Dissertação
Aplicação de componentes principais e regressões logísticas múltiplas em sistema de informações geográficas para a predição e o mapeamento digital de solos
Fecha
2008-10-31Registro en:
CATEN, Alexandre Ten. Application of principal components and multiple logistic regression in a geographical information system for prediction and digital soil mapping. 2008. 130 f. Dissertação (Mestrado em Agronomia) - Universidade Federal de Santa Maria, Santa Maria, 2008.
Autor
Caten, Alexandre Ten
Institución
Resumen
Social demands on soil information have grown dramatically, meanwhile the soil surveys are seldom carried out in the country. Digital soil mapping techniques
can be applied to infer the spatial distribution of soil from existing soil maps or from reference areas, extrapolating this information to areas not mapped. The purpose of this study was to apply in a Geographic Information System the Multiple Logistic Regressions (MLR) using Principal Components (PC) as explanatory variables to
predict soil classes spatial distribution. The study area was the region of municipality São Pedro do Sul / RS. For the development of predictive models a set of nine
terrain attributes were used. Model training was executed on an existing soil map and with a survey carried out in a reference area, both in a 1:50.000 scale. The first three
retained PC explained 65.57% of the data variability. The predictive models which used PC had lower values of kappa index. The most accurate predicted map reached
a kappa value of 63.20% and was generated by using the nine attributes of land as predictive covariates. The mapping accuracy is sensitive to similarities between the
mapped classes, and mapping in a more homogeneous categorical level reduces the accuracy of the predicted maps. Soil classes relatively not representative in the
training maps are not properly spatialized. The use of MLR allows spatializing of soil classes to areas not mapped, although the use of PC needs to be tested with a larger
number of covariates.