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| Registros recuperados : 30 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Acre. |
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Data corrente: |
07/07/2017 |
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Data da última atualização: |
16/11/2023 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
MORAS FILHO, L. O.; FIGUEIREDO, E. O.; ISAAC JÚNIOR, M. A.; BARROS, V. C. C. de; HOTT, M. C.; BORGES, L. A. C. |
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Afiliação: |
Luiz Otávio Moras Filho, Universidade Federal de Lavras (Ufla); EVANDRO ORFANO FIGUEIREDO, CPAF-Acre; Marcos Antônio Isaac Júnior, Universidade Federal de Lavras (Ufla); Vanessa Cabral Costa de Barros, Universidade Federal de Lavras (Ufla); Marcos Cicarini Hott, Universidade Federal de Lavras (Ufla); Luís Antônio Coimbra Borges, Universidade Federal de Lavras (Ufla). |
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Título: |
Classificador de máxima verossimilhança aplicado à identificação de espécies nativas na Floresta Amazônica. |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 18., 2017, Santos. Anais... Santos: Inpe, 2017. |
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Páginas: |
6 p. |
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ISBN: |
978-85-11-00088-1 |
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Idioma: |
Português |
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Conteúdo: |
Among 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. |
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Palavras-Chave: |
Acre; Amazonia Occidental; Amazônia Ocidental; Análisis estadístico; Bosques tropicales; Especies nativas; Estimación; Identificación de plantas; Manejo florestal; Máxima verossimilhança; Maximum Likelihood; Método de classificação digital; Rio Branco (AC); Sistemas de información geográfica; Teledetección; Western Amazon. |
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Thesagro: |
Análise estatística; Espécie nativa; Estimativa; Floresta tropical; Identificação; Método estatístico; Sensoriamento remoto; Sistema de informação geográfica. |
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Thesaurus NAL: |
Estimation; Geographic information systems; Indigenous species; Plant identification; Remote sensing; Statistical analysis; Tropical forests. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/161507/1/26344.pdf
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Marc: |
LEADER 02999nam a2200565 a 4500
001 2072220
005 2023-11-16
008 2017 bl uuuu u00u1 u #d
020 $a978-85-11-00088-1
100 1 $aMORAS FILHO, L. O.
245 $aClassificador de máxima verossimilhança aplicado à identificação de espécies nativas na Floresta Amazônica.$h[electronic resource]
260 $aIn: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 18., 2017, Santos. Anais... Santos: Inpe$c2017
300 $a6 p.
520 $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.
650 $aEstimation
650 $aGeographic information systems
650 $aIndigenous species
650 $aPlant identification
650 $aRemote sensing
650 $aStatistical analysis
650 $aTropical forests
650 $aAnálise estatística
650 $aEspécie nativa
650 $aEstimativa
650 $aFloresta tropical
650 $aIdentificação
650 $aMétodo estatístico
650 $aSensoriamento remoto
650 $aSistema de informação geográfica
653 $aAcre
653 $aAmazonia Occidental
653 $aAmazônia Ocidental
653 $aAnálisis estadístico
653 $aBosques tropicales
653 $aEspecies nativas
653 $aEstimación
653 $aIdentificación de plantas
653 $aManejo florestal
653 $aMáxima verossimilhança
653 $aMaximum Likelihood
653 $aMétodo de classificação digital
653 $aRio Branco (AC)
653 $aSistemas de información geográfica
653 $aTeledetección
653 $aWestern Amazon
700 1 $aFIGUEIREDO, E. O.
700 1 $aISAAC JÚNIOR, M. A.
700 1 $aBARROS, V. C. C. de
700 1 $aHOTT, M. C.
700 1 $aBORGES, L. A. C.
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Embrapa Acre (CPAF-AC) |
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