Individual Tree Biomass Equations Research Articles

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.

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.

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.

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.

Assessing the effect of pruning and thinning on crown fire hazard in young Atlantic maritime pine forests

Management of fuel to minimize crown fire hazard is a key challenge in Atlantic forests, particularly for pine species. However, a better understanding of effectiveness of silvicultural treatments, especially forest pruning, for hazard reduction is required. Here we evaluate pruning and thinning as two essential silvicultural treatments for timber pine forests. Data came from a network of permanent plots of young maritime pine stands in northwestern Spain. Vertical profiles of canopy bulk density were estimated for field data and simulated scenarios of pruning and thinning using individual tree biomass equations. Analyses of variance were conducted to establish the influence of each silvicultural treatment on canopy fuel variables. Results confirm the important role of both pruning and thinning in the mitigation of crown fire hazard, and that the effectiveness of the treatments is related to their intensity. Finally, models to directly estimate the vertical profile of canopy bulk density (CBD) were fitted using the Weibull probability density function and usual stand variables as regressors. The models developed include variables sensitive to pruning and thinning interventions and provide useful information to prevent extreme fire behavior through effective silviculture.

Individual tree biomass equations and growth models sensitive to climate variables for Larix spp. in China

Climate change has increased the need of information on amount of forest biomass. The biomass and carbon storage for larch (Larix spp.) in large geographic regions in China were failed to be accurately estimated from current biomass equations, because they were usually based on a few sample trees on local sites, generally incompatible to volume estimation, and not additive between components and total biomass. China needs reliable biomass estimation of the important species in the whole country. This study was based on the mensuration data of above- and belowground biomass from 600 and 198 destructive sample trees of larch from four regions in China, respectively. The main purpose was to develop compatible individual tree equations on both national and regional levels for above- and belowground biomass, biomass conversion factor and root-to-shoot ratio, using the nonlinear error-in-variable simultaneous equation approach. In addition, diameter at breast height (D) and tree height (H) growth models were also developed, and effects of key climate variables on biomass variation and growth process were analyzed. The results showed that mean prediction errors (MPEs) of regional aboveground biomass models were from 3.86 to 7.52%, and total relative errors (TREs) are within ±3%; and for regional belowground biomass equations, the MPEs are from 9.91 to 28.85%, and the TREs are within ±4%. The above- and belowground biomass and D- and H-growth were significantly related to mean annual temperature and mean annual precipitation. The biomass equations and growth models developed in this paper will provide good basis for estimating and predicting biomass of larch forests in China.

Spatiotemporal Assessment of Forest Biomass Carbon Sinks: The Relative Roles of Forest Expansion and Growth in Sichuan Province, China.

Spatiotemporal patterns of forest carbon (C) sinks and accurate estimation of such patterns are crucial to sustainable forest management. We combined individual tree biomass equations and a Random Forest algorithm to assess the spatiotemporal changes in biomass C sequestration and to further quantify the relative contributions of forest areal expansion and growth to biomass C sinks in Sichuan Province, China, over the past 25 yr. Forest area and average biomass C density increased from 10.5 million ha and 45.7 Mg C ha in 1988 to 14.2 million ha and 52.3 Mg C ha in 2012. Average C density was generally larger in the north and west of Sichuan Province compared with other regions. The expanded forest area and enhanced C density have jointly led to a rise in total C storage by 54.9% over this period in Sichuan Province. It was estimated that the forest areal expansion has been a larger contributor to C sinks than forest growth in Sichuan Province (69 vs. 31%), especially in the regions of the northwestern high mountains and the hilly country of the Sichuan basin. However, the relative contributions of areal expansion exhibited different trends in five subregions and 15 forest species groups in this province. Our study suggests that it is necessary to develop a new forestry management mode to maintain the long-term health of forest ecosystems in Sichuan Province, which should attach more importance to improving forest quality and selecting tree species in different subregions while increasing forested area in the future.

Integrated individual tree biomass simultaneous equations for two larch species in northeastern and northern China

Forest biomass estimation at large scale has become an important topic in the background of facing global climate change, and it is fundamental to develop individual tree biomass equations suitable for large-scale estimation. Based on the measured data of biomass components and stem volume from 100 sample trees of two larch species (Larix gmelinii and L. principis-rupprechtii) in northeastern and northern China, an integrated equation system including individual tree biomass equations, stem volume equation and height–diameter regression model were constructed using the dummy variable model and error-in-variable simultaneous equations. In the system, all the parameters of equations were estimated simultaneously, so that the aboveground biomass equation was compatible to stem volume equation and biomass conversion factor (BCF) function; the belowground biomass equation was compatible to root-to-shoot ratio (RSR) function; and stem wood, stem bark, branch and foliage biomass equations were additive to aboveground biomass equation. In addition, the system also ensured the compatibility between one- and two-variable models. The results showed that: (1) whether aboveground biomass equations or belowground biomass equations and stem volume equations, the estimates for larch in northeastern China were greater than those in northern China; (2) BCF of a larch tree decreased with the growing diameter while RSR increased with the growing diameter; (3) the proportion of stem wood biomass to aboveground biomass increased with the growing diameter while those of stem bark, branch, and foliage biomass decreased.

Using nonlinear mixed model and dummy variable model approaches to develop origin-based individual tree biomass equations

Based on above- and below-ground biomass measurements from 604 and 212 sample trees respectively, aboveground biomass models for different origins didn’t have significant difference while belowground biomass models did. Based on the measurement data of aboveground biomass from 604 sample trees and belowground biomass from 212 sample trees of Chinese fir (Cunninghamia lanceolata) and Masson pine (Pinus massoniana) in southern China, the individual tree above- and below-ground biomass models involving forest origin were developed using nonlinear mixed model and dummy variable model approaches, and the effect of forest origin on biomass models was analyzed. The results showed that the aboveground biomass models for different origins had no significant difference, while the belowground biomass models were significantly different; and the belowground biomass estimate of a natural tree was highly greater than that of a planted tree with the same diameter and height. Specially, the belowground biomass estimates of natural trees were nearly 30 % and about 45 % greater than those of planted trees for Chinese fir and Masson pine, respectively. The mean prediction errors of aboveground biomass models and belowground biomass models developed in this study were less than 5 % and 15 %, respectively, which meant the biomass models could be applied to estimate forest biomass of the two species at large scale.

Aboveground stand-level biomass estimation: a comparison of two methods for major forest species in northwest Spain

The scaling-up approach (which requires the use of individual tree biomass equations and data) is one of the most commonly used methods for estimating stand biomass at a local scale. However, biomass prediction over large management areas requires more efficient methods. Two methods of estimating aboveground stand biomass were developed and compared: stand biomass equations (SBE) including observed stand variables, and SBE including biomass expansion factors (BEF) and stand volume. Two types of systems of additive equations were fitted simultaneously for components and total aboveground stand biomass, to ensure additivity. Inherent correlations among biomass components were also taken into account in the fitting process. The systems explained a high percentage of the observed variability. The SBE systems that included observed stand variables provided more accurate estimates than those that included BEF and stand volume. However, the latter were found to be more precise for stem wood and total aboveground biomass prediction. Both approaches provide a direct link between forest inventory data, outputs from whole-stand growth models, and biomass estimates at stand level. Taking into account that the inventory effort is similar for both alternatives, the choice of which to use will depend on the data available and on the relative importance of the biomass components for the end-users.

Individual tree biomass equations or biomass expansion factors for assessment of carbon stock changes in living biomass – A comparative study

Abstract Signatory countries to the United Nations Framework Convention on Climate Change (UNFCCC) and its supplementary Kyoto Protocol (KP) are obliged to report greenhouse gas emissions and removals. Changes in the carbon stock of living biomass should be reported using either the default or stock change methods of the Intergovernmental Panel on Climate Change (IPCC) under the Land Use, Land-Use Change and Forestry sector. Traditionally, volume estimates are used as a forestry measures. Changes in living biomass may be assessed by first estimating the change in the volume of stem wood and then converting this volume to whole tree biomass using biomass expansion factors (BEFs). However, this conversion is often non-trivial because the proportion of stem wood increases with tree size at the expense of branches, foliage, stump and roots. Therefore, BEFs typically vary over time and their use may result in biased estimates. The objective of this study was to evaluate differences between biomass estimates obtained using biomass equations and BEFs with particular focus on uncertainty analysis. Assuming that the development of tree fractions in different ways can be handled by individual biomass equations, BEFs for standing stock were shown to overestimate the biomass sink capacity (Sweden). Although estimates for BEFs derived for changes in stock were found to be unbiased, the estimated BEFs varied substantially over time (0.85–1.22 ton CO2/m3). However, to some extent this variation may be due to random sampling errors rather than actual changes. The highest accuracy was obtained for estimates based on biomass equations for different tree fractions, applied to data from the Swedish National Forest Inventory using a permanent sample design (estimated change in stock 1990–2005: 420 million tons CO2, with a standard error amounting to 26.7 million tons CO2) Many countries have adopted such a design combined with the stock change method for reporting carbon stock changes under the UNFCCC/KP.

Additive prediction of aboveground biomass for Pinus radiata (D. Don) plantations

A general and two country-specific systems of additive equations were developed to predict aboveground biomass of Pinus radiata plantations from stand variables that are routinely measured in inventory plots and predicted by conventional growth and yield models. The data for this work consisted of 319 plot-based biomass estimates that were derived from individual tree biomass equations developed in situ. These plot-based biomass estimates were compiled from studies reported in the forestry and ecological literature since 1960 and also from personal communications. They represent more than 60 sites worldwide with a majority in Australia and New Zealand. The systems of additive biomass equations developed from these data provide an alternative and addition to the current methods of estimating the aboveground biomass of P. radiata plantations. They also provide a direct linkage between forest inventory measures, outputs from conventional growth and yield models and biomass and carbon stock estimates at the same spatial scale. This direct linkage provides a new basis for scaling to a remote sensing image from which biomass and carbon stocks across the landscape can be mapped. Comparisons of prediction accuracies between this approach and other methods such as scaling up from individual tree biomass estimates and biomass expansion factors highlighted considerable methodological differences in the estimates of aboveground biomass and associated uncertainties over a range of stand age and conditions. These differences should be carefully evaluated before adopting a particular method to estimate aboveground biomass and carbon stocks of P. radiata plantations at a local, regional or national scale.