Using Discriminant Analysis and Logistic Regression in Mapping Quaternary Sediments

Mapping the occurrence and thickness of layers within a soil profile is a prerequisite for soil characterization. The objective of this paper is to compare the applicability of two statistical methods-discriminant analysis (DA) and logistic regression (LR)-used to calculate the thickness of Quaternary sediments in a formal way and to identify parameters controlling the occurrence of these sediments. The investigations were carried out in southern Bavaria in an area of about 150 ha presenting a large variability in relief and parent material (Tertiary material, Pleistocene loess, colluvial/alluvial sediments). Comparisons between the two statistical methods were carried out with a training dataset and an evaluation dataset. The results show that DA was preferable under the assumptions of normality and equal variance/covariance matrices. The analyses produced models with 80 % and 79 % correctly reclassified assignments and a canonical correlation coefficient of approximately 0.60. From the simulations, it was found (i) that the determining predictors were altitude, slope, and upslope catchment area (partly expressed as topographical wetness index), SAGA wetness index and specific catchment area; and (ii) that a disadvantage of LR was that trial and error was frequently necessary to find the optimal composition of variables. In this study, a hierarchical combination of binary and ordinal LR was used and revealed (iii) that when the probabilities in LR between adjacent categories were similar, the possibility of incorrect calculations increased and (iv) that visual inspections as well as RMSE showed that DA with weighted depths (5 cm-stepwise DA) provided the best prediction accuracy. This information can help improve soil surveys and the predictability of the spatial heterogeneity in landscapes.