| |
|
|
 | Acesso ao texto completo restrito à biblioteca da Embrapa Meio Ambiente. Para informações adicionais entre em contato com cnpma.biblioteca@embrapa.br. |
|
Registro Completo |
|
Biblioteca(s): |
Embrapa Meio Ambiente. |
|
Data corrente: |
02/08/2018 |
|
Data da última atualização: |
02/08/2018 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Autoria: |
FRIEDEL, M. J.; BUSCEMA, M.; VICENTE, L. E.; IWASHITA, F.; KOGA-VICENTE, A. |
|
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. |
|
Título: |
Mapping fractional landscape soils and vegetation components from Hyperion satellite imagery using an unsupervised machine-learning workflow. |
|
Ano de publicação: |
2018 |
|
Fonte/Imprenta: |
International Journal of Digital Earth, v. 11, n. 7, p. 670-690, 2018. |
|
ISSN: |
1753-8955 |
|
DOI: |
https://doi.org/10.1080/17538947.2017.1349841 |
|
Idioma: |
Inglês |
|
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. |
|
Palavras-Chave: |
Machine learning. |
|
Thesagro: |
Satélite; Sensoriamento Remoto; Solo; Vegetação. |
|
Thesaurus Nal: |
Hyperspectral imagery; Remote sensing; Soil; Vegetation cover. |
|
Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
|
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.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Meio Ambiente (CNPMA) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
|
|
|
Registro Completo
|
Biblioteca(s): |
Embrapa Cerrados. |
|
Data corrente: |
10/10/1994 |
|
Data da última atualização: |
22/08/2014 |
|
Autoria: |
CARDOSO, A. N.; FERREIRA, G.; SOUSA, D. M. G. de; REIN, T. A.; OLIVEIRA, S. A. de; COTTA, T. A. |
|
Título: |
Adsorcao de defensivos agricolas em solos dos cerrados: caso do aldicarb. |
|
Ano de publicação: |
1994 |
|
Fonte/Imprenta: |
Planaltina: EMBRAPA-CPAC, 1994. |
|
Páginas: |
2p. |
|
Série: |
(EMBRAPA-CPAC. Pesquisa em Andamento, 70). |
|
Idioma: |
Português |
|
Palavras-Chave: |
Defensivo agrícola. |
|
Thesagro: |
Adsorção; Agricultura; Cerrado; Defensivo; Solo. |
|
Thesaurus NAL: |
adsorption; agriculture; aldicarb; soil. |
|
Categoria do assunto: |
--
X Pesquisa, Tecnologia e Engenharia |
|
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/107116/1/pesq-70.pdf
|
|
Marc: |
LEADER 00788nam a2200301 a 4500
001 1549409
005 2014-08-22
008 1994 bl uuuu u0uu1 u #d
100 1 $aCARDOSO, A. N.
245 $aAdsorcao de defensivos agricolas em solos dos cerrados$bcaso do aldicarb.
260 $aPlanaltina: EMBRAPA-CPAC$c1994
300 $a2p.
490 $a(EMBRAPA-CPAC. Pesquisa em Andamento, 70).
650 $aadsorption
650 $aagriculture
650 $aaldicarb
650 $asoil
650 $aAdsorção
650 $aAgricultura
650 $aCerrado
650 $aDefensivo
650 $aSolo
653 $aDefensivo agrícola
700 1 $aFERREIRA, G.
700 1 $aSOUSA, D. M. G. de
700 1 $aREIN, T. A.
700 1 $aOLIVEIRA, S. A. de
700 1 $aCOTTA, T. A.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Cerrados (CPAC) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
| Expressão de busca inválida. Verifique!!! |
|
|
