 This study examines the significance of LiDAR data acquisition, point density, for modeling forest inventory variables in Ontario, Canada. Field data were collected for three study sites with varying LiDAR densities, 3.2, 1.6, and 0.5 pulses M2. Stepwise regression models were developed to estimate forest inventory variables such as average height, top height, quadratic mean diameter, basal area, gross total volume, gross merchantable volume, total above ground biomass, and stem density. The results suggest that a mean density of 0.5 pulses M2 is sufficient for plot and stand level modeling under diverse forest conditions across Ontario, with no decimation effect observed for the precision of prediction of the majority of forest variables. This article was authored by Dave Etheridge, Dave Nesbit, Doug Pitt, and others.