at 
                         the individual tree level; (vi) to develop a modeling approach to 
                         simulate the spectral response of the species at the canopy level 
                         using three-dimensional (3D) radiative transfer modeling and (vii) 
                         to compare simulated and measured spectral responses of the 
                         species. Three classifiers were tested to discriminate eight tree 
                         species: Linear Discriminant Analysis (LDA), Support Vector 
                         Machines with linear kernel (L-SVM) and radial base function 
                         (RBF-SVM) and Random Forest (RF). An average classification 
                         accuracy of 70\% was obtained when using the 
                         visible/near-infrared bands (VNIR, 450-919 nm). The inclusion of 
                         short-wave infrared (SWIR, 1045-2400 nm) bands changed the 
                         accuracy to 84\%. Narrow-band vegetation indices were also tested 
                         and increased the classification accuracy by up to 5\% when 
                         combined with VNIR features. Experimental and simulated data of 
                         the WorldView-3 (WV-3) sensor were used for classification 
                         purposes. While the simulated VNIR sensor bands provided an 
                         accuracy of 57.4\%, the VNIR + SWIR set increased accuracy to 
                         74.8\%. This pattern was also observed in the classification of 
                         experimental WV-3 data (increase of 3.2\% after inclusion of SWIR 
                         bands). The degree of overlap between the within- and 
                         among-species spectral variability influenced the classifications. 
                         The developed tree mapping method produced reliable maps of the 
                         spatial distribution of the species and increased accuracy in 
                         relation to pixel-level classifications by up to 6\%. The use of 
                         a reduced set of hyperspectral bands did not significantly affect 
                         the classification accuracies but allowed us to depict the most 
                         important wavelengths to discriminate the species. These 
                         wavelengths were located around the green reflectance peak (550 
                         nm), at the red absorption feature (650 nm) and in the SWIR range 
                         at 1200, 1700, 2100 and 2300 nm. The spectral response of the 
                         species in the VNIR was accurately simulated by the Discrete 
                         Anisotropic Radiative Transfer (DART) model that operates in 3D. 
                         By means of a simplified crown structure, the spectral response of 
                         the species at the top of the canopy was simulated with an error 
                         of 1.5\%. The inversion of the model in the hyperspectral image 
                         provided statistical significant relationships (R\$^{}\$=0.65) 
                         between chlorophyll content (C\$_{ab}\$) and MCARI (Modified 
                         Chlorophyll Absorption in Reflectance Index), being C\$_{ab}\$ 
                         statistically different among the species. The developed modeling 
                         approach can be used to simulate hyperspectral acquisitions of 
                         tropical forests.",
            committee = "Galv{\~a}o, L{\^e}nio Soares (presidente) and Shimabukuro, Yosio 
                         Edemir (orientador) and Souza Filho, Carlos Roberto de 
                         (orientador) and Arag{\~a}o, Luiz Eduardo Oliveira de Cruz de and 
                         Almeida, Teodoro Isnard Ribeiro de and Silva, Thiago Sanna 
                         Freire",
         englishtitle = "Tree species discrimination in tropical semi-deciduous forest with 
                         remotely sensed data and radiative transfer modeling",
             language = "pt",
                pages = "148",
                  ibi = "8JMKD3MGP3W34P/3NSEFF5",
                  url = "http://urlib.net/rep/8JMKD3MGP3W34P/3NSEFF5",
           targetfile = "publicacao.pdf",
        urlaccessdate = "23 nov. 2020"
}


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