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• W2124583432 abstract "Neste trabalho, avaliou-se a acuracia da classificacao e identificacao de areas cultivadas com florestas plantadas com finalidade energetica em imagens orbitais do sensor Landsat5 TM, por meio de tecnicas estatisticas de mineracao de dados. Os pixels foram convertidos em valores de refletância de superficie, nas vizinhancas dos municipios de Sao Miguel do Tocantins, Sao Bento do Tocantins, Araguatins, Babaculândia, Darcinopolis e Wanderlândia, na regiao Norte do Estado do Tocantins. Foram utilizados atributos de textura para identificar melhorias nos resultados da classificacao. As tecnicas de mineracao de dados utilizadas se mostraram eficientes na identificacao precisa de florestas plantadas em imagens do satelite Landsat 5, tanto pelo desempenho da classificacao, quanto pela reducao da quantidade de informacao necessaria para realizar a identificacao. Os modelos de arvore de decisao, por meio do algoritmo J48, alcancaram taxas de acerto superiores a 90% na identificacao de especies plantadas em meio a outros alvos. Assim, as tecnicas empregadas neste estudo possibilitaram o desenvolvimento de modelos de classificacao robustos no auxilio ao planejamento e a tomada de decisao sobre o plantio de florestas no territorio brasileiro. PALAVRAS-CHAVE: Florestas plantadas, agroenergia, selecao de atributos, arvores de decisao, classificacao de imagens. IDENTIFICATION OF PLANTED FORESTS DESTINED TO BIOENERGY PRODUCTION THROUGH DATA MINING USING SATELLITE IMAGES ABSTRACT: In this work, we evaluated the accuracy of the classification and identification of areas cultivated with planted forests by satellite images of Landsat 5 TM, through data mining statistics techniques statistics. The pixels were converted to surface reflectance values from the cities of Sao Miguel do Tocantins, Sao Bento do Tocantins, Araguatins, Babaculândia, Darcinopolis and Wanderlandia, located in northern state of Tocantins. Texture attributes were used to identify improvements in the classification results. The data mining techniques used were effective in the accurate identification of planted forests in Landsat 5 satellite images, both by the classification performance and by reducing the amount of information needed to perform identification. The tree models, through the J48 algorithm, achieved success rates above 90% in the identification of species planted among other targets. Thus, the techniques used in this study enabled the development of robust classification models to assist the planning and decision-making on forest plantations in Brazil. KEYWORDS: Planted Forests, agro-energy, feature selection, decision trees, and pictures classification." @default.
• W2124583432 created "2016-06-24" @default.
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• W2124583432 date "2015-11-05" @default.
• W2124583432 modified "2023-10-03" @default.
• W2124583432 title "MINERAÇÃO DE DADOS PARA IDENTIFICAÇÃO DE FLORESTAS PLANTADAS DESTINADAS À PRODUÇÃO DE BIOENERGIA UTILIZANDO IMAGENS DE SATÉLITE" @default.
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• W2124583432 doi "https://doi.org/10.17224/energagric.2015v30n3p294-301" @default.
• W2124583432 hasPublicationYear "2015" @default.
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