scholarly journals The Effects of Combining the Variables in Allometric Biomass Models on Biomass Estimates over Large Forest Areas: A European Beech Case Study

Forests ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1428
Author(s):  
Erick O. Osewe ◽  
Ioan Dutcă
Keyword(s):  
Nonlinear Models ◽  
Forest Biomass ◽  
Tree Height ◽  
European Beech ◽  
Biomass Estimation ◽  
Parameter Estimates ◽  
Variable Model ◽  
Separate Variable ◽  
Biomass Models ◽  
Fagus Sylvatica L

Effective initiatives for forest-based mitigation of climate change rely on continuous efforts to improve the estimation of forest biomass. Allometric biomass models, which are nonlinear models that predict aboveground biomass (AGB) as a function of diameter at breast height (D) and tree height (H), are typically used in forest biomass estimations. A combined variable D2H may be used instead of two separate predictors. The Q-ratio (i.e., the ratio between the parameter estimates of D and parameter estimates of H, in a separate variable model) was proposed recently as a measure to guide the decision on whether D and H can be safely combined into D2H, being shown that the two model forms are similar when Q = 2.0. Here, using five European beech (Fagus sylvatica L.) biomass datasets (of different Q-ratios ranging from 1.50 to 5.05) and an inventory dataset for the same species, we investigated the effects of combining the variables in allometric models on biomass estimation over large forest areas. The results showed that using a combined variable model instead of a separate variable model to predict biomass of European beech trees resulted in overestimation of mean AGB per hectare for Q > 2.0 (i.e., by 6.3% for Q = 5.05), underestimation for Q < 2.0 (i.e., by –3.9% for Q = 1.50), whereas for Q = 2.03, the differences were minimum (0.1%). The standard errors of mean AGB per hectare were similar for Q = 2.03 (differences up to 0.2%), and the differences increased with the Q-ratio, by up 10.2% for Q = 5.05. Therefore, we demonstrated for European beech that combining the variables in allometric biomass models when Q ≠ 2.0 resulted in biased estimates of mean AGB per hectare and of uncertainty.

scholarly journals Generic additive allometric models and biomass allocation for two natural oak species in northeastern China

2020 ◽  
Author(s):  
Shengwang Meng ◽  
Fan Yang ◽  
Haibin Wang ◽  
Wei Wang ◽  
Sheng Hu ◽  
...  
Keyword(s):  
Biomass Allocation ◽  
Large Scale ◽  
Forest Biomass ◽  
Tree Height ◽  
Northeastern China ◽  
Carbon Accounting ◽  
Biomass Estimation ◽  
Stem Biomass ◽  
Biomass Models ◽  
Mongolian Oak

Abstract Background: Accurate quantification of forest biomass through allometric equations is crucial for global carbon accounting and climate change mitigation. Current models for oak species could not accurately estimate biomass in northeastern China, since they were usually established limited to Mongolian oak (Quercus mongolica) on local sites, and specifically, no biomass models were available for Liaodong oak (Quercus wutaishanica). The goal of this study was, therefore, to develop generic biomass models for both oak species on large scale and evaluate biomass allocation patterns within tree components. Results: The stem biomass accounts for about two-thirds of the aboveground biomass. The ratio of wood biomass holds constant and that of branch increases with increasing D, H, CW, CL, while a reverse trend was found for bark and foliage. The root-shoot ratio nonlinearly decreased with D, ranging from 1.06 to 0.11. Tree diameter proved to be a good predictor, especially for root biomass. Tree height is more prominent than crown size for improving stem biomass models, yet it puts negative effects on crown biomass models with non-significant coefficients. Crown width could help improve fitting results of branch and foliage biomass models. Conclusion: We conclude that the selected generic biomass models for Mongolian oak and Liaodong oak will vigorously promote the accuracy of biomass estimation.

scholarly journals Generic Additive Allometric Models and Biomass Allocation for Two Natural Oak Species in Northeastern China

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 715
Author(s):  
Shengwang Meng ◽  
Fan Yang ◽  
Sheng Hu ◽  
Haibin Wang ◽  
Huimin Wang
Keyword(s):  
Biomass Allocation ◽  
Tree Height ◽  
Northeastern China ◽  
Seemingly Unrelated Regression ◽  
Biomass Estimation ◽  
Model Parameters ◽  
Stem Biomass ◽  
Crown Width ◽  
Biomass Models ◽  
Mongolian Oak

Current models for oak species could not accurately estimate biomass in northeastern China, since they are usually restricted to Mongolian oak (Quercus mongolica Fisch. ex Ledeb.) on local sites, and specifically, no biomass models are available for Liaodong oak (Quercuswutaishanica Mayr). The goal of this study was, therefore, to develop generic biomass models for both oak species on a large scale and evaluate the biomass allocation patterns within tree components. A total of 159 sample trees consisting of 120 Mongolian oak and 39 Liaodong oak were harvested and measured for wood (inside bark), bark, branch and foliage biomass. To account for the belowground biomass, 53 root systems were excavated following the aboveground harvest. The share of biomass allocated to different components was assessed by calculating the ratios. An aboveground additive system of biomass models and belowground equations were fitted based on predictors considering diameter (D), tree height (H), crown width (CW) and crown length (CL). Model parameters were estimated by jointly fitting the total and the components’ equations using the weighted nonlinear seemingly unrelated regression method. A leave-one-out cross-validation procedure was used to evaluate the predictive ability. The results revealed that stem biomass accounts for about two-thirds of the aboveground biomass. The ratio of wood biomass holds constant and that of branches increases with increasing D, H, CW and CL, while a reverse trend was found for bark and foliage. The root-to-shoot ratio nonlinearly decreased with D, ranging from 1.06 to 0.11. Tree diameter proved to be a good predictor, especially for root biomass. Tree height is more prominent than crown size for improving stem biomass models, yet it puts negative effects on crown biomass models with non-significant coefficients. Crown width could help improve the fitting results of the branch and foliage biomass models. We conclude that the selected generic biomass models for Mongolian oak and Liaodong oak will vigorously promote the accuracy of biomass estimation.

scholarly journals Using linear mixed model and dummy variable  model approaches to construct compatible single-tree biomass equations at different scales – A case study for Masson pine in Southern China

2012 ◽  
Vol 58 (No. 3) ◽  
pp. 101-115 ◽  
Author(s):  
L.Y. Fu ◽  
W.S. Zeng ◽  
S.Z. Tang ◽  
R.P. Sharma ◽  
H.K. Li
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Southern China ◽  
Forest Biomass ◽  
Tree Biomass ◽  
Variable Model ◽  
Masson Pine ◽  
Dummy Variable ◽  
Single Tree ◽  
Biomass Models

The estimation of forest biomass is important for practical issues and scientific purposes in forestry. The estimation of forest biomass on a large-scale level would be merely possible with the application of generalized single-tree biomass models. The aboveground biomass data on Masson pine (Pinus massoniana) from nine provinces in southern China were used to develop generalized single-tree biomass models using both linear mixed model and dummy variable model methods. An allometric function requiring only diameter at breast height was used as a base model for this purpose. The results showed that the aboveground biomass estimates of individual trees with identical diameters were different among the forest origins (natural and planted) and geographic regions (provinces). The linear mixed model with random effect parameters and dummy model with site-specific (local) parameters showed better fit and prediction performance than the population average model. The linear mixed model appears more flexible than the dummy variable model for the construction of generalized single-tree biomass models or compatible biomass models at different scales. The linear mixed model method can also be applied to develop other types of generalized single-tree models such as basal area growth and volume models. &nbsp;

scholarly journals Modelling individual tree height to crown base of Norway spruce (Picea abies (L.) Karst.) and European beech (Fagus sylvatica L.)

PLoS ONE ◽  
2017 ◽  
Vol 12 (10) ◽  
pp. e0186394 ◽  
Author(s):  
Ram P. Sharma ◽  
Zdeněk Vacek ◽  
Stanislav Vacek ◽  
Vilém Podrázský ◽  
Václav Jansa
Keyword(s):  
Picea Abies ◽  
Norway Spruce ◽  
Fagus Sylvatica ◽  
Tree Height ◽  
European Beech ◽  
Individual Tree ◽  
Fagus Sylvatica L

scholarly journals Field methods for sampling tree height for tropical forest biomass estimation

2018 ◽  
Vol 9 (5) ◽  
pp. 1179-1189 ◽  
Author(s):  
Martin J. P. Sullivan ◽  
Simon L. Lewis ◽  
Wannes Hubau ◽  
Lan Qie ◽  
Timothy R. Baker ◽  
...  
Keyword(s):  
Tropical Forest ◽  
Forest Biomass ◽  
Tree Height ◽  
Biomass Estimation ◽  
Field Methods

scholarly journals Biomass functions applicable to European beech

2012 ◽  
Vol 51 (No. 4) ◽  
pp. 147-154 ◽  
Author(s):  
E. Cienciala ◽  
M. Černý ◽  
J. Apltauer ◽  
Z. Exnerová
Keyword(s):  
Tree Height ◽  
European Beech ◽  
Biomass Estimation ◽  
Managed Forests ◽  
Independent Variables ◽  
Breast Height ◽  
Allometric Functions ◽  
Branch Biomass ◽  
Independent Variable ◽  
Total Tree

This material describes parameterization of allometric functions applicable to biomass estimation of European beech trees. It is based on field data from destructive measurements of 20 full-grown trees with diameter at breast height (dbh) from 5.7 to 62.1 cm. The parameterization was performed for total tree aboveground biomass (AB; besides stump), stem and branch biomass, respectively. The allometric functions contained two or three parameters and used dbh either as a single independent variable or in combination with tree height (H). These functions explained 97 to 99% of the variability in the measured AB. The most successful equation was that using both dbh and H as independent variables in combination with three fitted parameters. H, as the second independent variable, had rather a small effect on improving the estimation: in the case of AB, H as independent variable improved prediction accuracy by 1&ndash;2% whereas in the case of branch biomass by about 5%. The parameterized biomass equations are applicable to tree specimens of European beech grown in typically managed forests.

Using the dummy variable model approach to construct compatible single-tree biomass equations at different scales — a case study for Masson pine (Pinus massoniana) in southern China

2011 ◽  
Vol 41 (7) ◽  
pp. 1547-1554 ◽  
Author(s):  
Wei Sheng Zeng ◽  
Hui Ru Zhang ◽  
Shou Zheng Tang
Keyword(s):  
Southern China ◽  
Forest Biomass ◽  
Pinus Massoniana ◽  
Biomass Estimation ◽  
Tree Biomass ◽  
Variable Model ◽  
Masson Pine ◽  
Dummy Variable ◽  
Single Tree ◽  
Biomass Equations

It is fundamental for monitoring and assessment of national forest biomass to develop generalized single-tree biomass models suitable for large-scale forest biomass estimation. However, the compatibility of forest biomass estimates among different scales is a real problem. Based on the aboveground biomass data of Masson pine (Pinus massoniana Lamb.) in southern China, generalized single-tree biomass equations applying to national and regional forest biomass estimation were constructed using the dummy variable model method, which provided an effective approach to solving the compatibility of forest biomass estimates among different scales. The results show that aboveground biomass estimates of individual trees with the same diameter are different to some extent among the forest origins and geographic regions and that a dummy model with specific (local) parameters is better than a population-average model. The dummy variable model can be applied to develop other generalized compatible models such as tree volume equations.

scholarly journals A practical measure for determining if diameter (D) and height (H) should be combined into D2H in allometric biomass models

2019 ◽  
Vol 92 (5) ◽  
pp. 627-634 ◽  
Author(s):  
I Dutcă ◽  
R E McRoberts ◽  
E Næsset ◽  
V N B Blujdea
Keyword(s):  
Aboveground Biomass ◽  
Prediction Accuracy ◽  
Fixed Ratio ◽  
Tree Height ◽  
Parameter Estimates ◽  
Tree Biomass ◽  
Variable Model ◽  
Individual Tree ◽  
Model Constraint ◽  
Practical Measure

AbstractTree diameter at breast height (D) and tree height (H) are often used as predictors of individual tree biomass. Because D and H are correlated, the combined variable D2H is frequently used in regression models instead of two separate independent variables, to avoid collinearity related issues. The justification for D2H is that aboveground biomass is proportional to the volume of a cylinder of diameter, D, and height, H. However, the D2H predictor constrains the model to produce parameter estimates for D and H that have a fixed ratio, in this case, 2.0. In this paper we investigate the degree to which the D2H predictor reduces prediction accuracy relative to D and H separately and propose a practical measure, Q-ratio, to guide the decision as to whether D and H should or should not be combined into D2H. Using five training biomass datasets and two fitting approaches, weighted nonlinear regression and linear regression following logarithmic transformations, we showed that the D2H predictor becomes less efficient in predicting aboveground biomass as the Q-ratio deviates from 2.0. Because of the model constraint, the D2H-based model performed less well than the separate variable model by as much as 12 per cent with regard to mean absolute percentage residual and as much as 18 per cent with regard to sum of squares of log accuracy ratios. For the analysed datasets, we observed a wide variation in Q-ratios, ranging from 2.5 to 5.1, and a large decrease in efficiency for the combined variable model. Therefore, we recommend using the Q-ratio as a measure to guide the decision as to whether D and H may be combined further into D2H without the adverse effects of loss in biomass prediction accuracy.

Konkurrenz zwischen Stieleiche und Buche auf Lothar-Sturmflächen | Competition between pedunculate oak and European beech on Lothar windthrow areas

2009 ◽  
Vol 160 (5) ◽  
pp. 114-123 ◽  
Author(s):  
Daniel Otto ◽  
Sven Wagner ◽  
Peter Brang
Keyword(s):  
Large Scale ◽  
Early Stage ◽  
Tree Height ◽  
European Beech ◽  
Threshold Value ◽  
Test Methods ◽  
Permanent Plots ◽  
Pedunculate Oak ◽  
Competitive Pressure ◽  
Competition Index

The competitive pressure of naturally regenerated European beech (Fagus sylvatica) saplings on planted pedunculate oak (Quercus robur) was investigated on two 1.8 ha permanent plots near Habsburg and Murten (Switzerland). The plots were established with the aim to test methods of artificial oak regeneration after large-scale windthrow. On both plots, 80 oaks exposed to varying levels of competitive pressure from at most 10 neighbouring beech trees were selected. The height of each oak as well as stem and branch diameters were measured. The competitive pressure was assessed using Schütz's competition index, which is based on relative tree height, crown overlap and distance from competing neighbours. Oak trees growing without or with only slight competition from beech were equally tall, while oaks exposed to moderate to strong competition were smaller. A threshold value for the competition index was found above which oak height decreased strongly. The stem and branch diameters of the oaks started to decrease even if the competition from beech was slight, and decreased much further with more competition. The oak stems started to become more slender even with only slight competition from beech. On the moderately acid beech sites studied here, beech grow taller faster than oak. Thus where beech is competing with oak and the aim is to maintain the oak, competitive pressure on the oak must be reduced at an early stage. The degree of the intervention should, however, take the individual competitive interaction into account, with more intervention if the competition is strong.

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