Biomass Equations Research Articles

Carbon Pools in a 77 Year-Old Oak Forest under Conversion from Coppice to High Forest

Recent model projections and many research results across the world suggest that forests could be significant carbon sinks or sources in the future, contributing in a such a way to global warming mitigation. Conversion of coppice forest to high forest may play an important role towards this direction. This study deals with the estimation of biomass, carbon pool and accumulation rates in all IPCC biomass categories of a 77 year-old oak ecosystem, which has been subjected to conversion from coppice to high forest through repeated tending measures. The research includes a plethora of field tree measurements, destructive sampling of representative oak trees and a systematic sampling of dead wood (standing and fallen), litter and soil. Furthermore, for the estimation of above ground tree living biomass at the stand level, we developed and tested appropriate allometric biomass equations based on the relationships between various independent tree variables (morphological characteristics) and the different tree biomass compartments or leaf biomass. Data analysis shows that coppice conversion results in large accumulation of carbon in all ecosystem pools, with an average annual carbon rate accumulation of 1.97 Mg ha–1 in living above and below ground tree biomass and small amounts to dead wood and litter. The developed allometric equations indicate that above ground tree living biomass can be reliable and precisely predicted by the simple measurement of tree diameter.

Evaluation of Carbon Stock, Nitrogen, and Phosphorus Contents in Forest Soil and Litter at Bintulu’s Acacia mangium Chronosequence Age Stand Plantation, Sarawak, Malaysia

Acacia mangium is the major species used in the forest plantation industry due to its fast-growing feature. However, there is still a lack of research on the nutrient concentration, specifically nitrogen (N) and phosphorus (P), as well as carbon content in Malaysia’s forest plantations. Hence, this study aimed to assess the total N and P concentrations in the soil and forest litter. Carbon content in different ages (Year 2, Year 4, and Year 9) of A. mangium plantation (together with a natural forest as a comparison) was also determined. This study was conducted in a Licensed Planted Forest, Bintulu, Sarawak, Malaysia. The natural forest was a control variable in this study. The Kjeldahl method was used to determine the total N. In contrast, dry ashing and double acid (Mehlich-1) methods were used to determine the total P in forest litter and available P in forest soil. The allometric biomass equations were used to estimate the carbon content. Total N in forest litter and forest soil was similar in all treatments. Total P in the Year 4 stand was significantly higher than in the Year 2 stand, yet, no differences were observed when compared with the control. Whereas soil available P showed no significant difference among all treatments. Acacia mangium stands recorded significantly lower total carbon content compared to the control. Old plantation stands contained much more total carbon stock than the younger stands. Also, deadwood is important in determining total carbon stock when it can account for almost 59% of above-ground biomass (AGB) carbon stock. This study revealed that forest plantations could function well in providing an adequate supply of available nutrients as well as have a potential role in carbon sink.

Additive Allometric Equations to Improve Aboveground Biomass Estimation for Mongolian Pine Plantations in Mu Us Sandy Land, Inner Mongolia, China

Afforestation is conducive to improving ecosystem service functions and ecosystem diversity in the Mu Us Sandy Land, however, the important attribute of biomass for Mongolian pine (Pinus sylvestris var. mongolica Litv.) plantations has yet to be accurately evaluated. This study aimed to develop additive allometric biomass equations for the species and evaluate biomass partitioning patterns within tree components. A total of 131 trees were measured for stem, branch, and leaf biomass by destructively sampling and tree climbing, with the latter as a supplement. For each biomass component, we tested three equations with the diameter at breast (D) alone, height (H) as additional, and diameter in combination with height (D2H) as predictors using the weighted least squared method. Weighted nonlinear seemingly unrelated regression was adopted to fit a system of additive allometric biomass equations utilizing the selected equations. A leave-one-out cross-validation method (the jackknife procedure) was used to assess the predictive ability. The biomass partitioning pattern was evaluated by calculating the ratios. The results revealed that the diameter alone is a good predictor for branches and foliage biomass estimates, while the stem requires H included to improve estimation accuracy. Mongolian pine allocates relatively more biomass to the crown (51.4%) compared to the stem (48.6%). Branch biomass fraction increased monotonously with increasing tree size while a reverse trend was observed for foliage. In conclusion, the additive models developed in this study provide a robust biomass estimation and can be extensively used to estimate Mongolian pine forests biomass in Mu Us Sandy Land.

Allometric equations for biomass and contents of macronutrients in a young Tectona grandis stand

Evaluations of biomass and nutrient contents in teak stands are necessary alternatives for avoiding early cutting to provide supplemental data for the plant module of fertilization recommendation systems. This study aimed to adjust allometric equations to estimate the accumulation of biomass and nutrient contents in teak plantations in the central region of Brazil. Plots in seminal and clonal stands contained 81 trees aged 75 months, that had not received previous fertilization or thinning. Additional clonal stands aged 15, 51 [...]

Ecological Response of Urban Forest Carbon Density to Site Conditions: A Case Study of a Typical Karst Mountainous Regions in Southwest China

Understanding the ecological constraints of limiting the magnitude and the allocation of carbon density is critical for executing adaptive forest management programs and upgrading the size of carbon sinks. Taking a typical karst mountainous region Guiyang City as a case study area, this study computed the biomass of different forest types using the volume–biomass equations and mapped the carbon density distribution of forests. Furthermore, the random forest algorithm and spatial autoregression model were adopted to reveal the effects of site conditions on carbon density in karst forests. The results indicate that the forest ecosystems of Guiyang City had a high carbon sequestration potential, and there was a significant difference in the carbon density of forests in terms of space dimensions. Road density, 0 cm cation exchange capacity, and soil parent materials were the dominant factors determining the spatial distribution of homogeneous units with different levels of carbon density. This study underlines the urgency adapting to the growing conditions of forests in terms of complex site conditions, and provides a scientific basis for optimizing forest management practices, to maintain their carbon sequestration capacity under urbanization pressure and fragile habitats.

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.

Tree allometric equations for estimating biomass and volume of Ethiopian forests and establishing a database: Review

Biomass measurements are central to understanding and monitoring carbon stocks in the forests. They also assists in assessing the growing stock of a species and biomass energy. Efforts in allometric equation development have grown. This compiled information is an important tool for assessing biomass and associated carbon stocks, the growing stock of a species and biomass energy. Despite this, except for a few attempts, the compilation of available equations has received little attention, particularly in Ethiopia. The uncertainty involved in biomass estimation is also associated with the choice and errors of the equations. The aim of the present paper is thus to review and compile available biomass and volume allometric equations and to critically synthesise quality control procedures for the equations. A total of 767 equations for both volume and biomass were collected from 47 articles, theses and dissertations, representing 47 sites in Ethiopia. Of these, 49% of the equations were developed for the Dry Afromontane biome, 22% for the Acacia-Commiphora biome, 15% for the Combretum-Terminalia biome and 14% for the Moist Afromontane biome. Among the compiled equations, biomass equations comprised 86.7% while the remainder 13.3% were for volume. Although a wide range of natural variations resulted in diverse species in Ethiopia, the already available equations were constrained by the size of individual samples, tree diameter range, tree species and spatial coverage. Moreover, variability in equation presentation and the missing of some relevant statistical parameters or inconsistent statistical analysis were among the problems observed. A quality control procedure revealed that 45 allometric equations had verification problems and were inappropriate for application. Invalid numeric values that may over- or underestimated values were predicted in 42% of the equations. Moreover, seven equations predicted “unrealistic” values, which were low values (4 equations) or both negative and low estimates (3 equations) within their interval of calibration. Altogether, 174 equations (22.7%) were statistically credible equations. These missing information affected the use of the equations and led to the preference of the generalised equations to assess forest biomass stocks. This highlighted important research gaps. The compiled database will also be useful to minimise duplication of efforts and enable efficient utilisation of limited resources, particularly in developing countries such as those in Eastern Africa and beyond in the tropics.

Species Composition Affects the Accuracy of Stand-Level Biomass Models in Hemiboreal Forests

Various tree species contribute differently to total biomass stock, making the development of species-specific stand-level equations critical for better estimation of forest biomass and quantification of carbon stocks. Previously derived dry weight biomass models did not assess the effect of dominant species composition according to stand growing stock. Growing stock definitions and forest species composition differ by country, justifying the need for national stand-level biomass equations. We explored the relationship between growing stock volume and stand biomass density of above- and below-ground components in six common forest categories in Latvia using plot-level data from the National Forest Inventory from 2016 to 2020. Additionally, we explored model dependence on region, forest type, and species composition index. Models that considered growing stock and dominant species composition index performed better than models with growing stock as the only variable, especially for heterogeneous deciduous forests with greater species diversity. The elaborated models are a useful alternative to individual-level assessment for estimating forest biomass stocks in circumstances where individual tree data are not available.

Allometric Equations for the Biomass Estimation of Calophyllum inophyllum L. in Java, Indonesia

Reliable data on CO2 quantification is increasingly important to quantify the climate benefits of forest landscape restoration and international commitments, such as the Warsaw REDD+ Framework and Nationally Determined Contributions under the Paris Agreement. Calophyllum inophyllum L. (nyamplung as a local name or tamanu tree for the commercial name) is an increasingly popular tree species in forest landscape restoration and bioenergy production for a variety of reasons. In this paper, we present allometric equations for aboveground biomass (AGB), belowground biomass (BGB), and total above- and belowground biomass (TABGB) predictions of C. inophyllum L. Data collection was carried out twice (2017 and 2021) from 40 trees in Java, Indonesia. Allometric equations using the natural logarithm of diameter at breast height (lnDBH) and ln height (lnH) for biomass prediction qualified the model’s fit with statistical significance at 95% of the confidence interval for AGB, BGB, and TABGB predictions. The results showed that the linear models using both lnDBH and lnH were well fit and accurate. However, the model with lnDBH is more precise than the model using lnH. Using lnDBH as a predictor, the R2 values were 0.923, 0.945, and 0.932, and MAPE were 24.7, 37.0, and 25.8 for AGB, BGB, and TABGB, respectively. Using lnH as a predictor, the R2 values were 0.887, 0.918, and 0.898 and MAPE were 37.4, 49.0, and 39.8 for AGB, BGB, and TABGB, respectively. Consequently, the driven allometric equations can help accurate biomass quantification for carbon-trading schemes of C. inophyllum L.

Allometric equations for biomass and carbon stock estimation of small diameter woody species from tropical dry deciduous forests: Support to REDD+

In order to assess the contribution to the overall carbon stock and generate carbon credits under REDD+, it is essential to have an accurate estimation of biomass of different forest components. Forest cover in India is gradually increasing due to the active restoration of degraded land and plantation on waste and barren lands leading to an increase in the abundance of small diameter woody species. However, these were not included in biomass studies due to the non- availability of adequate allometric equations and low carbon stocks compared to mature individuals. We have harvested 589 individuals belonging to 23 woody species at the seedling and sapling stage from a tropical dry deciduous forest and developed species specific allometric equation and general allometric equation for aboveground biomass estimation. Further, the belowground biomass equation of 9 species were also developed using above ground biomass and root to shoot ratio as predictor variable. In the case of general equation, the combination of diameter with height and diameter, height and wood specific gravity exhibited highest adjusted R2 value. In case of species-specific allometric equations, combination of diameter with height predicts above ground biomass more precisely as compared to the diameter and wood specific gravity. Since the estimation of wood specific gravity requires destruction of lower diameter individuals, usage of diameter and height for biomass estimation would help protecting regeneration as both methods yield same results. All the equations developed in the present study for below ground biomass predicts biomass precisely. We suggest use of species specific allometric model developed with diameter and height for estimation of biomass. Further, general models consisting of height and diameter may be used for biomass estimation in case of non-availability of species specific equations without destroying the regeneration.

Ustilago maydis Metabolic Characterization and Growth Quantification with a Genome-Scale Metabolic Model.

Ustilago maydis is an important plant pathogen that causes corn smut disease and serves as an effective biotechnological production host. The lack of a comprehensive metabolic overview hinders a full understanding of the organism’s environmental adaptation and a full use of its metabolic potential. Here, we report the first genome-scale metabolic model (GSMM) of Ustilago maydis (iUma22) for the simulation of metabolic activities. iUma22 was reconstructed from sequencing and annotation using PathwayTools, and the biomass equation was derived from literature values and from the codon composition. The final model contains over 25% annotated genes (6909) in the sequenced genome. Substrate utilization was corrected by BIOLOG phenotype arrays, and exponential batch cultivations were used to test growth predictions. The growth data revealed a decrease in glucose uptake rate with rising glucose concentration. A pangenome of four different U. maydis strains highlighted missing metabolic pathways in iUma22. The new model allows for studies of metabolic adaptations to different environmental niches as well as for biotechnological applications.

Additive Root Biomass Equations for Betula platyphylla Suk. Plantations in Northeast China

Most of the forest biomass models that have been developed so far focus on the study of the aboveground biomass of forest trees and the prediction of belowground biomass remains obviously insufficient. Moreover, most of the existing studies on the estimation of the belowground biomass of trees have considered roots as a whole, ignoring the differences in composition and function of roots within different diameter classes. In this study, we measured the root biomass of birch plantation forests in northeastern China using extensive destructive sampling, in which we divided the root system into three parts: coarse, medium, and fine roots. We selected the best model base form from three common allometric biomass equations and determined the most appropriate error structure for the two sets of models using likelihood comparisons. The additive and disaggregated models were fitted using maximum likelihood with open-source software. We also added the site factor as a dummy variable into the two models. Finally, the competency of the two models was tested using ten-fold cross-validation. The results showed that both models could provide relatively accurate estimates of birch root biomass but that the disaggregated model performed slightly better than the additive model.

Uncertainty of biomass stocks in Spanish forests: a comprehensive comparison of allometric equations

Biomass and carbon content are essential indicators for monitoring forest ecosystems and their role in climate action, but their estimation is not straightforward. A typical approach to solve these limitations has been the estimation of tree or stand biomass based on forest inventory data, using either allometric equations or biomass expansion factors. Many allometric equations exist, but very few studies have assessed how the calculation methods used may impact outcomes and how this impact depends on genera, functional group, climate or forest structural attributes. In this study we evaluate the differences in biomass estimates yielded by the most widely used biomass equations in Spain. We first quantify the discrepancies at tree level and among the main forest tree species. We observed that the divergences in carbon estimations between different equations increased with tree size, especially in the case of hardwoods and for diameters beyond the range used to calibrate the equations. At the plot level, we found considerable differences between the biomass values predicted using different methods (above 25% in one out of three plots), which constitutes a warning against the uncritical choice of equations to determine biomass or carbon values. The spatial representation of the differences revealed geographical patterns related to the dominance of fast-growing species such as Eucalyptus or Pinus pinaster, with a minor effect of forest structure, and almost no effect of climate. Finally, we observed that differences were mostly due to the data source rather than the modelling approach or equation used. Based on our results, BEF equations seem a valid and unbiased option to provide nation-level estimations of carbon balance, although local equations should preferably be used if they are available for the target area.

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.

Additional biomass estimation alternatives: nonlinear two- and three-stage least squares and full information maximum likelihood for slash pine

A system of nonlinear biomass component equations was developed for slash pine (Pinus elliottii Engelm. var. elliottii) trees using an econometric approach in which endogenous right-hand-side variables were included in some equations. The system was fitted to component biomass data from 306 slash pine trees sampled in the southeastern United States with weighted two-stage (2SLS) and three-stage (3SLS) least squares and full information maximum-likelihood (FIML) estimation methods. The predictive performances of the system fitted with these three estimation methods were ranked based on an array of statistics, and the ranking follows the order of FIML > 3SLS > 2SLS. The new system performed as well or better than previously published biomass equation systems developed using the aggregation and disaggregation approaches and fitted to the same data. The results demonstrated that the econometric approaches such as FIML and 3SLS have the potential to be useful for tree biomass modeling.

Crown area predicts total biomass for Rhodomyrtus tomentosa, an invasive shrub in Florida

AbstractPredictive models of aboveground plant biomass derived from nondestructive measurements greatly assist in monitoring and surveying natural areas. Where invasive species are concerned, these models can provide insights to the impacts of invasions and efficacy of management strategies. Furthermore, tools that facilitate a rapid inventory allow for multiple assessments of impact over larger areas. Downy rose myrtle [Rhodomyrtus tomentosa (Aiton) Hassk.] is an invasive shrub in Florida and Hawaii that is native to southeastern Asia. Rhodomyrtus tomentosa was imported into Florida in the early 20th century through the ornamental plant trade and produces pink flowers and edible purple globe fruits. This woody shrub is particularly problematic in the understory of Florida’s mesic pine forests, where it forms dense, impenetrable thickets. To characterize the populations more accurately in Florida and build predictive equations for biomass that could be used to inform control methods, we established a network of sites from which we harvested individuals over 3 yr. Based on these measurements, we built a simple predictive equation for R. tomentosa dry biomass. Crown area strongly associates with biomass in a linear relationship (P < 0.001, R2 = 0.82). Fruit production is highly variable, but positively correlates to plant height in individuals that have reached reproductive size (plants below 1 m generally do not produce fruit), albeit weakly (P < 0.002, R2 = 0.27). We demonstrate here that two simple measurements—height and crown area—can accurately predict biomass and, to some degree, fruit production for R. tomentosa in Florida and may guide control methods by focusing on removing individuals larger than 1 m tall.

Effects of enzymatic hydrolysis and alkalization pretreatment on biohydrogen production by chlorella photosynthesis

The effects of alkalization pretreatment and enzymolysis on biohydrogen production with Chlorella vulgaris microalgae biomass by photosynthesis were studied, the alkalization pretreatment enzymolysis was to alkalize biomass raw materials before enzymolysis, the biohydrogen production kinetics equation of microalgae biomass was put forward by comparing the biohydrogen process of enzymatic hydrolysis with that of alkaline pretreatment enzymatic hydrolysis. The experimental results show: the optimum initial substrate concentration for biohydrogen production by enzymatic hydrolysis and alkaline pretreatment was 24 g/L, the maximum biohydrogen was 132.1 mL and 294.5 mL, the maximum specific biohydrogen production was 22.0 mL/g and 49.1 mL/g, and the maximum biohydrogen content was 43.9% and 56.8%. The effect of biohydrogen production by enzymatic hydrolysis after alkaline pretreatment of microalgae biomass is obviously better than that by direct enzymatic hydrolysis, which provides scientific reference and development of high efficiency and low cost biohydrogen production technology by photosynthesis of microalgae biomass.

Genome-scale metabolic model of oleaginous yeast Papiliotrema laurentii

Oleaginous yeasts are promising candidates as single cell oil (SCO) platforms to meet energy demand due to their high growth rate, easy scaling and the possibility of being cultivated in culture media based on lignocellulosic biomass. The oleaginous yeast Papiliotrema laurentii UFV-1 is able to reach high lipid contents in short periods, besides its composition of fatty acids is suitable in terms of quality standards required for biodiesel production. However, little is known about the regulation of its metabolism. We present here the first genome-scale metabolic reconstruction of P. laurentii, papla-GEM. The reconstruction was based on homology to another oleaginous yeast and the model was subjected to intensive manual curation throughout the reconstruction stages to find out the metabolic specificities of this yeast. The final model includes 796 genes, 2465 reactions and 2127 metabolites, and its biomass equation is based on direct measurements of all major biomass components. The validation step was performed using experimental data obtained in this work, and simulation results evaluated the growth and lipid accumulation physiology of P. laurentii. Therefore, the papla-GEM will lead to a better understanding of the metabolic capabilities of P. laurentii and thus will be useful in systems metabolic engineering approaches.

Estimation of parameters characterizing a steady-state synthesis process with nonlinear microorganism growth kinetics

The paper describes a theoretical basis developed for estimating the parameters of a steady-state biotechnological process characterized by nonlinear microorganism growth kinetics. This study aimed to obtain a common methodological basis for estimating input parameters that determine actual technology implementation, taking into account all possible restrictions on the concentration of incoming substrate Sf (g/l) and dilution rate D (h-1 ). The theory development was based on a mathematical model describing one of the most common processes of lactic acid production. This mathematical model includes three mass balance equations (for biomass, substrate, and product), as well as an equation of microorganism growth kinetics. The study established relations for calculating the ultimate value of the dilution rate D ult at a given Sf , relations for the maximum and minimum values of Sf , as well as Sf and D providing the maximum productivity value QP, g/(l·h), where QP = PD (P – product concentration, g/l). These relations were designed to calculate the parameters of possible process implementation for two options at the same value of QP: two values of D calculated for a given Sf and two values of Sf calculated for a given D. A numerical experiment is described using the constants of the mathematical model confirmed by foreign studies. This numerical experiment is illustrated using an Sf-D dependence pattern determining an acceptable value range for Sf and D, with the separate calculation of parameters according to Sf sections. For each of these sections, calculation formulas are provided. It is concluded that the developed theoretical basis is sufficiently general in nature to be applied to biotechnological processes that involve other kinetic relations, as well as microorganism strains creating by-products and using raw materials that are employed to reproduce the substrate in the process of synthesis.

Estimation of Individual Tree Biomass in Natural Secondary Forests Based on ALS Data and WorldView-3 Imagery

Individual-tree aboveground biomass (AGB) estimation can highlight the spatial distribution of AGB and is vital for precision forestry. Accurately estimating individual tree AGB is a requisite for accurate forest carbon stock assessment of natural secondary forests (NSFs). In this study, we investigated the performance of three machine learning and three ensemble learning algorithms in tree species classification based on airborne laser scanning (ALS) and WorldView-3 imagery, inversed the diameter at breast height (DBH) using an optimal tree height curve model, and mapped individual tree AGB for a site in northeast China using additive biomass equations, tree species, and inversed DBH. The results showed that the combination of ALS and WorldView-3 performed better than either single data source in tree species classification, and ensemble learning algorithms outperformed machine learning algorithms (except CNN). Seven tree species had satisfactory accuracy of individual tree AGB estimation, with R2 values ranging from 0.68 to 0.85 and RMSE ranging from 7.47 kg to 36.83 kg. The average individual tree AGB was 125.32 kg and the forest AGB was 113.58 Mg/ha in the Maoershan study site in Heilongjiang Province, China. This study provides a way to classify tree species and estimate individual tree AGB of NSFs based on ALS data and WorldView-3 imagery.