Error propagation in biomass estimation in tropical forests

Abstract

Summary1. Reliable above-ground biomass (AGB) estimates are required for studies of carbon fluxes and stocks. However, there is a huge lack of knowledge concerning the precision of AGB estimates and the sources of this uncertainty. At the tree level, the tree height is predicted using the tree diameter at breast height (DBH) and a height sub-model. The wood-specific gravity (WSG) is predicted with taxonomic information and a WSG sub-model. The tree mass is predicted using the predicted height, the predicted WSG and the biomass sub-model. 2. Our models were inferred with Bayesian methods and the uncertainty propagated with a Monte Carlo scheme. The uncertainties in the predictions of tree height, tree WSG and tree mass were neglected sequentially to quantify their contributions to the uncertainty in AGB. The study was conducted in French Guiana where long-term research on forest ecosystems provided an outstanding data collection on tree height, tree dynamics, tree mass and species WSG. 3. We found that the uncertainty in the AGB estimates was found to derive primarily from the biomass sub-model. The models used to predict the tree heights and WSG contributed negligible uncertainty to the final estimate. 4. Considering our results, a poor knowledge of WSG and the height-diameter relationship does not increase the uncertainty in AGB estimates. However, it could lead to bias. Therefore, models and databases should be used with care. 5. This study provides a methodological framework that can be broadly used by foresters and plant ecologist. It provides the accurate confidence intervals associated with forest AGB estimates made from inventory data. When estimating region-scale AGB values (through spatial interpolation, spatial modelling or satellite signal treatment), the uncertainty of the forest AGB value in the reference forest plots has to be taken in account. We believe that in the light of the Reducing Emissions from Deforestation and Degradation debate, our method is a crucial step in monitoring carbon stocks and their spatio-temporal evolution.

Influence of wood density in tree-ring-based annual productivity assessments and its errors in Norway spruce

“…Recent studies have brought evidence of such compensation, proving that neglecting annual wood density fluctuations could lead to substantial errors or bias in estimating the biomass (Molto et al, 2013;Babst et al, 2014a). The errors generated by neglecting the variations in wood density have been considered small compared to those resulting from the volume increment estimation, but to our knowledge, such assumptions have never been tested and the consequences not documented.…”

“…Such chains can be decomposed, and the impact of each step was studied by modelling the steps into a single Markov chain Monte Carlo (MCMC) process (e.g. Molto et al, 2013). Analytical solutions to estimate the biomass estimation error, based e.g.…”

An overview of existing and promising technologies for national forest monitoring

“…Last, but not least, a significant part of the uncertainties associated with biomass or volume estimation is due to their calculation from allometric equations (Molto et al 2013;Chave et al 2014). Here, we show that some new technologies, such as terrestrial LiDAR or stereo-photogrammetry may be a promising way to improve significantly tree volume or biomass estimation without any destructive measurement.…”

Recommendations for the use of tree models to estimate national forest biomass and assess their uncertainty

“…This can be costly and requires additional fieldwork and coordination with local authorities. The second method uses Monte Carlo methods or Bootstrapping (Molto et al 2013). These statistical methods are not field intensive but require advanced skills not readily available in all countries.…”