Adam Michael Erickson , Nicholas Coops 

Reliable estimates of canopy light transmission are critical to understanding the structure and function of vegetation communities but are difficult and costly to attain by traditional field inventory methods. Airborne laser scanning (ALS) data uniquely provide multi-angular vertically resolved representation of canopy geometry across large geographic areas. While previous studies have proposed ALS indices of canopy light transmission, new algorithms based on theoretical advancements may improve existing models. Herein, we propose two new models of canopy light transmission (i.e., gap fraction, or Po, the inverse of angular canopy closure). We demonstrate the models against a suite of existing models and ancillary metrics, validated against convex spherical densiometer measurements for 950 field plots in the foothills of Alberta, Canada. We also tested the effects of synthetic hemispherical lens models on the performance of the proposed hemispherical Voronoi gap fraction (Phv) index. While vertical canopy cover metrics showed the best overall fit to field measurements, one new metric, point-density-normalized gap fraction (Ppdn), outperformed all other gap fraction metrics by two-fold. We provide suggestions for further algorithm enhancements based on validation data improvements. We argue that traditional field measurements are no longer appropriate for ‘ground-truthing’ modern LiDAR or SfM point cloud models, as the latter provide orders of magnitude greater sampling and coverage. We discuss the implications of this finding for LiDAR applications in forestry.