Improving aboveground biomass estimates by taking into account density variations between tree components

Billard, Antoine; Bauer, Rodolphe; Mothe, Frédéric; Jonard, Mathieu; Colin, Francis; Longuetaud, Fleur

doi:10.1007/s13595-020-00999-1

Cited by 11 publications

(3 citation statements)

References 46 publications

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“…However, our research found that the proportion of root biomass increased with increasing age and then decreased gradually after the trees matured. Generally, the part below the ground from the cutting place is treated as roots, so the root usually contains part of the stem, and the root has more knots and higher density [61], which may be the reason why the proportion of root biomass increases with age.…”

Section: Discussionmentioning

confidence: 99%

Allocation Patterns and Temporal Dynamics of Chinese Fir Biomass in Hunan Province, China

Deng

et al. 2023

Forests

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How trees allocate their biomass among different components has important implications for their survival and growth and ecosystem carbon cycling. Data on the distribution pattern and dynamics of tree biomass are essential for fully exploiting forest carbon sequestration potential and achieving the goal of carbon neutralization. However, there has not been enough research to-date on tree biomass spatial allocation and temporal dynamics in different site qualities at specific tree species scales. This study aimed to evaluate the biomass allocation patterns within tree components of Chinese fir and to examine how they are affected by tree age and site quality. A total of 87 trees were destructively sampled and measured for stem, branch, leaf, bark and root biomass. The biomass proportion difference of tree components in different age stages (8-40 years) was analysed, and the influence process of tree age and site quality on biomass allocation was examined. Our results indicate that the biomass allocation varied with tree age and was also affected by site quality. Stem biomass accounted for the largest proportion of total tree biomass, followed by leaf, root, branch and bark biomass in young forests, and it was followed by root, bark, branch and leaf biomass in other age groups. The biomass proportion of each component all nonlinearly changed with tree age. The proportion of stem biomass increased with increasing tree age, and the biomass proportion of branches and leaves decreased with increasing tree age. The proportion of root biomass first increased and then decreased with tree age, while the bark biomass proportion first decreased and then increased with increasing tree age. Site quality had a positive effect on the biomass proportion of stems but a negative effect on the biomass proportion of branches and bark. The interaction of tree age and site quality also had a significant effect on the proportion of stem biomass as well as root biomass. Therefore, to obtain accurate estimates of Chinese fir forest biomass and carbon stocks, age-specific changes and the influence of site conditions on it need to be considered.

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Section: Discussionmentioning

confidence: 99%

Allocation Patterns and Temporal Dynamics of Chinese Fir Biomass in Hunan Province, China

Deng

et al. 2023

Forests

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“…However, economic improvement can only occur if the selling price remains the same and the cost advantage will not be passed on to RCF. It also must be noted that a change in wood characteristics could affect carbon efficiency (Billard et al, 2020) that consequently has an impact on the cost. Hence, the decision-maker is advised to carefully assess the potential trade-offs between the cost and carbon efficiency reduction.…”

Section: Interaction Between Costs and Cost Growth Ratementioning

confidence: 99%

Optimal timing of multiple investment decisions in a wood value chain: A real options approach

Tschulkow

Compernolle

Passel

2021

Journal of Environmental Management

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“…Vertical variation along the stem must be considered when estimating forest biomass because not considering this source of systematic bias results in significant errors in estimates for vast areas, such as the Amazon [3,17,[24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Wood Basic Density in Large Trees: Impacts on Biomass Estimates in the Southwestern Brazilian Amazon

Romero,

de Nazaré Oliveira Novais,

Jacovine

et al. 2024

Forests

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Wood basic density (WD) plays a crucial role in estimating forest biomass; moreover, improving wood-density estimates is needed to reduce uncertainties in the estimates of tropical forest biomass and carbon stocks. Understanding variations in this density along the tree trunk and its impact on biomass estimates is underexplored in the literature. In this study, the vertical variability of WD was assessed along the stems of large trees that had a diameter at breast height (DBH) ≥ 50 cm from a dense ombrophilous forest on terra firme (unflooded uplands) in Acre, Brazil. A total of 224 trees were sampled, including 20 species, classified by wood type. The average WD along the stem was determined by the ratio of oven-dry mass to saturated volume. Five models were tested, including linear and nonlinear ones, to fit equations for WD, selecting the best model. The variation among species was notable, ranging from 0.288 g cm−3 (Ceiba pentandra, L., Gaertn) to 0.825 g cm−3 (Handroanthus serratifolius, Vahl., S. Grose), with an average of 0.560 g cm−3 (±0.164, standard deviation). Significant variation was observed among individuals, such as in Schizolobium parahyba var. amazonicum (H. ex D.), which ranged from 0.305 to 0.655 g cm−3. WD was classified as low (≤0.40 g cm−3), medium (0.41–0.60 g cm−3), and high (≥0.61 g cm−3). The variability in WD along the stem differs by wood type. In trees with low-density wood, density shows irregular variation but tends to increase along the stem, whereas it decreases in species with medium- and high-density wood. The variation in WD along the stem can lead to underestimations or overestimations, not only in individual trees and species but also in total stocks when estimating forest biomass. Not considering this systematic bias results in significant errors, especially in extrapolations to vast areas, such as the Amazon.

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Improving aboveground biomass estimates by taking into account density variations between tree components

Cited by 11 publications

References 46 publications

Allocation Patterns and Temporal Dynamics of Chinese Fir Biomass in Hunan Province, China

Allocation Patterns and Temporal Dynamics of Chinese Fir Biomass in Hunan Province, China

Optimal timing of multiple investment decisions in a wood value chain: A real options approach

Wood Basic Density in Large Trees: Impacts on Biomass Estimates in the Southwestern Brazilian Amazon

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