Destructive sampling-based allometric equations for biomass and carbon estimation in Acacia hybrid plantations in Southeastern Vietnam

There are large areas of big-leaf mahogany (Swietenia macrophylla King) afforestation and reforestation plantations for reducing carbon dioxide due to climate change in Taiwan. In Taiwan, out of the total area of mahogany plantations of approximately 2982.00 ha, 125.40 ha is in national forests and 232.00 is at the Hsin-Hua Experimental Forest Station. Biomass is a plant attribute that accumulates over time. It is an important indicator of growth and is used in analysis and management processes. Above-ground biomass is the key parameter in many allometric relationships. However, there are few studies on below-ground biomass estimations of mahogany, for it is difficult to excavate and quantify these portions. The aim of this study was to establish an allometric relationship to estimate the above-ground (stem wood, stem bark, branches, and foliage) and belowground (roots) biomass using an easily measured value, such as the diameter at breast height (DBH), diameter at the stem base (DSB) and tree height (H). Forty-six mahogany saplings (0 cm ≤ DBH ≤ 10 cm), with different ages in the second compartment of this forest station, were used to establish the allometric functions of DBH and biomass, and functions of DSB and biomass. A significance test of the correlation was used to test the relationship between DBH and biomass in different sections, including foliage, branches, stems, above-ground, below-ground, and the entire tree. The DSB was also tested. The results showed that the power regression function was superior to other functions. The correlation between DBH and biomass was higher than the correlation between DSB and biomass. The allometric functions for the entire tree biomass, above-ground biomass, and below-ground biomass were W = 175.67×DBH^2.29 (R^2 = 0.9692), Wabove = 112.21×DBH^2.34 (R^2 = 0.9621), and Wbelow = 61.65×DBH^2.19 (R^2 = 0.9610), respectively. The carbon content of each part of mahogany trees was as follows: stem wood (45.83±0.92%), roots (45.09±0.89%), foliage (44.95±1.21%), branches (43.74±1.09%), and stem bark (42.64±1.01%). Managers can estimate the biomass, carbon content ratio, and carbon storage of mahogany without destroying trees.

Light Detection and Ranging (LiDAR) provides three-dimensional information that can be used to extract tree parameter measurements such as height (H), canopy volume (CV), canopy diameter (CD), canopy area (CA), and tree stand density. LiDAR data does not directly give diameter at breast height (DBH), an important input into allometric equations to estimate biomass. The main objective of this study is to estimate tree DBH using existing allometric models. Specifically, it compares three global DBH pantropical models to calculate DBH and to estimate the aboveground biomass (AGB) of the Lake Broadwater Forest located in Southeast (SE) Queensland, Australia. LiDAR data collected in mid-2022 was used to test these models, with field validation data collected at the beginning of 2024. The three DBH estimation models—the Jucker model, Gonzalez-Benecke model 1, and Gonzalez-Benecke model 2—all used tree H, and the Jucker and Gonzalez-Benecke model 2 additionally used CD and CA, respectively. Model performance was assessed using five statistical metrics: root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), percentage bias (MBias), and the coefficient of determination (R2). The Jucker model was the best-performing model, followed by Gonzalez-Benecke model 2 and Gonzalez-Benecke model 1. The Jucker model had an RMSE of 8.7 cm, an MAE of −13.54 cm, an MAPE of 7%, an MBias of 13.73 cm, and an R2 of 0.9005. The Chave AGB model was used to estimate the AGB at the tree, plot, and per hectare levels using the Jucker model-calculated DBH and the field-measured DBH. AGB was used to estimate total biomass, dry weight, carbon (C), and carbon dioxide (CO2) sequestered per hectare. The Lake Broadwater Forest was estimated to have an AGB of 161.5 Mg/ha in 2022, a Total C of 65.6 Mg/ha, and a CO2 sequestered of 240.7 Mg/ha in 2022. These findings highlight the substantial carbon storage potential of the Lake Broadwater Forest, reinforcing the opportunity for landholders to participate in the carbon credit systems, which offer financial benefits and enable contributions to carbon mitigation programs, thereby helping to meet national and global carbon reduction targets.

Background and Objectives: Climate change has become a critical global issue affecting ecosystems worldwide, especially forests, which play a key role in carbon sequestration. While assessments often focus on large forests, small green spaces such as green university, nature trails, and urban green areas—also contribute to carbon storage and serve as platforms for environmental education. This study aimed to assess carbon storage of individual trees and generate spatially interactive carbon map the Khao Nam Sub Forest area at Kasetsart University, Sriracha Campus, Chonburi Province. This knowledge can apply the sustainable management of urban small green areas, including serve as a database for supporting environmental learning, as well as to supports the Sustainable Development Goals (SDGs 13 and 15) and aligns with Thailand’s Bio-Circular-Green (BCG) Economy Model. Methodology: The study was conducted within Khao Nam Sub area comprising dry evergreen forest (DEF) and mixed deciduous forest (MDF) types. Field data were collected along two designated nature trails: the summit trail (1.5 km) and the foothill loop trail (2.5 km), during August–October 2024. All trees and lianas with a diameter at breast height (DBH) ≥ 4.5 cm were identified, measured for DBH and total height, and georeferenced using the WGS84 coordinate system via the Google Earth mobile application. Some morphological characteristics were also photographed. Aboveground biomass (AGB) was estimated using forest-type-specific allometric equations, and belowground biomass (BGB) was derived as 0.27 × AGB. Carbon stock was calculated as 47% of total biomass (AGB + BGB) and expressed in tones of CO₂ equivalent (tCO₂e). The integrated application between Google Earth and Spreadsheet LESS-FOR-01Version 6 which developed by Thailand Greenhouse Gas Management Organization (TGO) was used to create the interactive map that included information of scientific names, GBH, carbon values, and morphological images of each tree in both 2D and 3D formats. The research was done during August to October 2024. Main Results: A total of 467 individual trees and climbing lianas were recorded along two designated nature trails, representing 70 species, 59 genera, and 33 families, indicating relatively high species richness within the study area. The most abundant climbing species was Lasiobema scandens. Across all individuals, the average diameter at breast height (DBH) was 15.40 ± 8.00 cm, and the average height was 8.76 ± 4.21 m, reflecting a structurally mixed stand composed of both small and maturing tree stages. The estimated total tree biomass for the entire surveyed area was 61.56 tons, comprising 48.47 tons of aboveground biomass (AGB) and 13.09 tons of belowground biomass. This corresponded to a total carbon stock of 28.93 tons carbon (tC), or 106.07 tons of CO₂-equivalent (tCO₂e). Upon analysis, clear structural differences were evident. The Summit trail contained the highest number of individuals (321 stems representing 48 species), which were mostly small in size, with a mean DBH of 14.37 ± 7.47 cm and a mean height of 7.89 ± 2.45 m. The total biomass was 31.51 tons (24.81 tons AGB and 6.70 tons BGB), storing 14.81 tC or 54.29 tCO₂e. In contrast, the Foothill trail contained fewer trees (146 stems from 52 species), but trees were generally larger and taller, with a mean DBH of 17.87 ± 8.16 cm and a mean height of 10.65 ± 5.04 m. Its total biomass was 30.05 tons (23.66 tons AGB and 6.39 tons BGB), sequestering 14.12 tC or 51.78 tCO₂e. Despite the smaller number of individuals, the foothill trail showed comparable carbon stocks to the summit trail, highlighting the greater influence of large-sized trees on carbon storage compared with high densities of small-sized trees. The forest composition and structure along both routes reflect a transitional mosaic of DEF and MDF tree species, with stratified canopy layers and varied stem sizes indicating ongoing regeneration and partial recovery from past disturbance. However, the accumulated carbon stock was lower compared with estimates from full-plot assessments of the entire Khao Nam Sub Forest. This discrepancy may be attributed to several limitations, including measurement uncertainties of tree height measured, the use of a fixed root-to-shoot ratio (0.27), and the linear-based sampling design along nature trails, which access forest edges and cannot fully represent the entire landscape. Although the positional accuracy of Google Earth Mobile depends on mobile signal quality, it proved suitable for generating interactive mapping and spatial visualization in the Khao Nam Sub Forest. The resulting map enables users to easily and clearly access tree-specific information along the nature trails, including scientific name, GBH, total height, carbon stock, and morphological images, with georeferenced positions displayed in both 2D and 3D formats. Such tools facilitate participatory learning, future research applications, and effective management of small-scale green spaces. Conclusion: Integrating Google Earth with the LESS-FOR-01 spreadsheet proved to be an effective method for assessing and visualizing tree-level carbon storage in small green areas. The resulting interactive Tree Carbon Storage Map enhances the assessment of carbon sequestration potential at a micro-scale level and spatial-based learning while raising awareness of each tree’s contribution to carbon storage in an ecosystem. This low-cost and replicable approach provides practical support for green space management, environmental education, and Thailand’s pursuit of Carbon Neutrality and the Sustainable Development Goals (SDGs 13 and 15) within academic institutions and local communities.

Mangrove restoration is widely recognized as a nature-based solution for enhancing blue carbon storage and mitigating climate change. However, quantitative assessments of biomass and carbon recovery in restored mangrove systems remain limited, particularly in Indonesia's ecotourism landscapes. This study evaluated aboveground and belowground biomass and associated carbon stocks in rehabilitated mangrove stands in the Balang Baru Ecotourism Area, Jeneponto Regency, South Sulawesi. Field measurements were conducted in April 2025 across five sampling stations, with 15 nested plots. Tree diameter at breast height (DBH) and height were recorded for all individuals with DBH ≥ 5 cm. Aboveground biomass (AGB) and belowground biomass (BGB) were estimated using allometric equations, and carbon stocks were derived using standard biomass-to-carbon conversion factors. The results indicated substantial biomass accumulation in the restored mangrove stands. Mean AGB and BGB were estimated at 95.20 Mg ha⁻¹ and 41.23 Mg ha⁻¹, respectively. Corresponding carbon stocks averaged 45.70 Mg C ha⁻¹ for aboveground carbon (AGC) and 16.08 Mg C ha⁻¹ for belowground carbon (BGC). Biomass and carbon stocks varied spatially among stations, with the highest values recorded at Station 5 (AGB: 126.19 Mg ha⁻¹) and the lowest at Station 4 (AGB: 67.30 Mg ha⁻¹). However, one-way ANOVA showed no statistically significant differences among stations (p > 0.05), indicating relatively comparable biomass recovery across the rehabilitated area. These findings suggest that restored mangrove stands in Balang Baru have developed substantial biomass and carbon storage capacity despite their relatively young age. The results highlight the potential contribution of community-based mangrove restoration to blue carbon initiatives and coastal ecosystem recovery. Continued long-term monitoring, along with the inclusion of soil organic carbon assessments, is recommended to obtain more comprehensive estimates of total carbon storage in restored mangrove ecosystems. Highlights Restored mangroves in Balang Baru demonstrate significant biomass recovery. AGB reached 20 ± 10.56 Mg ha⁻¹ and BGB 41.23 ± 3.83 Mg ha⁻¹. Carbon stocks averaged 70 ± 5.07 Mg C ha⁻¹ (AGC) and 16.08 ± 1.49 Mg C ha⁻¹ (BGC). Biomass distribution among stations showed no significant differences. Restored mangroves contribute to blue carbon and climate mitigation.

Globally, belowground biomass (BGB) accounts for 20–26% of total biomass, and as such it is an important carbon (C) pool for many vegetation types. However, large uncertainty exists for belowground biomass C compared to aboveground stocks. Using data from 108 destructively harvested trees belonging to 36 miombo species, we estimated root to shoot ratios, and developed models for estimation of aboveground biomass (AGB), BGB and total biomass C stocks in the Copperbelt province of Zambia. We also validated our models using independent datasets from elsewhere in Zambia and Malawi. The C fractions in wood ranged between 51.9 and 58.9%, which was higher than the IPCC default value. The root to shoot ratio was found to be 0.303. The analysis also demonstrated isometric scaling of BGB with AGB. According to cross-validation results, the model that incorporated wood density (ρ), diameter at breast height (D) and total stem height (H) formulated as AGB =0.093(ρD2H)0.97 *1.08 outperformed existing models developed for the miombo woodlands in Zambia. The best model for BGB was BGB = 0.476(AGB)0.88*1.126. Using the top-ranked models, the stand-level AGB stocks were estimated at 222.2 Mg ha−1, while BGB stocks were estimated at 52.4 Mg ha−1. Aboveground and belowground C stocks were 125.3 Mg ha−1 and 29.5 Mg ha−1, respectively. Total biomass C stocks were estimated at 152.1 Mg ha−1 or 558.3 Mg ha−1 CO2 equivalent sequestered in tree biomass. These estimates may be used as baseline data for future carbon management and for emerging payment for ecosystem services projects in miombo woodlands.

Allometric equations, wood density and partitioning of aboveground biomass in the arboretum of Ruhande, Rwanda

Rubber tree allometry, biomass partitioning and carbon stocks in mountainous landscapes of sub-tropical China

Tree allometric models that are used to predict the biomass of individual tree are critical to forest carbon accounting and ecosystem service modeling. To enhance the accuracy of such predictions, the development of site-specific, rather than generalized, allometric models is advised whenever possible. Subtropical forests are important carbon sinks and have a huge potential for mitigating climate change. However, few biomass models compared to the diversity of forest ecosystems are currently available for the subtropical forests of China. This study developed site-specific allometric models to estimate the aboveground and the belowground biomass for south subtropical humid forest in Guangzhou, Southern China. Destructive methods were used to measure the aboveground biomass with a sample of 144 trees from 26 species, and the belowground biomass was measured with a subsample of 116 of them. Linear regression with logarithmic transformation was used to model biomass according to dendrometric parameters. The mixed-species regressions with diameter at breast height (DBH) as a single predictor were able to adequately estimate aboveground, belowground and total biomass. The coefficients of determination (R2) were 0.955, 0.914 and 0.954, respectively, and the mean prediction errors were −1.96, −5.84 and 2.26%, respectively. Adding tree height (H) compounded with DBH as one variable (DBH2H) did not improve model performance. Using H as a second variable in the equation can improve the model fitness in estimation of belowground biomass, but there are collinearity effects, resulting in an increased standard error of regression coefficients. Therefore, it is not recommended to add H in the allometric models. Adding wood density (WD) compounded with DBH as one variable (DBH2WD) slightly improved model fitness for prediction of belowground biomass, but there was no positive effect on the prediction of aboveground and total biomass. Using WD as a second variable in the equation, the best-fitting allometric relationship for biomass estimation of the aboveground, belowground, and total biomass was given, indicating that WD is a crucial factor in biomass models of subtropical forest. Root-shoot ratio of subtropical forest in this study varies with species and tree size, and it is not suitable to apply it to estimate belowground biomass. These findings are of great significance for accurately measuring regional forest carbon sinks, and having reference value for forest management.

Allometric equations for estimating tree aboveground biomass in evergreen broadleaf forests of Viet Nam

Simultaneous estimation of above- and below-ground biomass in tropical forests of Viet Nam

This research estimates the carbon stock of the subtropical broad-leaved evergreen scrub forest of Lehtrar, a revenue estate of Kotli Sattian, Rawalpindi, Punjab, Pakistan. A total of six nested co-centric plots of 17.84 m2 each were laid out in the forest, having two sub-plots of 5.64 m2 and 1 m2 each, for shrubs and litter, respectively. Stem density, tree height, diameter at breast height (DBH), total tree biomass, and total carbon stock were calculated. In each plot, parameters like latitude, longitude, aspect, slope, elevation, tree count, etc., were catalogued. The carbon value was calculated in pools such as aboveground biomass (AGB), belowground biomass (BGB), litter, shrubs, etc. The tree height was measured using Abney’s level and the diameter at breast height (DBH) with diameter tape, while factors such as volume, shrub mass, litter mass, total tree biomass, and total carbon stock were calculated by using standard formulas. Results showed Olea ferrugineae to be the most abundant tree species in the study area, followed by Acacia modesta. The total average DBH and height were calculated as 17.03 and 16.79, respectively, with the species Dalbergia sissoo having the greatest DBH value. The mean carbon stock came out to be 47.75 tons/ha, with plot number 3 having the highest value of carbon stock, owing to the greatest stem count. The results of the study were significant and reflected a rich stem density, rich biomass, and an adequate carbon stocking capacity. The scrub forests of the study area, being important carbon sinks, are prone to deforestation and forest degradation activities that need to be controlled by using proper forest management practices to keep their carbon sequestration ability intact, as suggested under various reducing emissions from deforestation and forest degradation (REDD initiatives of UNFCCC.

Tree diameter measurement is one of the most important stages of forest inventories to assess growing stock, aboveground biomass, and landscape restoration options, among others. This study investigates the accuracy of measuring tree diameters using a Light Detection and Ranging (LiDAR)-equipped smartphone vs. a normal caliper (reference data) and the opportunity to use low-cost smartphone-based applications in forest inventories. To estimate the diameter at breast height (DBH) of single trees, we used a smartphone with a third-party app that automatically analyzed three-dimensional (3D) point clouds. For two different measurement techniques, we compared the two measurement techniques based on DBH data from 55 Calabrian pine (Pinus brutia Ten.) and 50 oriental plane (Platanus orientalis L.) trees using the paired-sample t-test and Wilcoxon signed-rank test. Mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE), percent bias (PBIAS), and coefficient of determination (R2) were used as precision and error statistics. Statistical differences were observed between the reference and smartphone-based DBH according to the paired-sample t-test and Wilcoxon signed-rank test. The R2 values obtained were determined as 0.91, 0.88, and 0.88 for Calabrian pine, oriental plane, and all tree species (105 trees), respectively. In addition to the overall accuracy performance of the comparison between reference and estimations, MAE, MSE, RMSE, and PBIAS values for the DBH of 105 tree stems were calculated as 1.56cm, 5.42cm, 2.33cm, and - 5.10%, respectively. The estimation accuracies increased in regular stem forms compared with forked stems particularly observed on plane trees. Further experiments are needed to investigate the uncertainties associated with trees of different stem forms, species (coniferous or deciduous), different work environments, and different types of LiDAR and LiDAR-based app scanners.

There are few allometric equations available for dipterocarp forests, despite the fact that this forest type covers extensive areas in tropical Southeast Asia. This study aims to develop a set of equations to estimate tree aboveground biomass (AGB) in dipterocarp forests in Vietnam and to validate and compare their predictive performance with allometric equations used for dipterocarps in Indonesia and pantropical areas. Diameter at breast height (DBH), total tree height (H), and wood density (WD) were used as input variables of the nonlinear weighted least square models. Akaike information criterion (AIC) and residual plots were used to select the best models; while percent bias, root mean square percentage error, and mean absolute percent error were used to compare their performance to published models. For mixed-species, the best equation was AGB = 0.06203 × DBH 2.26430 × H 0.51415 × WD 0.79456 . When applied to a random independent validation dataset, the predicted values from the generic equations and the dipterocarp equations in Indonesia overestimated the AGB for different sites, indicating the need for region-specific equations. At the genus level, the selected equations were AGB = 0.03713 × DBH 2.73813 and AGB = 0.07483 × DBH 2.54496 for two genera, Dipterocarpus and Shorea, respectively, in Vietnam. Compared to the mixed-species equations, the genus-specific equations improved the accuracy of the AGB estimates. Additionally, the genus-specific equations showed no significant differences in predictive performance in different regions (e.g., Indonesia, Vietnam) of Southeast Asia.

Deep learning models for improved reliability of tree aboveground biomass prediction in the tropical evergreen broadleaf forests

Allometric regression models are widely used to estimate tropical forest biomass, but balancing model accuracy with efficiency of implementation remains a major challenge. In addition, while numerous models exist for aboveground mass, very few exist for roots. We developed allometric equations for aboveground biomass (AGB) and root biomass (RB) based on 300 (of 45 species) and 40 (of 25 species) sample trees respectively, in an evergreen forest in Vietnam. The biomass estimations from these local models were compared to regional and pan-tropical models. For AGB we also compared local models that distinguish functional types to an aggregated model, to assess the degree of specificity needed in local models. Besides diameter at breast height (DBH) and tree height (H), wood density (WD) was found to be an important parameter in AGB models. Existing pan-tropical models resulted in up to 27% higher estimates of AGB, and overestimated RB by nearly 150%, indicating the greater accuracy of local models at the plot level. Our functional group aggregated local model which combined data for all species, was as accurate in estimating AGB as functional type specific models, indicating that a local aggregated model is the best choice for predicting plot level AGB in tropical forests. Finally our study presents the first allometric biomass models for aboveground and root biomass in forests in Vietnam.