Over the past two decades there has been an abundance of research demonstrating the utility of airborne light detection and ranging (LiDAR) for predicting forest biophysical/inventory variables at the plot and stand levels. However, to date there has been little effort to develop a set of protocols for data acquisition and processing that would move governments or the forest industry towards cost-effective implementation of this technology for strategic and tactical (i.e.
, operational) forest resource inventories. The goal of this paper is to initiate this process by examining the significance of LiDAR data acquisition (i.e.
, point density) for modeling forest inventory variables for the range of species and stand conditions representing much of Ontario, Canada. Field data for approximately 200 plots, sampling a broad range of forest types and conditions across Ontario, were collected for three study sites. Airborne LiDAR data, characterized by a mean density of 3.2 pulses m−2
were systematically decimated to produce additional datasets with densities of approximately 1.6 and 0.5 pulses m−2
. Stepwise regression models, incorporating LiDAR height and density metrics, were developed for each of the three LiDAR datasets across a range of forest types to estimate the following forest inventory variables: (1) average height (R2
(adj) = 0.75–0.95); (2) top height (R2
(adj) = 0.74–0.98); (3) quadratic mean diameter (R2
(adj) = 0.55–0.85); (4) basal area (R2
(adj) = 0.22–0.93); (5) gross total volume (R2
(adj) = 0.42–0.94); (6) gross merchantable volume (R2
(adj) = 0.35–0.93); (7) total aboveground biomass (R2
(adj) = 0.23–0.93); and (8) stem density (R2
(adj) = 0.17–0.86). Aside from a few cases (i.e.
, average height and density for some stand types), no decimation effect was observed with respect to the precision of the prediction of the majority of forest variables, which suggests that a mean density of 0.5 pulses m−2
is sufficient for plot and stand level modeling under these diverse forest conditions across Ontario.