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Forests 2016, 7(12), 311;

Predictive Modeling of Black Spruce (Picea mariana (Mill.) B.S.P.) Wood Density Using Stand Structure Variables Derived from Airborne LiDAR Data in Boreal Forests of Ontario

Department of Agricultural and Environmental Sciences, Tennessee State University, Nashville, TN 37209-1561, USA
Natural Resources Canada, Canadian Forest Service, Canadian Wood Fibre Centre, 1219 Queen St. E., Sault Ste. Marie, ON P6A 2E5, Canada
Ontario Ministry of Natural Resources and Forestry, FRI Unit, 3301 Trout Lake Rd., North Bay, ON P1A 4L7, Canada
Department of Biology and Chemistry, Nipissing University, North Bay, ON P1B 8L7, Canada
Author to whom correspondence should be addressed.
Academic Editors: Chris Hopkinson, Laura Chasmer and Craig Mahoney
Received: 23 September 2016 / Revised: 21 November 2016 / Accepted: 29 November 2016 / Published: 8 December 2016
(This article belongs to the Special Issue LiDAR Remote Sensing of Forest Resources)
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Our objective was to model the average wood density in black spruce trees in representative stands across a boreal forest landscape based on relationships with predictor variables extracted from airborne light detection and ranging (LiDAR) point cloud data. Increment core samples were collected from dominant or co-dominant black spruce trees in a network of 400 m2 plots distributed among forest stands representing the full range of species composition and stand development across a 1,231,707 ha forest management unit in northeastern Ontario, Canada. Wood quality data were generated from optical microscopy, image analysis, X-ray densitometry and diffractometry as employed in SilviScan™. Each increment core was associated with a set of field measurements at the plot level as well as a suite of LiDAR-derived variables calculated on a 20 × 20 m raster from a wall-to-wall coverage at a resolution of ~1 point m−2. We used a multiple linear regression approach to identify important predictor variables and describe relationships between stand structure and wood density for average black spruce trees in the stands we observed. A hierarchical classification model was then fitted using random forests to make spatial predictions of mean wood density for average trees in black spruce stands. The model explained 39 percent of the variance in the response variable, with an estimated root mean square error of 38.8 (kg·m−3). Among the predictor variables, P20 (second decile LiDAR height in m) and quadratic mean diameter were most important. Other predictors describing canopy depth and cover were of secondary importance and differed according to the modeling approach. LiDAR-derived variables appear to capture differences in stand structure that reflect different constraints on growth rates, determining the proportion of thin-walled earlywood cells in black spruce stems, and ultimately influencing the pattern of variation in important wood quality attributes such as wood density. A spatial characterization of variation in a desirable wood quality attribute, such as density, enhances the possibility for value chain optimization, which could allow the forest industry to be more competitive through efficient planning for black spruce management by including an indication of suitability for specific products as a modeled variable derived from standard inventory data. View Full-Text
Keywords: LiDAR; wood quality; wood density; random forests; wood quality mapping; black spruce LiDAR; wood quality; wood density; random forests; wood quality mapping; black spruce

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Pokharel, B.; Groot, A.; Pitt, D.G.; Woods, M.; Dech, J.P. Predictive Modeling of Black Spruce (Picea mariana (Mill.) B.S.P.) Wood Density Using Stand Structure Variables Derived from Airborne LiDAR Data in Boreal Forests of Ontario. Forests 2016, 7, 311.

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