The tail behavior of the probability distributions of timber material characteristics plays an important role in the overall probabilistic modeling.
We compare our cross-scale density estimates and discuss the relative merits of studying the distribution of non-timber forest products (NTFP) at more than one spatial scale.
Figure 3 demonstrates the distribution of forest stands with four timber stock classes based on aerial-imagery interpretations as of 2001.
Figure 1 presents the initial situation in the year 2000 for the spatial distribution of standing timber volume [m3/ha], main tree species [spruce, pine, oak, beech, mixed species stands], stand age [in 30-year classes], and carbon stock [t/ha].
The main objective of this study was to evaluate the ability of modelling to improve knowledge on the distribution of six timber species and six palm trees in the Amazon, and whether these models can benefit from using data from field surveys added to those of biological collections.
Figure 1 presents the initial situation in the year 2000 for the spatial distribution of standing timber volume [m/ha], main tree species [spruce, pine, oak, beech, mixed species stands], stand age [in 30-year classes], and carbon stock [t/ha].
Projecting the fate of these services has proven to be challenging, however, as major uncertainties exist regarding the principal drivers of boreal ecosystem dynamics, including the future spatial and temporal distribution of wildfire and timber harvesting.
It is primarily engaged in the growing and harvesting of timber; the distribution and sale of forest products; and real estate construction and development.
When timber price was stochastic the distribution of outcomes was much wider indicating that timber price is a more significant source of uncertainty than the biological growth process of trees.
