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.

Details

Title
LiDAR Sampling Density for Forest Resource Inventories in Ontario, Canada
Author
Treitz, Paul; Lim, Kevin; Woods, Murray; Pitt, Doug; Nesbitt, Dave; Etheridge, Dave
Pages
830-848
Publication year
2012
Publication date
2012
Publisher
MDPI AG
e-ISSN
20724292
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
1537381281
Copyright
Copyright MDPI AG 2012