Douglas G. Pitt

Early vegetation control for the regeneration of a single-cohort, intimate mixture of white spruce and trembling aspen on upland boreal sites

In Canada’s boreal forest region, there is increasing demand for practical regeneration strategies that will recreate mixed stands of white spruce (Picea glauca (Moench) Voss) and trembling aspen (...

Competitive effects of woody and herbaceous vegetation in a young boreal mixedwood stand

The influence of aspen (Populus tremuloides Michx.) and herbaceous (forb and grass) vegetation on resource availability and white spruce (Picea glauca (Moench) Voss) growth were examined as part of a long-term experiment established in 2002 near Whitecourt, Alberta, Canada. During the 2005 growing season, we examined the effects of herbicide treatments designed to control only woody (triclopyr ester) or both woody and herbaceous (glyphosate) vegetation on leaf area index (LAI) of both the woody and herbaceous components and relationships among LAI and light, soil moisture, air temperature, soil temperature, nitrogen availability, and spruce growth. Treatments reduced LAI and increased light, soil nitrogen availability, and white spruce growth. There were no apparent effects of the treatments on soil moisture in 2005. Both the woody and herb–grass layers appear to be competing for light and soil nitrogen in this young plantation. Controlling only woody vegetation resulted in an increase in herbaceous and t...

Imputing Tree Lists for New Brunswick Spruce Plantations Through Nearest-Neighbor Matching of Airborne Laser Scan and Inventory Plot Data

ABSTRACT Light detection and ranging (LiDAR) has greatly improved the spatial resolution and accuracy of operational forest inventories. However, a cost-effective method to impute species-specific tree-level inventory is needed, to be used as input to tree or stand growth models to project single-point-in-time LiDAR estimates. We evaluated a method to match stand structural variables estimated from LiDAR to those in a library of over 5,500 sample plot measurements to impute tree lists for LiDAR grid cells across 83,000 ha of spruce (Picea sp.) plantations. Matches were determined based on planted species and minimum sum of squared difference between 6 inventory variables. Forest inventory variables obtained by the plot matches were highly correlated (r = 0.91–0.99) with those measured on 98 validation plots. Basal area distributions derived from plot matching were statistically equivalent to those observed on the validation plots 86% of the time (α = 0.05). When we aggregated the predictions for all validation plots, there was minimal difference between predicted and actual basal area distributions by planted species and species compositions were similar. Plot matching is a valid method to impute tree lists for LiDAR cells that combine the wealth of existing plot data with high resolution LiDAR-derived variables.