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

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.

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The Effect of Stand Age and Climate on Aboveground Biomass Accumulation of Pinus tabulaeformis Forests

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.726-731.4266 ◽

2013 ◽

Vol 726-731 ◽

pp. 4266-4269

Author(s):

Fei Li ◽

Hua Yong Zhang ◽

Zhong Yu Wang ◽

Yang Su ◽

Lu Han

Keyword(s):

Aboveground Biomass ◽

Predictive Power ◽

Explanatory Power ◽

Biomass Accumulation ◽

Pinus Tabulaeformis ◽

Stepwise Multiple Regression ◽

Stand Age ◽

Mean Annual Precipitation ◽

Mean Annual Temperature ◽

Better Than

In order to investigate the effect of stand age and climate hydrothermic factors on aboveground biomass accumulation (ABA), data from 65 typical Pinus tabulaeformis forest stands were compiled from published literatures. By means of stepwise multiple regression, the variations in ABA were examined across the range of stand age and gradients of mean annual precipitation (MAP) and mean annual temperature (MAT). For comparison, stand age was also used as explaining variable alone. The results show that, stand age and MAP could explain 85.1% of variation in ABA, the predictive power is much better than stand age alone. The explanatory power of stand age and MAP were 70.7% and 15.3% respectively. In comparison with stand age, MAP has a relatively poor but significant effect. ABA is not significantly related to MAT, which implies that water availability is more important than thermal condition for ABA of Pinus tabulaeformis forests.

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Effects of Stand Age on Biomass Allocation and Allometry of Quercus Acutissima in the Central Loess Plateau of China

Forests ◽

10.3390/f10010041 ◽

2019 ◽

Vol 10 (1) ◽

pp. 41 ◽

Cited By ~ 7

Author(s):

Bin Yang ◽

Wenyan Xue ◽

Shichuan Yu ◽

Jianyun Zhou ◽

Wenhui Zhang

Keyword(s):

Biomass Allocation ◽

Aboveground Biomass ◽

Belowground Biomass ◽

Stem Bark ◽

Stand Age ◽

Tree Biomass ◽

Quercus Acutissima ◽

Stem Wood ◽

Biomass Equations ◽

Total Tree

We studied the effects of stand age on allocation and equation fitting of aboveground and below-ground biomass in four Quercus acutissima stands (14, 31, 46, and 63 years old) in the Central Loess Plateau of China. The stem wood, stem bark, branch, foliage, and belowground biomass of each of the 20 destructive harvesting trees were quantified. The mean total biomass of each tree was 28.8, 106.8, 380.6, and 603.4 kg/tree in the 14-, 31-, 46-, and 63-year-old stands, respectively. Aboveground biomass accounted for 72.25%, 73.05%, 76.14%, and 80.37% of the total tree biomass in the 14-, 31-, 46-, and 63-year-old stands, respectively, and stem wood was the major component of tree biomass. The proportion of stem (with bark) biomass to total tree biomass increased with stand age while the proportions of branch, foliage, and belowground biomass to total tree biomass decreased with stand age. The ratio of belowground biomass to aboveground biomass decreased from 0.39 in the 14-year-old stand to 0.37, 0.31, and 0.24 in the 31-, 46-, and 63-year-old stands, respectively. Age-specific biomass equations in each stand were developed for stem wood, stem bark, aboveground, and total tree. The inclusion of tree height as a second variable improved the total tree biomass equation fitting for middle-aged (31-year-old and 46-year-old) stands but not young (14 years old) and mature (63 years old) stands. Moreover, biomass conversion and expansion factors (BCEFs) varied with stand age, showing a decreasing trend with increasing stand age. These results indicate that stand age alters the biomass allocation of Q. acutissima and results in age-specific allometric biomass equations and BCEFs. Therefore, to obtain accurate estimates of Q. acutissima forest biomass and carbon stocks, age-specific changes need to be considered.

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Calibrating individual tree biomass models for contrasting tropical species at an uneven-aged site in the native Atlantic Forest of Brazil: A direct comparison of alternative approaches, sample sizes, and sample selection methods

Forest Ecology and Management ◽

10.1016/j.foreco.2020.118306 ◽

2020 ◽

Vol 473 ◽

pp. 118306

Author(s):

Michel Anderson Almeida Colmanetti ◽

Aaron Weiskittel ◽

Henrique Ferraço Scolforo ◽

Jaime Felipe Medina Sotomayor ◽

Hilton Thadeu Zarate do Couto

Keyword(s):

Atlantic Forest ◽

Sample Selection ◽

Tropical Species ◽

Selection Methods ◽

Sample Sizes ◽

Tree Biomass ◽

Individual Tree ◽

Biomass Models ◽

Alternative Approaches

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Compacting Coastal Plain soils changes midrotation loblolly pine allometry by reducing root biomass

Canadian Journal of Forest Research ◽

10.1139/x08-060 ◽

2008 ◽

Vol 38 (8) ◽

pp. 2169-2176 ◽

Cited By ~ 6

Author(s):

Kim H. Ludovici

Keyword(s):

Loblolly Pine ◽

Soil Compaction ◽

Belowground Biomass ◽

Stem Volume ◽

Root Production ◽

Tree Biomass ◽

Site Productivity ◽

Allocation Patterns ◽

Below Ground ◽

Pinus Taeda L

Factorial combinations of soil compaction and organic matter removal were replicated at the Long Term Site Productivity study in the Croatan National Forest, near New Bern, North Carolina, USA. Ten years after planting, 18 preselected loblolly pine ( Pinus taeda L.) trees were destructively harvested to quantify treatment effects on total above- and below-ground tree biomass and to detect any changes in the absolute and relative allocation patterns. Stem volume at year 10 was not affected by compaction treatments, even though the ultisols on these sites continued to have higher bulk densities than noncompacted plots. However, even when site preparation treatments were undetectable aboveground, the treatments significantly altered absolute root growth and tree biomass allocation patterns. Soil compaction decreased taproot production and significantly increased the ratio of aboveground to belowground biomass. Decreased root production will decrease carbon and nutrient stores belowground, which may impact future site productivity.

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

Forest Science ◽

10.5849/fs-2016-055 ◽

2017 ◽

Vol 63 (3) ◽

pp. 241-249 ◽

Cited By ~ 4

Author(s):

LiYong Fu ◽

WeiSheng Zeng ◽

ShouZheng Tang

Keyword(s):

Forest Biomass ◽

Tree Biomass ◽

Individual Tree ◽

Biomass Models ◽

Pine Species

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Comparison of Tree Biomass Modeling Approaches for Larch (Larix olgensis Henry) Trees in Northeast China

Forests ◽

10.3390/f11020202 ◽

2020 ◽

Vol 11 (2) ◽

pp. 202 ◽

Cited By ~ 4

Author(s):

Lihu Dong ◽

Yue Zhang ◽

Zhuo Zhang ◽

Longfei Xie ◽

Fengri Li

Keyword(s):

Random Effects ◽

Aboveground Biomass ◽

Northeast China ◽

Model Systems ◽

Larix Olgensis ◽

Tree Biomass ◽

Modeling Approaches ◽

Biomass Models ◽

Larix Olgensis Henry ◽

Biomass Modeling

Accurate quantification of tree biomass is critical and essential for calculating carbon storage, as well as for studying climate change, forest health, forest productivity, nutrient cycling, etc. Tree biomass is typically estimated using statistical models. Although various biomass models have been developed thus far, most of them lack a detailed investigation of the additivity properties of biomass components and inherent correlations among the components and aboveground biomass. This study compared the nonadditive and additive biomass models for larch (Larix olgensis Henry) trees in Northeast China. For the nonadditive models, the base model (BM) and mixed effects model (MEM) separately fit the aboveground and component biomass, and they ignore the inherent correlation between the aboveground and component biomass of the same tree sample. For the additive models, two aggregated model systems with one (AMS1) and no constraints (AMS2) and two disaggregated model systems without (DMS1) and with an aboveground biomass model (DMS2) were fitted simultaneously by weighted nonlinear seemingly unrelated regression (NSUR) and applied to ensure additivity properties. Following this, the six biomass modeling approaches were compared to improve the prediction accuracy of these models. The results showed that the MEM with random effects had better model fitting and performance than the BM, AMS1, AMS2, DMS1, and DMS2; however, when no subsample was available to calculate random effects, AMS1, AMS2, DMS1, and DMS2 could be recommended. There was no single biomass modeling approach to predict biomass that was best for all aboveground and component biomass except for MEM. The overall ranking of models based on the fit and validation statistics obeyed the following order: MEM > DMS1 > AMS2 > AMS1> DMS2 > BM. This article emphasized more on the methodologies and it was expected that the methods could be applied by other researchers to develop similar systems of the biomass models for other species, and to verify the differences between the aggregated and disaggregated model systems. Overall, all biomass models in this study have the benefit of being able to predict aboveground and component biomass for larch trees and to be used to predict biomass of larch plantations in Northeast China.

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Tree biomass allometry during the early growth of Norway spruce (Picea abies) varies between pure stands and mixtures with European beech (Fagus sylvatica)

Canadian Journal of Forest Research ◽

10.1139/cjfr-2017-0177 ◽

2018 ◽

Vol 48 (1) ◽

pp. 77-84 ◽

Cited By ~ 6

Author(s):

Ioan Dutcă ◽

Richard Mather ◽

Florin Ioraş

Keyword(s):

Picea Abies ◽

Norway Spruce ◽

Fagus Sylvatica ◽

Aboveground Biomass ◽

European Beech ◽

Mixed Stand ◽

Pure Stand ◽

Tree Biomass ◽

Mixed Stands ◽

Individual Tree

In this paper, we report an investigation of how forest stand mixture may affect biomass allometric relationships in Norway spruce (Picea abies (L.) Karst.). Analysis of aboveground biomass data was conducted for 50 trees: 25 sample trees from a pure Norway spruce stand and 25 from a mixed stand of Norway spruce with European beech (Fagus sylvatica L.). ANCOVA results demonstrated that individual-tree biomass allometry of the pure stand significantly differed from that of the mixed stand. Allometric characteristics depended on the biomass component recorded and the type of biomass predictor used. When predicted by diameter at breast height and (or) height, the total aboveground biomass of mixed-stand trees was significantly less than that for pure-stand trees. This “apparent” lower aboveground biomass was attributed to the lower branch and needle biomass proportions of trees growing in mixed stand. The findings indicate that caution should be exercised when applying biomass allometric models developed from pure stands to predict tree biomass in mixed stands (and vice versa), as such data treatment may introduce significant bias.

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A Novel Tree Biomass Estimation Model Applying the Pipe Model Theory and Adaptable to UAV-Derived Canopy Height Models

Forests ◽

10.3390/f12020258 ◽

2021 ◽

Vol 12 (2) ◽

pp. 258

Author(s):

Takashi Machimura ◽

Ayana Fujimoto ◽

Kiichiro Hayashi ◽

Hiroaki Takagi ◽

Satoru Sugita

Keyword(s):

Model Theory ◽

Canopy Height ◽

Stem Volume ◽

Biomass Estimation ◽

Tree Biomass ◽

Estimation Model ◽

Individual Tree ◽

Pipe Model ◽

Plant Form ◽

Pipe Model Theory

Aiming to develop a new tree biomass estimation model that is adaptable to airborne observations of forest canopies by unmanned aerial vehicles (UAVs), we applied two theories of plant form; the pipe model theory (PMT) and the statical model of plant form as an extension of the PMT for tall trees. Based on these theories, tree biomass was formulated using an individual tree canopy height model derived from a UAV. The advantage of this model is that it does not depend on diameter at breast height which is difficult to observe using remote-sensing techniques. We also proposed a treetop detection method based on the fractal geometry of the crown and stand. Comparing surveys in plantations of Japanese cedar (Cryptomeria japonica D. Don) and Japanese cypress (Chamaecyparis obtusa Endl.) in Japan, the root mean square error (RMSE) of the estimated stem volume was 0.26 m3 and was smaller than or comparative to that of models using different methodologies. The significance of this model is that it contains only one empirical parameter to be adjusted which was found to be rather stable among different species and sites, suggesting the wide adaptability of the model. Finally, we demonstrated the potential applicability of the model to light detection and ranging (LiDAR) data which can provide vertical leaf density distribution.

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