scholarly journals A review of biomass equations for China's tree species

2020 ◽  
Vol 12 (1) ◽  
pp. 21-40 ◽  
Author(s):  
Yunjian Luo ◽  
Xiaoke Wang ◽  
Zhiyun Ouyang ◽  
Fei Lu ◽  
Liguo Feng ◽  
...  
Keyword(s):  
Tree Species ◽  
Carbon Accounting ◽  
Sub Saharan Africa ◽  
Background Information ◽  
Vegetation Coverage ◽  
Biomass Estimation ◽  
Tree Biomass ◽  
Biomass Equation ◽  
Geographical Regions ◽  
Biomass Equations

Abstract. Tree biomass equations are the most commonly used method to estimate tree and forest biomasses at various spatial and temporal scales because of their high accuracy, efficiency and conciseness. For decades, many tree biomass equations have been reported in diverse types of literature (e.g., journals, books and reports). These scattered equations are being compiled, and tree biomass equation datasets are currently available for many geographical regions (e.g., Europe, North America and sub-Saharan Africa) and countries (e.g., Australia, Indonesia and Mexico). However, one important country stands out as an area where a large number of biomass equations have not yet been reviewed and inventoried extensively: China. Therefore, in this study, we carried out a broad survey and critical review of the literature (from 1978 to 2013) on biomass equations in China and compiled a normalized tree biomass equation dataset for China. This dataset consists of 5924 biomass equations for nearly 200 tree species and their associated background information (e.g., geographical location, climate and stand description), showing sound geographical, climatic and forest vegetation coverage across China. The dataset is freely available at https://doi.org/10.1594/PANGAEA.895244 (Luo et al., 2018) for noncommercial scientific applications, and this dataset fills an important regional gap in global biomass equations and provides key parameters for biomass estimation in forest inventory and carbon accounting studies in China.

scholarly journals ChinAllomeTree 1.0: China's normalized tree biomass equation dataset

2019 ◽  
Author(s):  
Yunjian Luo ◽  
Xiaoke Wang ◽  
Zhiyun Ouyang ◽  
Fei Lu ◽  
Liguo Feng ◽  
...  
Keyword(s):  
Geographical Location ◽  
Allometric Equation ◽  
Carbon Accounting ◽  
Sub Saharan Africa ◽  
Background Information ◽  
Biomass Estimation ◽  
Tree Biomass ◽  
Biomass Equation ◽  
Geographical Regions ◽  
Biomass Equations

Abstract. The tree biomass equation, which is also called the tree allometric equation, is the most commonly used method to estimate tree and forest biomass at various spatial-temporal scales because of its high accuracy, efficiency and conciseness. For decades, many tree biomass equations have been reported in diverse types of literature (e.g., journals, books and reports). These scattered equations are being compiled, and tree biomass equation datasets are currently available for many geographical regions (e.g., Europe, North America and Sub-Saharan Africa) and countries (e.g., Australia, Indonesia and Mexico) except for in an important region of the world, Eastern Asia, specifically China. Therefore, in this study, we carried out an extensive survey and critical review of the literature (from 1978–2013) on biomass equations conducted in China and developed China’s normalized tree biomass equation dataset (ChinAllomeTree version 1.0). This dataset consists of 5,924 biomass component equations for nearly 200 species and their associated background information (e.g., geographical location, climate and stand description), showing sound geographical, climatic and forest vegetation coverages across China. The dataset is freely available at https://doi.pangaea.de/10.1594/PANGAEA.895244 for noncommercial scientific applications, which fills an important regional gap in global biomass datasets and provides key parameters for biomass estimation in forest inventory and carbon accounting in China.

scholarly journals Biomass and stem volume equations for tree species in Europe

2005 ◽  
Vol 2005 (4) ◽  
pp. 1-63
Author(s):  
Dimitris Zianis ◽  
Petteri Muukkonen ◽  
Raisa Mäkipää ◽  
Maurizio Mencuccini
Keyword(s):  
Tree Species ◽  
Forest Ecosystems ◽  
Nutrient Balance ◽  
Theoretical Models ◽  
Stem Volume ◽  
Biomass Estimation ◽  
Statistical Parameters ◽  
Geographical Regions ◽  
Biomass Equations ◽  
Volume Equations

A review of stem volume and biomass equations for tree species growing in Europe is presented. The mathematical forms of the empirical models, the associated statistical parameters and information about the size of the trees and the country of origin were collated from scientific articles and from technical reports. The total number of the compiled equations for biomass estimation was 607 and for stem volume prediction it was 230. The analysis indicated that most of the biomass equations were developed for aboveground tree components. A relatively small number of equations were developed for southern Europe. Most of the biomass equations were based on a few sampled sites with a very limited number of sampled trees. The volume equations were, in general, based on more representative data covering larger geographical regions. The volume equations were available for major tree species in Europe. The collected information provides a basic tool for estimation of carbon stocks and nutrient balance of forest ecosystems across Europe as well as for validation of theoretical models of biomass allocation.

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 Allometric equations for estimating tree biomass in restored mixed-species Atlantic Forest stands

2014 ◽  
Vol 14 (2) ◽  
Author(s):  
Lauro Rodrigues Nogueira Júnior ◽  
Vera Lex Engel ◽  
John A. Parrotta ◽  
Antonio Carlos Galvão de Melo ◽  
Danilo Scorzoni Ré
Keyword(s):  
Atlantic Forest ◽  
Forest Restoration ◽  
Global Climate ◽  
Allometric Equations ◽  
Predictor Variables ◽  
Forest Stands ◽  
Tree Biomass ◽  
Biomass Equation ◽  
South Central ◽  
Biomass Equations

Restoration of Atlantic Forests is receiving increasing attention because of its role in both biodiversity conservation and carbon sequestration for global climate change mitigation. This study was carried out in an Atlantic Forest restoration project in the south-central region of São Paulo State - Brazil to develop allometric equations to estimate tree biomass of indigenous tree species in mixed plantations. Above and below-ground biomass (AGB and BGB, respectively), stem diameter (DBH: diameter at 1.3 m height), tree height (H: total height) and specific wood density (WD) were measured for 60 trees of 19 species. Different biomass equations (linear and nonlinear-transformed) were adjusted to estimate AGB and BGB as a function of DBH, H and WD. For estimating AGB and BGB, the linear biomass equation models were the least accurate. The transformed nonlinear biomass equation that used log DBH2, log H and log WD as predictor variables were the most accurate for AGB and the transformed nonlinear biomass equations that used log DBH2*WD as predictor variables were the most accurate for BGB. It is concluded that these adjusted equations can be used to estimate the AGB and BGB in areas of the studied project. The adjusted equations can be recommended for use elsewhere in the region for forest stands of similar age, tree size ranges, species composition and site characteristics.

scholarly journals Biomass estimation equations for mesquite trees in the Americas

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6782 ◽  
Author(s):  
Jose Navar ◽  
Felipa de Jesus Rodriguez-Flores ◽  
Julio Rios-Saucedo
Keyword(s):  
Aboveground Biomass ◽  
Biomass Estimation ◽  
Data Sets ◽  
South American ◽  
Biomass Equation ◽  
Northern Mexico ◽  
Biomass Equations ◽  
Semi Empirical ◽  
North And South ◽  
Semi Arid

Mesquite trees are the preferred dendroenergy sources in arid and semi-arid forests. In spite of their relative importance, regional aboveground biomass (AGB) equations for mesquite trees are scarce in the scientific literature. For that reason, the aims of this study were to: (a) harvest trees and develop regional biomass equations; (b) contrast measured data with equations developed previously; and (c) test the applicability of the fitted equation for mesquite trees in the arid and semi-arid forests of the Americas. We harvested 206 new mesquite trees from arid and semi-arid forests in northern Mexico (Coahuila, Nuevo Leon, and Tamaulipas) in addition to using two other previously compiled data sets from Mexico (N = 304) to develop a regional equation. To test the validity of this equation, for biomass equations reported for the rest of the country, as well as for North and South American mesquite trees, we contrasted AGB measurements with predictions of fitted equations. Statistical analysis revealed the need for a single, regional, semi-empirical equation as together the three data sets represented the variability of the aboveground biomass of mesquite trees across northern Mexico, as well as mesquite trees in America’s arid and semiarid regions. Due to the large quantity of mesquite trees harvested for sampling and their variability, the regional biomass equation developed encompasses all other North and South American equations, and is representative of mesquite trees throughout the arid and semi-arid forests of the Americas.

Generalized additive models reveal among-stand variation in live tree biomass equations

2020 ◽  
Author(s):  
Jacob I. Levine ◽  
Perry de Valpine ◽  
John J. Battles
Keyword(s):  
Linear Models ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Carbon Accounting ◽  
Stand Age ◽  
Accurate Estimation ◽  
Biomass Estimation ◽  
Allometric Relationships ◽  
Tree Biomass ◽  
Live Tree

Accurate estimation of forest biomass is important for scientists and policymakers interested in carbon accounting, nutrient cycling, and forest resilience. Estimates often rely on the allometry of trees; however, limited datasets, uncertainty in model form, and unaccounted for sources of variation warrant a re-examination of allometric relationships using modern statistical techniques. We asked the following questions: (1) Is there among-stand variation in allometric relationships? (2) Is there nonlinearity in allometric relationships? (3) Can among-stand variation or nonlinearities in allometric equations be attributed to differences in stand age? (4) What are the implications for biomass estimation? To answer these questions, we synthesized a dataset of small trees from six different studies in the White Mountains of New Hampshire. We compared the performance of generalized additive models (GAMs) and linear models and found that GAMs consistently outperform linear models. The best-fitting model indicates that allometries vary among both stands and species and contain subtle nonlinearities which are themselves variable by species. Using a planned contrasts analysis, we were able to attribute some of the observed among-stand heterogeneity to differences in stand age. However, variability in these results point to additional sources of stand-level heterogeneity, which if identified could improve the accuracy of live-tree biomass estimation.

scholarly journals Deriving Individual-Tree Biomass from Effective Crown Data Generated by Terrestrial Laser Scanning

2019 ◽  
Vol 11 (23) ◽  
pp. 2793
Author(s):  
Yujie Zheng ◽  
Weiwei Jia ◽  
Qiang Wang ◽  
Xu Huang
Keyword(s):  
Laser Scanning ◽  
Three Dimensional ◽  
Terrestrial Laser Scanning ◽  
Tree Height ◽  
Biomass Estimation ◽  
Tree Biomass ◽  
Crown Height ◽  
Individual Tree ◽  
Cloud Data ◽  
Biomass Equations

Biomass reflects the state of forest management and is critical for assessing forest benefits and carbon storage. The effective crown is the region above the lower limit of the forest crown that includes the maximum vertical distribution density of branches and leaves; this component plays an important role in tree growth. Adding the effective crown to biomass equations can enhance the accuracy of the derived biomass. Six sample plots in a larch plantation (ranging in area from 0.06 ha to 0.12 ha and in number of trees from 63 to 96) at the Mengjiagang forest farm in Huanan County, Jiamusi City, Heilongjiang Province, China, were analyzed in this study. Terrestrial laser scanning (TLS) was used to obtain three-dimensional point cloud data on the trees, from which crown parameters at different heights were extracted. These parameters were used to determine the position of the effective crown. Moreover, effective crown parameters were added to biomass equations with tree height as the sole variable to improve the accuracy of the derived individual-tree biomass estimates. The results showed that the minimum crown contact height was very similar to the effective crown height, and an increase in model accuracy was apparent (with R a 2 increasing from 0.846 to 0.910 and root-mean-square error (RMSE) decreasing from 0.372 kg to 0.286 kg). The optimal model for deriving biomass included tree height, crown length from minimum contact height, crown height from minimum contact height, and crown surface area from minimum contact height. The novelty of the article is that it improves the fit of individual-tree biomass models by adding crown-related variables and investigates how the accuracy of biomass estimation can be enhanced by using remote sensing methods without obtaining diameter at breast height.

Additive biomass equations for slash pine trees: comparing three modeling approaches

2019 ◽  
Vol 49 (1) ◽  
pp. 27-40 ◽  
Author(s):  
Dehai Zhao ◽  
James Westfall ◽  
John W. Coulston ◽  
Thomas B. Lynch ◽  
Bronson P. Bullock ◽  
...  
Keyword(s):  
Pinus Elliottii ◽  
Slash Pine ◽  
Seemingly Unrelated Regression ◽  
Total Biomass ◽  
Tree Biomass ◽  
Biomass Equation ◽  
Fit Statistics ◽  
Biomass Equations ◽  
Pine Trees ◽  
Total Tree

Both aggregative and disaggregative strategies were used to develop additive nonlinear biomass equations for slash pine (Pinus elliottii Engelm. var. elliottii) trees in the southeastern United States. In the aggregative approach, the total tree biomass equation was specified by aggregating the expectations of component biomass models, and their parameters were estimated by jointly fitting all component and total biomass equations using weighted nonlinear seemingly unrelated regression (NSUR) (SUR1) or by jointly fitting component biomass equations using weighted NSUR (SUR2). In an alternative disaggregative approach (DRM), the biomass component proportions were modeled using Dirichlet regression, and the estimated total tree biomass was disaggregated into biomass components based on their estimated proportions. There was no single system to predict biomass that was best for all components and total tree biomass. The ranking of the three systems based on an array of fit statistics followed the order of SUR2 > SUR1 > DRM. All three systems provided more accurate biomass predictions than previously published equations.

scholarly journals A biomass equation dataset for common shrub species in China

2021 ◽  
Author(s):  
Yang Wang ◽  
Wenting Xu ◽  
Zhiyao Tang ◽  
Zongqiang Xie
Keyword(s):  
Terrestrial Ecosystem ◽  
Structure And Function ◽  
Biomass Estimation ◽  
Ecosystem Structure ◽  
Biomass Equation ◽  
Shrub Species ◽  
Biomass Equations ◽  
Shrub Biomass ◽  
And Function ◽  
Ecosystem Structure And Function

Abstract. Shrub biomass equations provide an accurate, efficient and convenient method in estimating biomass of shrubland ecosystems and biomass of the shrub layer in forest ecosystems at various spatial and temporal scales. In recent decades, many shrub biomass equations have been reported mainly in journals, books and postgraduate's dissertations. However, these biomass equations are applicable for limited shrub species with respect to a large number of shrub species widely distributed in China, which severely restricted the study of terrestrial ecosystem structure and function, such as biomass, production, and carbon budge. Therefore, we firstly carried out a critical review of published literature (from 1982 to 2019) on shrub biomass equations in China, and then developed biomass equations for the dominant shrub species using a unified method based on field measurements of 738 sites in shrubland ecosystems across China. Finally, we constructed the first comprehensive biomass equation dataset for China’s common shrub species. This dataset consists of 822 biomass equations specific to 167 shrub species and has significant representativeness to the geographical, climatic and shrubland vegetation features across China. The dataset is freely available at https://doi.org/10.11922/sciencedb.00641 for noncommercial scientific applications, and this dataset fills a significant gap in woody biomass equations and provides key parameters for biomass estimation in studies on terrestrial ecosystem structure and function.

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.

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