Establishment of allometric models and estimation of biomass recovery of swidden cultivation fallows in mixed deciduous forests of the Bago Mountains, Myanmar

Chan, Nyein; Takeda, Satoshi; Suzuki, Reiji; Yamamoto, Sota

doi:10.1016/j.foreco.2013.05.038

Cited by 40 publications

(24 citation statements)

References 16 publications

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“…Others fitted the nonlinear power function (NLR) directly to the biomass data of original scale and believed that nonlinear models provided model fitting as good as the logtransformed models (Parresol 2001;Bi et al 2004;Lambert et al 2005;Chan et al 2013). However, Xiao et al (2011) pointed out that the choice between LR and NLR depends Trees on the distribution of model errors, and provided a likelihood analysis to compare the appropriateness of the two error structures.…”

Section: Discussionmentioning

confidence: 99%

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Developing additive systems of biomass equations for nine hardwood species in Northeast China

Dong

Zhang

2015

Trees

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Key message We developed two additive systems of biomass equations based on diameter and tree height for nine hardwood species by SUR, and used a likelihood analysis to evaluate the model error structures. Abstract In this study, a total of 472 trees were harvested and measured for stem, root, branch, and foliage biomass from nine hardwood species in Northeast China. Two additive systems of biomass equations were developed, one based on tree diameter (D) only and one based on both tree diameter (D) and height (H). For each system, three constraints were set up to account for the cross-equation error correlations between four tree component biomass, two sub-total biomass, and total biomass. The model coefficients were simultaneously estimated using seemly unrelated regression (SUR). Likelihood analysis was used to verify the error structures of power functions in order to determine if logarithmic transformation should be applied on both sides of biomass equations. Jackknifing model residuals were used to validate the prediction performance of biomass equations. The results indicated that (1) stem biomass accounted for the largest proportion (62 %) of the total tree biomass; (2) the two additive systems of biomass equations obtained good model fitting and prediction, of which the model R a 2 was [0.89, and the mean absolute percent bias (MAB %) was \35 %; (3) the system of biomass equations based on both D and H significantly improved model fitting and performance, especially for total, aboveground, and stem biomass; and (4) the anti-log correction was not necessary in this study. The established additive systems of biomass equations can provide reliable and accurate estimation for individual tree biomass of the nine hardwood species in Chinese National Forest Inventory.

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Section: Discussionmentioning

confidence: 99%

“…(in kg dry weight) of nine hardwood species from tree diameter (D, cm) and height (H, m) based on the literature(Bi et al 2004;Balboa-Murias et al 2006; Hosoda and Iehara 2010;Chan et al 2013):…”

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confidence: 99%

Developing additive systems of biomass equations for nine hardwood species in Northeast China

Dong

Zhang

2015

Trees

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“…Similar allometric equations using the variable of DBH 2 H to estimate forest biomass and biomass components could be found in other studies [25][26][27]. Tree component biomass values were calculated for individual tree within each plot, and then summed to obtain a summary of tree biomass for each plot.…”

Section: Field-based Biomass Estimatesmentioning

confidence: 92%

Above-Ground Biomass and Biomass Components Estimation Using LiDAR Data in a Coniferous Forest

Chen

et al. 2013

Forests

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This study aims to estimate forest above-ground biomass and biomass components in a stand of Picea crassifolia (a coniferous tree) located on Qilian Mountain, western China via low density small-footprint airborne LiDAR data. LiDAR points were first classified into ground points and vegetation points. After, vegetation statistics, including height quantiles, mean height, and fractional cover were calculated. Stepwise multiple regression models were used to develop equations that relate the vegetation statistics from field inventory data with field-based estimates of biomass for each sample plot. The results showed that stem, branch, and above-ground biomass may be estimated with relatively higher accuracies; estimates have adjusted , and a relative RMSE of 26.953%. Finally, above-ground biomass and biomass component spatial maps were established using stepwise multiple regression equations. These maps are very useful for updating and modifying forest base maps and registries. OPEN ACCESSForests 2013, 4 985

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“…To develop biomass equations, total tree biomass and tree component biomass (e.g., stems, roots, crown, branches and foliage) are regressed to tree diameter, or both diameter and height using linear or nonlinear regression (e.g., Burrows et al, 2000;Ketterings et al, 2001;Wang, 2006;Wang et al, 2006;Cai et al, 2013;Chan et al, 2013). However, if there is more than one tree component involved, fitting a biomass equation separately to each component ignores the inherent correlations among the biomass of tree components that are measured on the same sample trees.…”

Section: Introductionmentioning

confidence: 99%

A compatible system of biomass equations for three conifer species in Northeast, China

Dong

Zhang

2014

Forest Ecology and Management

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Establishment of allometric models and estimation of biomass recovery of swidden cultivation fallows in mixed deciduous forests of the Bago Mountains, Myanmar

Cited by 40 publications

References 16 publications

Developing additive systems of biomass equations for nine hardwood species in Northeast China

Developing additive systems of biomass equations for nine hardwood species in Northeast China

Above-Ground Biomass and Biomass Components Estimation Using LiDAR Data in a Coniferous Forest

A compatible system of biomass equations for three conifer species in Northeast, China

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