Using Ground Penetrating Radar (GPR) to Predict Log Moisture Content of Commercially Important Canadian Softwoods

Abstract

The non-destructive testing of wood fibre properties is crucial for informing forest management decisions and achieving optimal resource utilization. Moisture content (MC) is an important indicator of wood freshness and may reveal the presence of wood degradation. However, efficient methods are still needed to better monitor this property along the forest–wood value chain. The objective of the study was to develop prediction models to evaluate log MC based on the propagation of ground penetrating radar (GPR) signals. A total of 165 trees representing four species (black spruce (Picea mariana (Mill.) B.S.P.), white spruce (Picea glauca (Moench) Voss), red spruce (Picea rubens Sarg.), and balsam fir (Abies balsamea (L.) Mill.)) were harvested in two regions of the province of Quebec. GPR signals were acquired in the green (fresh) state and at three subsequent drying stages. Partial least squares regression (PLSR) and locally weighted PLSR (LWPLSR) were employed to establish relationships between GPR signals (antenna frequency: 1.6 GHz) and log properties. The models were fitted on three calibration sets containing four drying stages and different species mixes. The LWPLSR models performed better than the PLSR models for predicting log MC, with a lower root mean square error (RMSEp range: 10.8%–20.2% vs. 13.0%–20.5%) and a higher R2p (0.63–0.87 vs. 0.62–0.82). Spruce-only models performed considerably better than fir-only models while multi-species models were in-between. Despite the complex anisotropy of wood and the physics of wave propagation, the GPR technology can be successfully used to estimate log moisture content, but the GPR-based MC models should be calibrated for each specific type of wood material.