Abstract:

Four LiDAR-based models of canopy fractional cover (FC_LiDAR) have been tested against hemispherical photography fractional cover measurements (FC_HP) and compared across five ecozones, eight forest species and multiple LiDAR survey configurations. The four models compared are based on: i) a canopy-to-total first returns ratio (FC_LiDAR(FR)) method; ii) a canopy-to-total returns ratio (FC_LiDAR(RR)); iii) an intensity return ratio (FC_LiDAR(IR)); and iv) a Beer's Law modified (two-way transmission loss) intensity return ratio (FC_LiDAR(BL)). It is found that for the entire dataset, the FC_LiDAR(RR)model demonstrates the lowest overall predictive capability of overhead FC (annulus rings 1–4) (r² = 0.70), with a slight improvement for the FC_LiDAR(FR) model (r² = 0.74). The intensity-based FC_LiDAR(IR) model displays the best results (r² = 0.78). However, the FC_LiDAR(BL) model is considered generally more useful (r² = 0.75) because the associated line of best fit passes through the origin, has a slope near unity and produces a mean estimate of FC_HP within 5%. Therefore, FC_LiDAR(BL) requires the least calibration across a broad range of forest cover types. The FC_LiDAR(FR) and FC_LiDAR(RR) models, on the other hand, were found to be sensitive to variations in both canopy height and sensor pulse repetition frequency (or pulse power); i.e. changing the repetition frequency led to a systematic shift of up to 11% in the mean FC_LiDAR(RR) estimates while it had no effect on the intensity-based FC_LiDAR(IR) or FC_LiDAR(BL) models. While the intensity-based models were generally more robust, all four models displayed at least some sensitivity to variations in canopy structural class, suggesting that some calibration of FC_LiDAR might be necessary regardless of the model used. Short (< 2 m tall) or open canopy forest plots posed the greatest challenge to accurate FC estimation regardless of the model used.