Four LiDAR-based models of canopy fractional cover (FCLiDAR) have been tested against hemispherical photography fractional cover measurements (FCHP) 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 (FCLiDAR(FR)) method; ii) a canopy-to-total returns ratio (FCLiDAR(RR)); iii) an intensity return ratio (FCLiDAR(IR)); and iv) a Beer's Law modified (two-way transmission loss) intensity return ratio (FCLiDAR(BL)). It is found that for the entire dataset, the FCLiDAR(RR)model demonstrates the lowest overall predictive capability of overhead FC (annulus rings 1–4) (r2 = 0.70), with a slight improvement for the FCLiDAR(FR) model (r2 = 0.74). The intensity-based FCLiDAR(IR) model displays the best results (r2 = 0.78). However, the FCLiDAR(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 FCHP within 5%. Therefore, FCLiDAR(BL) requires the least calibration across a broad range of forest cover types. The FCLiDAR(FR) and FCLiDAR(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 FCLiDAR(RR) estimates while it had no effect on the intensity-based FCLiDAR(IR) or FCLiDAR(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 FCLiDAR 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.