Conventional tree height–diameter relationships significantly overestimate aboveground carbon stocks in the Central Congo Basin

Kearsley, Elizabeth; Haulleville, Thalès de; Hufkens, Koen; Kidimbu, Alidé; Toirambe, Benjamin; Baert, Geert; Huygens, Dries; Kebede, Yodit; Defourny, Pierre; Bogaert, Jan; Beeckman, Hans; Steppe, Kathy; Boeckx, Pascal; Verbeeck, Hans

doi:10.1038/ncomms3269

Cited by 116 publications

(138 citation statements)

References 45 publications

(70 reference statements)

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“…Based on 13 1-ha plots, Kearsley et al (2013) estimated the aboveground biomass to be 324 ± 40 (95 % CI) tonne ha −1 at Yangambi, significantly different from the estimate of 396 ± 14.3 (95 % CI) tonne ha −1 given by Lewis et al (2013) for central African moist forests. However, neither Kearsley et al nor Lewis et al took account of all sources of errors.…”

Section: Implication For the Confidence Intervals Of Biomass Estimatesmentioning

confidence: 68%

“…Nevertheless, plugging height models into biomass models also means that the errors of the former propagate into the latter (Fortin and DeBlois 2010). The choice of the height model may also strongly influence the biomass estimate, as recently shown by Kearsley et al (2013) whose reevaluation of the height:diameter relationship in central Congo basin led to a lower estimate of carbon stocks.…”

Section: Introductionmentioning

confidence: 98%

“…, μ, a, b, c (Dorisca et al 2011;Fayolle et al 2013;), but this is not even always the case (e.g. Chave et al 2005;Kearsley et al 2013). As often with polynomial regression (on log-or non-transformed data), the estimates of the model coefficients are strongly correlated, and approximating the variance matrix of the estimator of α, β, .…”

Section: Prediction Error Error Due To the Uncertainty On The Model'smentioning

confidence: 99%

“…This data set includes 12 trees from three species. We also used the dbh:height relationship fitted by Kearsley et al (2013) at Yangambi to convert twoentries allometric equations (depending on height and dbh) into single-entry equations (depending on dbh alone). It is a Mitscherlisch equation fitted to 487 trees between 10 and 125 cm dbh, with a residual standard deviation of 4.221 m:…”

Section: Biomass Datamentioning

confidence: 99%

“…Lewis et al (2013) and Kearsley et al (2013) estimated the biomass at the landscape level as if the biomass allometric equation was exact, with no associated model error. On the contrary, Cohen et al (2013) took account of both the sampling error and of the model error, and showed that the latter significantly contributed to the variability of the per hectare biomass estimate at the landscape level.…”

Section: Introductionmentioning

confidence: 99%

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Reducing the error in biomass estimates strongly depends on model selection

Picard

Bosela

Rossi

2014

Annals of Forest Science

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Abstract• Key message Improving the precision of forest biomass estimates requires prioritizing the different sources of errors. In a tropical moist forest in central Africa, the choice of the allometric equation was found to be the main source of error.• Context When estimating the forest biomass at the landscape level using forest inventory data and allometric models, there is a chain of propagation of errors including the measurement errors, the models' prediction error, the error due to the model choice, and the sampling error.• Aims This study aims at comparing the contributions of these different sources of error to the total error, to prioritize them, and improve the precision of biomass estimates. • Methods Using a 9-ha permanent sample plot in a moist forest near Kisangani in the Democratic Republic of Congo and seven competing allometric models, we estimated the contributions of the different sources of error to the total error of the per hectare biomass estimate, for plot sizes ranging from 0.04 to 1 ha.• Results When there was no a priori on which model being the best and for 1-ha plots, the error due to the model choice was the largest source of error (76 % of the total error). Using weights to combine the predictions of the different models into a single ensemble prediction strongly reduced this error.• Conclusion Collecting training data sets on tree biomass at many sites would be needed to improve the precision of forest biomass estimates in central Africa.

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Section: Implication For the Confidence Intervals Of Biomass Estimatesmentioning

confidence: 68%

Section: Introductionmentioning

confidence: 98%

Section: Prediction Error Error Due To the Uncertainty On The Model'smentioning

confidence: 99%

Section: Biomass Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Reducing the error in biomass estimates strongly depends on model selection

Picard

Bosela

Rossi

2014

Annals of Forest Science

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Forest Mapping of the Congo Basin using Synthetic Aperture Radar (SAR)

Wheeler

Rodríguez-Veiga

Balzter

et al. 2017

Earth Observation for Land and Emergency Monitoring

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Landscape‐level validation of allometric relationships for carbon stock estimation reveals bias driven by soil type

Beirne

Miao

Núñez

et al. 2019

Ecological Applications

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Mitigation of climate change depends on accurate estimation and mapping of terrestrial carbon stocks, particularly in carbon dense tropical forests. Allometric equations can be used to robustly estimate biomass of tropical trees, but often require tree height, which is frequently unknown. Researchers and practitioners must, therefore, decide whether to directly measure a subset of tree heights to develop diameter : height (D:H) equations or rely on previously published generic equations. To date, studies comparing the two approaches have been spatially restricted and/or not randomly allocated across the landscape of interest, making the implications of deciding whether or not to measure tree heights difficult to determine. To address this issue, we use inventory data from a systematic-random forest inventory across Gabon (102 forest sites; 42,627 trees, including 7,036 height-measured trees). Using plot-specific models of D:H as a benchmark, we compare the performance of a suite of locally fitted and commonly used generic models (parameterized national, georegional, and pantropical equations) across a variety of scales, and assess which abiotic, anthropogenic, and topographical covariates contribute the most to bias in height estimation. We reveal marked spatial structure in the magnitude and direction of bias in tree height estimation using all generic models, due at least in part to soil type, which compounded to substantial error in site-level AGB estimates (of up to 38% or 150 Mg/ha). However, two generic pantropical models (Chave 2014; Feldpausch 2012) converged to within 2.5% of mean AGB at the landscape scale. Our results suggest that some (not all) pantropical equations can extrapolate AGB across large spatial scales with minimal bias in estimated mean AGB. However, extreme caution must be taken when interpreting the AGB estimates from generic models at the site-level as they fail to capture substantial spatial variation in D:H relationships, which could lead to dramatic under-or over-estimation of site-level carbon stocks. Validated allometric models derived at site-or soil-type-levels may be the best way to reduce such biases arising from landscape-level heterogeneity in D:H model fit in the Afrotropics.

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Conventional tree height–diameter relationships significantly overestimate aboveground carbon stocks in the Central Congo Basin

Cited by 116 publications

References 45 publications

Reducing the error in biomass estimates strongly depends on model selection

Reducing the error in biomass estimates strongly depends on model selection

Forest Mapping of the Congo Basin using Synthetic Aperture Radar (SAR)

Landscape‐level validation of allometric relationships for carbon stock estimation reveals bias driven by soil type

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