A solution for reducing high bias in estimates of stored carbon in tropical forests (aboveground biomass)
H. Arellano-P., J. O. Rangel-Ch

TL;DR
This paper introduces a nondestructive, neural network-based method for estimating tropical forest aboveground carbon, addressing biases in traditional allometric models and improving accuracy across diverse plant architectures.
Contribution
The study presents a novel neural network approach that replaces destructive sampling for carbon estimation, accounting for plant diversity and architecture in tropical forests.
Findings
Carbon in tropical forests could reach 724 Pg C
The method improves accuracy over traditional allometric formulas
Reevaluation of climate models is necessary based on new estimates
Abstract
A nondestructive method for estimating the amount of carbon stored by individuals, communities, vegetation types, and coverages, as well as their volume and aboveground biomass, is presented. This methodology is based on information on carbon stocks obtained through three-dimensional analysis of tree architecture and artificial neural networks. This technique accurately incorporates the diversity of plant forms measured in plots, transects, and relev\'es. Stored carbon in any vegetation type is usually calculated as half the biomass of sampled individuals, estimated with allometric formulas. The most complete of these formulas incorporate diameter, height, and specific gravity of wood but do not consider the variation in carbon stored in different organs or different species, nor do they include information on the wide array of architectures present in different plant communities. To…
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Taxonomy
TopicsForest ecology and management · Plant Water Relations and Carbon Dynamics · Ecology and Vegetation Dynamics Studies
