Using volume-weighted average wood specific gravity of trees reduces bias in aboveground biomass predictions from forest volume data

Sagang, Le Bienfaiteur Takougoum; Takoudjou, Stéphane Momo; Rossi, Vivien; Fonton, Noël; Libalah, Moses B.; Kamdem, Narcisse Guy; Nguetsop, Victor François; Sonké, Bonaventure; Ploton, Pierre; Barbier, Nicolas

doi:10.1016/j.foreco.2018.04.054

Cited by 22 publications

(26 citation statements)

References 34 publications

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“…S1B). This relationship is similar to that previously reported by Hietz et al 37 in the case of radial WD gradients [Panamanian moist forest and Ecuadorian rain forest, 304 species] and Sagang et al 41 for vertical gradients [semideciduous forest of Taxonomic, geographical and functional grouping. Using PCA scores to describe the relative vertical profiles in the WD of individual trees, we examined whether these profiles were clustered taxonomically, geographically or functionally.…”

Section: Characterizing Wd Vertical Profilessupporting

confidence: 91%

“…The implications for stand-level error propagation in AGB estimation were not specifically studied here, but decreasing vertical variation in WD tended to be more frequent, explaining the overall mean positive bias obtained across our destructive (c. 9%) and terrestrial LiDAR (c. 11%) datasets and the 9% overestimation at the stand level found by Sagang et al 41 with a subset of our data. Since the vertical profile is correlated with species mean WD, and the latter value is close to 0.6 g cm −3 in wet tropical forests overall 44 , we may expect dominance of decreasing vertical WD patterns (see Fig.…”

Section: Characterizing Wd Vertical Profilesmentioning

confidence: 45%

“…For each tree, we weighted each compartment's WD by its volume to calculate the compartment volume-weighted WD, which were summed to obtain the tree level VWWD (see Methods). We then developed linear prediction models for VWWD using easily accessible variables characterizing WD (i.e., WD Stu , WD GWD ) and tree structure (tree DBH and the stem morphology index, Sm, used in Sagang et al 41 ). Prediction models based on WD alone explained most of the variance in tree VWWD (R² = 0.87 for m 1 based on WD Stu and 0.78 for m 4 based on species average WD GWD , Table 2).…”

Section: Characterizing Wd Vertical Profilesmentioning

confidence: 99%

“…Indeed, primary (vertical) growth precedes secondary lateral growth, and outer wood will exhibit the same age as wood closer to the pith higher up along the trunk or branch. However, there is currently little to no available information on the vertical variation in WD, particularly in highly diverse tropical rainforests [40][41][42] .…”

mentioning

confidence: 99%

“…These samples rarely if ever account for variation in WD within trees 43 . Since TLS tree volume is directly multiplied by WD to obtain an AGB estimate, any bias in WD will be directly propagated in AGB estimates (average 9% overestimation of AGB estimates at the scale of 1-ha forest stands using tree basal WD, see Sagang et al 41 ). As it is logistically challenging to sample WD and its vertical variation for each tree during forest inventories or LiDAR scanning campaigns, there is a critical need for a better understanding of vertical variations in WD across a wide range of tree species to improve AGB estimates.…”

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confidence: 99%

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Leveraging Signatures of Plant Functional Strategies in Wood Density Profiles of African Trees to Correct Mass Estimations From Terrestrial Laser Data

Takoudjou¹,

Ploton²,

Martin-Ducup³

et al. 2020

Sci Rep

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Wood density (WD) relates to important tree functions such as stem mechanics and resistance against pathogens. this functional trait can exhibit high intraindividual variability both radially and vertically. With the rise of LiDAR-based methodologies allowing nondestructive tree volume estimations, failing to account for WD variations related to tree function and biomass investment strategies may lead to large systematic bias in AGB estimations. Here, we use a unique destructive dataset from 822 trees belonging to 51 phylogenetically dispersed tree species harvested across forest types in Central Africa to determine vertical gradients in WD from the stump to the branch tips, how these gradients relate to regeneration guilds and their implications for AGB estimations. We find that decreasing WD from the tree base to the branch tips is characteristic of shade-tolerant species, while light-demanding and pioneer species exhibit stationary or increasing vertical trends. Across all species, the WD range is narrower in tree crowns than at the tree base, reflecting more similar physiological and mechanical constraints in the canopy. Vertical gradients in WD induce significant bias (10%) in AGB estimates when using database-derived speciesaverage WD data. However, the correlation between the vertical gradients and basal WD allows the derivation of general correction models. With the ongoing development of remote sensing products providing 3D information for entire trees and forest stands, our findings indicate promising ways to improve greenhouse gas accounting in tropical countries and advance our understanding of adaptive strategies allowing trees to grow and survive in dense rainforests. Terrestrial plants account for 83% of the living carbon on Earth 1 , of which tropical forests are estimated to account for close to half 2 , principally contained within woody plant parts. Tropical forests are therefore becoming a key element in international carbon trading schemes despite obvious difficulties in accurately estimating stocks

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Section: Characterizing Wd Vertical Profilessupporting

confidence: 91%

Section: Characterizing Wd Vertical Profilesmentioning

confidence: 45%

Section: Characterizing Wd Vertical Profilesmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Leveraging Signatures of Plant Functional Strategies in Wood Density Profiles of African Trees to Correct Mass Estimations From Terrestrial Laser Data

Takoudjou¹,

Ploton²,

Martin-Ducup³

et al. 2020

Sci Rep

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Consequences of vertical basic wood density variation on the estimation of aboveground biomass with terrestrial laser scanning

Demol

Calders

Moorthy

et al. 2021

Trees

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Key message Stump-to-tip trends in basic wood density complicate the conversion of tree volume into aboveground biomass. We use 3D tree models from terrestrial laser scanning to obtain tree-level volume-weighted wood density. Abstract Terrestrial laser scanning (TLS) is used to generate realistic 3D tree models that enable a non-destructive way of quantifying tree volume. An accurate value for basic wood density is required to convert tree volume into aboveground biomass (AGB) for forest carbon assessments. However, basic density is characterised by high inter-, intra-species and within-tree variability and a likely source of error in TLS-derived biomass estimates. Here, 31 adult trees of 4 important European timber species (Fagus sylvatica, Larix decidua, Pinus sylvestris, Fraxinus excelsior) were scanned using TLS and then felled for several basic wood density measurements. We derived a reference volume-weighted basic density (ρw) by combining volume from 3D tree models with destructively assessed vertical density profiles. We compared this to basic density retrieved from a single basal disc over bark (ρbd), two perpendicular pith-to-bark increment cores at breast height (ρic), and sourcing the best available local basic wood density from publications. Stump-to-tip trends in basic wood density caused site-average woody AGB estimation biases ranging from −3.3 to + 7.8% when using ρbd and from −4.1 to + 11.8% when using ρic. Basic wood density from publications was in general a bad predictor for ρw as the bias ranged from −3.2 to + 17.2%, with little consistency across different density repositories. Overall, our density-attributed biases were similar to several recently reported biases in TLS-derived tree volume, leading to potentially large compound errors in biomass assessments with TLS if patterns of vertical basic wood density variation are not properly accounted for.

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Accuracy differences in aboveground woody biomass estimation with terrestrial laser scanning for trees in urban and rural forests and different leaf conditions

Arseniou

MacFarlane

Calders

et al. 2023

Trees

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We want to acknowledge the Michigan State University W.J. Beal Botanical Gardens and Campus Arboretum (Frank W. Telewski, and Jeffrey Wilson) and the Michigan State University Department of Infrastructure, Planning and Facilities (Jerry Wahl) who assisted with the urban data collection. Furthermore, we want to thank the research staff of Harvard Forest (David Orwig, Audrey Barker Plotkin), Alan Strahler and UNAVCO for coordinating the rural forest data collection. We would also like to thank Samuel Clark, Garret Dettmann and the field group of Michigan State University, Jereme Frank (University of Maine), David Walker and Phil Radtke (Virginia Tech University) for their contribution to the collection of destructive tree measurements in Harvard Forest. Finally, we want to acknowledge the contribution of Matheus 2 B. Vicari (University College London) for processing the point clouds of the evergreen species scanned by K. Calders in Harvard Forest.Key Message: TLS data can be converted to reliable woody AGB estimates, but estimation quality is influenced by growing environment, leaf condition, and variation in tree density affecting volume to mass conversion.

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Using volume-weighted average wood specific gravity of trees reduces bias in aboveground biomass predictions from forest volume data

Cited by 22 publications

References 34 publications

Leveraging Signatures of Plant Functional Strategies in Wood Density Profiles of African Trees to Correct Mass Estimations From Terrestrial Laser Data

Leveraging Signatures of Plant Functional Strategies in Wood Density Profiles of African Trees to Correct Mass Estimations From Terrestrial Laser Data

Consequences of vertical basic wood density variation on the estimation of aboveground biomass with terrestrial laser scanning

Accuracy differences in aboveground woody biomass estimation with terrestrial laser scanning for trees in urban and rural forests and different leaf conditions

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