Our results show that published biomass allometric equations
from regional and national sources can give substantial variation in
plot-level biomass estimates, especially in denser plots. The variation
may suggest that large biomass plots are not well represented
in the national scale allometric equations derivation due to the
fact that it is more time consuming and labor intensive to harvest
large biomass plots. In spite of the fact that the biomass density for
two sets of allometric equations hold similar patterns statistically,
the differences in the biomass density are obvious, indicating the
importance of assessing the influence of varied allometric equations
in predicting biomass with small footprint lidar systems.
Since both sets of allometric equations employ DBH as input,
and the regional allometric equations use one more additional variable
(i.e., height), the difference between biomass density at the
plot level mainly reflects the aggregated variation of height for all
individual trees in a plot. As a result, the comparison of AGB using
those two sets of allometric equations shows that tree heights in
our study area may generally be higher than the average heights of
the same species group across the country.
In models with reference above ground biomass calculated from
regional biomass equations, the integration of a simplified, empirical
relationship between DBH and height generally improved the
performance of the regression models. Its utility can be explained
by the fact lidar-derived variables are directly related to height,
while reference AGBs calculated from either Jenkins allometric
equations or regional allometric equations share a common input,
DBH. The simplified transformation from height metrics to volumetric
metrics made the association between reference AGBs and
Our results show that published biomass allometric equationsfrom regional and national sources can give substantial variation inplot-level biomass estimates, especially in denser plots. The variationmay suggest that large biomass plots are not well representedin the national scale allometric equations derivation due to thefact that it is more time consuming and labor intensive to harvestlarge biomass plots. In spite of the fact that the biomass density fortwo sets of allometric equations hold similar patterns statistically,the differences in the biomass density are obvious, indicating theimportance of assessing the influence of varied allometric equationsin predicting biomass with small footprint lidar systems.Since both sets of allometric equations employ DBH as input,and the regional allometric equations use one more additional variable(i.e., height), the difference between biomass density at theplot level mainly reflects the aggregated variation of height for allindividual trees in a plot. As a result, the comparison of AGB usingthose two sets of allometric equations shows that tree heights inour study area may generally be higher than the average heights ofthe same species group across the country.In models with reference above ground biomass calculated fromregional biomass equations, the integration of a simplified, empiricalrelationship between DBH and height generally improved theperformance of the regression models. Its utility can be explainedby the fact lidar-derived variables are directly related to height,while reference AGBs calculated from either Jenkins allometricequations or regional allometric equations share a common input,DBH. The simplified transformation from height metrics to volumetricmetrics made the association between reference AGBs and
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