scholarly journals Separating Regressions for Model Fitting to Reduce the Uncertainty in Forest Volume-Biomass Relationship

Forests ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 658
Author(s):  
Caixia Liu ◽  
Xiaolu Zhou ◽  
Xiangdong Lei ◽  
Huabing Huang ◽  
Carl Zhou ◽  
...  
Keyword(s):  
Field Data ◽  
Forest Biomass ◽  
Model Fitting ◽  
Graphical Analysis ◽  
Biomass Estimation ◽  
Allometric Relationship ◽  
Stem Biomass ◽  
Intermediate Variable ◽  
Growing Stock ◽  
Pseudo Data

The method of forest biomass estimation based on a relationship between the volume and biomass has been applied conventionally for estimating stand above- and below-ground biomass (SABB, t ha−1) from mean growing stock volume (m3 ha−1). However, few studies have reported on the diagnosis of the volume-SABB equations fitted using field data. This paper addresses how to (i) check parameters of the volume-SABB equations, and (ii) reduce the bias while building these equations. In our analysis, all equations were applied based on the measurements of plots (biomass or volume per hectare) rather than individual trees. The volume-SABB equation is re-expressed by two Parametric Equations (PEs) for separating regressions. Stem biomass is an intermediate variable (parametric variable) in the PEs, of which one is established by regressing the relationship between stem biomass and volume, and the other is created by regressing the allometric relationship of stem biomass and SABB. A graphical analysis of the PEs proposes a concept of “restricted zone,” which helps to diagnose parameters of the volume-SABB equations in regression analyses of field data. The sampling simulations were performed using pseudo data (artificially generated in order to test a model) for the model test. Both analyses of the regression and simulation demonstrate that the wood density impacts the parameters more than the allometric relationship does. This paper presents an applicable method for testing the field data using reasonable wood densities, restricting the error in field data processing based on limited field plots, and achieving a better understanding of the uncertainty in building those equations.

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 Re-estimating the changes and ranges of forest biomass carbon in China during the past 40 years

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Xiaolu Zhou ◽  
Xiangdong Lei ◽  
Caixia Liu ◽  
Huabing Huang ◽  
Carl Zhou ◽  
...  
Keyword(s):  
Forest Ecosystem ◽  
Forest Biomass ◽  
Biomass Carbon ◽  
National Scale ◽  
Step Process ◽  
Stem Biomass ◽  
Growing Stock ◽  
True Value ◽  
Significant Bias ◽  
The Future

Abstract Background In recent decades the future of global forests has been a matter of increasing concern, particularly in relation to the threat of forest ecosystem responses under potential climate change. To the future predictions of these responses, the current forest biomass carbon storage (FCS) should first be clarified as much as possible, especially at national scales. However, few studies have introduced how to verify an FCS estimate by delimiting the reasonable ranges. This paper addresses an estimation of national FCS and its verification using two-step process to narrow the uncertainty. Our study focuses on a methodology for reducing the uncertainty resulted by converting from growing stock volume to above- and below-ground biomass (AB biomass), so as to eliminate the significant bias in national scale estimations. Methods We recommend splitting the estimation into two parts, one part for stem and the other part for AB biomass to preclude possible significant bias. Our method estimates the stem biomass from volume and wood density (WD), and converts the AB biomass from stem biomass by using allometric relationships. Results Based on the presented two-step process, the estimation of China’s FCS is performed as an example to explicate how to infer the ranges of national FCS. The experimental results demonstrate a national FCS estimation within the reasonable ranges (relative errors: + 4.46% and − 4.44%), e.g., 5.6–6.1 PgC for China’s forest ecosystem at the beginning of the 2010s. These ranges are less than 0.52 PgC for confirming each FCS estimate of different periods during the last 40 years. In addition, our results suggest the upper-limits by specifying a highly impractical value of WD (0.7 t∙m− 3) on the national scale. As a control reference, this value decides what estimate is impossible to achieve for the FCS estimates. Conclusions Presented methodological analysis highlights the possibility to determine a range that the true value could be located in. The two-step process will help to verify national FCS and also to reduce uncertainty in related studies. While the true value of national FCS is immeasurable, our work should motivate future studies that explore new estimations to approach the true value by narrowing the uncertainty in FCS estimations on national and global scales.

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 Tropical-Forest Biomass Estimation at X-Band From the Spaceborne TanDEM-X Interferometer

2015 ◽  
Vol 12 (2) ◽  
pp. 239-243 ◽  
Author(s):  
Robert Treuhaft ◽  
Fabio Gonzalves ◽  
Joao Roberto dos Santos ◽  
Michael Keller ◽  
Michael Palace ◽  
...  
Keyword(s):  
Tropical Forest ◽  
Forest Biomass ◽  
Biomass Estimation ◽  
X Band

Forest Biomass Estimation Based on Forest Inventory Data in Middle and Last Scales

2011 ◽  
Vol 183-185 ◽  
pp. 220-224
Author(s):  
Ming Ze Li ◽  
Wen Yi Fan ◽  
Ying Yu
Keyword(s):  
Tree Species ◽  
Forest Inventory ◽  
Forest Biomass ◽  
Biomass Estimation ◽  
Total Biomass ◽  
Inventory Data ◽  
Biomass Equation ◽  
Tree Species Composition ◽  
Forest Inventory Data ◽  
Ecosystem Carbon

The forest biomass (which is referred to the arbor aboveground biomass in this research) is one of the most primary factors to determine the forest ecosystem carbon storages. There are many kinds of estimating methods adapted to various scales. It is a suitable method to estimate forest biomass of the farm or the forestry bureau in middle and last scales. First each subcompartment forest biomass should be estimated, and then the farm or the forestry bureau forest biomass was estimated. In this research, based on maoershan farm region, first the single tree biomass equation of main tree species was established or collected. The biomass of each specie was calculated according to the materials of tally, such as height, diameter and so on in the forest inventory data. Secondly, each specie’s biomass and total biomass in subcompartment were calculated according to the tree species composition in forest management investigation data. Thus the forest biomass spatial distribution was obtained by taking subcompartment as a unit. And last the forest total biomass was estimated.

scholarly journals Forest Biomass Estimation Using SAR and Optical Images

2015 ◽  
Vol 3 (1) ◽  
pp. 15-26
Author(s):  
Mohamad Reza Ramezani ◽  
Mahmood Reza Sahebi ◽  
◽  
Keyword(s):  
Forest Biomass ◽  
Biomass Estimation ◽  
Optical Images

scholarly journals ABOVE GROUND TREES BIOMASS OF LORE LINDU NATIONAL PARK-CENTRAL SULAWESI : A STUDY COMBINING FIELD MEASUREMENT AND REMOTE SENSING

Agromet ◽  
2010 ◽  
Vol 24 (1) ◽  
pp. 33
Author(s):  
Naimatu Solicha ◽  
Tania June ◽  
M. Ardiansyah ◽  
Antonius B. W.
Keyword(s):  
Remote Sensing ◽  
Carbon Stock ◽  
Field Data ◽  
Forest Cover ◽  
Satellite Image ◽  
Forest Biomass ◽  
Allometric Equation ◽  
Natural Forest ◽  
Forest Cover Type ◽  
Cover Type

Forests play an important role in global carbon cycling, since they hold a large pool of carbon as well as potential carbon sinks and sources to the atmosphere. Accurate estimation of forest biomass is required for greenhouse gas inventories and terrestrial carbon accounting. The information on biomass is essential to assess the total and the annual capacity of forest vigor. Estimation of aboveground biomass is necessary for studying productivity, carbon cycles, nutrient allocation, and fuel accumulation in terrestrial ecosystem. The possibility that above ground forest biomass might be determined from space is a promising alternative to ground-based methods. Remote sensing has opened an effective way to estimate forest biomass and carbon. By the combination of data field measurement and allometric equation, the above ground trees biomass possible to be estimated over the large area. The objectives of this research are: (1) To estimate the above ground tree biomass and carbon stock of forest cover in Lore Lindu National Park by combination of field data observation, allometric equation and multispectral satellite image; (2) to find the equation model between parameter that determines the biomass estimation. The analysis showed that field data observation and satellite image classification influencing much on the accuracy of trees biomass and carbon stock estimation. The forest cover type A and B (natural forest with the minor timber extraction) has the higher biomass than C and D (natural forest with the major timber extraction and agro forestry), it is about 607 ton/ha and 603 ton/ha. Forest cover type C is 457 ton/ha. Forest cover type D has the lowest biomass is about 203 ton/ha. Natural forest has high biomass, because of the tropical vegetation trees heterogeneity. Forest cover D has the lowest trees biomass because its vegetation component as secondary forest with the homogeneity of cacao plantation. The forest biomass and carbon estimation for each cover type will be useful for the further equation analysis when using the remote sensing technology for estimating the total biomass and for the economic carbon analysis.Forests play an important role in global carbon cycling, since they hold a large pool of carbon as well as potential carbon sinks and sources to the atmosphere. Accurate estimation of forest biomass is required for greenhouse gas inventories and terrestrial carbon accounting. The information on biomass is essential to assess the total and the annual capacity of forest vigor. Estimation of aboveground biomass is necessary for studying productivity, carbon cycles, nutrient allocation, and fuel accumulation in terrestrial ecosystem. The possibility that above ground forest biomass might be determined from space is a promising alternative to ground-based methods. Remote sensing has opened an effective way to estimate forest biomass and carbon. By the combination of data field measurement and allometric equation, the above ground trees biomass possible to be estimated over the large area. The objectives of this research are: (1) To estimate the above ground tree biomass and carbon stock of forest cover in Lore Lindu National Park by combination of field data observation, allometric equation and multispectral satellite image; (2) to find the equation model between parameter that determines the biomass estimation. The analysis showed that field data observation and satellite image classification influencing much on the accuracy of trees biomass and carbon stock estimation. The forest cover type A and B (natural forest with the minor timber extraction) has the higher biomass than C and D (natural forest with the major timber extraction and agro forestry), it is about 607 ton/ha and 603 ton/ha. Forest cover type C is 457 ton/ha. Forest cover type D has the lowest biomass is about 203 ton/ha. Natural forest has high biomass, because of the tropical vegetation trees heterogeneity. Forest cover D has the lowest trees biomass because its vegetation component as secondary forest with the homogeneity of cacao plantation. The forest biomass and carbon estimation for each cover type will be useful for the further equation analysis when using the remote sensing technology for estimating the total biomass and for the economic carbon analysis.

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