Improvement of Soil Texture Classification with LiDAR Data

Abstract

Models for the prediction of soil texture for the Abitibi River Forest (ARF) region in the District of Cochrane in Ontario, Canada were created from environmental covariates generated from remotely-sensed data as soil formation factors. A novel approach of incorporating LiDAR (Light Detection and Ranging) retrievals for the entire study area to derive covariates of canopy height model (CHM) and gap fraction was investigated. CHM and gap fraction had high variable importance for the soil texture models fitted for the region, with CHM being the most important variable out of a set of 104 predictors, and gap fraction among the top predictors. Random forest (RF) and support vector machine with radial basis functions (SVM Radial) approaches were utilized for the soil texture classification. The inclusion of CHM and gap fraction with other environmental predictors improved upon the accuracy of soil texture models, with accuracy scores exceeding 0.7 and Cohen's kappa greater than 0.5. Prediction maps for soil texture were generated for the ARF study region.