LiDAR Sampling Density for Forest Resource Inventories in Ontario, Canada

Remote Sensing

Paul Treitz³  Kevin Lim¹  Murray Woods⁴  Doug Pitt²  Dave Nesbitt⁴ 

[1] Lim Geomatics Inc., P.O. Box 45089, 680 Eagleson Road, Ottawa, ON K2M 2G0, Canada; E-Mail:;Canadian Wood Fibre Centre, Canadian Forest Service, 1219 Queen St. E., Sault Ste. Marie, ON P6A 2E5, Canada; E-Mail:;Department of Geography, Queen’s University, Kingston, ON K7L 3N6, Canada;Southern Science & Information Section, Ontario Ministry of Natural Resources, 3301 Trout Lake Road, North Bay, ON P1A 4L7, Canada; E-Mail:

关键词: light detection and ranging;    LiDAR;    airborne laser scanning;    ALS;    laser pulse density;    forest resource inventory;    remote sensing;    forestry;   

DOI  :  10.3390/rs4040830

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2012 by the authors; licensee MDPI, Basel, Switzerland.