Search Results (97)

Teak (Tectona grandis L.f.) is a leading tropical plantation species valued for high-quality timber and carbon (C) storage. This study assessed stand growth across ages and sites, quantified biomass and C by tree component and stand, and developed DBH-based allometric equations for biomass and C estimation. Six stand ages (5, 6, 9, 11, 14, and 17 years) were assessed in three municipalities of Nayarit, Mexico. Dendrometric inventories in permanent plots and destructive sampling of 35 trees provided calibration data for leaves, branches, stem, and roots. C concentration was determined with an elemental analyzer, and nonlinear regression models were adjusted and validated. Stand biomass and C increased with age, peaking at ages 11–14 (>130 Mg ha⁻¹; >60 Mg C ha⁻¹), with lower values at age 17. San Blas and Rosamorada accumulated significantly more than Tuxpan, reflecting site quality. C concentration was stable across sites and ages, with stem and roots consistently ranging between 48% and 50%, and leaves and branches averaging 45%–46%. Allometric equations were most accurate for stem and total biomass/C (R² = 0.73–0.79), while foliage showed higher variability. On average, 60%–70% of biomass was allocated to the stem and 15%–20% to roots. Indicators were stable, with an aboveground-to-belowground ratio (A:B) ≈ 4.9 and a biomass expansion factor (BEF) ≈ 1.5. The current annual increment (CAI) presented two main peaks: ~20 Mg ha⁻¹ yr⁻¹ at ages 5–6 and ~11 Mg ha⁻¹ yr⁻¹ at ages 9–11, followed by a decline after age 14. Teak in western Mexico reaches peak productivity at ages 6–11, with belowground biomass essential for accurate C accounting. Full article

Our study focused on the quantification of aboveground biomass stock and aboveground net primary productivity (ANPP) in young, planted beech (Fagus sylvatica L.). We selected 15 young even-aged stands targeting moderately fertile sites. Three rectangular plots were established within each stand, and all trees were annually measured for height and stem basal diameter from 2020 to 2024. For biomass modeling, we conducted destructive sampling of 111 beech trees. Each tree was separated into foliage and woody components, oven-dried, and weighed to determine dry mass. Allometric models were developed using these predictors: tree height, stem basal diameter, and their combination. Biomass accumulation was closely correlated with stand age, allowing us to scale tree-level models to stand-level predictions using age as a common predictor. Biomass stocks of both woody parts and foliage increased with stand age, reaching 48 Mg ha⁻¹ and 6 Mg ha⁻¹, respectively, at the age of 15 years. A comparative analysis indicated generally higher biomass in naturally regenerated stands, except for foliage at age 16, where planted stands caught up with the naturally regenerated ones. Our findings contribute to a better understanding of forest productivity dynamics and offer practical models for estimating carbon sequestration potential in managed forest ecosystems. Full article

Mulching is a sustainable agronomic practice that can improve soil quality and fruit characteristics in crops. This study investigated the influence of sheep wool mulch and a soil conditioner on growth, the accumulation of bioactive compounds, and soil enzymatic activity in apple orchards. A two-year field experiment (2023–2024) was conducted using three experimental methods: mulching with sheep wool (V2), application of a soil conditioner, corn starch-based polymer (V3), and a combination of sheep wool and corn starch-based polymer (V4) along with a control (V1). Tree growth parameters, fruit physicochemical properties, total phenolic and flavonoid content, and soil enzyme activities (dehydrogenase, catalase, phosphatase) were assessed. Data were analyzed using Principal Component Analysis (PCA) and Pearson’s correlation. PCA showed that the combined variant (V4) improved fruit size, weight, and bioactive compound content, while wool mulch alone (V2) was associated with higher fruit yield and better vegetative growth. Catalase activity correlated positively and consistently with bioactive compounds in both years, while phosphatase activity showed an intensified positive relationship in 2024. Dehydrogenase activity was negatively correlated with phenolic content in both seasons. Organic and integrated mulching practices can beneficially modulate both aboveground and belowground plant–soil interactions. The combined variant proved to be the most effective strategy, enhancing fruit nutritional quality and supporting sustainable apple orchard management. Full article

Pinus yunnanensis, a native tree species in southwest China, is shading-tolerant and ecologically significant. Light has a critical impact on plant physiology, and decapitation improves canopy light penetration and utilization efficiency. The study of allometric relationships is well-known in forestry, forest ecology, and related fields. Under control (full daylight exposure, 0% shading), L1 (partial shading, 25% shading), L2 (medium shading, 50% shading), and L3 (serious shading, 75% shading) levels, this study used the decapitation method. The results confirmed the effectiveness of decapitation in annual P. yunnanensis and showed that the main stem maintained isometric growth in all shading treatments, accounting for 26.8% of the individual plant biomass, and exhibited dominance in biomass allocation and high shading sensitivity. These results also showed that lateral roots exhibited a substantial biomass proportion of 12.8% and maintained more than 0.5 of higher plasticity indices across most treatments. Moreover, the lateral root exhibited both the lowest slope in 0.5817 and the highest significance (p = 0.023), transitioning from isometric to allometric growth under L1 shading treatment. Importantly, there was a positive correlation between the biomass allocation of an individual plant and that of all components of annual P. yunnanensis. In addition, the synchronized allocation between main roots and lateral branches, as well as between main stems and lateral roots, suggested functional integration between corresponding belowground and aboveground structures to maintain balanced resource acquisition and architectural stability. At the same time, it has been proved that the growth of lateral roots can be accelerated through decapitation. Important scientific implications for annual P. yunnanensis management were derived from these shading experiments on allometric growth. Full article

Canada’s vast forests play a substantial role in the global carbon balance but require laborious and expensive forest inventory campaigns to monitor changes in aboveground biomass (AGB). Light detection and ranging (LiDAR) or reflectance observations onboard airborne or unoccupied aerial vehicles (UAVs) may address scalability limitations associated with traditional forest inventory but require simple forest structures or large sets of manually delineated crowns. Here, we introduce a deep learning approach for crown delineation and AGB estimation reproducible for complex forest structures without relying on hand annotations for training. Firstly, we detect treetops and delineate crowns with a LiDAR point cloud using marker-controlled watershed segmentation (MCWS). Then we train a deep learning model on annotations derived from MCWS to make crown predictions on UAV red, blue, and green (RGB) tiles. Finally, we estimate AGB metrics from tree height- and crown diameter-based allometric equations, all derived from UAV data. We validate our approach using 14 ha mixed forest stands with various experimental tree densities in Southern Ontario, Canada. Our results show that using an unsupervised LiDAR-only algorithm for tree crown delineation alongside a self-supervised RGB deep learning model trained on LiDAR-derived annotations leads to an 18% improvement in AGB estimation accuracy. In unharvested stands, the self-supervised RGB model performs well for height (adjusted R², R_a² = 0.79) and AGB (R_a² = 0.80) estimation. In thinned stands, the performance of both unsupervised and self-supervised methods varied with stand density, crown clumping, canopy height variation, and species diversity. These findings suggest that MCWS can be supplemented with self-supervised deep learning to directly estimate biomass components in complex forest structures as well as atypical forest conditions where stand density and spatial patterns are manipulated. Full article

Aboveground biomass models are useful for assessing vegetation conditions and providing valuable information on the availability of ecosystem goods and services, including carbon stock and forest/rangeland products. This study aimed to develop aboveground biomass estimation models for the common woody species found in Borana woodland. Multispecies and species-specific models for aboveground biomass were developed using 114 destructively sampled trees representing five species. The dendrometric variables selected as predictors of the trees’ aboveground dry biomass for both multispecies and species-specific models were diameter at breast height, tree height, wood basic density (ρ), crown area (ca) and crown diameter (cd). The distribution of biomass across trees’ aboveground components was estimated using destructively sampled trees. Most tree biomass is allocated to branches, followed by the stems. The tree diameter, wood basic density, and crown diameter were significant predictors in generic and species-specific biomass models across all tree components. Incorporating wood basic density into the model significantly improved prediction accuracy, while tree height had a minimal effect on biomass estimation. The stem and twig biomasses were the highest and least predictable plant parts, respectively. Compared with the existing models, our newly developed models significantly reduced prediction errors, reinforcing the importance of location-specific models for accurate biomass estimation. Hence, this study fills the geographic and ecological gaps by developing models tailored with the unique conditions of the Borana lowland forest. The accuracy of species-specific biomass models varied among tree species, indicating the need for species-specific models that account for variations in growth architecture, ecological factors, and bioclimatic conditions. Full article

In Vietnam, models for estimating Above-Ground Biomass (AGB) to predict carbon stock are primarily based on diameter at breast height (DBH), tree height (H), and wood density (WD). However, remote sensing has increasingly been recognized as a cost-effective and accurate alternative. Within this context, the present study aimed to develop correlation equations between Total Above-Ground Carbon (TAGC) and vegetation indices derived from Sentinel-2 imagery to enable direct estimation of carbon stock for assessing emissions and removals. In this study, the remote sensing indices most strongly associated with TAGC were identified using principal component analysis (PCA). TAGC values were calculated based on forest inventory data from 115 sample plots. Regression models were developed using Ordinary Least Squares and Maximum Likelihood methods and were validated through Monte Carlo cross-validation. The results revealed that Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Near Infrared Reflectance (NIR), as well as three variable combinations—(NDVI, ARVI), (SAVI, SIPI), and (NIR, EVI — Enhanced Vegetation Index)—had strong influences on TAGC. A total of 36 weighted linear and non-linear models were constructed using these selected variables. Among them, the quadratic models incorporating NIR and the (NIR, EVI) combination were identified as optimal, with AIC values of 756.924 and 752.493, R² values of 0.86 and 0.87, and Mean Percentage Standard Errors (MPSEs) of 22.04% and 21.63%, respectively. Consequently, these two models are recommended for predicting carbon stocks in Evergreen Broadleaf (EBL) forests within Vietnam’s South Central Coastal Ecoregion. Full article

Elephants exhibit remarkable cognitive and social abilities, which are integral to their navigation, resource acquisition, and responses to environmental challenges such as climate change and human–wildlife conflict. Their capacity to acquire, recall, and utilise spatial information enables them to traverse large, fragmented landscapes, locate essential resources, and mitigate risks. While older elephants, particularly matriarchs, are often regarded as repositories of ecological knowledge, the mechanisms by which younger individuals acquire this information remain uncertain. Existing research suggests that elephants follow established movement patterns, yet direct evidence of intergenerational knowledge transfer is limited. This review synthesises current literature on elephant navigation and decision-making, exploring how their behavioural strategies contribute to resilience amid increasing anthropogenic pressures. Empirical studies indicate that elephants integrate environmental and social cues when selecting routes, accessing water, and avoiding human-dominated areas. However, the extent to which these behaviours arise from individual memory, social learning, or passive exposure to experienced individuals requires further investigation. Additionally, elephants function as ecosystem engineers, shaping landscapes, maintaining biodiversity, and contributing to climate resilience. Recent research highlights that elephants’ ecological functions can indeed contribute to climate resilience, though the mechanisms are complex and context-dependent. In tropical forests, forest elephants (Loxodonta cyclotis) disproportionately disperse large-seeded, high-carbon-density tree species, which contribute significantly to above-ground carbon storage. Forest elephants can improve tropical forest carbon storage by 7%, as these elephants enhance the relative abundance of slow-growing, high-biomass trees through selective browsing and seed dispersal. In savannah ecosystems, elephants facilitate the turnover of woody vegetation and maintain grassland structure, which can increase albedo and promote carbon sequestration in soil through enhanced grass productivity and fire dynamics. However, the ecological benefits of such behaviours depend on population density and landscape context. While bulldozing vegetation may appear destructive, these behaviours often mimic natural disturbance regimes, promoting biodiversity and landscape heterogeneity, key components of climate-resilient ecosystems. Unlike anthropogenic clearing, elephant-led habitat modification is part of a long-evolved ecological process that supports nutrient cycling and seedling recruitment. Therefore, promoting connectivity through wildlife corridors supports not only elephant movement but also ecosystem functions that enhance resilience to climate variability. Future research should prioritise quantifying the net carbon impact of elephant movement and browsing in different biomes to further clarify their role in mitigating climate change. Conservation strategies informed by their movement patterns, such as wildlife corridors, conflict-reducing infrastructure, and habitat restoration, may enhance human–elephant coexistence while preserving their ecological roles. Protecting older individuals, who may retain critical environmental knowledge, is essential for sustaining elephant populations and the ecosystems they influence. Advancing research on elephant navigation and decision-making can provide valuable insights for biodiversity conservation and conflict mitigation efforts. Full article

Urban street trees often exhibit distinct architectural characteristics compared to their counterparts in natural forests. Allometric equations for the stem (M_S), branches (M_B), and total dry aboveground biomass of urban trees (M_T) were developed, based on 52 destructively sampled specimens, belonging to 10 different species, growing in the Municipality of Athens, Greece. Linear, log-linear, and nonlinear regression analyses were applied, and fit statistics were used to select the most appropriate model. The results indicated that diameter at breast height (D_1.3) and tree height (H) are needed for accurately predicting M_S, while M_B may be estimated based on D_1.3. To circumvent the caveat of the additivity property for estimating the biomass of different tree component, nonlinear seemingly unrelated regression (NSUR) was implemented. The 95% prediction intervals for M_S, M_B, and M_T efficiently captured the variability of the sampled trees. Finally, the predictions were compared with estimates from i-Tree, the most widely used model suite for urban and rural forestry analysis, and a mean deviation of 134% (ranging from 3% to 520%) was reported. Therefore, in the absence of urban-specific allometries, the obtained empirical models are proposed for estimating biomass in street trees, particularly in cities with Mediterranean-like climatic influences. Full article

Vegetation characteristics significantly influence the impact of wildfires on individual building structures, and these effects can be systematically analyzed using heat transfer modelling software. Close-range light detection and ranging (LiDAR) data obtained from uncrewed aerial systems (UASs) capture detailed vegetation morphology; however, the integration of dense vegetation and merged canopies into three-dimensional (3D) models for fire modelling software poses significant challenges. This study proposes a method for integrating the UAS–LiDAR-derived geometric features of vegetation components—such as bark, wooden core, and foliage—into heat transfer models. The data were collected from the natural woodland surrounding an elevated building in Samford, Queensland, Australia. Aboveground biomass (AGB) was estimated for 21 trees utilizing three 3D tree reconstruction tools, with validation against biomass allometric equations (BAEs) derived from field measurements. The most accurate reconstruction tool produced a tree mesh utilized for modelling vegetation geometry. A proof of concept was established with Eucalyptus siderophloia, incorporating vegetation data into heat transfer models. This non-destructive framework leverages available technologies to create reliable 3D tree reconstructions of complex vegetation in wildland–urban interfaces (WUIs). It facilitates realistic wildfire risk assessments by providing accurate heat flux estimations, which are critical for evaluating building safety during fire events, while addressing the limitations associated with direct measurements. Full article

We studied the effects of stand age on the allocation of biomass and allometric relationships among component biomass in five stands ages (1, 3, 5, 7, and 8 years old) of two eucalypts hybrids, including Eucalyptus urophylla × E. grandis and E. urophylla × E. tereticornis, in the Leizhou Peninsula, China. The stem, bark, branch, leaf, and root biomass from 60 destructively harvested trees were quantified. Allometric models were applied to examine the relationship between the tree component biomass and predictor variable (diameter at breast height, D, and height, H). Stand age was introduced into the allometric models to explore the effect of stand age on biomass estimation. The results showed the following: (1) Stand age significantly affected the distribution of biomass in each component. The proportion of stem biomass to total tree biomass increased with stand age, the proportions of bark, branch, and leaf biomass to total tree biomass decreased with stand age, and the proportion of root biomass to total tree biomass first decreased and then increased with stand age. (2) There were close allometric relationships between biomass (i.e., the components biomass, aboveground biomass, and total biomass per tree) and diameter at breast height (D), height (H), the product of diameter at breast height and tree height (DH), and the product of the square of the diameter at breast height and tree height (D²H). The allometric relationship between biomass and measurement parameters (D, H, DH, D²H) could be applied to the biomass assessment of eucalypts plantation. (3) Allometric equations that included stand age as a complementary variable significantly improved the fit and enhanced the accuracy of biomass estimates. The optimal independent variable for the biomass prediction model varied according to each organ. These results indicate that stand age has an important influence on biomass allocation. Allometric equations considering stand age could improve the accuracy of carbon sequestration estimates in plantations. Full article

Carbon (C) losses due to the conversion of natural forests adversely affect the biotic and abiotic components of terrestrial ecosystems. In tropical China, rubber cultivation often extends from its traditional range to elevations of up to 1400 m. However, C stock in rubber plantations along elevation gradients is poorly understood. In this study, we investigated biomass and C stock along elevation gradients in two age groups (8- and 12-year-old) of rubber monoculture plantations in Xishuangbanna, Southwest China. The C distribution across various tree sections, ranging from aboveground biomass (AGB) to belowground biomass (BGB), including litter, big dead branches, and different soil depths were measured. A significant negative correlation was observed between AGB, BGB, litter, and total ecosystem C stocks and elevation gradients in both age groups. However, no correlation was observed between the total soil C stock and elevation gradients in 8-year-old rubber plantations, while significant decline was detected in 12-year-old rubber plantations. The highest ecosystem C stock of 197.90 Mg C ha⁻¹ was recorded at 900 m in 8-year-old plantations; whereas, in 12-year-old rubber plantations, the highest value of 183.12 Mg C ha⁻¹ was found at 700 m. The total ecosystem C stock decreased to their lowest level at 1000 m in both the 8-year-old and 12-year-old plantations, ranging between 113.05 Mg C ha⁻¹ and 125.75 Mg C ha⁻¹, respectively. Moreover, total ecosystem C stock significantly decreased from 51.55% to 8.05% and from 42.96% to 11.46% between 700 m and 1100 m, in both 8-year-old and 12-year-old plantations, respectively. Regardless of elevation gradients, the total ecosystem C stock of 12-year-old rubber plantations was 1.98% greater than that of 8-year-old rubber plantations. Biomass was the second largest contributor, while soil accounted for 82% to 90%, and the other components contributed less than 2% of the total ecosystem C stock in both age groups. These fluctuations in C stock along elevation gradients in both 8- and 12-year-old plantations suggested that rubber growth, biomass, and C stock capacity decreased above 900 m, and that age and elevation are key factors for biomass and C stock in rubber monoculture plantations. Full article

Bamboo forests, as some of the integral components of forest ecosystems, have emerged as focal points in forestry research due to their rapid growth and substantial carbon sequestration capacities. In this paper, satellite-borne lidar data from GEDI and ICESat-2/ATLAS are utilized as the main information sources, with Landsat 9 and DEM data as covariates, combined with 51 pieces of ground-measured data. Using random forest regression (RFR), boosted regression tree (BRT), k-nearest neighbor (KNN), Cubist, extreme gradient boosting (XGBoost), and Stacking-ridge regression (RR) machine learning methods, an aboveground carbon (AGC) storage model was constructed at a regional scale. The model evaluation indices were the coefficient of determination (R²), root mean square error (RMSE), and overall estimation accuracy (P). The results showed that (1) The best-fit semivariogram models for c_dem, f_dem, f_ndvi, p_dem, and a_ndvi were Gaussian models, while those for h1_b7, h2_b7, h3_b7, and h4_b7 were spherical models; (2) According to Pearson correlation analysis, the AGC of Dendrocalamus giganteus showed an extremely significant correlation (p < 0.01) with c_dem and p_dem from GEDI, and also showed an extremely significant correlation with a_ndvi, h1_b7, h2_b7, h3_b7, and h4_b7 from ICESat-2/ATLAS; moreover, AGC showed a significant correlation (0.01 < p < 0.05) with f_dem and f_ndvi from GEDI; (3) The estimation accuracy of the GEDI model was superior to that of the ICESat-2/ATLAS model; additionally, the estimation accuracy of the Stacking-RR model, which integrates GEDI and ICESat-2/ATLAS (R² = 0.92, RMSE = 5.73 Mg/ha, p = 86.19%), was better than that of any single model (XGBoost, RFR, BRT, KNN, Cubist); (4) Based on the Stacking-RR model, the estimated AGC of Dendrocalamus giganteus within the study area was 1.02 × 10⁷ Mg. The average AGC was 43.61 Mg/ha, with a maximum value of 76.43 Mg/ha and a minimum value of 15.52 Mg/ha. This achievement can serve as a reference for estimating other bamboo species using GEDI and ICESat-2/ATLAS remote sensing technologies and provide decision support for the scientific operation and management of Dendrocalamus giganteus. Full article

The traditional volume-derived biomass method is limited because it does not fully consider the carbon sink of young trees, which leads to the underestimation of the carbon sink capacity of a forest ecosystem. Therefore, there is an urgent need to establish an allometric biomass model of young trees to provide a quantitative basis for accurately estimating the carbon storage and carbon sink of young trees. The destructive data that were used in this study included the biomass of the young trees of the two dominant species (Betula pendula subsp. mandshurica (Regel) Ashburner & McAll and Populus × tomentosa Carrière) in China, which was composed of the aboveground biomass (B_a), belowground biomass (B_b), and total biomass (B_t). Univariate and bivariate dimensions were selected and five candidate biomass models were independently tested. Two additive allometric biomass model systems of young trees were established using the proportional function control method and algebraic sum control method, respectively. We found that the logistic function was the most suitable for explaining the allometric growth relationship between the B_a, B_t, and diameter at breast height (D) of young trees; the power function was the most suitable for explaining the allometric growth relationship between the B_b and D of young trees. When compared with the independent fitting model, the two additive allometric biomass model systems provide additive biomass prediction which reflects the conditions in reality. The accuracy of the B_t models and B_a models was higher, while the accuracy of the B_b models was lower. In terms of the two dimensions—univariate and bivariate, we found that the bivariate additive allometric biomass model system was more accurate. In the univariate dimension, the proportional function control method was superior to the algebraic sum control method. In the bivariate dimension, the algebraic sum control method was superior to the proportional function control method. The additive allometric biomass models provide a reliable basis for estimating the biomass of young trees and realizing the additivity of the biomass components, which has broad application prospects, such as the monitoring of carbon stocks and carbon sink evaluation. Full article

Forests play an important role in promoting carbon cycling and mitigating the urban heat island effect as one of the world’s major carbon storages. Scientifically quantifying tree biomass is the basis for assessing tree carbon storage and other ecosystem functions. In this study, a sample plot of Populus tomentosa plantation in the Olympic Forest Park in Beijing was selected as the research object. Point cloud data from three types of laser scanners, including terrestrial laser scanner (TLS), backpack laser scanner (BLS), and handheld laser scanner (HLS), were used to estimate the biomass of single tree trunks, branches, leaves, and aboveground total biomass based on the Allometric Biomass Model (ABM) and Advanced Quantitative Structure Model (AdQSM). The following conclusions were drawn from the estimation results: (1) For the three types of laser scanner point clouds, the biomass estimation values obtained using the AdQSM model were generally higher than those obtained using the Allometric Biomass Model. However, the estimation values obtained using the two models were similar, especially for tree trunks and total biomass. (2) For total biomass and individual biomass components of single trees, the results obtained from handheld and terrestrial laser scanner point clouds are consistent; however, they show some differences from the results obtained from backpack-mounted point clouds. This study further enriches the methodological system for estimating forest biomass, providing a theoretical basis and reference for more accurate estimates of forest biomass and more sustainable forest management. Full article