Aboveground tree biomass of Araucaria araucana in southern Chile: measurements and multi-objective optimization of biomass models
Abstract
Estimating carbon stocks in wooded systems is crucial to quantify national
greenhouse gas balance estimates. However, inaccurate estimates are likely
due to the divergent architecture of many species. The monkey puzzle tree
Araucaria araucana, with its umbrella-like architecture is a vivid example.
This species, often found in monodominant stands at high elevations, is the
greatest carbon reservoir in the landscape, hence estimating its carbon storage
is crucial. To provide the necessary basis for these estimations, we documented
the variation in basic density and moisture content along the stem
profile, identified the most suitable biomass estimation models, and quantified
biomass allocation for three age ranges. We measured, felled, weighed, and
separated trees into three categories: stem wood, stem bark, and foliage
(branches + scaly leaves). The log-linear form of the simple allometric equation
Y = aXb, based on diameter at breast height as the explanatory variable,
covered a large part of the variation and showed good cross-validation performance
(>0.96). Models using more covariates achieved lower absolute errors,
but the estimation of the additional model parameters was associated with
greater uncertainty. A multi-objective model comparison revealed that the
best additional covariate to further improve biomass estimation was total tree
height. The mean absolute percentage error was 9.8% for the total aboveground
biomass, 8% for stem wood, 12% for stem bark and 24% for foliage.
Changes in biomass distribution among tree components were related to age.
For older trees, there was a relative increase in stem wood, a decreased proportion
of foliage, but no change in stem bark. The proportion of stem bark
biomass is similar to that of Araucaria angustifolia, but higher than in other
conifers and most trees in general. Our results provide key properties for A.
araucana and general guidance for the selection of easily-measurable variables
allowing for excellent predictive power for local biomass estimation.
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