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. Full article

The 10th-growing season performance of planted eastern white pine (Pinus strobus L.) seedlings was evaluated in response to herbaceous and woody vegetation control treatments within a clearcut and two variants of the uniform shelterwood regeneration system (single vs. multiple future removal cuts). Herbaceous vegetation control involved the suppression of grasses, forbs, ferns and low shrubs for the first 2 or 4 growing seasons after planting. Deciduous woody vegetation control treatments, conducted in combination with the herbaceous treatments within a response-surface design, involved the permanent removal of all tall shrubs and deciduous trees at the time of planting, at the end of the 2nd or 5th growing seasons, or not at all. In general, the average size of planted pine was related positively to the duration of herbaceous vegetation control and negatively to delays in woody control. White pine weevil (Pissodes strobi Peck) altered these trends, reducing the height of pine on plots with little or no overtopping deciduous woody vegetation or mature tree cover. Where natural pine regeneration occurred on these plots, growth was similar but subordinate to the planted pine. Data from the three sites indicate that at least 60% of planted pine may be expected to reach an age-10 height target of 2.5 m when overtopping cover (residual overstory + regenerating deciduous) is managed at approximately 65% ± 10%, and total herbaceous cover is suppressed to levels not exceeding 50% in the first five years. On productive sites, this combination may be difficult to achieve in a clearcut, and requires fairly rigorous vegetation management in shelterwood regeneration systems. Currently, synthetic herbicides offer the only affordable and effective means of achieving such vegetation control. Full article

Airborne Laser Scanning (ALS) metrics have been used to develop area-based forest inventories; these metrics generally include estimates of stand-level, per hectare values and mean tree attributes. Tree-based ALS inventories contain desirable information on individual tree dimensions and how much they vary within a stand. Adding size class distribution information to area-based inventories helps to bridge the gap between area- and tree-based inventories. This study examines the potential of ALS and stereo-imagery point clouds to predict size class distributions in a boreal forest. With an accurate digital terrain model, both ALS and imagery point clouds can be used to estimate size class distributions with comparable accuracy. Nonparametric imputations were generally superior to parametric imputations; this may be related to the limitation of using a unimodal Weibull function on a relatively small prediction unit (e.g., 400 m²). Full article