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

Henry, Matieu; Cifuentes-Jara, Miguel; Réjou‐Méchain, Maxime; Piotto, Daniel; Fuentes, José María Michel; Wayson, Craig; Guier, Federico Alice; Lombis, Héctor Castañeda; Castellanos, Edwin; Lara, Ruby Cuenca; Rojas, Kelvin Cueva; Pasquel, Jhon del Águila; Montoya, Álvaro Duque; Vega, Javier Fernández; Galo, Abner Jiménez; López, Omar R.; Marklund, Lars Gunnar; Milla, Fabián; Cahidez, José de Jesús Návar; Malavassi, Edgar Ortiz; Pérez, J. A. López; Zea, Carla Ramírez; García, Luis Rangel; Rubilar, Rafael; Sanquetta, Carlos Roberto; Scott, Charles T.; Westfall, James A.; Zapata-Cuartas, Mauricio; Saint‐André, Laurent

doi:10.1007/s13595-015-0465-x

Cited by 24 publications

(17 citation statements)

References 36 publications

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“…McRoberts and Westfall (2015), Henry et al (2015), Temesgen et a.l (2015), and Picard et al (2014) distinguish four sources of errors (surrogate of uncertainty) in model prediction: (1) model misspecification (also known as statistical model; i.e. : error due to model selection (Cunia 1986a)), (2) uncertainty in the values of independent variables, (3) uncertainty in the model parameter estimates, and (4) residual variability around model prediction.…”

Section: Tree Component Biomassmentioning

confidence: 99%

Live above- and belowground biomass of a Mozambican evergreen forest: a comparison of estimates based on regression equations and biomass expansion factors

Magalhães

2015

For. Ecosyst.

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Background: Biomass regression equations are claimed to yield the most accurate biomass estimates than biomass expansion factors (BEFs). Yet, national and regional biomass estimates are generally calculated based on BEFs, especially when using national forest inventory data. Comparison of regression equations based and BEF-based biomass estimates are scarce. Thus, this study was intended to compare these two commonly used methods for estimating tree and forest biomass with regard to errors and biases. Methods: The data were collected in 2012 and 2014. In 2012, a two-phase sampling design was used to fit tree component biomass regression models and determine tree BEFs. In 2014, additional trees were felled outside sampling plots to estimate the biases associated with regression equation based and BEF-based biomass estimates; those estimates were then compared in terms of the following sources of error: plot selection and variability, biomass model, model parameter estimates, and residual variability around model prediction. Results: The regression equation based below-, aboveground and whole tree biomass stocks were, approximately, 7.7, 8.5 and 8.3 % larger than the BEF-based ones. For the whole tree biomass stock, the percentage of the total error attributed to first phase (random plot selection and variability) was 90 and 88 % for regression-and BEF-based estimates, respectively, being the remaining attributed to biomass models (regression and BEF models, respectively). The percent bias of regression equation based and BEF-based biomass estimates for the whole tree biomass stock were −2.7 and 5.4 %, respectively. The errors due to model parameter estimates, those due to residual variability around model prediction, and the percentage of the total error attributed to biomass model were larger for BEF models (than for regression models), except for stem and stem wood components. Conclusions: The regression equation based biomass stocks were found to be slightly larger, associated with relatively smaller errors and least biased than the BEF-based ones. For stem and stem wood, the percentages of their total errors (as total variance) attributed to BEF model were considerably smaller than those attributed to biomass regression equations.

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Section: Tree Component Biomassmentioning

confidence: 99%

Live above- and belowground biomass of a Mozambican evergreen forest: a comparison of estimates based on regression equations and biomass expansion factors

Magalhães

2015

For. Ecosyst.

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“…Generally, the volume-based method is called the indirect method (Somogyi et al 2007) which is a realistic method based on building the relationships between volume and biomass (Brown and Lugo 1984;Brown 1997). These relationships have been expressed in different definitions, such as BEFs and allometric equations of the biomass with volume predictor(s) (Henry et al 2015). These equations can be derived empirically from tree sampling as well as from estimates per unit area.…”

Section: National Forest Biomass and The Second Uncertaintymentioning

confidence: 99%

“…Using volume-based methods would lead to uncertainty in national FCS estimates (Jenkins et al 2003;Henry et al 2015), and on certain conditions, could generate apparent inconsistency between the estimates for the same area (Ni 2013). This is particularly noticeable for monitoring dynamics of the biomass in those countries, in which the promotions of afforestation and reforestation programs have continued for decades and increasingly affect world FCS.…”

Section: Introductionmentioning

confidence: 99%

Re-estimating the changes and ranges of forest biomass carbon in China during the past 40 years

et al. 2019

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Background: In recent decades the future of global forests has been a matter of increasing concern, particularly in relation to the threat of forest ecosystem responses under potential climate change. To the future predictions of these responses, the current forest biomass carbon storage (FCS) should first be clarified as much as possible, especially at national scales. However, few studies have introduced how to verify an FCS estimate by delimiting the reasonable ranges. This paper addresses an estimation of national FCS and its verification using two-step process to narrow the uncertainty. Our study focuses on a methodology for reducing the uncertainty resulted by converting from growing stock volume to above-and below-ground biomass (AB biomass), so as to eliminate the significant bias in national scale estimations. Methods: We recommend splitting the estimation into two parts, one part for stem and the other part for AB biomass to preclude possible significant bias. Our method estimates the stem biomass from volume and wood density (WD), and converts the AB biomass from stem biomass by using allometric relationships. Results: Based on the presented two-step process, the estimation of China's FCS is performed as an example to explicate how to infer the ranges of national FCS. The experimental results demonstrate a national FCS estimation within the reasonable ranges (relative errors: + 4.46% and − 4.44%), e.g., 5.6-6.1 PgC for China's forest ecosystem at the beginning of the 2010s. These ranges are less than 0.52 PgC for confirming each FCS estimate of different periods during the last 40 years. In addition, our results suggest the upper-limits by specifying a highly impractical value of WD (0.7 t•m − 3 ) on the national scale. As a control reference, this value decides what estimate is impossible to achieve for the FCS estimates.Conclusions: Presented methodological analysis highlights the possibility to determine a range that the true value could be located in. The two-step process will help to verify national FCS and also to reduce uncertainty in related studies. While the true value of national FCS is immeasurable, our work should motivate future studies that explore new estimations to approach the true value by narrowing the uncertainty in FCS estimations on national and global scales.

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“…Além de produtos, geram serviços ambientais vitais como por exemplo, os ciclos de ar e água, clima, polinização, e ciclagem de nutrientes (HENRY et al, 2014), entre os quais a capacidade de armazenar carbono por meio da fixação biológica via fotossíntese.…”

Section: Introductionunclassified

EQUAÇÕES DE BIOMASSA PARA AROEIRA-PIMENTEIRA (Schinus terebinthifolius RADDI)

Sanquetta¹,

Sanquetta²,

Durigan³

et al. 2015

Enciclopédia Biosfera

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RESUMOEste estudo objetivou avaliar quatro modelos de regressão para estimar a biomassa seca aérea de Schinus terebinthifolius. Os dados foram coletados no município de Pinhais, PR, em um plantio estabelecido em 2003. Mensurou-se diretamente o dap (diâmetro à altura do peito) e a ht (altura total) de 30 árvores, que após o corte e a derrubada foram objeto de quantificação de biomassa verde pelo método direto. Foram selecionadas porções amostrais representativas da biomassa, com o objetivo de realizar as determinações da biomassa seca, estas foram levadas à em estufa a 70 o C até peso constante para obter a biomassa seca aérea de cada indivíduo (ba). Foram testados quatro modelos matemáticos, a saber: Kopezky-Gehrhardt, Spurr, Husch e Schumacher-Hall. Para os modelos logarítmicos aplicou-se o Fator de Correção de Meyer. As equações foram obtidas pelo método dos mínimos quadrados ordinários. Foram utilizados três critérios de seleção de modelos para o julgamento do melhor entre eles: coeficiente de determinação ajustado (R 2 aj. ) e erro padrão da estimativa em porcentagem (S yx% ) e a análise gráfica dos resíduos. O conjunto de dados avaliado apresentou dap médio de 8,02±1,75 cm e altura total média de 7,52±0,65, enquanto a biomassa aérea seca variou de 16,38±7,94 kg. A equação selecionada foi ba = -0,325050 + 0,261156. dap 2 , correspondente ao modelo de Kopezky-Gehrhardt. Os valores observados para R 2 aj . e S yx% do modelo selecionado foram 0,781 e 25,54%, respectivamente. Todos os modelos geraram estimativas satisfatórias, mas que o modelo de Kopezky-Gehrhardt apresentou leve superioridade em relação aos demais e que esse modelo é mais simples por requerer apenas o dap como variável independente, sendo recomendado para uso. PALAVRAS-CHAVE: ajuste, análise de resíduos, modelagem, massa seca, regressão.

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

Abstract: Abstract& Key message Three options are proposed to improve the accuracy of national forest biomass estimates and decrease the uncertainty related to tree model selection depending on available data and national contexts.

Cited by 24 publications

References 36 publications

Live above- and belowground biomass of a Mozambican evergreen forest: a comparison of estimates based on regression equations and biomass expansion factors

Live above- and belowground biomass of a Mozambican evergreen forest: a comparison of estimates based on regression equations and biomass expansion factors

Re-estimating the changes and ranges of forest biomass carbon in China during the past 40 years

EQUAÇÕES DE BIOMASSA PARA AROEIRA-PIMENTEIRA (Schinus terebinthifolius RADDI)

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