Sentence examples for predictions for tree from inspiring English sources
Cao ([2010]) also found that use of observed rather than predicted stand attributes for disaggregation led to improved predictions for tree survival and diameter growth, i.e. the quality of the tree-level predictions in disaggregation depended on the reliability of the stand predictions.
Predictions for each tree from the model are shown in Figure 4.
Compared to PNWGap predictions and observed stands, percent critical errors (α=0.05) of predictions for dominant tree species and stand-level measures with LandMod ranged from 1.4 to 29% with the majority of critical errors less than 15%.
While the light detection and ranging (LiDAR) technology provides a good basis for predictions of tree height and biomass, tree species identification based on this type of data is particularly challenging in structurally heterogeneous forests.
We conclude that predictions for individual trees remain in a likely data range for very dense stands.
Methods for validating short-term predictions of tree or stand change are documented in numerous sources.
Volume (total aboveground, sawtimber and pulpwood) prediction errors for trees in the independent-validation data set also exhibited similar trends (residual plots not shown).
Predictions of tree total volume and aboveground biomass were within the expected range for these plots.
Site assessment has been mainly used for the prediction of tree performance but it can be extended to form the basis for site-specific management.
Comparison of estimated aboveground total live-tree C (8 single-source predictions) for the same trees produced a range that spanned 43% of our output range, whereas stem wood plus bark live-tree C (11 single-source estimates) yielded a range that covered 53% of ours.
In agreement with our results, most frequently there is an over-prediction for small trees and an under-prediction for larger trees (Sterba et al., 2001; Schmid et al., 2006; Froese and Robinson, 2007; Mette et al., 2009).
