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Open AccessArticle

LiDAR Sampling Density for Forest Resource Inventories in Ontario, Canada

1
Department of Geography, Queen’s University, Kingston, ON K7L 3N6, Canada
2
Lim Geomatics Inc., P.O. Box 45089, 680 Eagleson Road, Ottawa, ON K2M 2G0, Canada
3
Southern Science & Information Section, Ontario Ministry of Natural Resources, 3301 Trout Lake Road, North Bay, ON P1A 4L7, Canada
4
Canadian Wood Fibre Centre, Canadian Forest Service, 1219 Queen St. E., Sault Ste. Marie, ON P6A 2E5, Canada
5
Northeast Science & Information Section, Ontario Ministry of Natural Resources, P.O. Bag 3020, South Porcupine, ON P0N 1H0, Canada
*
Author to whom correspondence should be addressed.
Remote Sens. 2012, 4(4), 830-848; https://doi.org/10.3390/rs4040830
Received: 22 February 2012 / Revised: 16 March 2012 / Accepted: 16 March 2012 / Published: 27 March 2012
(This article belongs to the Special Issue Laser Scanning in Forests)
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. View Full-Text
Keywords: light detection and ranging; LiDAR; airborne laser scanning; ALS; laser pulse density; forest resource inventory; remote sensing; forestry light detection and ranging; LiDAR; airborne laser scanning; ALS; laser pulse density; forest resource inventory; remote sensing; forestry
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MDPI and ACS Style

Treitz, P.; Lim, K.; Woods, M.; Pitt, D.; Nesbitt, D.; Etheridge, D. LiDAR Sampling Density for Forest Resource Inventories in Ontario, Canada. Remote Sens. 2012, 4, 830-848.

AMA Style

Treitz P, Lim K, Woods M, Pitt D, Nesbitt D, Etheridge D. LiDAR Sampling Density for Forest Resource Inventories in Ontario, Canada. Remote Sensing. 2012; 4(4):830-848.

Chicago/Turabian Style

Treitz, Paul; Lim, Kevin; Woods, Murray; Pitt, Doug; Nesbitt, Dave; Etheridge, Dave. 2012. "LiDAR Sampling Density for Forest Resource Inventories in Ontario, Canada" Remote Sens. 4, no. 4: 830-848.

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