Tree Biomass Equations Research Articles

A dataset of woody plant biomass models for subtropical mixed evergreen and deciduous broad-leaved forests

Tree biomass equations are the most commonly used method to estimate tree and forest biomass on various spatial and temporal scales because of their minor damage, ease of use, and high relative accuracy. The systematic compilation of models for the biomass of mixed evergreen and deciduous broad-leaved forest (a unique and essential class of typical vegetation types in the subtropical zone of China) has not been reported so far. In this paper, we compiled a species list through an inventory of 28.9 hm2 of mixed evergreen deciduous broad-leaved forests in southwestern Hubei Province with fixed detection sample plots, produced a species list, and used it to retrieve, collect and establish a model dataset of woody plant biomass in subtropical mixed evergreen and deciduous broad-leaved forests. The species list contains a total of 665 biomass models in 167 groups as well as the information corresponding to each mode, such as the plant species name, Latin name, plant life type, plant components calculated by the model, model independent variables, measurement units and ranges of independent variables, model correlation coefficient or coefficient of determination, model sample size, climate zone, and soil type. is also recorded. By establishing this dataset, this paper not only provides essential information for in-depth research on the productivity and carbon sink of this specific vegetation but also serve as a scientific basis for the management of this type of forest, the conservation of biodiversity, and the evaluation of forest ecological benefits.

Biomass distribution, allocation and growth efficiency in European beech trees of different ages in pure even-aged stands in northeast France

Abstract Determination of the biomass and biomass increment of trees in managed stands is a pre-requisite for estimating the carbon stocks and fluxes, in order to adapt the forests to new climatic requirements, which impose to maximize the CO2 retained by forests. Tree biomass and biomass increment equations were formerly developed in two young experimental beech stands in the Hesse forest (NE France). To extend such a study to beech stands of different age classes, it was necessary to build biomass and biomass increment equations that could be used for any age, called generalized biomass equations. For that, trees were sampled in plots covering a large age range in Hesse forest, and in each plot several trees were chosen to represent the different social classes. Compatible biomass and biomass increment equations for the different tree compartments and their combination in above and belowground tree parts were developed and fitted, allowing the analysis of the variations of the biomass distribution and allocation with tree age. Stem growth efficiency (stem growth per unit of leaf area) appeared dependent on tree age and tree social status. The biomass and biomass increment equations established for beech allow the estimation of the biomass and carbon stocks and fluxes (NPP) for the even-aged beech stands of the Hesse forest, whatever their age. These equations could also be used to analyze the effects of silvicultural treatments on the biomass and carbon stocks and fluxes of beech stands, using the available stand growth and yield models of beech.

Estimation and Spatial Mapping of Residue Biomass following CTL Harvesting in Pinus radiata Plantations: An Application of Harvester Data Analytics

The utilization of forest harvest residues for renewable bioenergy production and bioproducts has increasingly become an integrated part of forestry that helps to meet the needs of climate change mitigation and a future carbon neutral economy. An essential element in the planning of any harvesting residue recovery operation is a reliable assessment of the quantity and quality of residue biomass and its composition over a harvest area. With the now widely adopted cut-to-length (CTL) at the stump harvesting system in Pinus radiata plantations in Australia, harvesting residues left on site are significantly larger in quantity and spatially more dispersed over a harvest area in comparison to the more traditional whole-tree harvesting. The conventional approach of assessing forest harvest residues through sample plots, transects, or small study blocks has provided site-specific estimates of residue biomass. However, these estimates cannot be readily extrapolated over the plantation landscape, which varies in silviculture, site, and stand conditions. To overcome this limitation, this study relied on harvester data analytics to obtain spatially explicit estimates of residue biomass using an example data set from harvested plantations of three stand types: unthinned (T0), thinned once (T1), and thinned twice (T2). Three methods of integrating biomass equations with harvester data were compared for residue biomass estimation: (1) applying individual tree biomass equations to harvested stems, (2) applying stand-level biomass equations to gridded harvester data, and (3) integrating estimates from the first approach with recorded and estimated waste volumes of harvested stems. The estimates of total residue biomass obtained using the three methods through harvester data analytics varied between 56.2 and 156.4 t/ha in green weight across the three stand types. These estimates were validated indirectly through ex situ sample plots and proved to be comparable to the quantities of residue biomass assessed using conventional sample plots, transects, or small blocks following CTL harvesting of rotation age P. radiata plantations elsewhere in Australia. Among the three methods, the third method made the most intensive use of the harvester data and provided the most realistic estimates of residue biomass. Spatial mapping of the estimated total and component residue biomass will assist the operational planning of residue recovery and site-specific nutrient management for the long-term sustainability of P. radiata plantations.

Allometry of tree biomass and carbon partitioning in ponderosa pine plantations grown under diverse conditions

Forests not only provide forest products to meet people’s demands, but also sequester enough carbon to offset more than 11% of anthropogenic CO2 release in the United States. An accurate estimate of forest carbon sequestration over different forest and forest product combinations requires the species-specific and site-specific allometric biomass equations for trees and understory vegetation at particular forest developmental stages. Here, we constructed the above- and below-ground allometric equations by harvesting 108 20-year-old ponderosa pine (Pinus ponderosa) trees grown under four different silvicultural treatments (four combinations of with and without competing vegetation control (H) and fertilization (F): C = control with no vegetation control and no fertilization, H, F, and HF) at three diverse site conditions in northern California. We found that the logarithmic regressions provided superior estimations of biomass for total, aboveground, bole, coarse roots, branches, and foliage compared to simple or scaled power-law fits. Treatment effect in allometric equations was generally lacking but site effect was significant, suggesting site specific allometric relations exist. With the best allometric equations, we estimated the biomass and carbon for each plot among treatments and sites. In addition, understory woody plant biomass was also measured with fixed area subplots. Results show significantly more total carbon sequestration at the high-quality sites with 113.1–162.5 Mg ha−1 than the lower ones with 85.8–105.9 Mg ha−1 at the intermediate site and 29.3–70.4 Mg ha−1 at the poorest site, respectively. The range of carbon stocks indicates the treatment differences at each site, with the largest effect (240%) at the poorest site and similar at other two sites (~130%). If planted trees only were considered, a trend among sites exists with 93.4 – 149.3 Mg ha−1 at the richest site, 26.2–88.1 Mg ha−1 at the intermediate site, and 17.1–64.2 Mg ha−1 at the poorest site, respectively. Therefore, if we manage forests for carbon or forest products, intensive management practices will substantially enhance plantation productivity and stand development while site quality cannot be changed.

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

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 reexamination of allometric relationships using modern statistical techniques. We asked the following questions: (i) Is there among-stand variation in allometric relationships? (ii) Is there nonlinearity in allometric relationships? (iii) Can among-stand variation or nonlinearities in allometric equations be attributed to differences in stand age? (iv) 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 that 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 points to additional sources of stand-level heterogeneity, which if identified could improve the accuracy of live tree biomass estimation.

Effects of afforestation with different species on carbon pools and soil and forest floor properties

Land use and land use change are factors that affect carbon and nutrient stocks in ecosystems. The aim of this research was to determine the effects of forest land use types on carbon pools and soil and forest floor features. This study was conducted in areas afforested with black pine (Pinus nigra Arn. subsp. pallasiana Lamb. Holmboe) and Scots pine (Pinus sylvestris L.) and on adjacent bare land within the Akdağ Nature Park, which is located in the West-Central Anatolia Region of Turkey. Three 20 × 20 m sample plots were selected within each forest land use, and the diameters at breast height and heights of all trees were measured. Tree biomass and carbon stocks in the unit areas were calculated using tree biomass equations, and carbon conversion factors were developed for two tree species. Within each sample plot, disturbed and core soil samples and forest floor samples were taken at three points at depths of 0–10, 10–20 and 20–30 cm. The physical and chemical properties of the soil and forest floor samples were determined in the laboratory and measurements were converted to a unit area using volume values. The data were evaluated using independent sample t-tests and analysis of variance. The results showed that the ecosystem carbon (C) stocks differed significantly with forest land use type; black pine plantations, Scots pine plantations and bare land accumulated 235.2 t C ha−1, 206.1 t C ha−1 and 37.4 t C ha−1, respectively. We found that in addition to the positive effect of afforestation on soil, black pine had a greater impact on some forest floor and soil characteristics in the region than Scots pine. Thus, we suggest that priority should be given to black pine in afforestation of the region and in other ecosystems with similar climates. Additionally, our results reveal the importance of afforestation on bare lands for reducing the impact of global climate change.

Allometric equations to estimate above-ground biomass of small-diameter mixed tree species in secondary tropical forests

Abstract: Accounting for small-size tree biomass is critical to improve total stand biomass estimates of secondary tropical forests, and is essential to quantify their vital role in mitigating climate change. However, owing to the scarcity of equations available for small-size trees, their contribution to total biomass is unknown. The objective of this study was to generate allometric equations to estimate total biomass of 22 tree species ≤ 10 cm in diameter at breast height (DBH), in the Yucatan peninsula, Mexico, by using two methods. First, the additive approach involved the development of biomass equations by tree component (stem, branch and foliage) with simultaneous fit. In the tree-level approach, total tree biomass equations were fit for multi-species and wood density groups. Further, we compared the performance of total tree biomass equations that we generated with multi-species equations of previous studies. Data of total and by tree component biomass were fitted from eight non-linear models as a function of DBH, total height (H) and wood density (ρ). Results showed that two models, identified as model I and II, best fitted our data. Model I has the form AGB = β0 (ρ·DBH2·H)β1 + e and model II: AGB = exp(-β0)(DBH2·H)β1 + e, where AGB is biomass (kg). Both models explained between 53% and 95% of the total observed variance in biomass, by tree-structural component and total tree biomass. The variance of total tree biomass explained by fit models related to wood density group was 96%-97%. Compared foreign equations showed between 30% and 45% mean error in total biomass estimation compared to 0.05%-0.36% error showed by equations developed in this study. At the local level, the biomass contribution of small trees based on foreign models was between 24.38 and 29.51 Mg ha-1, and model I was 35.97 Mg ha-1. Thus, from 6.5 up to 11.59 Mg ha-1 could be excluded when using foreign equations, which account for about 21.8% of the total stand biomass. Local equations provided more accurate biomass estimates with the inclusion of ρ and H as predictors variables and proved to be better than foreign equations. Therefore, our equations are suitable to improve the accuracy estimates of carbon forest stocks in the secondary forests of the Yucatan peninsula.

Can crown variables increase the generality of individual tree biomass equations?

Crown variables and height can improve biomass predictions and may help to quantify how tree biomass is influenced by inter-tree competition (stand density, species composition), climate, edaphic conditions, and age. Tree biomass is influenced by tree age, stand structural characteristics, and climatic and edaphic site factors. However, most allometric equations for predicting tree biomass do not quantify these effects and when these characteristics are included as variables, they typically only reflect the average or current conditions even though tree allometry is the result of past growing conditions as well. An alternative to using site and stand characteristics is to use tree variables that have been influenced by site and stand structure for the whole lifetime of the tree. The objective of this study was to compare stem diameter-only equations that predict above-ground biomass or foliage mass with equations that also contain a climatic variable (aridity index) and stand variables, such as age and basal area (as a proxy for competition), or equations containing another tree size variable, such as crown diameter, crown length and tree height. This was done using 348 trees from 24 sites and 10 Eucalyptus species in southern Australia. Stand basal area and age were significant in 2 of 23 final equations and the aridity index was not significant in any equations. Adding another tree size variable to the diameter-only equations reduced the bias and increased the precision in 6 of the final equations. This study shows that the addition of crown variables (or height) to diameter-only biomass equations can quantify some of the effects that site and stand structural characteristics have on biomass. Variables such as crown diameter and height could also be useful because they are easier to measure accurately using remote sensing methods than stem diameters and related stand structural characteristics, such as basal area.

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

Tree biomass equations are important yet difficult, time-intensive, and expensive to develop. However, the calibration of previously developed, species-specific models could be a viable alternative, particularly for highly diverse and protected forests like the Atlantic Forest of Brazil. Consequently, the primary research goal of this study was to conduct a comprehensive evaluation of the potential to calibrate an existing individual tree aboveground biomass model for a new species and/or site by using linear mixed-effects. Specific research objectives were to determine the optimal approach for effective calibration by allowing sample selection method, sample size, and range of tree sizes sampled to vary. In particular, a certain set of species was used as a primary dataset to fit both generalized and species-specific biomass models, that were then calibrated for a secondary dataset at a different site and location. Both similar and divergent species at the secondary site were used to calibrate and evaluate the previous models. Our results suggested that species-level calibration was efficient for the majority of the species or individuals examined that can greatly improve the performance at much lower sample sizes required to develop a new equation, especially for the larger trees in the stand. In general, one to three randomly selected trees were sufficient to effectively calibrate a biomass model for a new species. We expect the combination of model calibration for abundant species associated with the use of the previous developed generalized model for less abundant species can drastically reduce the need for destructive sampling and improve predictions, which is important for highly threatened forests like the Atlantic Forest in Brazil. Overall, the results highlight the potential of model calibration to significantly improve both biomass and carbon estimates in species-rich forests like those in the tropics.

A review of biomass equations for China's tree species

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.

Allometric equations for total aboveground dry biomass and carbon content of Pinus occidentalis trees

Although precise species-specific aboveground tree biomass equations are needed in the Dominican Republic to quantify potential carbon storage in the context of climate change and sustainable forest management, there has been a lack of available information concerning total aboveground biomass and/or carbon content for naturally occurring Pinus occidentalis trees. The objectives of the study were to develop species-specific allometric biomass models for P. occidentalis, as well as to assess variation in carbon concentration among stem plus bark, branches and foliage by means of chemical analyses. Predictor variables included diameter at breast height (DBH) and total tree height (H), alone and in combination. Model fitting methods employing linear and nonlinear regression techniques were used and evaluated based on goodness-of-fit criteria. Two competing biomass models produced similar goodness-of-fit statistics: model 4, fitted by the Weighted Non-linear Least Squares technique, , and model 6, fitted by modeling the variance component and applying the Maximum Likelihood method, . Average carbon concentration (%) was highest in foliage tissue (49.8%), followed by branches (46.37%) and, lastly, stem plus bark (45.95%). On average, each individual tree is storing 175 kg of total aboveground carbon. To our knowledge, this is the first account reporting carbon fraction for this tropical species. Calculated wood carbon fractions from the study can be easily incorporated into forest C accounting, and may reduce errors in forest C valuations, which nowadays are performed using global estimates published elsewhere.

SPECIES-SPECIFIC BIOMASS EQUATIONS FOR SMALL-SIZE TREE SPECIES IN SECONDARY TROPICAL FORESTS

<p>Secondary tropical forests are largely dominated by small-diameter trees. Their contribution to the total stand productivity may be determinant for ecosystem functioning. However, the biomass contained in small-size trees is frequently ignored because they are rarely included in forest inventories, as well as due to the lack of biomass equations for such trees. The aim of this study was to generate specific biomass equations by tree structural components and total-tree biomass for 22 species (diameter at breast height-DBH ≤ 10 cm diameter) structurally important of secondary tropical forests in southeast Yucatán peninsula. The biomass equations were generated using two fitting approaches. First, using the additive approach where the biomass equations by structural tree components (stem, branches and foliage) were simultaneously fitted. Second, the tree-level approach where the total-tree biomass was independently fitted. We used DBH, total height (TH) and wood density (WD) as predictor variables during the fitting process. The performance of each total-tree biomass equation was compared to generalized biomass equations developed in other tropical forests. The variance explained of observed biomass across tree species were stem, 64% to 99%; branch, 24% to 95%; foliage, 35% to 94% for equations by structural tree components; while the explained variance for total tree biomass equations was between 94 to 99 %. The inclusion of TH and WD as predictor variables in the equation structure, significantly improved the goodness of fit statistics. These are the first equations developed for small-size tree species in secondary forests of Yucatán peninsula. We observed that generalized equations developed for other tropical forests, in some cases overestimated up to ~ 44.2% and in other cases underestimated up to ~ 48.4% the mean total-tree biomass. When we applied our equations to forest inventory data, they showed a better predicting performance than generalized equations. Therefore, they are reliable for estimating biomass of small-size tree species in similar forest types. Our equations could be used for biomass-carbon estimation in tropical secondary forests composed by high densities of small-size tree species; therefore, they are relevant within climate change contexts.</p>

Allometric biomass equations for young trees of four broadleaved species in Albania

Background: Biomass assessment of young forest stands is important because of their role in the carbon cycling. The aim of this study was to develop biomass equations for young broadleaved species growing in natural conditions in Albania. Methods: Five forest stands were investigated using circular sample plots. Diameter at breast height (DBH) and height (H) from 58 sampled trees ranging in age from 4 to 34 years old of Turkey oak (Quercus cerris L.), sweet chestnut (Castanea sativa Mill.), European hornbeam (Carpinus betulus L.) and manna ash (Fraxinus ornus L.) were measured in situ. Logarithmic regression equations were used and tested for their performance to estimate aboveground and tree-components biomass for each species using DBH, H and their combination DBH2 x H as predictors. Results: We found that DBH was a reliable predictor for estimation of aboveground and components biomass for young trees but the inclusion of height in biomass allometry did not improve the biomass estimation. We observed differences in scale (?0) and exponent (?1) coefficients of biomass models, not only between broadleaved species, but also among tree-components within species. Both coefficients were strongly species-specific and their values reflect differences in biomass stocking rate due to different growth strategies of each species in early development phases. Conclusions: Allometric equations to estimate aboveground and tree-component biomass appeared to be species-specific, meaning that such models are applicable for species growing at sites with similar ecological conditions. From the tree variables used, DBH was the most reliable predictor of aboveground and individual components biomass, whereas height proved to be a promising predictor for stand biomass. These allometric equations developed for young trees will improve the accuracy of current estimates of forest carbon stock in Albania.

Tree Biomass Equations from Terrestrial LiDAR: A Case Study in Guyana

Large uncertainties in tree and forest carbon estimates weaken national efforts to accurately estimate aboveground biomass (AGB) for their national monitoring, measurement, reporting and verification system. Allometric equations to estimate biomass have improved, but remain limited. They rely on destructive sampling; large trees are under-represented in the data used to create them; and they cannot always be applied to different regions. These factors lead to uncertainties and systematic errors in biomass estimations. We developed allometric models to estimate tree AGB in Guyana. These models were based on tree attributes (diameter, height, crown diameter) obtained from terrestrial laser scanning (TLS) point clouds from 72 tropical trees and wood density. We validated our methods and models with data from 26 additional destructively harvested trees. We found that our best TLS-derived allometric models included crown diameter, provided more accurate AGB estimates ( R 2 = 0.92–0.93) than traditional pantropical models ( R 2 = 0.85–0.89), and were especially accurate for large trees (diameter > 70 cm). The assessed pantropical models underestimated AGB by 4 to 13%. Nevertheless, one pantropical model (Chave et al. 2005 without height) consistently performed best among the pantropical models tested ( R 2 = 0.89) and predicted AGB accurately across all size classes—which but for this could not be known without destructive or TLS-derived validation data. Our methods also demonstrate that tree height is difficult to measure in situ, and the inclusion of height in allometric models consistently worsened AGB estimates. We determined that TLS-derived AGB estimates were unbiased. Our approach advances methods to be able to develop, test, and choose allometric models without the need to harvest trees.

Biomass estimation equations for mesquite trees in the Americas.

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.

Biomass estimation of individual trees for coppice-originated oak forests

In this study, individual tree-biomass equations are developed for the different biomass components (foliage, branch, bark, stem, and total aboveground biomass) of Quercus frainetto Ten., Quercus cerris L., and Quercus petraea (Matt.) Liebl. species, which are the main oak species in coppice-originated oak forests. This research was conducted in northern Turkish Thrace, an important coppice-oak area. A total of 474 oak trees at different development stages over different sites were destructively sampled to obtain biomass models of the major oak species in the region. Two main biomass models are established to estimate the biomass components: according to the diameter at breast height (DBH) alone and according to the d2h variable, which is the combined DBH and tree height. The goodness-of-fit statistics of the models are calculated to compare these models. The models that use the DBH alone as an independent variable explain 68–95% of the variation in the biomass of the tree components. After adding the tree height to the model, the accuracy for the bark-, stem-, and total aboveground-biomass estimates increased, while the accuracy of the foliage- and branch-biomass estimates decreased. Different oak species have different biomass components depending on their development stages. The amount of biomass components in the total aboveground biomass and foliage decreased from 20% (SDF) to 4% (LDF), the amount of bark decreased from 17% (SDF) to 12% (LDF), and the amount of stems increased from 42% (SDF) to 66% (LDF) when increasing the mean DBH of the sample plots.

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

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.

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

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.

Additive aboveground biomass equations based on different predictors for natural Tilia Linn

Biomass is a basic quantitative character of forest ecosystem. Biomass data are foundation of researching many forestry and ecology problems. 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. Constructing biomass models is considered a good approach to estimate forest biomass. Based on biomass data of 97 sampling trees of natural Tilia Linn. in Xiaoxing'an Mountains and Zhangguangcai ranges, three additive systems of individual tree biomass equations were developed: based on tree diameter at breast height (D) only, based on tree diameter at breast height and height (H), and based on the best models. The nonlinear seemly unrelated regression was used to estimate the parameters in the additive system of biomass equations. The heteroscedasticity in model residuals was addressed by applying a unique weight function to each equation. The individual tree biomass model validation was accomplished by Jackknifing technique. The results showed that three additive systems of individual tree biomass equations could fit and predict the biomass of Tilia Linn. well (adjusted coefficient of determination Ra2>0.84, mean predicted error percentage MPE<8.5%, mean absolute error MAE<16.3 kg,mean standard error percentage MPSE<28.5%). The biomass equations of stem and aboveground were better than biomass equations of branch, foliage and crown. Adding total tree height and crown factor in the additive systems of biomass equations could significantly improve model fitting performance and predicting precision (Ra2 improved from 0.01 to 0.04, MAE decreased from 0.01 to 4.55 kg), narrow the confidence interval of the predicted value and the biomass of stem, foliage and aboveground were increased more than the biomass of branch and crown. In general, the equations of the additive system based on the best models produced the best model fitting, followed by that of the additive system based on D and H, and that based on D. It was essential to develop biomass model by adding total tree height and crown factor.

Aboveground biomass equations for the predominant conifer species of the Inland Northwest USA

Assessment of aboveground biomass stocks in the coniferous forests of the inland northwest USA is important for timber, bioenergy, and carbon inventories, as well as for wildfire risk determination. In this study, individual tree biomass equation systems are developed for 7 regionally important conifer species using data from 470 felled trees sampled across 84 stands and spanning a range of diameters at breast height (1.37 m; dbh) running from 5 cm to 105 cm. The equation systems permit estimation of crown biomass components (i.e., foliage, dead branches, and live branches by size class) and stem components (abovestump stemwood and stembark), as well as compatible estimates of (sub)totals. The systems draw on commonly collected inventory variables including dbh, tree height, and live crown length. All biomass components scaled approximately linearly with dbh on the logarithmic scale, but equation systems drawing on both dbh and height provided more accurate estimates for all species; systems drawing additionally on live crown length provided more accurate estimates still for all species but one. In line with previous work, incorporation of live crown length improved live crown component equations most, but also improved stem component equations for two species. Across species and systems, stem components and subtotals were most accurately estimated (mean absolute errors ∼10%) while dead branch biomass estimation proved least tractable (mean absolute errors >50%). Overall, the reported biomass equation systems draw on the largest felled tree samples collected from the region, and provide the most comprehensive basis developed to date for regional forest biomass assessments over the inland northwest.