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Registro Completo |
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Biblioteca(s): |
Embrapa Hortaliças. |
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Data corrente: |
19/01/2016 |
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Data da última atualização: |
19/01/2016 |
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Autoria: |
BATISTA, M. A. V.; BEZERRA NETO, F.; SILVA, M. L.; AMBRÓSIO, M. M. Q.; CUNHA, J. L. X. L. |
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Afiliação: |
MARCOS A. V. BATISTA, INSTITUTO FEDERAL DE EDUCAÇÃO CIÊNCIA E TECNOLOGIA DO CEARÁ - IGUATU.; FRANCISCO BEZERRA NETO, UNIVERSIDADE FEDERAL RURAL DO SEMI-ÁRIDO, MOSSORÓ; MAIELE L. SILVA, UNIVERSIDADE ESTADUAL MATO GROSSO DO SUL; MÁRCIA M. Q. AMBRÓSIO, UFERSA- Mossoró-RN; JORGE L. X. L. CUNHA, UEMS. |
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Título: |
Atributos de solo-planta e de produção de beterraba influenciados pela adubação com espécies da Caatinga. |
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Ano de publicação: |
2016 |
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Fonte/Imprenta: |
Horticultura Brasíleira, Brasília, DF, v. 34, n. 1, p. 31-38, jan./fev. 2016. |
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ISSN: |
0102-0536 |
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Idioma: |
Português |
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Palavras-Chave: |
Eficiência agronômica; Merremia aegyptia; Senna uniflora. |
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Thesagro: |
Adubo verde; Beta vulgaris. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 00722naa a2200229 a 4500
001 2034388
005 2016-01-19
008 2016 bl uuuu u00u1 u #d
022 $a0102-0536
100 1 $aBATISTA, M. A. V.
245 $aAtributos de solo-planta e de produção de beterraba influenciados pela adubação com espécies da Caatinga.
260 $c2016
650 $aAdubo verde
650 $aBeta vulgaris
653 $aEficiência agronômica
653 $aMerremia aegyptia
653 $aSenna uniflora
700 1 $aBEZERRA NETO, F.
700 1 $aSILVA, M. L.
700 1 $aAMBRÓSIO, M. M. Q.
700 1 $aCUNHA, J. L. X. L.
773 $tHorticultura Brasíleira, Brasília, DF$gv. 34, n. 1, p. 31-38, jan./fev. 2016.
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Registro original: |
Embrapa Hortaliças (CNPH) |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Meio Ambiente. Para informações adicionais entre em contato com cnpma.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Meio Ambiente. |
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Data corrente: |
02/08/2018 |
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Data da última atualização: |
02/08/2018 |
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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: |
FRIEDEL, M. J.; BUSCEMA, M.; VICENTE, L. E.; IWASHITA, F.; KOGA-VICENTE, A. |
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Afiliação: |
MICHAEL J FRIEDEL, Hydrogeology Department - GNS Science; MASSIMO BUSCEMA, University of Colorado; LUIZ EDUARDO VICENTE, CNPMA; FABIO IWASHITA, University of Florence; ANDREA KOGA-VICENTE, CEPAGRI-UNICAMP. |
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Título: |
Mapping fractional landscape soils and vegetation components from Hyperion satellite imagery using an unsupervised machine-learning workflow. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
International Journal of Digital Earth, v. 11, n. 7, p. 670-690, 2018. |
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ISSN: |
1753-8955 |
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DOI: |
https://doi.org/10.1080/17538947.2017.1349841 |
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Idioma: |
Inglês |
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Conteúdo: |
An unsupervised machine-learning workflow is proposed for estimating fractional landscape soils and vegetation components from remotely sensed hyperspectral imagery. The workflow is applied to EO-1 Hyperion satellite imagery collected near Ibirací, Minas Gerais, Brazil. The proposed workflow includes subset feature selection, learning, and estimation algorithms. Network training with landscape feature class realizations provide a hypersurface from which to estimate mixtures of soil (e.g. 0.5 exceedance for pixels: 75% clay-rich Nitisols, 15% iron-rich Latosols, and 1% quartz-rich Arenosols) and vegetation (e.g. 0.5 exceedance for pixels: 4% Aspen-like trees, 7% Blackberry-like trees, 0% live grass, and 2% dead grass). The process correctly maps forests and iron-rich Latosols as being coincident with existing drainages, and correctly classifies the clay-rich Nitisols and grasses on the intervening hills. These classifications are independently corroborated visually (Google Earth) and quantitatively (random soil samples and crossplots of field spectra). Some mapping challenges are the underestimation of forest fractions and overestimation of soil fractions where steep valley shadows exist, and the under representation of classified grass in some dry areas of the Hyperion image. These preliminary results provide impetus for future hyperspectral studies involving airborne and satellite sensors with higher signal-to-noise and smaller footprints. |
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Palavras-Chave: |
Machine learning. |
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Thesagro: |
Satélite; Sensoriamento Remoto; Solo; Vegetação. |
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Thesaurus NAL: |
Hyperspectral imagery; Remote sensing; Soil; Vegetation cover. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 02406naa a2200301 a 4500
001 2093828
005 2018-08-02
008 2018 bl uuuu u00u1 u #d
022 $a1753-8955
024 7 $ahttps://doi.org/10.1080/17538947.2017.1349841$2DOI
100 1 $aFRIEDEL, M. J.
245 $aMapping fractional landscape soils and vegetation components from Hyperion satellite imagery using an unsupervised machine-learning workflow.$h[electronic resource]
260 $c2018
520 $aAn unsupervised machine-learning workflow is proposed for estimating fractional landscape soils and vegetation components from remotely sensed hyperspectral imagery. The workflow is applied to EO-1 Hyperion satellite imagery collected near Ibirací, Minas Gerais, Brazil. The proposed workflow includes subset feature selection, learning, and estimation algorithms. Network training with landscape feature class realizations provide a hypersurface from which to estimate mixtures of soil (e.g. 0.5 exceedance for pixels: 75% clay-rich Nitisols, 15% iron-rich Latosols, and 1% quartz-rich Arenosols) and vegetation (e.g. 0.5 exceedance for pixels: 4% Aspen-like trees, 7% Blackberry-like trees, 0% live grass, and 2% dead grass). The process correctly maps forests and iron-rich Latosols as being coincident with existing drainages, and correctly classifies the clay-rich Nitisols and grasses on the intervening hills. These classifications are independently corroborated visually (Google Earth) and quantitatively (random soil samples and crossplots of field spectra). Some mapping challenges are the underestimation of forest fractions and overestimation of soil fractions where steep valley shadows exist, and the under representation of classified grass in some dry areas of the Hyperion image. These preliminary results provide impetus for future hyperspectral studies involving airborne and satellite sensors with higher signal-to-noise and smaller footprints.
650 $aHyperspectral imagery
650 $aRemote sensing
650 $aSoil
650 $aVegetation cover
650 $aSatélite
650 $aSensoriamento Remoto
650 $aSolo
650 $aVegetação
653 $aMachine learning
700 1 $aBUSCEMA, M.
700 1 $aVICENTE, L. E.
700 1 $aIWASHITA, F.
700 1 $aKOGA-VICENTE, A.
773 $tInternational Journal of Digital Earth$gv. 11, n. 7, p. 670-690, 2018.
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Embrapa Meio Ambiente (CNPMA) |
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