Digital mapping of soil properties in Canadian managed forests at 250 m of resolution using the k-nearest neighbor method

Abstract:

Large-scale mapping of soil properties is increasingly important for environmental resource management. While
forested areas play critical environmental roles at local and global scales, forest soil maps are typically at lowresolution.
The objective of this study was to generate continuous national maps of selected soil variables (C, N and
soil texture) for the Canadian managed forest landbase at 250 m resolution. We produced these maps using the
kNN method with a training dataset of 538 ground-plots fromthe National Forest Inventory (NFI) across Canada,
and 18 environmental predictor variables. The best predictor variables were selected (7 topographic and 5 climatic
variables) using the Least Absolute Shrinkage and Selection Operator method. On average, for all soil variables,
topographic predictors explained 37% of the total variance versus 64% for the climatic predictors. The
relative root mean square error (RMSE%) calculated with the leave-one-out cross-validation method gave values
ranging between 22% and 99%, depending on the soil variables tested. RMSE values b 40% can be considered a
good imputation in light of the low density of points used in this study. The study demonstrates strong capabilities
for mapping forest soil properties at 250m resolution, compared with the current Soil Landscape of Canada
System, which is largely oriented towards the agricultural landbase. The methodology used here can potentially
contribute to the national and international need for spatially explicit soil information in resource management
science.