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Registro Completo |
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Biblioteca(s): |
Embrapa Florestas. |
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Data corrente: |
06/06/1995 |
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Data da última atualização: |
18/03/2009 |
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Autoria: |
LIU, C. J. |
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Título: |
Multivariate taper function of loblolly pine. |
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Ano de publicação: |
1973 |
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Fonte/Imprenta: |
1973. |
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Páginas: |
53 f. |
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Idioma: |
Inglês |
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Notas: |
Thesis (Master of Science) - Louisiana State University, Louisiana. |
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Conteúdo: |
The least-squares aproximation by a multiple regression approach was used to interpolate the recorded sectional diameters of each tree to their positional diameters at every 0.1 of the total stem length. the general form of the interpolation regression equation was as follows. Where xi is the predicted diameter at zi position. A variance-covariance matrix of positional diameters was constructed to carry out the principal component analysis. The calculation involved in the analysis was to find the roots of an eigen equation of the variance-covariance matrix of the original variates is equivalent to performing a linear transformation from the set of original variates to the set of new variates. The significance of the newly transformed variates was decided by the ratio of the magnitude of the eigenvalue associated with a particular variate to the sum of all eigenvalues. A significant variate was considered as a principal component in the multivariate analysis. Interpretation of the components was performed by a graphical analysis of corresponding eigenvectors.The trend of mutation of entries of the revealed that the first significant principal stem and was consequently interpred as stem taper. On the other hand, the second and the third significant of the lack of information in the original records. The cumulative percentaje of eigenvalues showed that the first three variates countedfor more than 99 percent of the total variance. A step wise regression procedure was then used to derive polynomial expressions of einvectors to the first three significant variates.The multiple regression modeladopted in this curve-fitting techinique read as folows:were thay are entries of the eigenvector,is the exponent of y and the are the regression coefficients. A correlation analysis between predicted and interpolated diameters was carried out to detect the goodness of fit of the final multivariate prediction function.The correlation indicates the applicability of the costructed taper function. MenosThe least-squares aproximation by a multiple regression approach was used to interpolate the recorded sectional diameters of each tree to their positional diameters at every 0.1 of the total stem length. the general form of the interpolation regression equation was as follows. Where xi is the predicted diameter at zi position. A variance-covariance matrix of positional diameters was constructed to carry out the principal component analysis. The calculation involved in the analysis was to find the roots of an eigen equation of the variance-covariance matrix of the original variates is equivalent to performing a linear transformation from the set of original variates to the set of new variates. The significance of the newly transformed variates was decided by the ratio of the magnitude of the eigenvalue associated with a particular variate to the sum of all eigenvalues. A significant variate was considered as a principal component in the multivariate analysis. Interpretation of the components was performed by a graphical analysis of corresponding eigenvectors.The trend of mutation of entries of the revealed that the first significant principal stem and was consequently interpred as stem taper. On the other hand, the second and the third significant of the lack of information in the original records. The cumulative percentaje of eigenvalues showed that the first three variates countedfor more than 99 percent of the total variance. A step wise regression procedure was then used... Mostrar Tudo |
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Palavras-Chave: |
Análise multivariada; Mensuração florestal; Tabelas de volume; Taper; Volume tables. |
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Thesagro: |
Pinus Taeda. |
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Thesaurus Nal: |
forest mensuration; multivariate analysis. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02638nam a2200229 a 4500
001 1305219
005 2009-03-18
008 1973 bl uuuu m 00u1 u #d
100 1 $aLIU, C. J.
245 $aMultivariate taper function of loblolly pine.
260 $a1973.$c1973
300 $a53 f.
500 $aThesis (Master of Science) - Louisiana State University, Louisiana.
520 $aThe least-squares aproximation by a multiple regression approach was used to interpolate the recorded sectional diameters of each tree to their positional diameters at every 0.1 of the total stem length. the general form of the interpolation regression equation was as follows. Where xi is the predicted diameter at zi position. A variance-covariance matrix of positional diameters was constructed to carry out the principal component analysis. The calculation involved in the analysis was to find the roots of an eigen equation of the variance-covariance matrix of the original variates is equivalent to performing a linear transformation from the set of original variates to the set of new variates. The significance of the newly transformed variates was decided by the ratio of the magnitude of the eigenvalue associated with a particular variate to the sum of all eigenvalues. A significant variate was considered as a principal component in the multivariate analysis. Interpretation of the components was performed by a graphical analysis of corresponding eigenvectors.The trend of mutation of entries of the revealed that the first significant principal stem and was consequently interpred as stem taper. On the other hand, the second and the third significant of the lack of information in the original records. The cumulative percentaje of eigenvalues showed that the first three variates countedfor more than 99 percent of the total variance. A step wise regression procedure was then used to derive polynomial expressions of einvectors to the first three significant variates.The multiple regression modeladopted in this curve-fitting techinique read as folows:were thay are entries of the eigenvector,is the exponent of y and the are the regression coefficients. A correlation analysis between predicted and interpolated diameters was carried out to detect the goodness of fit of the final multivariate prediction function.The correlation indicates the applicability of the costructed taper function.
650 $aforest mensuration
650 $amultivariate analysis
650 $aPinus Taeda
653 $aAnálise multivariada
653 $aMensuração florestal
653 $aTabelas de volume
653 $aTaper
653 $aVolume tables
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Registro original: |
Embrapa Florestas (CNPF) |
