Improvement of soil property mapping in the Great Clay Belt of northern Ontario using multi-source remotely sensed data

This study aimed to improve the accuracy in modelling the properties of forest soil by employing a variety of remotely sensed data. In addition to the commonly used multispectral satellite imagery, airborne LiDAR (light detection and ranging) data were exploited to derive detailed vegetation properties and topographic variables. Random forest (RF) and support vector machine (SVM) approaches were applied to classify soil types in terms of texture, calcareous substrate reaction to hydrochloric acid, and ELC (Ecological Land Classification) moisture regime. The developed methods were tested on data acquired over a boreal region (49 degrees - 50 degrees N, 81 degrees - 84 degrees W) with a combined area of 4,085 km(2) in the Great Clay Belt (GCB) region, Ontario, Canada. Compared with the fieldcollected data, the overall accuracies and kappa coefficients of the retrieved soil properties were greater than 0.7 and 0.5, respectively. The accuracies attained between the RF and SVM approaches were similar, but in general the highest accuracies were achieved by the RF method. The models developed for the whole GCB regions generated accuracies comparable to those for the three sub-regions. The lowest modelling uncertainties occurred in areas dominated by peatland, whereas the highest modelling uncertainties existed in the regions with dry moisture regime or clayey soil at the surface. The results also showed that environmental covariates corresponding to vegetation were most important in the prediction of soil properties. Specifically, canopy height model (CHM) and gap fraction derived from LiDAR data, were among the most important variables. The inclusion of LiDAR-derived covariates demonstrated potential, applied in addition with topographic and climatic covariates and optical imagery. CHM pertains to the vertical dimension, and gap fraction relates to the density of the canopy layer, respectively; both covariates that offer supplemental detail that is not necessarily ascertained for the canopy by optical imagery.

Automated summary

This study aimed to improve the accuracy of forest soil properties modeling by utilizing various remotely sensed data, such as airborne LiDAR. Random forest and support vector machine approaches were applied for classification, showing that LiDAR-derived covariates and environmental factors related to vegetation were crucial for predicting soil properties accurately.