No abstract

Wind damage to forests is an important ecological disturbance factor. At the same time, it can have serious economic consequences due to a reduction in timber production. Current models for predicting the risk of wind damage are useful, but generally only focus on the ''mean'' tree within uniform stands. This paper presents measurements made of wind loading on trees of different sizes within four forest stands of different structure and management history, but all well-acclimated to current wind conditions. Each tree demonstrated a linear relationship between the maximum hourly turning moment and the square of the average hourly wind speed at the canopy top; we defined this ratio (the gradient of the line M max vs. u 2 ) as the turning moment coefficient (T C ). T C was correlated with tree size, in a relationship that differed little between the four forest sites despite the differences between the stands. The relationship between T C and individual tree competition within each stand was investigated, using both distance-independent and distance-dependent competition indices. All sites showed decreasing T C with increasing competition. However, the relationships differed between sites and would also be expected to change through time for a single site. The distance-dependent indices offered no improvement over the simpler, non-spatial indices that required only a diameter distribution. We suggest how, subject to further work, the results presented could be applied to calculate the risk of wind damage to trees of different sizes within a forest stand, and how the risk of wind damage to individual trees might change in response to thinning.

There is a growing interest to use acoustic sensors for selection in tree breeding to ensure high wood quality of future plantations. In this study, we assessed acoustic velocity as a selection trait for the improvement of mechanical wood properties in two 15-and 32-year-old white spruce (Picea glauca [Moench.] Voss) genetic tests. Individual heritability of acoustic velocity was moderate and of the same magnitude as heritability of wood density. Considerable genetic gain could be expected for acoustic velocity and a measure combining velocity and wood density. The relationship between acoustic velocity and cellulose microfibril angle (MFA) was strong on the genetic level and selection based on velocity could effectively improve MFA, which is one of the most important determinants of wood mechanical properties. Although low, the positive relationship between acoustic velocity and tree height presents an interesting opportunity for the improvement of both tree growth and wood quality. On the phenotypic level, MFA was more strongly correlated to acoustic velocity in mature trees than in young trees. The addition of easily obtainable traits such as diameter at breast height (DBH), height-to-diameter ratio as well as wood density to velocity determinations could improve models of MFA at the young and the mature age. OPEN ACCESSForests 2013, 4 576We conclude that juvenile acoustic velocity is an appropriate trait to select for wood quality in a tree breeding context.

Airborne laser scanning (LiDAR) is used in forest inventories to quantify stand structure with three dimensional point clouds. However, the structure of point clouds depends not only on stand structure, but also on the LiDAR instrument, its settings, and the pattern of flight. The resulting variation between and within datasets (particularly variation in pulse density and footprint size) can induce spurious variation in LiDAR metrics such as maximum height (h max) and mean height of the canopy surface model (C mean). In this study, we first compare two LiDAR datasets acquired with different parameters, and observe that h max and C mean are 56 cm and 1.0 m higher, respectively, when calculated using the high-density dataset with a small footprint. Then, we present a model that explains the observed bias using probability theory, and allows us to recompute the metrics as if the density of pulses were infinite and the size of the two footprints were equivalent. The model is our first step in developing methods for correcting various LiDAR metrics that are used for area-based prediction of stand structure. Such methods may be particularly useful for monitoring forest growth over time, given that acquisition parameters often change between inventories.