Orientador: CéliaRegina Lopes Zimback / Banca: Zacarias Xavier de Barros / Banca: Marco Antonio M.Biaggioni / Banca: Marcelo Giovaneti Canteri / Banca: Ivo Mario Mathias / Resumo: Este trabalho teve por objetivo a discriminação de classes de cobertura do solo em imagens de sensoriamento remoto do satélite CBERS-2 por meio do Classificador Árvore de Decisão. O estudo incluiu a avaliação de combinações de atributos da imagem para melhor discriminação entre classes e a verificação da acurácia da metodologia proposta comparativamente ao Classificador Máxima Verossimilhança (MAXVER). A área de estudo está localizada na região dos Campos Gerais, no Estado do Paraná, que apresenta diversidade quanto aos tipos de vegetação: culturas de inverno e de verão, áreas de reflorestamento, mata natural e pastagens. Foi utilizado um conjunto de dezesseis (16) atributos a partir das imagens, composto por: bandas do sensor CCD (1, 2, 3, 4), índices de vegetação (CTVI, DVI, GEMI, NDVI, SR, SAVI, TVI), componentes de mistura (solo, sombra, vegetação) e os dois primeiros componentes principais. A acurácia da classificação foi avaliada por meio da matriz de erros de classificação e do coeficiente kappa. A coleta de amostras de verdade terrestre foi realizada utilizando-se um aparelho GPS de navegação para o processo de georreferenciamento, para serem usadas na fase de treinamento dos classificadores e também na verificação da acurácia. O processamento das imagens e a geração dos mapas temáticos foram realizados por meio do Sistema de Informações Geográficas SPRING, sendo as rotinas desenvolvidas na linguagem de programação LEGAL. Para a geração do Classificador Árvore de Decisão foi utilizada a ferramenta See5. Na definição das classes, buscou-se um alto nível discriminatório a fim de permitir a separação dos diferentes tipos de culturas presentes na região nas épocas de inverno e de verão. A classificação por árvore de decisão apresentou uma acurácia total de 94,5% e coeficiente kappa igual a 0,9389, para a cena 157/128; para... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: This work aimed to discriminate classes of land cover in remote sensing images of the satellite CBERS-2, using the Decision Tree Classifier. The study includes the evaluation of combinations of attributes of the image to a better discrimination between classes and the verification of the accuracy of the proposed methodology, comparatively to the Maximum Likelihood Classifier (MLC). The geographical area used is situated in the region of the "Campos Gerais", in the Paraná State, which presents diversities concerning the different kinds of vegetations: summer and winter crops, reforestation areas, natural forests and pastures. It was used a set of sixteen (16) attributes from images, composed by bands of the sensor CCD (1, 2, 3, 4), vegetation indices (CTVI, DVI, GEMI, NDVI, SR, SAVI, TVI), mixture components (soil, shadow, vegetation) and the two first principal components. The accuracy of the classifications was evaluated using the classification error matrix and the kappa coefficient. The collect of the samples of ground truth was performed using a navigation device GPS to the georeference process to be used in the training stage of the classifiers and in the verification of the accuracy, as well. The processing of the images and the generation of the thematic maps were made using the Geographic Information System SPRING, and the routines were developed in the programming language LEGAL. The generation of the Decision Tree Classifier was made using the tool See5. A high discriminatory level was aimed during the definition of the classes in order to allow the separation of the different kinds of winter and summer crops. The classification accuracy by decision tree was 94.5% and kappa coefficient was 0.9389 to the scene 157/128; to the scene 158/127, it presented the values 88% and 0.8667, respectively. Results showed that the performance of the Decision Tree Classifier was better... (Complete abstract click electronic access below) / Doutor

Orientador: Valdemir Antonio Rodrigues / Banca: Antônio de Pádua Sousa / Banca: Luiza Helena Duenhas / Abstract: The preservation of the biodiversity of the vegetation in the micro watershed is of basic importance for the maintenance of the animal and vegetal wild life, ambient services and protection of the biosfera. The morphometry is a tool of great importance as diagnostic of susceptibility to the ambient degradation or conservation and guides the planning and handling of the micro watershed. The present study had as objective the morphometric characterization and analysis of biodiversity in the micro watershed of the Edgárdia Farm, Botucatu - SP. The micro watershed of the Edgárdia Farm belongs to the College of Agronomicas Ciencias/UNESP, city of Botucatu - SP. The program used for the attainment of the morphometrical variable was the ILWIS 3.2 version and the topographical letter of the IBGE with 1:50.000 scale. In the study of biodiversity four transects in the micro watershed had been installed, in the parts high (superior third), medium high and medium low (medium third) and low (inferior third) of the micro watershed, in which parcels of 10 x 5 meters had been installed. In the four transects, the forest species had been quantified and commanded in popular and classified families, species, names in its respective ecological successions (pioneer, secondary or climax). It was measured in each tree the height and the diameter in the height of the chest (DAP). The morphometrical variable and the results were: area of 7,205 kmø; perimeter of 11,59 km; length and width of micro watershed 3,578 the equal 3,016 km; length of the draining net and quotas of 15,68 km and 1100,00 km; factor of form 0,79, considered very high; density of draining of 2,18 km.kmø, classified as average; the high declivity of 76,98%, in accordance with the classes of declivities and types of relief of the micro watershed, was classified as scarped; frequency of rivers the 3,19 segments of rivers... (Complete abstract click electronic access below) / Mestre