Construction of biomass models for Larix olgensis plantation using hierarchical Bayesian seemingly unrela-ted regression.

Accurate estimation of forest biomass in China is crucial for the study of carbon cycle and mechanisms underlying carbon storage in global terrestrial ecosystems. Based on the biomass data of 376 individuals of Larix olgensis in Heilongjiang Province, we used seemingly unrelated regression (SUR) method to build a univariate biomass SUR model with diameter at breast height as the independent variable and considering the random effect at the sampling site level. Then, a seemingly unrelated mixed effect (SURM) model was constructed. As the calculation of random effects of SURM model did not require the empirically measured values of all dependent variables, we analyzed the deviations from the following four types in detail: 1) SURM1, the random effect was calculated according to the measured biomass of stem, branch and foliage; 2) SURM2, the random effect was calculated according to the measured value of tree height (H); 3) SURM3, the random effect was calculated according to the measured crown length (CL); 4) SURM4, the random effect was calculated according to the measured values of H and CL. The results showed that the fitting effect of branch and foliage biomass models was improved significantly after considering the horizontal random effect of the sampling plot, with R2 being increased by more than 20%. The fitting effect of stem and root biomass models were improved slightly, with R2 being increased by 4.8% and 1.7%, respectively. When using five randomly selected trees to calculate the horizontal random effect of the sampling plot, the prediction performance of SURM model was better than that of SUR model and SURM model considering only fixed effects, especially SURM1 model (MAPE% of stem, branch, foliage and root was 10.4%, 29.7%, 32.1% and 19.5%, respectively). Except for SURM1 model, the deviation of SURM4 in predicting stem, branch, foliage and root biomass was smaller than that of SURM2 and SURM3 models. In actual prediction, although the prediction accuracy of SURM1 model was the highest, it needed to measure aboveground biomass of several trees, and the use cost was relatively high. Therefore, the SURM4 modelled on measured H and CL was recommended to predict the standing tree biomass of L. olgensis.

ABSTRACTMost terrestrial carbon is stored in forest biomass, which plays an important role in local, regional, and global climate change. Monitoring of forests and their status, and accurate estimation of forest biomass are important in mitigating the impacts of climate change. Empirical models developed using remote-sensing and field-measured forest data are commonly used to estimate forest biomass. In the present study, we used a mechanistic model to estimate height and biomass in the Three Gorges reservoir region (China) based on the allometric scale and resource limits (ASRL) model. The forests in the Three Gorges reservoir region are important and unique in view of the vertical distribution of vegetation and mixed needleleaf. Detailed information about the forest in this region is available from the Geoscience Laser Altimeter System (GLAS) and field measurements from 714 forest plots. The ASRL model parameters were adjusted using GLAS-derived forest tree height to reduce the deviation between modelled and observed forest height. The predicted maximum forest tree height from the optimized ASRL model was compared to measured tree heights, and a good correlation (R2 = 0.566) was found. The allometric scale function between forest height and diameter at breast height (DBH) is developed and the maximum forest tree height from the optimized ASRL model transferred to DBH. Moreover, the forest biomass was estimated from DBH according to the allometric scale function that was determined using DBH and biomass data. The results of maximum forest biomass using the ASRL model and the allometric scale function show a good accuracy (R2 = 0.887) in the Three Gorges reservoir region. Here, we present the forest biomass estimation approach following allometric theory for accurate estimation of maximum forest tree height and biomass. The proposed approach can be applied to forest species in all types of environmental conditions.

The accurate estimation of forest biomass is important to evaluate the structure and function of forest ecosystems, estimate carbon sinks in forests, and study matter cycle, energy flow, and the effects of climate change on forest ecosystems. Biomass additivity is a desirable characteristic to predict each component and the total biomass since it ensures consistency between the sum of the predicted values of components such as roots, stems, leaves, pods, and branches and the prediction for the total tree. In this study, 45 Robinia pseudoacacia L. trees were harvested to determine each component and the total biomass in the Loess Plateau of western Shanxi Province, China. Three additive systems of biomass equations of R. pseudoacacia L., based on the diameter at breast height (D) only and on the combination of D and tree height (H) with D2H and DbHc, were established. To ensure biomass model additivity, the additive system of biomass equations considers the correlation among different components using simultaneous equations and establishes constraints on the parameters of the equation. Seemingly uncorrelated regression (SUR) was used to estimate the parameters of the additive system of biomass equations, and the jackknifing technique was used to verify the accuracy of prediction of the additive system of biomass equations. The results showed that (1) the stem biomass contributed the most to the total biomass, comprising 51.82% of the total biomass, followed by the root biomass (24.63%) and by the pod and leaf biomass, which accounted for the smallest share, comprising 1.82% and 2.22%, respectively; (2) the three additive systems of biomass equations of R. pseudoacacia L. fit well with the models and were effective at making predictions, particularly for the root, stem, above-ground, and total biomass (R2adj > 0.812; root mean square error (RMSE) < 0.151). The mean absolute error (MAE) was less than 0.124, and the mean prediction error (MPE) was less than 0.037. (3) When the biomass model added the tree height predictor, the goodness of fit R2adj increased, RMSE decreased, and the accuracy of prediction was much improved. In particular, the additive system, which was developed based on DbHc combination prediction factors, was the most accurate. The additive system of biomass equations established in this study can provide a reliable and accurate estimation of the individual biomass of R. pseudoacacia L. in the Loess region of western Shanxi Province, China.

Considering random effects and sampling strategies improves individual compatible biomass models for mixed plantations of Larix olgensis and Fraxinus mandshurica in northeastern China

The accurate estimation of tree above-ground (AGB) and below-ground (BGB) biomass components and their root/shoot ratio play key roles in stand and country-level forest biomass and carbon stock estimation. Nevertheless, site-specific and appropriate biomass equations and root/shoot ratio are hardly available for natural larch (Larix sibirica Ledeb.) forests in Mongolia. The present study aimed (1) to develop allometric equations to estimate the above- and below-ground biomass of L. sibirica trees, and (2) to estimate the root/shoot ratio applicable for estimating the root biomass based on above-ground biomass of natural larch forests in northern Mongolia. A total of 40 trees with DBH ranging from 6.8 to 40.8 cm were sampled for tree biomass analyses. For each biomass component, we calculated the proportion of biomass allocated to different components, and also tested four allometric equations based on diameter at breast height (DBH) and height (H) as independent variables. Our results, based on measurements of oven-dried biomass, revealed that stem biomass on average accounted for 44.5% and followed by branch (28.6%) and root (19.9%) biomass, respectively. Stem and branch biomass proportions were gradually increased with increasing DBH, while a contrary trend was observed for needles. The root/shoot ratio averaged 0.25. A comparison of the allocation of root biomass by diameter fractions showed an ever-growing trend of coarse roots with an increase in stem diameter, which often exceeded more than 50% of the total root biomass. However, biomass equations, which include both DBH and H were more precise than equations that are solely based only on DBH. Consequently, among the proposed allometric regression models for estimating the AGB and BGB, the equation y = aD b H c was selected as the best-fitted equation for estimating each biomass component in Siberian larch forests. These allometric equations are available to be used for the estimation of natural larch forest biomass and carbon stocks in the Khentii Mountains of Mongolia, where extreme continental climate conditions dominate.

We studied the effects of stand age on the allocation of biomass and allometric relationships among component biomass in five stands ages (1, 3, 5, 7, and 8 years old) of two eucalypts hybrids, including Eucalyptus urophylla × E. grandis and E. urophylla × E. tereticornis, in the Leizhou Peninsula, China. The stem, bark, branch, leaf, and root biomass from 60 destructively harvested trees were quantified. Allometric models were applied to examine the relationship between the tree component biomass and predictor variable (diameter at breast height, D, and height, H). Stand age was introduced into the allometric models to explore the effect of stand age on biomass estimation. The results showed the following: (1) Stand age significantly affected the distribution of biomass in each component. The proportion of stem biomass to total tree biomass increased with stand age, the proportions of bark, branch, and leaf biomass to total tree biomass decreased with stand age, and the proportion of root biomass to total tree biomass first decreased and then increased with stand age. (2) There were close allometric relationships between biomass (i.e., the components biomass, aboveground biomass, and total biomass per tree) and diameter at breast height (D), height (H), the product of diameter at breast height and tree height (DH), and the product of the square of the diameter at breast height and tree height (D2H). The allometric relationship between biomass and measurement parameters (D, H, DH, D2H) could be applied to the biomass assessment of eucalypts plantation. (3) Allometric equations that included stand age as a complementary variable significantly improved the fit and enhanced the accuracy of biomass estimates. The optimal independent variable for the biomass prediction model varied according to each organ. These results indicate that stand age has an important influence on biomass allocation. Allometric equations considering stand age could improve the accuracy of carbon sequestration estimates in plantations.

Biomass equations were developed for different components of oak trees (Quercus cerris L.), which have been managed in coppices at different development stages-small-diameter forest (SDF) and medium-diameter forest (MDF). In this context, four biomass regression models-two based on diameter at breast height (DBH) alone and two based on DBH and total tree height (H)-were developed for each of the crown, stem, and total aboveground biomass components. Akaike's information criterion (AIC), root mean square error percentage (RMSE (%)), mean absolute error percentage (MAE (%)), adjusted coefficient of determination (Adj.R2), and bias values were used to evaluate and compare the suitability of a total of 12 regression models developed for biomass components. As a result, in the estimation of crown biomass, only DBH-based models provided higher estimation accuracy than DBH-H-based models. For the most suitable model, estimated values were Adj.R2 = 0.60, bias = - 0.009, RMSE = 66%, and MAE = 41%. In models developed to estimate stem biomass, the estimation accuracy of DBH-H-based models was higher. In the goodness-of-fit statistics calculated for the most suitable model, Adj.R2, bias, RMSE, and MAE were 0.89, 0.010, 38%, and 23%, respectively. The models developed to estimate the total aboveground biomass were all close in terms of estimation accuracy. The biomass components (crown andstem) in the total aboveground biomass were proportionally as follows: crown at 38% and stem at 62% in the SDF stage, and crown at 35% and stem at 65% in the MDF stage, indicating lower crown and higher stem partitioning as the development stage increased.

Accurate estimation of tree biomass is required for accounting for and monitoring forest carbon stocking. Allometric biomass equations constructed by classical statistical methods are widely used to predict tree biomass in forest ecosystems. In this study, a Bayesian approach was proposed and applied to develop two additive biomass model systems: one with tree diameter at breast height as the only predictor and the other with both tree diameter and total height as the predictors for planted Korean larch (Larix olgensis Henry) in the Northeast, P.R. China. The seemingly unrelated regression (SUR) was used to fit the simultaneous equations of four tree components (i.e., stem, branch, foliage, and root). The model parameters were estimated by feasible generalized least squares (FGLS) and Bayesian methods using either non-informative priors or informative priors. The results showed that adding tree height to the model systems improved the model fitting and performance for the stem, branch, and foliage biomass models, but much less for the root biomass models. The Bayesian methods on the SUR models produced narrower 95% prediction intervals than did the classical FGLS method, indicating higher computing efficiency and more stable model predictions, especially for small sample sizes. Furthermore, the Bayesian methods with informative priors performed better (smaller values of deviance information criterion (DIC)) than those with the non-informative priors. Therefore, our results demonstrated the advantages of applying the Bayesian methods on the SUR biomass models, not only obtaining better model fitting and predictions, but also offering the assessment and evaluation of the uncertainties for constructing and updating tree biomass models.

PDF HTML阅读 XML下载 导出引用 引用提醒 不同林分起源的相容性生物量模型构建 DOI: 10.5846/stxb201306181729 作者: 作者单位: 中国林业科学研究院资源信息研究所,中国林业科学研究院资源信息研究所,新疆农业大学计算机与信息工程学院,中国林业科学研究院资源信息研究所,国家林业局调查规划设计院 作者简介: 通讯作者: 中图分类号: 基金项目: 国家863重点项目（2012AA12A306）和国家自然科学基金项目（31170588，31300534） Development of compatible biomass models for trees from different stand origin Author: Affiliation: Research Institute of Forest Resources Information Thchniques, CAF,Research Institute of Forest Resources Information Thchniques, CAF,,, Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:目前为止已有不同方法构建生物量相容性模型，但不同林分起源的生物量相容性模型很少报道。针对此问题，以150株南方马尾松（Pinus masson iana）地上生物量数据为例，利用比例平差法和非线性联立方程组法建立不同起源地上生物量以及干材、干皮、树枝和树叶各分项生物量相容的通用性模型。根据分配层次不同，两种方法又各自考虑总量直接控制和分级联合控制两种方案。从直径、树高、地径、枝下高和冠幅5个林分变量中选取不同的变量构建一元、二元和三元生物量模型，并利用加权最小二乘回归法消除生物量模型中存在的异方差性。结果为：比例平差法和非线性联立方程组法都能有效保证各分项生物量总和等于总生物量，模型预测精度满足要求。总体而言，非线性联立方程组方法比比例平差方法精度高，同时两种方法中总量直接控制法比分级联合控制法预测效果好；各分项生物量模型本身作为权函数能有效消除异方差；各分项对应的三元生物量模型预测精度最高，其次是二元生物量模型，最低是一元生物量模型，但这些差异不是很大。总之，为权衡考虑模型预测精度和调查成本，建议把直径和树高作为协变量利用总量直接控制非线性联立方程组法对不同起源生物量建模。 Abstract:Biomass equations for individual-trees have appeared frequently in the ecological and forestry literature over the last 60 years as biomass estimation is a prerequisite for studies on forest productivity, nutrient cycling and for calculating carbon sequestration, storage and other structural and functional attributes of forest ecosystems. Over the same period of time, the methods of developing biomass equations for total tree and component biomass have evolved from single equation least squares to multivariate adjustment in proportion and simultaneous equations, both linear and nonlinear. The single equation approach relates total tree biomass and its components such as stem, wood, bark, branches, and foliage to predictor variables such as diameter at breast height, height and sometime also crown width using log transformed data through least squares regression. The equation for each component is estimated separately without taking into account (1) the inherent correlation among the biomass components measured on the same sample trees and (2) the logical constraint between the sum of predicted biomass for tree components and the prediction for the total tree. As a result, biomass equations developed through this approach fall short of statistical efficiency in parameter estimation and lack compatibility among the component equations (Parresol 1999). The lack of compatibility means inconsistency in logic in the sense that the predicted values from summing the biomass equations of tree components do not equal to the predicted value from the equation for the total tree biomass. Development of compatible individual-tree biomass models were well reported in the literature, while how to construct these biomass models for trees from different stand origin has not been investigated so far. In this paper, generalized models on total above-ground biomass and its four components (stem wood, stem bark, branch, and foliage) for trees from different stand origin were established using the methods of adjustment in proportion and nonlinear simultaneous equations. Totally 150 Masson pine (Pinus masson iana) trees were sampled for biomass investigation in southern China. For the two approaches mentioned above, i.e. adjustment in proportion and nonlinear simultaneous equations, controlling jointly from level to level by ratio functions and controlling directly under total biomass by proportion functions were employed. Covariate variables of one-, two- and three-variable biomass models were obtained from five stand variable candidates of diameter at breast height, tree total height, diameter at ground level, height to crown base and crown width. Weighted least square regression was used to remove the heteroscedasticity of biomass models. The results showed that both methods of adjustment in proportion and nonlinear simultaneous equations could efficiently ensure that the total biomass is equal to the summary of its components with high prediction accuracy. However, the prediction accuracy of nonlinear simultaneous equations was generally much higher than that of adjustment in proportion. The approach of controlling directly by proportion functions was slightly better than the one controlling jointly by ratio functions. The function of each component biomass itself as weighted function could remove heteroscedasticity effectively. The biomass models for each component with three variables (diameter at breast height, height and crown width) had the highest prediction accuracy, following by the two variables (diameter at breast height and height), and the single variable (diameter at breast height) model. The discrepancies among the models were very small, however. For balancing the model prediction accuracy and survey cost in constructing biomass model for trees from different stand origin, we suggest adapting the nonlinear simultaneous equations of controlling directly under total biomass with diameter at breast height and height as covariables. 参考文献 相似文献 引证文献

Wood specific gravity and aboveground biomass of Bombacopsis quinata plantations in Costa Rica

A massive plantation forests have been established in arid region of north western India for environmental, economic and livelihood benefits to the local people. However, their contribution to climate change mitigation is poorly understood, because of lack of allometric equations for biomass estimation. Objective of this study was to develop species-specific allometric models for estimating total, stem, branch, and leaf biomasses of Vachellia tortilis planted in western Rajasthan. Different linear and non-linear models were fitted to establish relationship between dry biomasses of different components of above-ground part of V. tortilis trees with diameter at breast height (DBH) and total height (H) and allometric equations were selected based on model performance statistics. Trees were 6.0-15.6 m tall, 10.50-54.10 cm in diameter, 19.0-773 kg tree -1 stem biomass, 28.0-2166 kg tree -1 branch biomass, 1.0-51.0 kg tree -1 leaf biomass and 58.0-2848 kg tree -1 total biomass. Model Y= a Exp bDBH was best fit with DBH and fulfilled the validation criterions with highest R 2 and lowest residual error (σ), Akaike information criteria and root mean square error values. The value of adjusted R 2 was >0.90 for the equations fitted on biomasses of different components except leaf biomass (adj. R 2 = 0.46). Statistical variables of all components were highly significant (p<0.01) indicating the accuracy and precision of the equations. The developed biomass regression models can be applied as a species-specific equation in predicting standing biomass and carbon sequestration benefits of V. tortilis in north western India.

Abstract. Ha NT, Bao TQ, Tuan NT, Rodríguez-Hernández DI, Dung NT, Ngoan TT. 2025. Destructive sampling-based allometric equations for biomass and carbon estimation in Acacia hybrid plantations in Southeastern Vietnam. Nusantara Bioscience 16: 203-217. This study developed accurate allometric equations for estimating aboveground and belowground biomass, as well as carbon stocks, for Acacia hybrid (Acacia mangium × Acacia auriculiformis) plantations in Southeastern, Vietnam. A dataset of 45 destructively sampled trees with varying ages and diameter classes was used to validate the models. The fresh biomass of the four tree components (stem, branches, leaves, and roots) was measured for a total of 180 samples. Samples were oven-dried at 105°C for stems and branches, and 80°C for leaves, to determine their biomass. Linear and non-linear equations were employed to model both individual tree and stand-level dry biomass (AGB: aboveground biomass, BGB: belowground biomass, TGBG: total biomass), and carbon stocks (AGC: aboveground carbon, BGC: belowground carbon, TGC: total carbon). Diameter at breast height (DBH), tree height (H), stand density (SD), and stand age (A) were included as predictor variables. The best-fitting models were selected based on coefficients of determination (R²), sum of squared errors (SEE), mean absolute error (MAE), sum of squared residuals (SSR), correction factors (CF), mean absolute percentage error (MAPE), and root mean square error (RMSE), with R² values greater than 0.895 and RMSE values less than 0.363. The results revealed strong relationships between aboveground and belowground biomass, and logarithmic functions of DBH and tree height were found to be good predictors for all biomass components. The key equations are: ln(AGB) = -3.03805 + 0.586847*ln(DBH*H) + 1.58329*ln(DBH); ln(BGB) = -0.597955 + 0.485409*ln(DBH)2; ln(TGB) = -2.65453 + 2.11674*ln(DBH) + 0.57522*ln(H). Among the variables, DBH was found to be particularly effective in estimating BGB. At the stand level, total biomass (TSB) has a significant correlation with stand density, mean diameter, and stand height, as shown in the following equation: Ln(TSB) = -9.85561 + 1.09128*ln(SD) + 1.96789*ln(Ds) + 0.608831*ln(Hs). These models provide foresters with valuable tools for estimating biomass and carbon accumulation in Acacia hybrid plantations. The total carbon stock of the Acacia hybrid population in the study area ranged from 29.0 tons/ha to 313.3 tons/ha. This information can support carbon accounting efforts and contribute to Vietnam's initiatives for carbon reduction and climate change mitigation.

Allometric models for improving aboveground biomass estimates in West African savanna ecosystems

Stem, branch, needle, and total aboveground biomass were assessed for three 9- to 12-year-old white spruce (Picea glauca (Moench) Voss) plantations, each subjected to three different stand tending options at age 4 to 7. Biomass components were predicted from measures of stem diameter with coefficients of variation between 24% and 29%. Diameter at breast height (DBH) generally provided lower prediction precision than did the lower stem measures tested (coefficient of variation > 35%). The addition of tree height in models reduced the standard error of the estimates for stem and total biomass by an average of 48% and 8%, respectively, and compensated for different height/diameter ratios imposed on the spruce by the stand tending treatments. Needle and branch biomass models were invariant to the tending treatments and, consequently, to the addition of height as an independent variable. Predictions from existing published white spruce equations suggest that extrapolation to this study area would have led to adequate stem biomass estimation but to serious (>55%) underestimates of branch, needle, and, correspondingly, total biomass. Slow self-pruning by plantation spruce, particularly before crown closure, is cited as a possible reason for these differences.

IntroductionAccurate biomass estimation is crucial for quantifying forest carbon storage and guiding sustainable management. In this study, we developed four biomass modeling systems for natural white birch (Betula platyphylla Sukaczev) in northeastern China using field data from 148 trees.MethodsThe data included diameter at breast height (DBH), tree height (H), crown dimensions, and biomass components (stem, branch, foliage, and root biomass), as well as soil and climate variables. We employed Seemingly Unrelated Regression (SUR) and mixed-effects models (SURM) to account for component correlations and spatial variability.ResultsThe base model (SURba), using only the DBH variable, explained 89-96% of the biomass variance (RMSE%: 1.34-19.94%). The second model (SURbio) incorporated H for stem/branch biomass and crown length (CL) for foliage, improving the predictions of stem, branch, and foliage biomass (R2 increased by 1.69–4.86%; RMSE% decreased by 10.76-59.04%). Next, the SURba-abio and SURbio-abio models integrated abiotic factors, including soil organic carbon content (SOC), mean annual precipitation (MAP), degree-days above 18°C (DD18), and soil bulk density (BD). Both models showed improvement, with the abiotic factor model SURba-abio performing similarly to the biotic factor model SURbio (ΔR2 < 4.36%), while the SURbio-abio model performed the best. Subsequently, random effects were introduced at the sampling point (Forestry Bureau) level, developing seemingly unrelated mixed-effects models (SURMba, SURMbio, SURMba-abio, SURMbio-abio), which improved model fitting and prediction accuracy. The gap between the SURMba-abio model (with abiotic factors) and the SURMbio-abio model (including both biotic and abiotic factors) was minimal (ΔR2 < 2.80%). The random effects model stabilized when calibrated with aboveground biomass measurements from four trees.DiscussionIn conclusion, these models provide an effective approach for estimating the biomass of natural white birch in northeastern China. In the absence of biotic factors, the SURba-abio and SURMba-abio models serve as reliable alternatives, emphasizing the importance of abiotic factors in biomass estimation and offering a practical solution for predicting birch biomass.