scholarly journals Accommodating heteroscedasticity in allometric biomass models

2021 ◽  
pp. 119865
Author(s):  
Ioan Dutcă ◽  
Ronald E. McRoberts ◽  
Erik Næsset ◽  
Viorel N.B. Blujdea
Keyword(s):  
Biomass Models

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 Stand-level biomass models for predicting C stock for the main Spanish pine species

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ana Aguirre ◽  
Miren del Río ◽  
Ricardo Ruiz-Peinado ◽  
Sonia Condés
Keyword(s):  
Form Factor ◽  
Pinus Halepensis ◽  
Dry Weight ◽  
Pinus Nigra ◽  
Carbon Stocks ◽  
Tree Size ◽  
Pine Forests ◽  
Stand Volume ◽  
Biomass Models ◽  
Pine Species

Abstract Background National and international institutions periodically demand information on forest indicators that are used for global reporting. Among other aspects, the carbon accumulated in the biomass of forest species must be reported. For this purpose, one of the main sources of data is the National Forest Inventory (NFI), which together with statistical empirical approaches and updating procedures can even allow annual estimates of the requested indicators. Methods Stand level biomass models, relating the dry weight of the biomass with the stand volume were developed for the five main pine species in the Iberian Peninsula (Pinus sylvestris, Pinus pinea, Pinus halepensis, Pinus nigra and Pinus pinaster). The dependence of the model on aridity and/or mean tree size was explored, as well as the importance of including the stand form factor to correct model bias. Furthermore, the capability of the models to estimate forest carbon stocks, updated for a given year, was also analysed. Results The strong relationship between stand dry weight biomass and stand volume was modulated by the mean tree size, although the effect varied among the five pine species. Site humidity, measured using the Martonne aridity index, increased the biomass for a given volume in the cases of Pinus sylvestris, Pinus halepensis and Pinus nigra. Models that consider both mean tree size and stand form factor were more accurate and less biased than those that do not. The models developed allow carbon stocks in the main Iberian Peninsula pine forests to be estimated at stand level with biases of less than 0.2 Mg∙ha− 1. Conclusions The results of this study reveal the importance of considering variables related with environmental conditions and stand structure when developing stand dry weight biomass models. The described methodology together with the models developed provide a precise tool that can be used for quantifying biomass and carbon stored in the Spanish pine forests in specific years when no field data are available.

scholarly journals Biomass models for estimating carbon storage in Areca palm plantations

2021 ◽  
Vol 10 ◽  
pp. 100115
Author(s):  
Milon Das ◽  
Panna Chandra Nath ◽  
Gudeta Weldesemayat Sileshi ◽  
Rajiv Pandey ◽  
Arun Jyoti Nath ◽  
...  
Keyword(s):  
Carbon Storage ◽  
Biomass Models

scholarly journals Belowground biomass models for young oligotrophic Scots pine stands in Latvia

2018 ◽  
Vol 11 (2) ◽  
pp. 206-211 ◽  
Author(s):  
L Kenina ◽  
A Bardulis ◽  
R Matisons ◽  
R Kapostins ◽  
A Jansons
Keyword(s):  
Scots Pine ◽  
Belowground Biomass ◽  
Biomass Models ◽  
Pine Stands ◽  
Scots Pine Stands

Development and evaluation of aboveground small tree biomass models for naturally regenerated and planted species in eastern Maine, U.S.A.

2014 ◽  
Vol 68 ◽  
pp. 215-227 ◽  
Author(s):  
Andrew S. Nelson ◽  
Aaron R. Weiskittel ◽  
Robert G. Wagner ◽  
Michael R. Saunders
Keyword(s):  
Tree Biomass ◽  
Small Tree ◽  
Biomass Models

Choosing the Dependent Variable in SAR Backscatter – Forest Biomass Models

2021 ◽  
Author(s):  
M.J. Ducey ◽  
X. Huang ◽  
B. Ziniti ◽  
N. Torbick
Keyword(s):  
Forest Biomass ◽  
Biomass Models

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.  

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