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@PhDThesis{GirolamoNeto:2018:IdFiCe,
               author = "Girolamo Neto, Cesare Di",
                title = "Identifica{\c{c}}{\~a}o de fitofisionomias de Cerrado no Parque 
                         Nacional de Bras{\'{\i}}lia utilizando random forest aplicado a 
                         imagens de alta e m{\'e}dia resolu{\c{c}}{\~o}es espaciais",
               school = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                 year = "2018",
              address = "S{\~a}o Jos{\'e} dos Campos",
                month = "2018-08-28",
             keywords = "sensoriamento remoto, classifica{\c{c}}{\~a}o, cerrado, textura, 
                         minera{\c{c}}{\~a}o de dados, remote sensing, classification, 
                         cerrado, texture, data mining.",
             abstract = "Depois da Mata Atl{\^a}ntica, o Cerrado {\'e} o bioma Brasileiro 
                         que mais passou por altera{\c{c}}{\~o}es com a 
                         ocupa{\c{c}}{\~a}o humana, com uma perda de cobertura vegetal de 
                         978.745 kmē. Portanto, {\'e} estrat{\'e}gico que o bioma seja 
                         monitorado para combater o desmatamento e manter as {\'a}reas de 
                         preserva{\c{c}}{\~a}o ambiental. Neste sentido, v{\'a}rias 
                         pesquisas t{\^e}m sido desenvolvidas para estudar as 
                         altera{\c{c}}{\~o}es da cobertura e uso da terra, estimar as 
                         emiss{\~o}es de carbono, estudar o impacto do desmatamento e 
                         degrada{\c{c}}{\~a}o da biodiversidade. A maioria dos trabalhos 
                         para classificar a cobertura vegetal do Cerrado tem utilizado 
                         imagens da classe Landsat, com 30 metros de resolu{\c{c}}{\~a}o 
                         espacial, discriminando fitofisionomias de Campo, Savana e 
                         Floresta com taxas de acerto superiores a 80%. Todavia, ainda 
                         s{\~a}o encontradas dificuldades em classificar as 
                         fitofisionomias com uma legenda de classifica{\c{c}}{\~a}o mais 
                         detalhada. Esse fato mostra a necessidade do uso de imagens de 
                         resolu{\c{c}}{\~a}o espacial melhor, as quais se mostraram 
                         capazes de identificar a estrutura da vegeta{\c{c}}{\~a}o em 
                         fitofisionomias semelhantes. Embora, o uso destas imagens permita 
                         classificar a cobertura vegetal do Cerrado com mais detalhes, 
                         n{\~a}o h{\'a} na literatura pesquisas conclusivas sobre quais 
                         fitofisionomias podem ser mais bem discriminadas com imagens de 
                         resolu{\c{c}}{\~a}o espacial da classe Landsat (30m) e alta 
                         resolu{\c{c}}{\~a}o (1 a 4 m). Dentro deste contexto, o 
                         principal objetivo deste trabalho foi avaliar imagens de alta e 
                         m{\'e}dia resolu{\c{c}}{\~o}es, combinadas com t{\'e}cnicas de 
                         extra{\c{c}}{\~a}o de atributos, para mapear as fitofisionomias 
                         do Cerrado com um maior n{\'{\i}}vel de detalhamento do que a 
                         literatura existente. Foram obtidas imagens (Landsat-8 e 
                         WorldView-2) para o Parque Nacional de Bras{\'{\i}}lia, 
                         regi{\~a}o que cont{\'e}m mais de 30 mil hectares de 
                         vegeta{\c{c}}{\~a}o nativa de Cerrado. A partir destas imagens 
                         foram gerados dados de Reflect{\^a}ncia, do Modelo Linear de 
                         Mistura Espectral, da Transformada Tasseled Cap, de 
                         {\'{\I}}ndices de Vegeta{\c{c}}{\~a}o e de textura. Foram 
                         coletados pontos em campo e com interpreta{\c{c}}{\~a}o visual 
                         para gerar um conjunto de dados com mais de mil amostras 
                         classificadas para quatro diferentes legendas. Estas legendas 
                         consideram as fitofisionomias de Ribeiro e Walter (2008) e foram 
                         propostas, contendo 3, 6, 8 e 10 diferentes classes, de acordo com 
                         seu n{\'{\i}}vel de detalhamento. A escala mais simples 
                         diferenciou apenas as classes Campestre, Sav{\^a}nica e 
                         Florestal, e conforme o aumento da complexidade, foram 
                         identificadas classes como Campo Limpo, Campo Limpo {\'U}mido, 
                         Campo Limpo {\'U}mido com Murundu, Campo Sujo, Campo Rupestre, 
                         Cerrado Ralo, Cerrado T{\'{\i}}pico, Cerrado Denso, Veredas e 
                         Mata de Galeria. O comportamento espectral destas fitofisionomias 
                         revelou que elas s{\~a}o diferenci{\'a}veis apenas para a escala 
                         mais simples. Para n{\'{\i}}veis mais complexos, existe uma 
                         maior dificuldade de discrimina{\c{c}}{\~a}o com dados de 
                         Reflect{\^a}ncia. A classifica{\c{c}}{\~a}o das imagens foi 
                         feita pelo algoritmo Random Forest. Dentre os principais 
                         resultados, a legenda mais simples de mapeamento mostra-se 
                         adequada para ambas {\`a}s resolu{\c{c}}{\~o}es espaciais, 
                         obtendo taxas de acerto superiores a 87%. Com o aumento da 
                         complexidade de legenda, a imagem Landsat-8 passou a apresentar 
                         limita{\c{c}}{\~o}es na discrimina{\c{c}}{\~a}o de classes 
                         como Campo Limpo, Campo Sujo e Campo Rupestre. As classes de 
                         Cerrado Ralo e Cerrado Denso apresentaram confus{\~a}o com a 
                         classe de Cerrado T{\'{\i}}pico. Ainda foi constatado que estas 
                         imagens s{\~a}o deficientes em representar a 
                         transi{\c{c}}{\~a}o entre as classes de Campo Sujo e Cerrado 
                         Ralo. A taxa de acerto para a legenda mais detalhada com a imagem 
                         Landsat-8 foi de 65,21%. Entretanto, a imagem WorldView-2 se 
                         mostrou capaz de identificar estas fitofisionomias com uma melhor 
                         taxa de acerto (74,17%). O uso de atributos relacionados {\`a} 
                         textura foi essencial para o aumento dessa taxa. Neste sentido, 
                         por meio da imagem WorldView-2 foi poss{\'{\i}}vel identificar a 
                         Classe de Campo Rupestre com melhor taxa de acerto, reduzindo 
                         erros entre as classes de Campo Limpo e Campo Sujo. xii As classes 
                         de Cerrado Ralo e Cerrado Denso reduziram sua confus{\~a}o com 
                         Cerrado T{\'{\i}}pico. O erro de transi{\c{c}}{\~a}o entre 
                         Campo Sujo e Cerrado Ralo ainda persiste na imagem de alta 
                         resolu{\c{c}}{\~a}o, por{\'e}m com uma menor magnitude. Algumas 
                         classes como Veredas, Campo Limpo {\'U}mido e Campo Limpo 
                         {\'U}mido com Murundu n{\~a}o foram identificadas com boa 
                         precis{\~a}o em ambas as imagens. Dentre as principais 
                         conclus{\~o}es deste trabalho destacam-se o uso de atributos de 
                         textura para melhorar a discrimina{\c{c}}{\~a}o de 
                         fitofisionomias do Cerrado. Estes atributos foram capazes de 
                         representar as varia{\c{c}}{\~o}es entre regi{\~o}es com 
                         vegeta{\c{c}}{\~a}o arb{\'o}rea intercaladas por regi{\~o}es 
                         com vegeta{\c{c}}{\~a}o herb{\'a}ceoarbustiva, melhorando a 
                         discrimina{\c{c}}{\~a}o de fitofisionomias como Campo Sujo, 
                         Cerrado Ralo, Cerrado T{\'{\i}}pico e Cerrado Denso. ABSTRACT: 
                         After the Atlantic Forest, the Cerrado is the Brazilian biome that 
                         has presented most changes with human occupation, with a loss of 
                         vegetation cover of 978,745 kmē. Therefore, it is strategic that 
                         this biome is monitored in order to decrease deforestation and 
                         maintain the areas of environmental protection. In this sense, 
                         several researches have been developed to study changes in land 
                         cover and use, to estimate carbon emissions, to study the impact 
                         of deforestation and biodiversity degradation. Most of these 
                         studies classify Cerrado vegetation using Landsat like images, 
                         with 30 meters of spatial resolution, discriminating classes such 
                         as Grassland, Savanna and Woodland with accuracy higher than 80%. 
                         However, it is still difficult to classify Cerrado 
                         phytophysiognomies with a more detailed classification legend. 
                         This fact shows the need of better spatial resolution images, 
                         which were able to identify vegetation structure in similar 
                         phytophysiognomies. Although the use of these images allows 
                         classifying the Cerrado vegetation cover with more details, there 
                         is no conclusive research in the literature about which 
                         phytophysiognomie can be discriminated with better accuracy with 
                         Landsat images (30m) and high resolution (1 to 4 m). In this 
                         context, the aim of this work was to evaluate high and medium 
                         resolution images, combined with feature extraction techniques, to 
                         map Cerrado phytophysiognomies with a higher level of detail than 
                         the existing literature. A Landsat-8 image and a WorldView-2 image 
                         were obtained for the Brasilia National Park, a region that 
                         contains more than 30 thousand hectares of Cerrado native 
                         vegetation. Data of Reflectance, Spectral Linear Mixture Model, 
                         Tasseled Cap Transformation, Vegetation Indices and texture were 
                         obtained for these images. Samples for the classification were 
                         collected on field and by visual interpretation, generating a 
                         dataset with more than one thousand samples classified for four 
                         different legends. These legends adopts the phytophysiognomies 
                         described by Ribeiro and Walter (2008) and were proposed 
                         containing 3, 6, 8 and 10 different classes, according to their 
                         level of detail. The simpler scale adopted the classes of 
                         Grassland, Savanna and Woodland. When the level of detail was 
                         increased, the following classes were used: Open Grassland, 
                         Flooded Grassland, Flooded Grassland with Murundu, Shrub 
                         Grassland, Rocky Grassland, Shrub Savanna, Typical Savanna, Dense 
                         Savanna, Flooded Plains with Palmtrees and Gallery Forest. The 
                         spectral behavior of these phytophysiognomies revealed that they 
                         are distinguishable only for the simplest scale, for more complex 
                         levels, there is a greater difficulty of discrimination with 
                         Reflectance data. The classification of the images was done by the 
                         algorithm Random Forest. Among the main results, the simplest 
                         mapping legend is adequate for both spatial resolutions, obtaining 
                         hit rates higher than 87%. With the increase of the legend 
                         complexity, the Landsat-8 images started to present difficulties 
                         in discriminating classes like Open Grassland, Shrub Grassland and 
                         Rocky Grassland. The classes of Shrub Savanna and Dense Savanna 
                         were misclassified as Typical Savanna. It was still observed that 
                         these images are deficient in representing the transition between 
                         the classes of Shrub Grassland and Shrub Savanna. The hit rate for 
                         the most detailed legend with the Landsat-8 image was 65.21%. 
                         However, the WorldView-2 image was able to identify these 
                         phytophysiognomies with a better accuracy (74.17%). The use of 
                         texture features was essential for this increase. In this sense, 
                         the WorldView-2 image xiv identified the Rock Grassland class with 
                         better accuracy and also reduced the misclassification between the 
                         Open Grasslands and Shrub Grasslands. The classes of Shrub Savanna 
                         and Dense Savanna reduced their confusion with Typical Savanna. 
                         The transition error between Shrub Grasslands and Shrub Savanna 
                         still persists in WorldView-2 image, but with a smaller magnitude. 
                         Some classes such as Flooded Plains with Palmtrees, Flooded 
                         Grassland and Flooded Grassland with Murundu were not identified 
                         with good accuracy on both images. The main conclusion of this 
                         study is that the use of texture features helped to improve the 
                         discrimination of Cerrado phytophysiognomies. These features were 
                         able to represent the variations between regions with arboreal 
                         vegetation interspersed by regions with herbaceous-shrub 
                         vegetation, improving the discrimination of phytophysiognomies 
                         such as Shrub Grasslands, Shrub Savanna, Typical Savanna and Dense 
                         Savanna.",
            committee = "Ponzoni, Fl{\'a}vio Jorge (presidente) and Fonseca, Leila Maria 
                         Garcia (orientadora) and K{\"o}rting, Thales Sehn (orientador) 
                         and Valeriano, Dalton de Morisson and Negri, Rog{\'e}rio Galante 
                         and Lacerda, Camila Souza dos Anjos",
         englishtitle = "Identification of Brazilian savannah physiognomies on 
                         Bras{\'{\i}}lia National Park using random forest on high and 
                         medium spatial resolution images",
             language = "pt",
                pages = "186",
                  ibi = "8JMKD3MGP3W34R/3RU6Q68",
                  url = "http://urlib.net/rep/8JMKD3MGP3W34R/3RU6Q68",
           targetfile = "publicacao.pdf",
        urlaccessdate = "04 dez. 2020"
}


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