Logistic Modeling to spatially predict the probability of soil drainage classes

Logistic models were developed to spatially predict the probability of drainage classes in a humid tropical area (58 900 ha) using sampled terrain attributes from a digital elevation model, and vegetation indices from a LANDSAT-5 Thematic Mapper image. Soil drainage classes were assigned on the basis of the local water table regime depth, determined by soil morphological indicators, to 295 pseudo-randomly selected soil auger hole observations (calibration data set) and 72 soil pedon observations (validation data set). Six drainage classes were identified: excessively (D1), well (D2), moderately well (D3), imperfectly (D4), poorly (D5), and very poorly (D6). A nested dichotomous modeling strategy of progressively separating the six drainage classes was adopted, and resulted in five multivariate logistic models. The best performing model, predicting the probability of nonhydric (D1D2) soils versus hydric (D3D4D5D6) soils had a concordance of 99%, and the worst performing model, predicting the probability of imperfectly (134) drained soils versus moderately well (133) drained soils had a concordance of 65%. The most important spatial determinants were: elevation, slope, distance-to-the-river channel (DC), and vegetation indices. The logistic models were combined in a geographic information system (GIS) to derive soil drainage class maps using the gridded data sets of the significant variables. The results showed that digital elevation models and vegetation indices from LANDSAT-5 Thematic Mapper provide complementary information for developing statistical models to spatially predict and map soil drainage classes.