Dataset Information

Allometric biomass equations for 12 tree species in coniferous and broadleaved mixed forests, Northeastern China.

ABSTRACT: Understanding forest carbon budget and dynamics for sustainable resource management and ecosystem functions requires quantification of above- and below-ground biomass at individual tree species and stand levels. In this study, a total of 122 trees (9-12 per species) were destructively sampled to determine above- and below-ground biomass of 12 tree species (Acer mandshuricum, Acer mono, Betula platyphylla, Carpinus cordata, Fraxinus mandshurica, Juglans mandshurica, Maackia amurensis, P. koraiensis, Populus ussuriensis, Quercus mongolica, Tilia amurensis and Ulmus japonica) in coniferous and broadleaved mixed forests of Northeastern China, an area of the largest natural forest in the country. Biomass allocation was examined and biomass models were developed using diameter as independent variable for individual tree species and all species combined. The results showed that the largest biomass allocation of all species combined was on stems (57.1%), followed by coarse root (21.3%), branch (18.7%), and foliage (2.9%). The log-transformed model was statistically significant for all biomass components, although predicting power was higher for species-specific models than for all species combined, general biomass models, and higher for stems, roots, above-ground biomass, and total tree biomass than for branch and foliage biomass. These findings supplement the previous studies on this forest type by additional sample trees, species and locations, and support biomass research on forest carbon budget and dynamics by management activities such as thinning and harvesting in the northeastern part of China.

SUBMITTER: He H

PROVIDER: S-EPMC5774681 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Allometric biomass equations for 12 tree species in coniferous and broadleaved mixed forests, Northeastern China.

He Huaijiang H Zhang Chunyu C Zhao Xiuhai X Fousseni Folega F Wang Jinsong J Dai Haijun H Yang Song S Zuo Qiang Q

PloS one 20180119 1

Understanding forest carbon budget and dynamics for sustainable resource management and ecosystem functions requires quantification of above- and below-ground biomass at individual tree species and stand levels. In this study, a total of 122 trees (9-12 per species) were destructively sampled to determine above- and below-ground biomass of 12 tree species (Acer mandshuricum, Acer mono, Betula platyphylla, Carpinus cordata, Fraxinus mandshurica, Juglans mandshurica, Maackia amurensis, P. koraiens ...[more]

PMID: 29351291

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Project description:BACKGROUND:Biomass maps are valuable tools for estimating forest carbon and forest planning. Individual-tree biomass estimates made using allometric equations are the foundation for these maps, yet the potentially-high uncertainty and bias associated with individual-tree estimates is commonly ignored in biomass map error. We developed allometric equations for lodgepole pine (Pinus contorta), ponderosa pine (P. ponderosa), and Douglas-fir (Pseudotsuga menziesii) in northern Colorado. Plot-level biomass estimates were combined with Landsat imagery and geomorphometric and climate layers to map aboveground tree biomass. We compared biomass estimates for individual trees, plots, and at the landscape-scale using our locally-developed allometric equations, nationwide equations applied across the U.S., and the Forest Inventory and Analysis Component Ratio Method (FIA-CRM). Total biomass map uncertainty was calculated by propagating errors from allometric equations and remote sensing model predictions. Two evaluation methods for the allometric equations were compared in the error propagation-errors calculated from the equation fit (equation-derived) and errors from an independent dataset of destructively-sampled trees (n?=?285). RESULTS:Tree-scale error and bias of allometric equations varied dramatically between species, but local equations were generally most accurate. Depending on allometric equation and evaluation method, allometric uncertainty contributed 30-75% of total uncertainty, while remote sensing model prediction uncertainty contributed 25-70%. When using equation-derived allometric error, local equations had the lowest total uncertainty (root mean square error percent of the mean [% RMSE]?=?50%). This is likely due to low-sample size (10-20 trees sampled per species) allometric equations and evaluation not representing true variability in tree growth forms. When independently evaluated, allometric uncertainty outsized remote sensing model prediction uncertainty. Biomass across the 1.56 million ha study area and uncertainties were similar for local (2.1 billion Mg; % RMSE?=?97%) and nationwide (2.2 billion Mg; % RMSE?=?94%) equations, while FIA-CRM estimates were lower and more uncertain (1.5 billion Mg; % RMSE?=?165%). CONCLUSIONS:Allometric equations should be selected carefully since they drive substantial differences in bias and uncertainty. Biomass quantification efforts should consider contributions of allometric uncertainty to total uncertainty, at a minimum, and independently evaluate allometric equations when suitable data are available.

Project description:BACKGROUND:Application of allometric equations for quantifying forests aboveground biomass is a crucial step related to efforts of climate change mitigation. Generalized allometric equations have been applied for estimating biomass and carbon storage of forests. However, adopting a generalized allometric equation to estimate the biomass of different forests generates uncertainty due to environmental variation. Therefore, formulating species-specific allometric equations is important to accurately quantify the biomass. Montane moist forest ecosystem comprises high forest type which is mainly found in the southwestern part of Ethiopia. Yayu Coffee Forest Biosphere Reserve is categorized into Afromontane Rainforest vegetation types in this ecosystem. This study was aimed to formulate species-specific allometric equations for Albizia grandibracteata Tuab. and Trichilia dregeana Sond. using the semi-destructive method. RESULTS:Allometric equations in form of power models were developed for each tree species by evaluating the statistical relationships of total aboveground biomass (TAGB) and dendrometric variables. TAGB was regressed against diameter at breast height (D), total height (H), and wood density (?) individually and in a combination. The allometric equations were selected based on model performance statistics. Equations with the higher coefficient of determination (adj.R2), lower residual standard error (RSE), and low Akaike information criterion (AIC) values were found best fitted. Relationships between TAGB and predictive variables were found statistically significant (p???0.001) for all selected equations. Higher bias was reported related to the application of pan-tropical or generalized allometric equations. CONCLUSIONS:Formulating species-specific allometric equations is found important for accurate tree biomass estimation and quantifying the carbon stock. The developed biomass regression models can be applied as a species-specific equation to the montane moist forest ecosystem of southwestern Ethiopia.

Project description:We investigated the residual rate and mass loss rate of litter, as well as the carbon release dynamics of litter and soil across seasons, to better understand the effects of seasonal fluctuations on carbon dynamics in mixed coniferous forests. The study was carried out in natural mixed coniferous forests in the Xiaoxinganling region of Heilongjiang Province, China, and the number of temperature cycles in the unfrozen season, freeze-thaw season, frozen season, and thaw season was controlled. The goal of the study was to examine how the carbon release dynamics of litter and soil respond to the freeze-thaw process and whether there are differences in carbon release dynamics under different seasons. Repeated-measures analysis of variance was used to analyze the residual mass rate and mass loss rate of litter, litter organic carbon and soil organic carbon during the unfrozen season, freeze-thaw season, frozen season, and thaw season. Litter decomposition was highest in the unfrozen season (15.9%~20.3%), and litter and soil carbon were sequestered throughout this process. Temperature swings above and below 0°C during the freeze-thaw season cause the litter to physically fragment and hasten its decomposition. Decomposition of litter was still feasible during the frozen season, and it was at its lowest during the thaw season (7.2%~7.8%), when its organic carbon was transported to the soil. Carbon migrates from undecomposed litter to semi-decomposed litter and then to soil. The carbon in the environment is fixed in the litter (11.3%~18.2%) and soil (34.4%~36.7%) in the unfrozen season, the carbon-fixing ability of the undecomposed litter in the freeze-thaw season is better, and the carbon in the semi-decomposed litter is mostly transferred to the soil; the carbon-fixing ability of the litter in the frozen season is worse (-3.9%~ -4.3%), and the organic carbon in the litter is gradually transferred to the soil. The carbon-fixing ability of the undecomposed litter in the thaw season is stronger, and the organic carbon in the semi-decomposed litter is mostly transferred to the soil. Both litter and soil can store carbon; however, from the unfrozen season until the thaw season, carbon is transported from undecomposed litter to semi-decomposed litter and to the soil over time.

Dataset Information

Allometric biomass equations for 12 tree species in coniferous and broadleaved mixed forests, Northeastern China.

Publications

Allometric biomass equations for 12 tree species in coniferous and broadleaved mixed forests, Northeastern China.

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