Predicting total and component biomass of Chinese fir using a forecast combination method

Zhang, X.; Cao, Quang V.; Xiang, Congwei; Duan, Aiguo; Zhang, J.

doi:10.3832/ifor2243-010

Cited by 14 publications

(14 citation statements)

References 21 publications

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“…In the context of global climate change, the quantification of biomass production in community forests is necessary as an indicator to assess carbon storage and forest productivity (Zhang et al 2017). Unfortunately, the direct measurement of tree biomass in community forests using a destructive method is expensive and timeconsuming.…”

Section: Introductionmentioning

confidence: 99%

Allometric equations for estimating biomass of community forest tree species in Madiun, Indonesia

et al. 2020

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Abstract. Wirabuana PYAP, Setiahadi R, Sadono R, Lukito M, Martono DS, Matatula J. 2020. Allometric equations for estimating biomass of community forest tree species in Madiun, Indonesia. Biodiversitas 21: 4291-4300. The capability of community forests for offsetting carbon emissions highly depends on their biomass production. Unfortunately, the measurement of tree biomass in community forests using a destructive method is expensive and time-consuming. It is also almost impossible to conduct this method for all trees in the observation area. Therefore, the development of allometric equations is essential to support tree biomass estimation in community forests. This study was designed to construct specific models for predicting individual tree biomass in community forests, located in Madiun, Indonesia. We destructively sampled approximately 120 trees from four different species (30 trees for each species), i.e., Falcataria moluccana, Melia azedarach, Swietenia macrophylla, and Tectona grandis. For every tree sample, the measurement of biomass was conducted in each tree’s component, namely roots, stem, branches, and leaves. The allometric equations were developed with regression analysis using predictor variables, like diameter at breast height (D), squared diameter at breast height combined with tree height (D2H), as well as D and H separately. Results found that for four species, the mean biomass in the stem was 50.3%, followed by branches 25.4%, roots 15.9%, and leaves 8.3%. The best equation for estimating biomass in every component and total of four species was different. However, our study showed that the equation lnŶ = -3.037 + 1.430 lnD + 1.684 was reliable to estimate total individual tree biomass of four species in the surveyed area since this model had accuracy of 90.8%. Referring to these findings, we recommended the utilization of an allometric equation as an alternative method for facilitating more efficient biomass measurement in the community forests.

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Section: Introductionmentioning

confidence: 99%

Allometric equations for estimating biomass of community forest tree species in Madiun, Indonesia

et al. 2020

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“…In the context of global climate change, quantifying and estimating the above-ground biomass (AGB) and C storage in forest ecosystems using tree biomass models are necessary [7,8]. The estimation of forest ecosystem biomass, including the biomass of tree components, such as stems, branches, foliage, and roots, on local, regional and national scales is essential for determining C storage and forest productivity [9,10]. The biomass and carbon stock changes estimated by the allometric equations are important for assessing the mitigation effect of forests on global climate change, and predicting the potential for C sequestration and emission reduction activities such as tree planting, protecting forests from wildfire and insect or disease outbreak, etc.…”

Section: Introductionmentioning

confidence: 99%

Allometric Equations for Estimating the Above-Ground Biomass of Five Forest Tree Species in Khangai, Mongolia

Altanzagas

Luo

Altansukh

et al. 2019

Forests

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Understanding the contribution of forest ecosystems to regulating greenhouse gas emissions and maintaining the atmospheric CO2 balance requires the accurate quantification of above-ground biomass (AGB) at the individual tree species level. The main objective of this study was to develop species-specific allometric equations for the total AGB and various biomass components, including stem, branch, and foliage biomass in Khangai region, northern Mongolia. We destructively sampled a total of 183 trees of five species (22–74 trees per species), including Siberian stone pine (Pinus sibirica Du Tour.), Asian white birch (Betula platyphylla Sukacz.), Mongolian poplar (Populus suaveolens Fisch.), Siberian spruce (Picea obovata Ldb.), and Siberian larch (Larix sibirica Ldb.), across this region. The results showed that for the five species, the average biomass proportion for the stems was 75%, followed by branches at 20% and foliage at 5%. The species-specific component and total AGB models for the Khangai region were developed using tree diameter at breast height (D) and D² and tree height (H) combined ( D 2 H ); and both D and H were used as independent variables. The best allometric model was lnŶ = lna + b × lnD + c × lnH for the various components and total AGB of B. platyphylla and L. sibirica, for the stems and total AGB of P. suaveolens, and for the stem and branch biomass of P. obovata. The equation lnŶ = lna + b × ln( D 2 × H ) was best for the various components and total AGB of P. sibirica, for the branch and foliage biomass of P. suaveolens, and for AGB of P. obovata. The equation lnŶ = lna + b × ln(D) was best only for the foliage biomass of P. obovata. Our results highlight that developing species-specific tree AGB models is very important for accurately estimating the biomass in the Khangai forest region of Mongolia. Our biomass models will be used at the tree level inventories with sample plots in the Khangai forest region.

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“…Aun cuando se ha observado que el Dn es la principal variable independiente para estimar la biomasa total y por componente estructural del árbol (Forrester et al, 2017), en este estudio, la inclusión de la At incrementó la precisión en la estimación de la biomasa en las seis especies muestreadas, aunque en el caso de follaje, la inclusión de esta última variable predictora, además del Dn, resultó en menor calificación de los parámetros de bondad de ajuste. Trabajos similares han señalado la necesidad de incluir la At para evitar sobreestimaciones de biomasa principalmente en árboles de grandes dimensiones (Feldpausch et al, 2012;Zhang et al, 2017).…”

Section: Discussionunclassified

“…simultaneas (Parresol, 2001;Bi, Turner y Lambert, 2004;Zhang et al, 2017). El ajuste simultaneo se realizó mediante el método de regresión iterativa aparentemente no relacionadas (Iterated Seemingly Unrelated Regression, ITSUR), utilizando el procedimiento MODEL del paquete estadístico de SAS/ETS ® ver.…”

Section: Modelo Ecuaciónunclassified

Ecuaciones alométricas para estimar biomasa en especies de encino en Guanajuato, México

Cortés-Sánchez

Ángeles‐Pérez

Posadas

et al. 2019

MYB

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Los bosques de encino son importantes en el equilibrio de los ecosistemas donde estos cohabitan, ya que son un grupo de especies con mayor capacidad para fijar y almacenar carbono atmosférico. El objetivo de este trabajo fue desarrollar ecuaciones alométricas para estimar biomasa aérea de seis especies de encino (Quercus rugosa, Q. obtusata, Q. sideroxyla, Q. coccolobifolia, Q. laurina y Q. candicans) con un muestreo destructivo de 165 árboles. Se ajustaron ecuaciones para estimar biomasa por componentes estructurales (fuste, ramas y follaje), y de forma aditiva se estimó la biomasa total, así como una ecuación generalizada para las seis especies, y ecuaciones para estas divididas en dos grupos. Para mejorar la capacidad predictiva de las ecuaciones se ajustaron modelos que incluyen las variables de diámetro normal, altura total y densidad de la madera. El mayor porcentaje de biomasa se concentró en el fuste con 60.6%, mientras que ramas y follaje representaron 36.1% y 3.28%, respectivamente. Las ecuaciones ajustadas explicaron más de 95% de la variabilidad en la biomasa total del árbol y, comparadas con ecuaciones desarrolladas en otros lugares para el mismo género, presentaron un menor sesgo porcentual. Las ecuaciones alométricas generadas pueden ser utilizadas para la región, así como en bosques con características estructurales y de crecimiento similares a las de las comunidades de encino de la sierra de Guanajuato.

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Predicting total and component biomass of Chinese fir using a forecast combination method

Cited by 14 publications

References 21 publications

Allometric equations for estimating biomass of community forest tree species in Madiun, Indonesia

Allometric equations for estimating biomass of community forest tree species in Madiun, Indonesia

Allometric Equations for Estimating the Above-Ground Biomass of Five Forest Tree Species in Khangai, Mongolia

Ecuaciones alométricas para estimar biomasa en especies de encino en Guanajuato, México

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