Geomorphons: Landform and property predictions in a glacial moraine in Indiana landscapes

Predicting soil property distribution from a catena in the digital environment has been explored by many researchers with only slightly better than modest results. In this study, the landform recognition algorithm geomorphons in the GRASS GIS environment was explored to determine if this landscape model could improve predictions of soil properties. For 74 borings on the Wabash glacial moraine in Wells County, Indiana, measurements were made for: A horizon thickness, depth to chroma 2 features, effervescence, dense glacial till, carbonate concretions, and autochthonous platy structure. A digital elevation model (DEM) generated from light detection and ranging (LiDAR) data was used for the study site. The geomorphons algorithm was used to generate 10 original landforms: flat, footslope, summit, ridge, shoulder, spur, slope, hollow, valley, and depression that were aggregated to new landforms coinciding with slope positions: toeslope, footslope, backslope, shoulder, summit', and depression recognized by soil surveyors. Linear Discriminant Analysis (LDA) and Multinomial Logistics Regression Analysis (MLR) were used to aggregate the measured soil properties into the landform groups. The aggregation of geomorphons groups improved the MRL predictions to 83% accuracy. Also, the aggregation of geomorphons to five landforms to predict soil property distribution on the landscape gave promising results for the low-relief and relatively flat area of northeast Indiana. To test if the true mean value of each soil property for each landform was reliable for generalizing population characteristics, relative standard error (RSE) was calculated as a proportion of standard error to population mean from a bootstrap estimation. The range of RSE values for all soil properties and landforms was between similar to 0.7% and similar to 19%. Since the estimates of the measured soil properties all have RSE values of less than 25%, they can be considered reliable. (C) 2016 Elsevier B.V. All rights reserved.