scholarly journals Individual Tree Biomass Models to Estimate Forest Biomass for Large Spatial Regions Developed Using Four Pine Species in China

2017 ◽  
Vol 63 (3) ◽  
pp. 241-249 ◽  
Author(s):  
LiYong Fu ◽  
WeiSheng Zeng ◽  
ShouZheng Tang
Keyword(s):  
Forest Biomass ◽  
Tree Biomass ◽  
Individual Tree ◽  
Biomass Models ◽  
Pine Species

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.  

Construction of compatible and additive individual-tree biomass models for Pinus tabulaeformis in China

2017 ◽  
Vol 47 (4) ◽  
pp. 467-475 ◽  
Author(s):  
WeiSheng Zeng ◽  
LianJin Zhang ◽  
XinYun Chen ◽  
ZhiChu Cheng ◽  
KeXi Ma ◽  
...  
Keyword(s):  
Aboveground Biomass ◽  
Belowground Biomass ◽  
Pinus Tabulaeformis ◽  
Stem Volume ◽  
Mean Annual Precipitation ◽  
Mean Annual Temperature ◽  
Tree Biomass ◽  
Individual Tree ◽  
Chinese Pine ◽  
Biomass Models

Current biomass models for Chinese pine (Pinus tabulaeformis Carr.) fail to accurately estimate biomass in large geographic regions because they were usually based on limited sample trees on local sites, incompatible with stem volume, and not additive among components and total biomass. This study was based on mensuration data of individual-tree biomass from large samples of Chinese pine. The purpose was to construct compatible and additive biomass models using the nonlinear error-in-variable simultaneous equations and dummy variable modeling approach. This approach could ensure compatibility of an aboveground biomass model with a biomass conversion factor (BCF) and a stem volume model and compatibility of a belowground biomass model with a root-to-shoot ratio (RSR) model. Also, stem, branch, and foliage biomass models were additive to the aboveground biomass model. Results showed that mean prediction errors (MPEs) of the developed one- and two-variable aboveground biomass models were less than 4% and MPEs of the three-component (stem, branch, and foliage) and belowground biomass models were less than 10%. Furthermore, the effects of main climate variables on above- and below-ground biomass were analyzed. Aboveground biomass was related to mean annual temperature (MAT), while belowground biomass had no significant relationship with either MAT or mean annual precipitation (MAP). The developed models provide a good basis for estimating biomass of Chinese pine forests.

Generic linear mixed-effects individual-tree biomass models forPinus massonianain southern China

2014 ◽  
Vol 76 (1) ◽  
pp. 47-56 ◽  
Author(s):  
Liyong Fu ◽  
Weisheng Zeng ◽  
Huiru Zhang ◽  
Guangxing Wang ◽  
Yuancai Lei ◽  
...  
Keyword(s):  
Southern China ◽  
Mixed Effects ◽  
Tree Biomass ◽  
Individual Tree ◽  
Linear Mixed Effects ◽  
Biomass Models

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 Tree height integrated into pantropical forest biomass estimates

2012 ◽  
Vol 9 (8) ◽  
pp. 3381-3403 ◽  
Author(s):  
T. R. Feldpausch ◽  
J. Lloyd ◽  
S. L. Lewis ◽  
R. J. W. Brienen ◽  
M. Gloor ◽  
...  
Keyword(s):  
East Africa ◽  
Carbon Storage ◽  
Stand Structure ◽  
Forest Biomass ◽  
Small Diameter ◽  
Tree Height ◽  
Total Biomass ◽  
Guiana Shield ◽  
Tree Biomass ◽  
Continental Scale

Abstract. Aboveground tropical tree biomass and carbon storage estimates commonly ignore tree height (H). We estimate the effect of incorporating H on tropics-wide forest biomass estimates in 327 plots across four continents using 42 656 H and diameter measurements and harvested trees from 20 sites to answer the following questions: 1. What is the best H-model form and geographic unit to include in biomass models to minimise site-level uncertainty in estimates of destructive biomass? 2. To what extent does including H estimates derived in (1) reduce uncertainty in biomass estimates across all 327 plots? 3. What effect does accounting for H have on plot- and continental-scale forest biomass estimates? The mean relative error in biomass estimates of destructively harvested trees when including H (mean 0.06), was half that when excluding H (mean 0.13). Power- and Weibull-H models provided the greatest reduction in uncertainty, with regional Weibull-H models preferred because they reduce uncertainty in smaller-diameter classes (≤40 cm D) that store about one-third of biomass per hectare in most forests. Propagating the relationships from destructively harvested tree biomass to each of the 327 plots from across the tropics shows that including H reduces errors from 41.8 Mg ha−1 (range 6.6 to 112.4) to 8.0 Mg ha−1 (−2.5 to 23.0). For all plots, aboveground live biomass was −52.2 Mg ha−1 (−82.0 to −20.3 bootstrapped 95% CI), or 13%, lower when including H estimates, with the greatest relative reductions in estimated biomass in forests of the Brazilian Shield, east Africa, and Australia, and relatively little change in the Guiana Shield, central Africa and southeast Asia. Appreciably different stand structure was observed among regions across the tropical continents, with some storing significantly more biomass in small diameter stems, which affects selection of the best height models to reduce uncertainty and biomass reductions due to H. After accounting for variation in H, total biomass per hectare is greatest in Australia, the Guiana Shield, Asia, central and east Africa, and lowest in east-central Amazonia, W. Africa, W. Amazonia, and the Brazilian Shield (descending order). Thus, if tropical forests span 1668 million km2 and store 285 Pg C (estimate including H), then applying our regional relationships implies that carbon storage is overestimated by 35 Pg C (31–39 bootstrapped 95% CI) if H is ignored, assuming that the sampled plots are an unbiased statistical representation of all tropical forest in terms of biomass and height factors. Our results show that tree H is an important allometric factor that needs to be included in future forest biomass estimates to reduce error in estimates of tropical carbon stocks and emissions due to deforestation.

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
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