scholarly journals Evaluation of Stand Biomass Estimation Methods for Major Forest Types in the Eastern Da Xing’an Mountains, Northeast China

Forests ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 715
Author(s):  
Dong ◽  
Zhang ◽  
Li
Keyword(s):  
Model Fitting ◽  
Prediction Performance ◽  
Model Systems ◽  
Estimation Methods ◽  
Biomass Estimation ◽  
Forest Types ◽  
Stand Volume ◽  
Additive System ◽  
Biomass Models ◽  
Biomass Equations

Currently, forest biomass estimation methods at the regional scale have attracted the greatest attention from researchers, and the development of stand biomass models has become popular a trend. In this study, a total of 5074 measurements on 1053 permanent sample plots were obtained in the Eastern Da Xing’an Mountains, and three additive systems of stand biomass equations were developed. The first additive system (M-1) used stand variables as the predictors (i.e., stand basal area and average height), the second additive system (M-2) utilized stand volume as the sole predictor, and the third additive system (M-3) included both stand volume and biomass expansion and conversion factors (BCEFs) as the predictors. The coefficients of the three model systems were estimated with nonlinear seemingly unrelated regression (NSUR), while the heteroscedasticity of the model residuals was solved with the weight function. The jackknifing technique was used on the residuals, and several statistics were used to assess the prediction performance of each model. We comprehensively evaluated four stand biomass estimation methods (i.e., M-1, M-2, M-3 and a constant BCEF (M-4)). Here, we showed that the (1) three additive systems of stand biomass equations showed good model fitting and prediction performance, (2) M-3 significantly improved the model fitting and performance and provided the most accurate predictions for most stand biomass components, and (3) the ranking of the four stand biomass estimation methods followed the order of M-3 > M-2 > M-4 > M-1. Our results demonstrated these additive stand biomass models could be used to estimate the stand aboveground and belowground biomass for the major forest types in the Eastern Da Xing’an Mountains, although the most appropriate method depends on the available data and forest type.

scholarly journals A Bayesian Approach to Estimating Seemingly Unrelated Regression for Tree Biomass Model Systems

Forests ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1302
Author(s):  
Longfei Xie ◽  
Fengri Li ◽  
Lianjun Zhang ◽  
Faris Rafi Almay Widagdo ◽  
Lihu Dong
Keyword(s):  
Bayesian Approach ◽  
Bayesian Methods ◽  
Model Fitting ◽  
Seemingly Unrelated Regression ◽  
Model Systems ◽  
Informative Priors ◽  
Tree Biomass ◽  
Tree Diameter ◽  
Biomass Models ◽  
Unrelated Regression

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.

scholarly journals Models of stand volume and biomass estimation based on LiDAR data for the main forest types in Calabria (southern Italy)

2017 ◽  
Vol 14 (3) ◽  
pp. 175-187 ◽  
Author(s):  
G Scrinzi ◽  
A Floris ◽  
F Clementel ◽  
V Bernardini ◽  
F Chianucci ◽  
...  
Keyword(s):  
Southern Italy ◽  
Biomass Estimation ◽  
Lidar Data ◽  
Forest Types ◽  
Stand Volume

A Comparison of Two Biomass Estimation Methods: A Case Study of Several Pine Forests in China

2013 ◽  
Vol 807-809 ◽  
pp. 806-809
Author(s):  
Fei Li ◽  
Hua Yong Zhang ◽  
Zhong Yu Wang ◽  
Li Zhang
Keyword(s):  
Linear Function ◽  
Power Function ◽  
Goodness Of Fit ◽  
Pinus Tabulaeformis ◽  
Estimation Methods ◽  
Pinus Koraiensis ◽  
Biomass Estimation ◽  
Function Model ◽  
Stand Volume ◽  
Function Models

Power function model and linear function model were commonly used to express the relationships between stand volume and biomass. However, the relative accuracy is still unclear. In order to compare the accuracy of the two types of model, field measurement data of 279 pine forest stands in China were collected from published literatures. Using the data collected, the relationships between stand volume and aboveground biomass (AGB) of Pinus koraiensis forest, Pinus armandii forest, Pinus massoniana forest, Pinus tabulaeformis forest and Pinus forest were established. The mean relative error and mean absolute values of relative errors were employed to test the errors of the established equations. The goodness-of-fit and errors of these two types of model were compared. The results show that the power function models could generally express the relationships better than the linear function models. Also, the errors of the power function models are generally lower than those of the linear function 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 and Volume Modeling along with Carbon Concentration Variations of Short-Rotation Poplar Plantations

Forests ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 780 ◽  
Author(s):  
Lihu Dong ◽  
Faris Rafi Almay Widagdo ◽  
Longfei Xie ◽  
Fengri Li
Keyword(s):  
Carbon Sink ◽  
Biomass Conversion ◽  
Model Fitting ◽  
National Forest Inventory ◽  
Model Systems ◽  
Stem Volume ◽  
Crown Length ◽  
Short Rotation ◽  
And Performance ◽  
Volume Models

Short-rotation forestry is of interest to provide biomass for bioenergy and act as a carbon sink to mitigate global warming. The Poplar tree (Populus × xiaohei) is a fast-growing and high-yielding tree species in Northeast China. In this study, a total of 128 Populus × xiaohei trees from the Songnen Plain, Heilongjiang Province, Northeastern China, were harvested. Several available independent variables, such as tree diameter at breast height (D), tree’s total height (H), crown width (CW), and crown length (CL), were differently combined to develop three additive biomass model systems and eight stem volume models for Populus × xiaohei tree. Variance explained within the three additive biomass model systems ranged from 83% to 98%, which was lowest for the foliage models, and highest for the stem biomass models. Similar findings were found in the stem volume models, in which the models explained more than 94% of the variance. The additional predictors, such as H, CL, or CW, evidently enhanced the model fitting and performance for the total and components biomass along with the stem volume models. Furthermore, the biomass conversion and expansion factors (BCEFs) of the root (118.2 kg/m3), stem (380.2 kg/m3), branch (90.7 kg/m3), and foliage (31.2 kg/m3) were also calculated. The carbon concentrations of Populus × xiaohei in root, stem, branch, and foliage components were 45.98%, 47.74%, 48.32%, and 48.46%, respectively. Overall, the newly established models in this study provided complete and comprehensive tools for quantifying the biomass and stem volume of Populus × xiaohei, which might be essential to be specifically utilized in the Chinese National Forest Inventory.

scholarly journals Aboveground Forest Biomass Estimation with Landsat and LiDAR Data and Uncertainty Analysis of the Estimates

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Dengsheng Lu ◽  
Qi Chen ◽  
Guangxing Wang ◽  
Emilio Moran ◽  
Mateus Batistella ◽  
...  
Keyword(s):  
Uncertainty Analysis ◽  
Optical Sensors ◽  
Forest Biomass ◽  
Structural Data ◽  
Data Availability ◽  
Estimation Methods ◽  
Biomass Estimation ◽  
Lidar Data ◽  
Availability Constraints ◽  
Data Saturation

Landsat Thematic mapper (TM) image has long been the dominate data source, and recently LiDAR has offered an important new structural data stream for forest biomass estimations. On the other hand, forest biomass uncertainty analysis research has only recently obtained sufficient attention due to the difficulty in collecting reference data. This paper provides a brief overview of current forest biomass estimation methods using both TM and LiDAR data. A case study is then presented that demonstrates the forest biomass estimation methods and uncertainty analysis. Results indicate that Landsat TM data can provide adequate biomass estimates for secondary succession but are not suitable for mature forest biomass estimates due to data saturation problems. LiDAR can overcome TM’s shortcoming providing better biomass estimation performance but has not been extensively applied in practice due to data availability constraints. The uncertainty analysis indicates that various sources affect the performance of forest biomass/carbon estimation. With that said, the clear dominate sources of uncertainty are the variation of input sample plot data and data saturation problem related to optical sensors. A possible solution to increasing the confidence in forest biomass estimates is to integrate the strengths of multisensor data.

A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems

2014 ◽  
Vol 9 (1) ◽  
pp. 63-105 ◽  
Author(s):  
Dengsheng Lu ◽  
Qi Chen ◽  
Guangxing Wang ◽  
Lijuan Liu ◽  
Guiying Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Aboveground Biomass ◽  
Forest Ecosystems ◽  
Estimation Methods ◽  
Biomass Estimation

Operational biomass recovery of small trees: equations for six central Ontario tree species

2015 ◽  
Vol 45 (3) ◽  
pp. 372-377 ◽  
Author(s):  
Matt Thiel ◽  
Nathan Basiliko ◽  
John Caspersen ◽  
Jeff Fera ◽  
Trevor Jones
Keyword(s):  
Pinus Strobus ◽  
Size Class ◽  
Forest Types ◽  
White Pine ◽  
Breast Height ◽  
Biomass Equations ◽  
Bioenergy Feedstocks ◽  
Biomass Recovery ◽  
The Difference ◽  
High Degree

Accurate estimates of the amount of biomass that can be recovered at the roadside are needed to make informed decisions about whether to implement an increased utilization harvesting system to supply additional bioenergy feedstocks. Current estimates of recovery are based on total aboveground biomass equations that do not always account for the volume lost to the unharvested stumps or to tops and branches broken during forestry operations. The study took place in a white pine (Pinus strobus L.) mixedwood forest at the Petawawa Research Forest in central Ontario. Equations to describe recoverable biomass were developed from 371 cut and skidded trees, which ranged from 3 to 24 cm in diameter at breast height, across six species. For each species and diameter size class, we evaluated the difference between estimates produced by locally developed equations and those from published equations produced for other locations and forest types. Our recovered biomass estimates were generally higher than the Canadian national averages but within the observed range of published values from across North America. We report that small trees are recovered nearly in their entirety, with little breakage and loss during operations. The high degree of variability among estimates produced by the various equations poses one of the biggest challenges in accurately estimating roadside biomass in an operational setting.

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