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| 1. |  | LAFETÁ, B. O.; MATOS, M. P. de; LAGE, P.; FERRARO, A. C.; PENIDO, T. M. A. Ácido indol-3-butírico (AIB) no enraizamento de estacas de fedegoso gigante. Pesquisa Florestal Brasileira, Colombo, v. 36, n. 88, p. 489-496, out./dez. 2016.| Biblioteca(s): Embrapa Florestas. |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Solos. Para informações adicionais entre em contato com cnps.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Solos. |
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Data corrente: |
12/12/2022 |
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Data da última atualização: |
12/12/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
BASTOS, B. P.; PINHEIRO, H. S. K; CARVALHO JUNIOR, W. de; ANJOS, L. H. C. dos; FERREIRA, F. J. F. |
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Afiliação: |
BLENDA PEREIRA BASTOS, UNIVERSIDADE FEDERAL RURAL DO RIO DE JANEIRO; HELENA SARAIVA KOENOW PINHEIRO, UNIVERSIDADE FEDERAL RURAL DO RIO DE JANEIRO; WALDIR DE CARVALHO JUNIOR, CNPS; LÚCIA HELENA CUNHA DOS ANJOS, UNIVERSIDADE FEDERAL RURAL DO RIO DE JANEIRO; FRANCISCO JOSÉ FONSECA FERREIRA, UNIVERSIDADE FEDERAL DO PARANÁ. |
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Título: |
Clustering airborne gamma-ray spectrometry data in Nova Friburgo, State of Rio de Janeiro, southeastern Brazil. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Journal of Applied Geophysics, v. 209, 104900, Feb. 2023. |
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DOI: |
https://doi.org/10.1016/j.jappgeo.2022.104900 |
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Idioma: |
Inglês |
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Conteúdo: |
The goal of this study was to test different image clustering techniques, using airborne gamma-ray spectrometry data to optimize the interpretation of areas with similar properties in large scale. The methodology applied was based on the comparison of two techniques of data-driven classification (K-means and Gaussian Mixture Models) and a technique of knowledge-driven classification (Simplified RGB) to discriminate domains from the primary variables potassium (K), uranium (eU), and thorium (eTh) obtained from airborne gamma-ray spectrometry surveys. The performance of these three methods was evaluated through pre-processing techniques and post-processing including the best number of clusters/classes, visual interpretation, internal validation, and boxplot analysis. The clustering performance was considered satisfactory through the visual interpretation and comparison with the geological map and the DEM, for all three methods. For a quantitative analysis, the simplest model of unsupervised clustering Gaussian Mixture Models prevailed. |
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Palavras-Chave: |
Data-driven classification; Gamma-ray spectrometry; Geological mapping; Knowledge-driven classification. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
LEADER 01840naa a2200229 a 4500
001 2149458
005 2022-12-12
008 2023 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.jappgeo.2022.104900$2DOI
100 1 $aBASTOS, B. P.
245 $aClustering airborne gamma-ray spectrometry data in Nova Friburgo, State of Rio de Janeiro, southeastern Brazil.$h[electronic resource]
260 $c2023
520 $aThe goal of this study was to test different image clustering techniques, using airborne gamma-ray spectrometry data to optimize the interpretation of areas with similar properties in large scale. The methodology applied was based on the comparison of two techniques of data-driven classification (K-means and Gaussian Mixture Models) and a technique of knowledge-driven classification (Simplified RGB) to discriminate domains from the primary variables potassium (K), uranium (eU), and thorium (eTh) obtained from airborne gamma-ray spectrometry surveys. The performance of these three methods was evaluated through pre-processing techniques and post-processing including the best number of clusters/classes, visual interpretation, internal validation, and boxplot analysis. The clustering performance was considered satisfactory through the visual interpretation and comparison with the geological map and the DEM, for all three methods. For a quantitative analysis, the simplest model of unsupervised clustering Gaussian Mixture Models prevailed.
653 $aData-driven classification
653 $aGamma-ray spectrometry
653 $aGeological mapping
653 $aKnowledge-driven classification
700 1 $aPINHEIRO, H. S. K
700 1 $aCARVALHO JUNIOR, W. de
700 1 $aANJOS, L. H. C. dos
700 1 $aFERREIRA, F. J. F.
773 $tJournal of Applied Geophysics$gv. 209, 104900, Feb. 2023.
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