Error propagation and scaling for tropical forest biomass estimates

Abstract

The above-ground biomass (AGB) of tropical forests is a crucial variable for ecologists, biogeochemists, foresters and policymakers. Tree inventories are an efficient way of assessing forest carbon stocks and emissions to the atmosphere during deforestation. To make correct inferences about long-term changes in biomass stocks, it is essential to know the uncertainty associated with AGB estimates, yet this uncertainty is rarely evaluated carefully. Here, we quantify four types of uncertainty that could lead to statistical error in AGB estimates: (i) error due to tree measurement; (ii) error due to the choice of an allometric model relating AGB to other tree dimensions; (iii) sampling uncertainty, related to the size of the study plot; (iv) representativeness of a network of small plots across a vast forest landscape. In previous studies, these sources of error were reported but rarely integrated into a consistent framework. We estimate all four terms in a 50 hectare (ha, where 1 ha = 10(4) m2) plot on Barro Colorado Island, Panama, and in a network of 1 ha plots scattered across central Panama. We find that the most important source of error is currently related to the choice of the allometric model. More work should be devoted to improving the predictive power of allometric models for biomass.

Densidade da madeira de árvores em savanas do norte da Amazônia brasileira

“…Esse padrão é o mesmo observado em outros ecossistemas abertos da Amazônia (Barbosa e Ferreira 2004), embora resultados discordantes possam ser relatados para DM de espécies florestais com DAP > 10 cm (Chave et al 2004). De qualquer forma, nossos valores médios de densidade da madeira, tomando como base a proporção de biomassa entre as diferentes classes de diâmetro, indicam ser mais realistas para estimativas de estoques de carbono arbóreo.…”

Assessment of Aboveground Woody Biomass Dynamics Using Terrestrial Laser Scanner and L-Band ALOS PALSAR Data in South African Savanna

“…Reliable allometric equations for AGB estimation in this region are few or poorly documented. This is partly attributed to the tedious work involved in the harvesting and weighing of tree species within an ecosystem, with large financial and time costs [5,6]. Hence, most allometric equations do not take into consideration the differences in tree species associated with variations in ecological regions.…”

“…However, the associated errors, mainly emanating from sampling procedures and errors associated with the allometric models, need to be taken into consideration. Until 2005, most allometries considered only the relationship between biomass, plant diameter, and wood specific density [5,6,8], leaving out the height variable. Examples of such allometries are presented in [6] where a linear relationship between AGB and log-transformed trees with D >33 cm in Kruger National Park (KNP) was derived, with 16% and 12% errors for woody and leaf biomass, respectively.…”

“…Allometric models are developed based on relationships between biomass and tree characteristics such as height and basal diameter [7]. Such relationships are therefore prone to errors associated with models used in their development [5,8]. Forest impenetrability, and time and financial constraints, have led to localized field inventories with few tree species [9], which are not representative over large areas [3,10].…”

REDD+ readiness implications for Sri Lanka in terms of reducing deforestation

“…Third, more sample plots in dry monsoon forests would be needed to reach the desired precision level. The literature also indicates that the choice of allometric equation can result in errors exceeding 20% of above-ground biomass and even more when large trees are abundant (Chave et al, 2004;Kirby and Potvin, 2007). Table 2 also compares our results with those of four other surveys, which should be regarded as Tier 1 information estimates of biomass carbon specifically for Sri Lankan forests.…”