02999nam a2200565 a 450000100080000000500110000800800410001902000220006010000230008224501420010526001010024730000090034852012260035765000150158365000350159865000230163365000250165665000190168165000250170065000210172565000260174665000200177265000150179265000220180765000200182965000250184965000250187465000400189965300090193965300240194865300240197265300270199665300230202365300210204665300160206765300310208365300210211465300290213565300230216465300390218765300200222665300410224665300190228765300190230670000220232570000250234770000240237270000160239670000210241220722202023-11-16       2017    bl uuuu     u00u1 u    #d  a978-85-11-00088-11 aMORAS FILHO, L. O.  aClassificador de máxima verossimilhança aplicado à identificação de espécies nativas na Floresta Amazônica.h[electronic resource]  aIn: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 18., 2017, Santos. Anais... Santos: Inpec2017  a6 p.  aAmong a variety of digital classification methods based on remote sensing images, the Maximum Likelihood (ML) is widely used in environmental studies, mainly for land cover and vegetation analysis. This study aimed to evaluate the effectiveness of supervised classification by ML technique in a forest management area of dense ombrophilous forest, using one RapidEye image. With this purpose, it was conducted the census of species over 30 cm in diameter at breast height and calculated the Cover Value Index (CVI), and selected the 20 species with the highest CVI as a parameter for classification in a Geographic Information System. 13 of the 20 species selected in the study area were not identified by the classification method, and among the seven identified species, two were underestimated and the others were overestimated. Both the maximum likelihood technique and the spatial resolution of the image used were not suitable for supervised classification of native vegetation, with Kappa index of 0.05 and global accuracy of 5.53%. Studies using spectral characterization in leaf level supported by higher or hyper spectral and spatial resolution images are recommended to increase the accuracy of classification.  aEstimation  aGeographic information systems  aIndigenous species  aPlant identification  aRemote sensing  aStatistical analysis  aTropical forests  aAnálise estatística  aEspécie nativa  aEstimativa  aFloresta tropical  aIdentificação  aMétodo estatístico  aSensoriamento remoto  aSistema de informação geográfica  aAcre  aAmazonia Occidental  aAmazônia Ocidental  aAnálisis estadístico  aBosques tropicales  aEspecies nativas  aEstimación  aIdentificación de plantas  aManejo florestal  aMáxima verossimilhança  aMaximum Likelihood  aMétodo de classificação digital  aRio Branco (AC)  aSistemas de información geográfica  aTeledetección  aWestern Amazon1 aFIGUEIREDO, E. O.1 aISAAC JÚNIOR, M. A.1 aBARROS, V. C. C. de1 aHOTT, M. C.1 aBORGES, L. A. C.