Multivariate analysis applied to reduce the number of predictors in digital soil mapping

Authors

  • Alexandre ten Caten UFSM
  • Ricardo Simão Diniz Dalmolin UFSM
  • Fabrício Araújo Pedron UFSM
  • Maria de Lourdes Mendonça-Santos EMBRAPA - CNPS

DOI:

https://doi.org/10.1590/S1678-3921.pab2011.v46.9731

Keywords:

principal components analysis, terrain attributes, pedometry, remote sensing

Abstract

The objective of this work was to assess the possibility of generating a smaller set of uncorrelated predictors, potentially applicable to digital soil mapping, by multivariate statistical analysis. The terrain attributes, elevation, slope, stream distance, planar curvature, profile curvature, relative available radiation, natural logarithm of the contributing area, topographic wetness index, and sediment transport capacity, were transformed by the Varimax method into the variables: altimetry, hydrology, and curvature. This transformation represented a variability concentration of 65.57% of the original data in the three new components. The new variables enable the use of a smaller amount of data set in prediction models, besides the fact that they are uncorrelated. Varimax rotation allows the relationship between environment and soil formation to be explicitly included in the prediction models.

Author Biographies

Alexandre ten Caten, UFSM

http://lattes.cnpq.br/4065267714747712

Ricardo Simão Diniz Dalmolin, UFSM

http://lattes.cnpq.br/3735884911693854

Fabrício Araújo Pedron, UFSM

http://lattes.cnpq.br/6868334304493274

Maria de Lourdes Mendonça-Santos, EMBRAPA - CNPS

http://lattes.cnpq.br/9502132296476941

Published

2011-07-28

How to Cite

Caten, A. ten, Dalmolin, R. S. D., Pedron, F. A., & Mendonça-Santos, M. de L. (2011). Multivariate analysis applied to reduce the number of predictors in digital soil mapping. Pesquisa Agropecuaria Brasileira, 46(5), 554–562. https://doi.org/10.1590/S1678-3921.pab2011.v46.9731

Issue

Section

SOIL SCIENCE