Estimate Tree Biomass Research Articles

Reducing tree volume overestimation in quantitative structure models using modeled branch topology and direct twig measurements

Abstract Quantitative Structure Models (QSMs) are fit to tree point clouds to represent the topology of trees as a network of cylinders. QSMs allow for the calculation of metrics difficult to measure without destructive sampling, including total tree volume. Current limitations in terrestrial laser scanning technology make small branches difficult to accurately resolve, causing overestimation of small branch volume in QSMs, which can translate into overestimating tree biomass. We present a new method called Real Twig to correct overestimated small branch and twig cylinders in QSMs. Real Twig differs from current methods by using twig diameters measured directly from corresponding tree species to model a unique taper for every path in the QSM, using the QSM’s inherent branching topology, but without relying on predefined mathematical or allometric relationships. To test Real Twig, we generated QSMs for different sets of trees that had detailed dry mass and density measurements obtained via felling after scanning. QSM-based biomass estimates were obtained by multiplying the tree’s QSM-based volume estimate by the tree’s specific basic density value. We trained our method with high-quality data consisting of five northern red oak (Quercus rubra L.) and five red maple (Acer rubrum L.) trees, using two different versions of TreeQSM, a widely used algorithm for generating QSMs. We further tested our method on three publicly available datasets, including managed forests and large tropical trees, collected with both phase-shift or time-of-flight sensors. QSMs corrected with our Real Twig method showed a very large improvement in tree biomass estimation, with a relative mean error of −1.2%, a relative root mean square error of 10.5%, and a concordance correlation coefficient of 0.999, compared to a relative mean error 76.8%, a relative root mean square error of 48.7%, and a concordance correlation coefficient of 0.982, when using the standard outputs of TreeQSM.

Getting allometry right at the Oak Ridge free‐air CO2 enrichment experiment: Old problems and new opportunities for global change experiments

Societal Impact StatementFree‐air CO2 enrichment (FACE) experiments provide essential data on forest responses to increasing atmospheric CO2 for evaluations of climate change impacts on humanity. Understanding and reducing the uncertainty in the experimental results is critical to ensure scientific and public confidence in the models and policy initiatives that derive therefrom. One source of uncertainty is the estimation of tree biomass using mathematical relationships between biomass and easily obtained and non‐destructive measurements (allometry). We evaluated the robustness of the allometric relationships established at the beginning of a FACE experiment and discuss the challenges and opportunities for the new generation of FACE experiments.Summary Long‐term field experiments to elucidate forest responses to rising atmospheric CO2 concentration require allometric equations to estimate tree biomass from non‐destructive measurements of tree size. We analyzed whether the allometric equations established at the beginning of a free‐air CO2 enrichment (FACE) experiment in a Liquidambar styraciflua plantation were still valid at the end of the 12 year experiment. Aboveground woody biomass was initially predicted by an equation that included bole diameter, taper, and height, assuming that including taper and height as predictors would accommodate changes in tree structure that might occur over time and in response to elevated CO2. At the conclusion of the FACE experiment, we harvested 23 trees, measured dimensions and dry mass of boles and branches, and extracted and measured the woody root mass of 10 trees. Although 10 of the harvested trees were larger than the trees used to establish the allometric relationship, measured aboveground woody biomass was well predicted by the original allometry. The initial linear equation between bole basal area and woody root biomass underestimated final root biomass by 28%, but root biomass was just 21% of total wood mass, and errors in aboveground and belowground estimates were offsetting. The allometry established at the beginning of the experiment provided valid predictions of tree biomass throughout the experiment. New allometric approaches using terrestrial laser scanning should reduce an important source of uncertainty in decade‐long forest experiments and in assessments of centuries‐long forest biomass accretion used in evaluating carbon offsets and climate mitigation.

Constructing and Validating Estimation Models for Individual-Tree Aboveground Biomass of Salix suchowensis in China

Biomass serves as a crucial indicator of plant productivity, and the development of biomass models has become an efficient way for estimating tree biomass production rapidly and accurately. This study aimed to develop a rapid and accurate model to estimate the individual aboveground biomass of Salix suchowensis. Growth parameters, including plant height (PH), ground diameter (GD), number of first branches (NFB), number of second branches (NSB) and aboveground fresh biomass weight (FW), were measured from 892 destructive sample trees. Correlation analysis indicated that GD had higher positive correlations with FW than PH, NFB and NSB. According to the biological features and field observations of S. suchowensis, the samples were classified into three categories: single-stemmed type, first-branched type and second-branched type. Based on the field measurement data, regression models were constructed separately between FW and each growth trait (PH, GD, NFB and NSB) using linear and nonlinear regression functions (linear, exponential and power). Then, multiple power regression and multiple linear regression were conducted to estimate the fresh biomass of three types of S. suchowensis. For the single-stemmed plant type, model M1 with GD as the single parameter had the highest adj R2, outperforming the other models. Among the 16 constructed biomass-estimating equations for the first-branched plant type, model M32 FW = 0.010371 × PH1.15862 × GD1.250581 × NFB0.190707 was found to have the best fit, with the highest coefficient of determination (adj R2 = 0.6627) and lowest Akaike Information Criterion (AIC = 5997.3081). When it comes to the second-branched plant type, the best-fitting equation was proved to be the multiple power model M43 with PH, GD, NFB and NSB as parameters, which had the highest adj R2 value and best-fitting effect. The stability and reliability of the models were confirmed by the F-test, repeated k-fold cross-validation and paired-sample t-tests. The models developed in this study could provide efficient tools for accurately estimating the total aboveground biomass for S. suchowensis at individual tree levels. The results of this study could also be useful for optimizing the economic productivity of shrub willow plantations.

Point cloud-based crown volume improves tree biomass estimation: Evaluating different crown volume extraction algorithms

Accurately estimating aboveground biomass (AGB) is increasingly crucial for a better understanding of terrestrial ecosystem carbon balance and its relationship with global climate change. Terrestrial laser scanning (TLS) enables detailed reconstruction of tree crown three-dimensional structures, such as tree crown volume (CV), which has been proven to have a strong correlation with AGB. In this study, a coupled algorithm framework for extracting “optimal biomass” CV to improve AGB estimation using allometric models is proposed. The coupled algorithm framework was compared with commonly used tree CV extraction algorithms (model prediction, convex hull, alpha-shape, and voxel) based on point cloud data from 19 artificial Korean pine plots. The results reveal that the alpha-shape coupled with the voxel algorithm provides the closest approximation to the “optimal biomass” CV, exhibiting the highest correlation with AGB (r = 0.7905). α values and voxel size significantly influence the accuracy of CV extraction, while small variations in point cloud density have minimal impact on the extracted CV. Furthermore, the explanatory power of the “optimal biomass” CV for tree AGB was tested using 30 destructively harvested sampled trees. It was observed that the allometric model incorporating both “optimal biomass” volume and DBH (R2 = 0.966, RMSE = 15.321 kg) outperformed the allometric model incorporating both H and DBH (R2 = 0.953, RMSE = 16.131 kg) and the DBH-only model (R2 = 0.948, RMSE = 16.615 kg), particularly in reducing underestimation of AGB for larger trees. These findings demonstrate that the proposed alpha-shape coupled with the voxel algorithm provides an effective solution for extracting “optimal biomass” CV, significantly improving the accuracy of tree-level AGB estimation.

Allometric equations for estimation of above- and below-ground biomass of Acacia mearnsii in northwestern Ethiopia

Correct estimation of tree biomass is important when calculating uptake or emission of CO2 in relation to land-use and land-use change. The objectives of this study were (1) to estimate the root/shoot ratio for the estimation of root biomass based on the above-ground biomass (AGB) of Acacia mearnsii, and (2) to develop allometric equations for the estimation of the above-and below-ground biomass (BGB) of Acacia mearnsii. To estimate the AGB and BGB, twenty-four trees of varying ages (3, 4, 5, and 6 years) were harvested, with six trees per age group. We measured the dry biomass for different tree parts and developed allometric models using tree height (H) and diameter at breast height (DBH) as independent variables. The results showed that the biomass of the stem accounted for 69% of the total biomass, followed by branches (14%), roots (8.1%), and leaves (7.3%). The recorded mean root/shoot ratio was 0.11. The biomass of the stem and coarse roots increased with increasing tree age, while a contrary trend was observed for the other tree components. Each component has its unique allometric model.

Urban trees' potential for regulatory services in the urban environment: an exploration of carbon sequestration.

Urbanisation has emerged as a formidable challenge for urban policymakers, reaching unparalleled heights and unsettling the ecological equilibrium of the cities. Urban areas now grapple with many issues encompassing climate change, resource depletion, population surges and increased pollution levels. Many planned cities have planted trees and other vegetation within the urban sectors to enhance air quality, mitigate climate effects and provide valuable ecosystem services. This study assessed tree species diversity and their potential for carbon sequestration in Panjab University Campus, Chandigarh. We established 188 plots, each comprising randomly selected quadrats measuring 10 m × 10 m, encompassing areas with varying levels of vegetation, ranging from low to moderate and high density. We used four different allometric equations to estimate tree biomass and carbon stock. Our findings revealed that 92 tree species belong to 72 genera and 35 families, with a total tree density of 975 ha-1. The total CO2 sequestration in form of carbon stock was 18,769.46 Mg C ha-1, with Manilkara hexandra (1239.20 Mg C ha-1), Ficus benghalensis (1072.24 Mg C ha-1), Kigelia pinnata (989.89 Mg C ha-1) and Lagerstroemia floribunda (716.88 Mg C ha-1) being the top contributors. Specifically, the equation of Chave et al. (2005) without tree height yielded the highest biomass and carbon stock estimates than other equations. The present study underscores the vital role of trees on the campus as potent carbon reservoirs meet to maintain an aesthetic sense for biotic components and alleviate rising levels of CO2 in the atmospheric environment. By emphasising the role of urban trees as potent carbon reservoirs, the study underscores the importance of integrating green infrastructure into urban planning strategies. Furthermore, it offers valuable guidance for urban planners. It suggests that strategic tree planting and maintenance can enhance green spaces, regulate temperatures and ultimately support regional and global climate change mitigation goals. Incorporating these findings into urban planning processes can aid policymakers in developing resilient, ecologically sustainable cities worldwide.

The distribution of tree biomass carbon within the Pacific Coastal Temperate Rainforest, a disproportionally carbon dense forest

Spatially explicit global estimates of forest carbon storage are typically coarsely scaled. While useful, these estimates do not account for the variability and distribution of carbon at management scales. We asked how climate, topography, and disturbance regimes interact across and within geopolitical boundaries to influence tree biomass carbon, using the perhumid region of the Pacific Coastal Temperate Rainforest, an infrequently disturbed carbon dense landscape, as a test case. We leveraged permanent sample plots in southeast Alaska and coastal British Columbia and used multiple quantile regression forests and generalized linear models to estimate tree biomass carbon stocks and the effects of topography, climate, and disturbance regimes. We estimate tree biomass carbon stocks are either 211 (SD = 163) Mg C ha−1 or 218 (SD = 169) Mg C ha−1. Natural disturbance regimes had no correlation with tree biomass but logging decreased tree biomass carbon and the effect diminished with increasing time since logging. Despite accounting for 0.3% of global forest area, this forest stores between 0.63% and 1.07% of global aboveground forest carbon as aboveground live tree biomass. The disparate impact of logging and natural disturbance regimes on tree biomass carbon suggests a mismatch between current forest management and disturbance history.

Forest carbon stock-based bioeconomy: Mixed models improve accuracy of tree biomass estimates

Tree biomass and carbon stock continue to be the most important contributors to a forest-based bioeconomy. Accurate estimation of tree biomass and carbon stock therefore is crucial for planning a sustainable bioeconomy based on sustained forest bioresource production and harvest. Precise carbon accounting is also of fundamental importance for a green carbon market as well as for an improved model of quantitative global carbon cycle. However, estimating biomass and carbon at a large spatial scale without causing significant environmental disruption has been posing a methodological challenge necessitating to evolve a robust yet accurate estimation method. Although forest-specific generalized allometric models are the most widely used method to estimate forest tree biomass and carbon stock, these either under- or over-estimate the values, particularly in mixed-species forests. To address this challenge, we employed mixed equations that included species-specific models or available models for a sister species that provided a more conservative estimate of stand-level tree biomass and carbon compared to the generalized equations. We gathered primary data on tree girth and height at the stand level from sub-tropical and temperate forests of Tawang river basin, Arunachal Pradesh, India for validation. We compiled 32 equations comprising 27 species-specific and 5 forest-specific generalized models from a global dataset of allometric models. The outputs from the mixed models were comparable with the results of the harvest studies conducted by other researchers in similar forest types validating the efficacy of mixed models at a landscape scale.

Tree Biomass Modeling Based on the Exploration of Regression and Artificial Neural Networks Approaches

Understanding the dynamics of tree biomass is a significant factor in forest ecosystems, and accurate quantitative knowledge of its development provides support for the optimization of forest management. This work aimed to employ innovative practices in tree biomass modeling, artificial neural network approaches along with the least-squares regression methodology, in order to construct reliable and accurate estimation and prediction models that contribute to solving the emerging problems in the field of sustainable forest management. Based on this aim, different modeling strategies were developed and explored. The nonlinear seemingly unrelated regression (NSUR) methodology, the generalized regression (GRNN), the resilient propagation (RPNN) and the Bayesian regularization (BRNN) artificial neural network algorithms were utilized for the construction of reliable biomass models to attain the most accurate and reliable tree biomass components and total tree biomass estimations. The work showed that GRNN models provided a significantly better performance compared with the other modeling methodologies tested. Considering the non-parametric nature of the GRNN neural network algorithm, the fact that it was designed for nonlinear regression-type problems capable of dealing with small datasets, this modeling approach warrants consideration as an effective alternative to nonlinear regression or to other neural network approaches to the field of tree biomass modeling.

INDIVIDUAL TREE SEGMENTATION FROM BLS DATA BASED ON GRAPH AUTOENCODER

Abstract. In the last two decades, Light detection and ranging (LiDAR) has been widely employed in forestry applications. Individual tree segmentation is essential to forest management because it is a prerequisite to tree reconstruction and biomass estimation. This paper introduces a general framework to extract individual trees from the LiDAR point cloud based on a graph link prediction problem. First, an undirected graph is generated from the point cloud based on K-nearest neighbors (KNN). Then, this graph is used to train a convolutional autoencoder that extracts the node embeddings to reconstruct the graph. Finally, the individual trees are defined by the separate sets of connected nodes of the reconstructed graph. A key advantage of the proposed method is that no further knowledge about tree or forest structure is required. Seven sample plots from a plantation forest with poplar and dawn redwood species have been employed in the experiments. Though the precision of the experimental results is up to 95 % for poplar species and 92 % for dawn redwood trees, the method still requires more investigations on natural forest types with mixed tree species.

Biomass estimation models for Acacia saligna trees in restored landscapes

Acacia saligna, originating from Australia, is a naturalized multipurpose tree species widely grown to restore degraded lands of Africa. The contribution of A. saligna in biomass restoration can be quantified using a precise estimation of tree biomass carbon. This study developed species-specific allometric models and evaluated the spatial variation of tree biomass across restored areas in exclosures and open grazing landscapes. These models could play a considerable role in the monitoring of carbon dynamics across A. saligna planation dominated areas. We harvested, excavated, and weighed twenty-one sample trees representing different size classes to develop allometric models for the estimation of aboveground (AGB), belowground (BGB) and total tree (TB) biomass. The average dry-to-fresh mass ratio and the root-to-shoot ratio was 0.47 (±0.13) and 0.28 (±0.14), respectively. Tree biomass significantly correlated with diameter at breast height (r = 0.93; P < 0.001), diameter at stump height (r = 0.88, P < 0.001) and tree height (r = 0.56, P < 0.05). Our best biomass estimation models explained 86%, 82% and 87% of variations in AGB, BGB, and TB, respectively. Models using DSH and DSH & H explained 70%–78% of the variation in AGB, BGB, and TB. Estimated C-stock showed a significant relationship with stem density (R 2 = 0.91, P < 0.01). Estimated TB varied between 1.5–18 Mg ha−1 on grazed land and exclosures. Estimated C-stocks in the exclosure exceeded the estimated C-stock in the open grazing land by ∼60%. This implies that with proper management practices and enrichment planting A. saligna significantly contributes to increasing carbon accumulation on degraded landscapes, playing a key role in climate change mitigation efforts while improving land productivity.

Nondestructive estimation of pomegranate tree biomass by using allometric equation

Nondestructive estimation of pomegranate tree biomass by using allometric equation

Allometric models for biomass prediction of Hevea brasilliensis

AbstractTrees contribute to combating global warming by absorbing CO2, thus enhancing the quality of the environment. Sri Lanka maintains about 112,000 ha of Hevea brasiliensis (rubber) plantations, where 85.7% is in the wet zone. When rubber trees decline in latex production, after 30–33 years of planting, they are harvested to regenerate the next cycle. Rubber timber in Sri Lanka is used mainly for manufacturing interior parts of furniture, tool handles, and toys, leaving minimal chances for the emission of CO2 by combustion. Therefore, rubber is a good source of carbon sequestration, which can be used to earn additional benefits for the farmers. Estimating tree biomass helps to assess the carbon sequestration of those trees. Therefore this study aimed to build allometric models to predict the biomass using diameter at breast height (dbh) and total tree height (h). Since uprooting rubber trees is not allowed till maturity, whole‐tree, above‐ground, and stem biomass prediction models were built by uprooting ten 30‐ and 31‐year‐old trees from two rubber estates of the low country wet zone of Sri Lanka. For the rest of the ages, dbh and h prediction models were built by age using data collected from six age classes of rubber trees from the same two estates so those variables could be used as inputs to the biomass prediction models. For dbh and h models, Michaelis–Menten functions performed better than power‐law functions. Biomass was estimated for the stem and large branches by converting respective volumes to weights. Weight measurements were taken directly for biomass assessment for small branches, foliage, and roots. The quality of models was determined based on both qualitative and quantitative evaluations. The finding proved that the biomass of rubber plantations could effectively be estimated by dbh and h over time.

Individual tree volume estimation with terrestrial laser scanning: Evaluating reconstructive and allometric approaches

Accurate estimates of above-ground tree biomass within forest inventories are essential for calibration and validation of biomass mapping products based on Earth observation data. Terrestrial laser scanning (TLS) enables detailed and non-destructive volume estimation of individual trees, which can be converted to biomass with wood basic density. Existing TLS-based approaches range from simple geometrical features to virtual 3D reconstruction of entire trees. Validating such approaches with weight measurements is a key step before the integration of TLS or other close-range technologies into operational applications such as forest inventories. In this study, we firstly evaluate individual tree volume estimation approaches based on 3D reconstruction through quantitative structure models (QSM) against destructive reference data of 60 trees and compare them to operational allometric scaling models (ASM). Secondly, we determine the explanatory power of TLS-derived geometric parameters regarding total wood, stem, coarse wood and fine branch volume. We observe similar accuracy in merchantable (¿7 cm) wood compartments for ASMs (NRMSE = 25 %) and QSMs (NRMSE = 29 %), with QSMs showing better results for broadleaves than conifers and generally overestimating fine branch volume. Feature selection shows that a combination of stem diameters and volume of convex hulls around tree crowns has the most potential to model the entire tree volume including branches, especially for conifers. In cases where the quality of available point clouds is insufficient for QSMs, 3D information can thus still be utilised by deriving geometric parameters. The integration of crown dimension parameters into new allometric models could substantially improve the estimation of branch wood volume.

Comparison of Parameter Estimation Methods Based on Two Additive Biomass Models with Small Samples

Accurately estimating tree biomass is crucial for monitoring and managing forest resources, and understanding regional climate change and material cycles. The additive model system has proven reliable for biomass estimation in Chinese forestry since it considers the inherent correlation among variables based on allometric equations. However, due to the increasing difficulty of obtaining a substantial amount of sample data, estimating parameters for the additive model equations becomes a formidable challenge when working with limited sample sizes. This study primarily focuses on analyzing these parameters using data extracted from a smaller sample. Here, we established two additive biomass model systems using the independent diameter and the combined variable that comprises diameter and tree height. The logarithmic Nonlinear Seemingly Uncorrelated (logarithmic NSUR) method and the Generalized Method of Moments (GMM) method were applied to estimate the parameters of these models. By comparing four distinct approaches, the following key results were obtained: (1) Both the GMM and logarithmic NSUR methods can yield satisfactory goodness of fit and estimation precision for the additive biomass equations, with the root mean square error (RMSE) were significantly low, and coefficients of determination (R2) were mostly higher than 0.9. (2) Comparatively, examining the fitted curves of predicted values, the GMM method provided better fitting than the NSUR method. The GMM method with the combined variable is the most suggested approach for the calculation and research of single-tree biomass models with a small sample size.

Ratio estimators for aboveground biomass and its parts in subtropical forests of Brazil

Researchers on forest biomass seek to develop or find methods of simple application, whose estimators reveals the best performance and provide the additivity of the biomass parts. Tree biomass normally is obtained by scaling tree parts (stem, branches, and leaves) separately. When the biomass is modeled by parts of the tree, their sum does not match the total, which means lack of additivity in the tree biomass estimation. We aimed to test and propose a modeling technique for estimating total aboveground biomass, so that consistency between tree parts will be achieved. The techniques ‘ratio estimators’ and ‘weighted apparently unrelated nonlinear regressions (WNSUR)’ were tested using a biomass dataset with 387 trees. Data were collected in eight sites located in Paraná and Rio Grande do Sul, Brazil, where information was collected on diameter at 1.30 m aboveground (dbh), heights (total, stem and crown), total biomass aboveground and its parts: stem, branches and leaves. All trees were identified at species level. To adjust the ratio equations, the cylinder volume (vcl), based on the mean square diameter (dqi2), was used as the independent variable and the total aboveground biomass and the respective parts: stem, branches, and leaves, as dependent variables. The occurrence of heteroscedasticity in the ratio estimators indicated that the data should be stratified into two stages, the first by dbh and the second by the slope of the ratio between the total aboveground biomass and the vcl of each tree. To group the trees in the second stage, a posteriori, a discriminant analysis was applied. The standard errors of the estimate resulted in less than 4% for the ratio estimators, while for the WNSUR they reached values larger than 40% for the total biomass aboveground and its parts. The statistics: bias, MAE, MSE and RMSE showed better performance by ratio estimators when compared to the WNSUR, for the total biomass and its parts except for the bias towards the stem. Ratio estimators naturally provide additivity to biomass parts making it possible to improve the precisions of the estimates. In future work with forest biomass and its parts, more canopy variables should be collected to improve the stratification of trees within the diameter stratum. The total volume of the trees should also be estimated, if possible, via xylometry, to approximate the ratio coefficients of the total aboveground biomass, as much as possible, to the actual values of the specific gravity of the wood.

Estimation of tree biomass and carbon stock in the Tangkoko Nature Reserve, North Sulawesi

The objective of this study is to tree biomass and forest carbon stocks contained in the Tangkoko Nature Reserve (TNR) of North Sulawesi. Phylogenetic diversity was analysed based on tree species composition weighted by aboveground biomass. A stratified systematic sampling was used in the study across transect lines. Field measurements follow the method described in the SNI 7724:2019 on ground-based forest carbon accounting. Based on allometric approach, this study produces a quantity of 255.45 Mg.ha−1 of total biomass estimation consisting of 201.14 Mg.ha−1 AGB and 54.31 Mg.ha−1 BGB. A standard approach of C-stock calculation (i.e. 47% of tree biomass and 50% of soil organic biomass) results in quantities of 94.53 and 27.15 Mg.ha−1 respectively). The importance of species traits for biomass estimation should be considered by initiatives in reforestation for carbon offsetting.

StemAnalysis: An R-package for reconstructing tree growth and carbon accumulation with stem analysis data

1. Accurate information about age dynamics of timber production and carbon storage in forest ecosystems is frequently required by scientists, stakeholders, and policymakers. However, we lack standardized tools to calculate tree growth dynamics, even though this could be obtained by combining typical tree inventory, allometry and stem analysis data.2. Here we present an integrated R package, StemAnalysis, designed to reconstruct stem growth profiles, construct height-diameter relationships, and consequently to compute growth trends in terms of diameter at breast height (DBH), tree height, stem volume, tree biomass and carbon for an individual tree.3. StemAnalysis performs a number of standard tasks on input stem analysis data: (i) age class determination; (ii) the cumulative growth, mean annual increment, and current annual increment of outside-bark DBH, tree height, and stem volume are estimated; (iii) height-diameter relationship is fitted with nonlinear models; and (iv) tree biomass and carbon storage estimation from allometric and volume models are calculated. These tasks can be performed optionally.4. StemAnalysis provides investigators with a set of procedures for exploring tree growth patterns, thereby contributing to the ongoing development of forest multifunctional management sciences.

Allometric Models to Estimate the Biomass of Tree Seedlings from Dry Evergreen Forest in Thailand

Seedlings are an important stage for plant populations, as the abundance and rigor of seedlings can indicate a changing forest structure in the future. Studying the different traits of the seedling can represent how the plant grows. Biomass is one of the traits that can represent the plant’s performance and many other growth processes of the seedling. Several allometric equations have been developed to estimate tree biomass. However, allometric equations for the biomass of seedlings remains poorly studied, especially those from the tropics. The objective of this research is to create and develop a model that can be used to predict the biomass of seedlings, including total biomass, aboveground biomass, and belowground biomass, from root collar diameter, shoot height, main stem length, and wood density from 205 two-year-old seedlings from twenty tree species found in dry evergreen forest in Huai Kha Khaeng Wildlife Sanctuary, Uthai Thani, Thailand. The results showed that the root collar diameter, shoot height, and wood density could be used to create a model to best predict the seedling biomass. This model should be tested with other seedlings in the wild and other datasets to evaluate the performance of the model. To our knowledge, this study is among the first to provide the first allometry for seedlings in tropical dry evergreen forest. The results from this study will allow ecologists to monitor and examine the growth of the seedlings at all stages of life in dynamic tropical environments in the future.

Individual tree segmentation and biomass estimation based on UAV Digital aerial photograph

Individual tree segmentation and biomass estimation based on UAV Digital aerial photograph