China's normalized tree biomass equation dataset

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.

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

Canadian Journal of Forest Research ◽

10.1139/cjfr-2018-0246 ◽

2019 ◽

Vol 49 (1) ◽

pp. 27-40 ◽

Cited By ~ 10

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.

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A review of biomass equations for China's tree species

Earth System Science Data ◽

10.5194/essd-12-21-2020 ◽

2020 ◽

Vol 12 (1) ◽

pp. 21-40 ◽

Cited By ~ 7

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.

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ChinAllomeTree 1.0: China's normalized tree biomass equation dataset

10.5194/essd-2019-1 ◽

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.

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Temporal variation in tree biomass and carbon stocks of Pinus roxburghii Sargent forests of Rajouri forest division in Jammu & Kashmir State

Environment Conservation Journal ◽

10.36953/ecj.2017.18316 ◽

2017 ◽

Vol 18 (3) ◽

pp. 123-133

Author(s):

Rajeshwar Singh Jasrotia ◽

◽

Anil K. Raina ◽

Keyword(s):

Temporal Variation ◽

Carbon Stocks ◽

Tree Biomass ◽

Pinus Roxburghii ◽

Forest Division

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Assessing Wood and Soil Carbon Losses from a Forest-Peat Fire in the Boreo-Nemoral Zone

Forests ◽

10.3390/f12070880 ◽

2021 ◽

Vol 12 (7) ◽

pp. 880

Author(s):

Andrey Sirin ◽

Alexander Maslov ◽

Dmitry Makarov ◽

Yakov Gulbe ◽

Hans Joosten

Keyword(s):

Soil Carbon ◽

Stand Structure ◽

Peat Soil ◽

Burned Area ◽

Tree Biomass ◽

Carbon Loss ◽

Forest Stand Structure ◽

Peat Fire ◽

Peat Fires ◽

Carbon Losses

Forest-peat fires are notable for their difficulty in estimating carbon losses. Combined carbon losses from tree biomass and peat soil were estimated at an 8 ha forest-peat fire in the Moscow region after catastrophic fires in 2010. The loss of tree biomass carbon was assessed by reconstructing forest stand structure using the classification of pre-fire high-resolution satellite imagery and after-fire ground survey of the same forest classes in adjacent areas. Soil carbon loss was assessed by using the root collars of stumps to reconstruct the pre-fire soil surface and interpolating the peat characteristics of adjacent non-burned areas. The mean (median) depth of peat losses across the burned area was 15 ± 8 (14) cm, varying from 13 ± 5 (11) to 20 ± 9 (19). Loss of soil carbon was 9.22 ± 3.75–11.0 ± 4.96 (mean) and 8.0–11.0 kg m−2 (median); values exceeding 100 tC ha−1 have also been found in other studies. The estimated soil carbon loss for the entire burned area, 98 (mean) and 92 (median) tC ha−1, significantly exceeds the carbon loss from live (tree) biomass, which averaged 58.8 tC ha−1. The loss of carbon in the forest-peat fire thus equals the release of nearly 400 (soil) and, including the biomass, almost 650 tCO2 ha−1 into the atmosphere, which illustrates the underestimated impact of boreal forest-peat fires on atmospheric gas concentrations and climate.

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