Photogrammetric point clouds (PPC) obtained by stereomatching of aerial photographs now have a resolution sufficient to discern individual trees. We have produced such PPCs of a boreal forest and delineated individual tree crowns using a segmentation algorithm applied to the canopy height model derived from the PPC and a lidar terrain model. The crowns were characterized in terms of height and species (spruce, fir, and deciduous). Species classification used the 3D shape of the single crowns and their reflectance properties. The same was performed on a lidar dataset. Results show that the quality of PPC data generally approaches that of airborne lidar. For pixel-based canopy height models, viewing geometry in aerial images, forest structure (dense vs. open canopies), and composition (deciduous vs. conifers) influenced the quality of the 3D reconstruction of PPCs relative to lidar. Nevertheless, when individual tree height distributions were analyzed, PPC-based results were very similar to those extracted from lidar. The random forest classification (RF) of individual trees performed better in the lidar case when only 3D metrics were used (83% accuracy for lidar, 79% for PPC). However, when 3D and intensity or multispectral data were used together, the accuracy of PPCs (89%) surpassed that of lidar (86%).

Within the managed boreal forest of Quebec (Canada), there is a need to ensure the maintenance of age diversity across the forest resource, and, as a result, fo

Underestimation of LiDAR heights is widely known but has never been evaluated for several sensors and for diverse types of ecological conditions. This underestimation is mainly linked to the probability of the pulse to reach the ground and the top of vegetation. Main causes of this underestimation are pulse density, pattern of scan (sensors), scan angles, specific contract parameters (flying altitude, pulse repetition frequency) and characteristics of the territory (slopes, stand density and species composition). This study, carried out at a resolution of 1 × 1 m, first assessed the possibility of making an adjustment model to correct the bias of the digital terrain model (DTM), and then proposed a global adjustment model to correct the bias on the canopy height model (CHM). For this study, the bias of both DTM and CHM were calculated by subtracting two LiDAR datasets: high-density pixels with 21 pulses/m² (first return) and more (DTM or CHM reference value pixels) and low-density pixels (DTM or CHM value to correct). After preliminary analyses, it was concluded that the DTM did not need specific adjustment. In contrast, the CHM needed adjustments. Among the variables studied, three were selected for the final CHM adjustment model: the maximum height of the pixel (H2Corr); the density of first returns by m² (D_first); and the standard deviation of nine maximum heights of the neighborhood cells (H_STD9). The modeling occurred in three steps. The first two steps enabled the determination of significant variables and the shape of the equation to be defined (linear mixed model and non-linear model). The third step made it possible to propose an empirical equation using a non-linear mixed model that can be applied to a 1 × 1 m CHM. The CHM underestimation correction could be used for a preliminary step to several uses of the CHM such as volume calculation, forest growth models or multi-temporal analysis.

Forest dynamics following a partial cut conducted in the early 1950s in the Lac Édouard Experimental Forest in the Mauricie National Park, Quebec, Canada, were studied. Changes in vegetation were analyzed for the period between 1946 and 2001 in 109 mesic stands of the sugar maple – yellow birch bioclimatic domain. Three cutting intensities were considered: 1) low intensity, 15–30% of the basal area; 2) moderate intensity, 30–45% of the basal area; and 3) uncut. More than fifty years following cutting, the original relative proportion of the deciduous and softwood species was maintained. The relative proportion of basal area occupied by softwood species relative to the total basal area varied only from 2.0% to 7.4%. Red spruce (Picea rubens Sarg.) basal area increased in forest types Cornus (Co) and Oxalis-Cornus (O-Co) whereas balsam fir basal area decreased. In the Viburnum-Oxalis (Vi-O) forest type, sugar maple (Acer saccharum Marsh.) basal area increased and yellow birch (Betula alleghaniensis Britt.) decreased. Cutting areas were not invaded by competition species such as mountain maple (Acer spicatum Lamb.). In the Co and O-Co forest types, the regeneration density is probably sufficient to maintain the current cover type. In the Vi-O forest type, where moderate partial cutting was conducted, the deciduous component could substantially increase. Stands reacted positively to partial cutting. The increased growth of residual stems and the arrival of new stems resulted in 2001 total basal areas that were equal to or larger than those observed before cutting (1946). Key words: forest dynamics, partial cutting, yellow birch, red spruce, balsam fir

The potential of near-infrared spectroscopy (NIRS) to determine the transition from juvenile to mature wood in black spruce (Picea mariana (Mill.) B.S.P.) was assessed. In total, 127 wood samples were harvested from 50 sites located across the black spruce–moss domain in the province of Québec, Canada. Mechanical wood properties were determined by SilviScan. NIR spectra were collected on the transverse face of the samples. Good to excellent calibration statistics (R², ratio of performance to deviation) were obtained for basic density (0.85, 1.8), microfibril angle (0.79, 2.2), and modulus of elasticity (0.88, 2.9). Two-segment linear regressions were applied to microfibril angle profiles to determine the transition age and then calculate the juvenile and mature wood properties. The values obtained using SilviScan data were compared with those obtained using NIRS predicted data. Using SilviScan data, the average transition age was 23 years, with a standard deviation of 7 years. The correlation was moderate for the transition age (r = 0.592, P < 0.0001), which was slightly underestimated by NIRS with a mean prediction error (and 95% limits of agreement) of −2.2 ± 6.3 years (−14.6/10.1). These results suggest that the transition age from juvenile to mature wood could be predicted by NIRS. This article makes some recommendations to improve method accuracy for operational use.