Abstract
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.
Highlights
There has been a rapid growth in the application of airborne light detection and ranging (LiDAR) data for forestry, especially with respect to the potential production of enhanced forest resource inventories and much improved land base feature delineation
We examined the impact of three point densities (3.2, 1.6, and 0.5 pulses m−2) derived from the same LiDAR data acquisition on the prediction of several forest inventory variables for forest types common across Ontario
The results from this research demonstrate that a point density of 0.5 pulses·m−2 is sufficient for the estimation of forest inventory variables at the plot and stand levels for the different forest types considered in this study
Summary
There has been a rapid growth in the application of airborne light detection and ranging (LiDAR) data for forestry, especially with respect to the potential production of enhanced forest resource inventories (eFRI) and much improved land base feature delineation. Data acquisition standards that determine the optimal acquisition of LiDAR data for forestry (in terms of forest variable estimation and cost efficiency) have not been universally defined, nor is there documentation of expert knowledge defining suitable acquisition criteria (i.e., survey design) for estimating forest variables. These standards are required for the forest industry to gain the best possible return from the technology across a range of forest conditions and for specific operational requirements, as well as to maintain consistency across surveys within regions. This deficiency must be addressed to provide the forest sector, both in industry and government, with a distinct competitive advantage in achieving truly sustainable forest management that encompasses economic, ecological, and social values
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