Search Results (64)

Accurate assessments of aboveground biomass (AGB) stocks and their changes in extensive Miombo forests are challenging due to the lack of site-specific allometric equations (AEs). Terrestrial Laser Scanning (TLS) is a non-destructive method that enables the calibration of AEs and has recently been validated by the IPCC guidelines for carbon accounting within the REDD+ framework. TLS surveys were carried out in five non-contiguous 1-ha plots in two study sites in the wet Miombo forest of Katanga, in the Democratic Republic Congo. Local wood densities (WD) were determined from wood cores taken from 619 trees on the sites. After a careful checking of Quantitative Structure Models (QSMs) output, the individual volumes of 213 trees derived from TLS data processing were converted to AGB using WD. Four AEs were calibrated using different predictors, and all presented strong performance metrics (e.g., R² ranging from 90 to 93%), low relative bias and relative individual mean error (11.73 to 16.34%). Multivariate analyses performed on plot floristic and structural data showed a strong contrast in terms of composition and structure between sites and between plots within sites. Even though the whole variability of the biome has not been sampled, we were thus able to confirm the transposability of results within the wet Miombo forests through two cross-validation approaches. The AGB predictions obtained with our best AE were also compared with AEs found in the literature. Overall, an underestimation of tree AGB varying from −35.04 to −19.97% was observed when AEs from the literature were used for predicting AGB in the Miombo of Katanga. 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

Forests are invaluable natural resources that provide essential ecosystem services, and their carbon storage capacity is critical for climate mitigation efforts. Quantifying this capacity would require accurate estimation of forest structural attributes for deriving their aboveground biomass (AGB). Traditional field measurements, while precise, are labor-intensive and often spatially limited. Handheld Mobile Laser Scanning (HMLS) offers a rapid alternative for building forest inventories; however, its effectiveness and accuracy in diverse subtropical forests with complex canopy structure remain under-investigated. In this study, we employed both HMLS and traditional surveys within structurally complex subtropical forest plots, including old-growth forests (Fung Shui Woods) and secondary forests. These forests are characterized by dense understories with abundant shrubs and lianas, as well as high stem density, which pose challenges in Light Detection and Ranging (LiDAR) point cloud data processing. We assessed tree detection rates and extracted tree attributes, including diameter at breast height (DBH) and canopy height. Additionally, we compared tree-level and plot-level AGB estimates using allometric equations. Our findings indicate that HMLS successfully detected over 90% of trees in both forest types and precisely measured DBH (R² > 0.96), although tree height detection exhibited relatively higher uncertainty (R² > 0.35). The AGB estimates derived from HMLS were comparable to those obtained from traditional field measurements. By producing highly accurate estimates of tree attributes, HMLS demonstrates its potential as an effective and non-destructive method for rapid forest inventory and AGB estimation in subtropical forests, making it a competitive option for aiding carbon storage estimations in complex forest environments. Full article

This text provides a comprehensive overview of structural timber old buildings, from an in-depth analysis of construction processes to laboratory-based research aimed at establishing a pattern for estimating the density of wood in buildings. It is now widely recognised by society that historic buildings should be subject to conservation or rehabilitation. This article discusses the good technical knowledge that those involved in old buildings should have: the understanding of and respect for old construction techniques; rigorous inspections and diagnosis before a project; and the recognition of the properties of wooden structural elements, either visually or by means of non-destructive or semi-destructive testing methods (NDT/SDT). The final section of this article presents a laboratory study that correlates penetration resistance test results with wood density and verifies them in situ by direct analysis with wood core extraction. The aim of this study is to establish and verify a reliable pattern that allows the user to estimate the density of Scots pine in any structural member in service in an old building. The results obtained in the laboratory and of wood in service show that Equation (1) is a suitable pattern to obtain wood density through the wood penetration resistance test. Full article

For jack pine (Pinus banksiana Lamb.) and red pine (Pinus resinosa Aiton) forest types, the goal of this study was to develop and demonstrate enhanced climate-smart crop planning models that are capable of simultaneously addressing both conventional and evolving forest management objectives, i.e., volumetric yield, wood quality, carbon storage-based harvestable wood product (HWP) production, and biodiversity-driven deadwood accumulation objectives. Procedurally, this involved the following: (1) development and integration of species-specific cambial age prediction equations and associated integration of whole-stem fibre attribute prediction equation suites, previously developed for wood density (W_d), microfibril angle (M_a), modulus of elasticity (M_e), fibre coarseness (C_o), tracheid wall thickness (W_t), tracheid radial (D_r) and tangential (D_t) diameters, and specific surface area (S_a), into climate-sensitive structural stand density management models (SSDMMs); (2) modification of the computational pathway of the SSDMMs to enable the estimation of abiotic stem volume production; and (3) given (1) and (2), exemplifying the potential utility of the enhanced SSDMMs in operational crop planning. Analytically, to generate whole-stem attribute predictions and derive HWP estimates, species-specific hierarchical mixed-effects cambial age models were specified, parameterized, and statistically validated. The previously developed attribute equation suites along with the new cambial age models were then integrated within the species-specific SSDMMs. In order to facilitate the calculation of accumulated deadwood production arising from density-dependent (self-thinning) and density-independent (non-self-thinning) mortality, the computational pathways of the SSDMMs were augmented and modified. The utility of the resultant enhanced SSDMMs was then exemplified by generating and contrasting rotational volumetric yield, wood quality attribute property maps, quantity and quality (grade) of solid wood and non-solid wood HWPs, and deadwood production forecasts, for species–locale–RCP-specific crop plan sets. These analytical model-based innovations, along with the crop planning exemplifications, confirmed the adaptability and potential utility of the enhanced SSDMMs in mitigating the complexities of multiple criteria decision-making when managing jack pine and red pine forest types under climate change. Full article

A biochar (BC) generated by the pyrogasification of wood chips from authorized forestry cuts was extensively characterized and evaluated for its efficacy in retaining/releasing two agrochemicals, namely the fungicide penconazole (PEN), the herbicide S-metolachlor (S-MET), and the xenoestrogen bisphenol A (BPA) widely present in industrial effluents. The elemental composition of BC was evaluated using CN elemental analysis and total reflection X-ray fluorescence (TXRF) spectroscopy which showed the abundance of elements typically found in BCs (Ca, K, P) along with essential trace elements such as Fe and Mn. Scanning electron microscopy coupled with energy-dispersive X-ray analysis (SEM-EDX) described the surface features of BC along with the major surface elements, while Brunauer–Emmett–Teller (BET) analysis revealed, as expected, a large specific surface area (366 m² g⁻¹). High porosity (0.07 cm³ g⁻¹) was demonstrated by the density functional theory (DFT) method, while Fourier transform infrared (FT-IR) spectroscopy highlighted the presence of a prominent aromatic structure and the abundance of reactive functional groups responsible for the binding of the compounds. The sorption/desorption capacity of BC was studied by means of sorption kinetics and isotherms in batch trials, and by modeling the experimental data with various theoretical equations. All compounds reached sorption equilibrium on BC very rapidly, following preferentially pseudo-second-order kinetics. Freundlich adsorption constants of PEN, S-MET, and BPA were 37.3, 13.2, and 11.6 L g⁻¹, respectively, thus demonstrating the great affinity of BC for hydrophobic pollutants. The adsorption process was hysteretic as only a small fraction of each compound was slowly desorbed from BC. The overall results obtained highlighted the great potential of BC of acting as a biosorbent of contaminants, which is of great importance for the containment of pollution in agricultural soils and for limiting the entry of toxic compounds into the human and animal food chain. Full article

The growing demand for wood products and ecosystem services in Chinese fir plantations has led to longer rotation ages and density control practices, raising concerns about their impact on soil fertility. This study assessed soil fertility of Chinese fir plantations in Fujian, Jiangxi, and Sichuan Provinces using the Nemerow index. The effects of stand age and initial planting density on soil fertility were analyzed using statistical models. In Fujian and Jiangxi, soil fertility was significantly higher at 11 and 30 years than at 5 and 25 years, while in Sichuan, it was higher at 25 and 30 years than at 5 and 11 years. In Fujian, soil fertility was higher at 6667 trees ha⁻² than at 1667 trees ha⁻². No significant differences were observed in Jiangxi, while in Sichuan, soil fertility at 6667 trees ha⁻² was significantly higher than at 5000 and 1667 trees ha⁻², and soil fertility at 10,000 trees ha⁻² exceeded that at 1667 trees ha⁻². Soil fertility typically increased with stand age, especially in Fujian and Sichuan. Soil fertility also increased with initial planting density, especially in Jiangxi and Sichuan. A structural equation model (SEM) explained 88% of the variance in soil fertility, with stand age directly affecting soil fertility and soil organic matter mediating the effects of stand age and planting density. These findings suggest that adjusting rotation age and planting density could help improve soil fertility, offering practical implications for sustainable forest management in Chinese fir plantations. Full article

Plant hydraulic traits are essential for understanding and predicting plant drought resistance. Investigations into the mechanisms of the xylem anatomical traits of desert shrubs in response to climate can help us to understand plant survival strategies in extreme environments. This study examined the xylem anatomical traits and related functional traits of the branches of seven Calligonum mongolicum populations along a precipitation gradient, to explore their adaptive responses to climatic factors. We found that (1) the vessel diameter (D), vessel diameter contributing to 95% of hydraulic conductivity (D₉₅), hydraulic weighted vessel diameter (Dh), vessel density (VD), percentage of conductive area (CA), thickness-to-span ratio of vessels ((t/b)²), and theoretical hydraulic conductivity (K_th) varied significantly across sites, while the vessel group index (V_g), wood density (WD), and vulnerability index (VI) showed no significant differences. (2) Principal component analysis revealed that efficiency-related traits (K_th, D_h, D₉₅) and safety-related traits (VI, VD, inter-wall thickness of the vessel (t)) were the primary factors driving trait variation. (3) Precipitation during the wettest month (PWM) had the strongest influence, positively correlating with (t/b)² and negatively with D, D₉₅, D_h, CA, and K_th. (4) Structural equation modeling confirmed PWM as the main driver of K_th, with indirect effects through CA. These findings indicate that C. mongolicum displays high plasticity in xylem traits, enabling adaptation to changing environments, and providing insight into the hydraulic strategies of desert shrubs under climate change. Full article

Many factors (material properties, drill bit type and size, drill bit wear, drilling parameters used, and machine-tool characteristics) affect the efficiency of the drilling process, which could be quantified through the delamination factor, thrust force, and drilling torque. To find the optimal combination among the factors that affect the desired responses during drilling of wood-based composites, various modelling techniques could be applied. In this work, an artificial neural network (ANN) and response surface methodology (RSM) were applied to predict and optimize the delamination factor at the inlet and outlet, thrust force, and drilling torque during drilling of prelaminated particleboards, medium- density fiberboard (MDF), and plywood. The artificial neural networks were used to design four models—one for each analyzed response. The coefficient of determination (R²) during the validation phase of designed ANN models was among 0.39 and 0.96. The response surface methodology was involved to reveal the individual influence of analyzed factors on the drilling process and also to figure out the optimum combination of factors. The regression equations obtained an R² among 0.88 and 0.99. The material type affects mostly the delamination factor. The thrust force is mostly influenced by the drill type. The chipload has a significant effect on the drilling torque. A twist drill with a tip angle equal to 30° and a chipload of 0.1 mm/rev. could be used to efficiently drill the analyzed wood-based composites. Full article

Tropical forests play an important role in mitigating global climate change, emphasizing the need for reliable estimates of forest carbon stocks at regional and global scales. This is essential for effective carbon management, which involves strategies like emission reduction and enhanced carbon sequestration through forest restoration and conservation. However, reliable sample-based estimations of forest carbon stocks require accurate allometric equations, which are lacking for the rainforests of the Atlantic Forest Domain (AFD). In this study, we fitted biomass equations for the three main AFD forest types and accurately estimated the amount of carbon stored in their above-ground biomass (AGB) in Rio de Janeiro state, Brazil. Using non-destructive methods, we measured the total wood volume and wood density of 172 trees from the most abundant species in the main remnants of rainforest, semideciduous forest, and restinga forest in the state. The biomass and carbon stocks were estimated with tree-level data from 185 plots obtained in the National Forest Inventory conducted in Rio de Janeiro. Our locally developed allometric equations estimated the state’s biomass stocks at 70.8 ± 5.4 Mg ha⁻¹ and carbon stocks at 35.4 ± 2.7 Mg ha⁻¹. Notably, our estimates were more accurate than those obtained using a widely applied pantropical allometric equation from the literature, which tended to overestimate biomass and carbon stocks. These findings can be used for establishing a baseline for monitoring carbon stocks in the Atlantic Forest, especially in the context of the growing voluntary carbon market, which demands more consistent and accurate carbon stock estimations. Full article

Bog bilberry (Vaccinium uliginosum L.) is considered a highly valued non-wood forest product (NWFP) species with edible and medicinal uses in East Asia. It grows in the northeastern forests of China, where stand attributes and structure jointly determine its population characteristics and individuals’ growth. Mapping the regional distributions of its population characteristics can be beneficial in the management of its natural resources, and this mapping should be predicted using machine learning modeling to obtain accurate results. In this study, a total of 60 stands were randomly chosen and screened to investigate natural bog bilberry populations in the eastern mountains of Heilongjiang and Jilin provinces in northeastern China. Individual height, canopy cover area, and fresh weight all increased in stands at higher latitudes, and shoot height was also higher in the eastern stands. The rootstock grove density showed a polynomial quadratic distribution pattern along increasing topographical gradients, resulting in a minimum density of 0.43–0.52 groves m⁻² in stands in the southern part (44.3016° N, 129.4558° E) of Heilongjiang. Multivariate linear regression indicated that the bog bilberry density was depressed by host forest tree species diversity; this was assessed using both the Simpson and Shannon–Wiener indices, which also showed polynomial quadratic distribution patterns (with a modeling minimum of 0.27 and a maximum of 1.21, respectively) in response to the increase in latitude. Structural equation models identified positive contributions of tree diameter at breast height and latitude to shoot height and a negative contribution of longitude to the bog bilberry canopy area. Random forest modeling indicated that dense populations with heavy individuals were distributed in eastern Heilongjiang, and large-canopy individuals were distributed in Mudanjiang and Tonghua. In conclusion, bog bilberry populations showed better attributes in northeastern stands where host forest trees had low species diversity, but the dominant species had strong trunks. Full article

To explore the radial variation in wood properties of slash pine (Pinus elliottii Engelm.) during its growth process and to achieve the early prediction of these properties, our study was carried out in three slash pine harvest-age plantations in Ganzhou, Jian, and Jingdezhen, Jiangxi province of South China. Wood core samples were collected from 360 sample trees from the three plantations. SilviScan technology was utilized to acquire wood property parameters, such as tangential fiber widths (TFWs), radial fiber widths (RFWs), fiber wall thickness (FWT), fiber coarseness (FC), microfibril angle (MFA), modulus of elasticity (MOE), wood density (WD) and ring width (RD). Subsequent systematic analysis focused on the phenotypic and radial variation patterns of wood properties, aiming to establish a clear boundary between juvenile and mature wood. Based on determining the boundary between juvenile and mature wood, a regression equation was used to establish the relationship between the properties of juvenile wood and the ring ages. This relationship was then extended to the mature wood section to predict the properties of mature wood. Our results indicated significant differences in wood properties across different locations. The coefficients of variation for RD and MOE were higher than other properties, suggesting a significant potential for selective breeding. Distinct radial variation patterns in wood properties from the pith to the bark were observed. The boundary between juvenile and mature wood was reached at the age of 22. The prediction models developed for each wood property showed high accuracy, with determination coefficients exceeding 0.87. Additionally, the relative and standard errors between the measured and predicted values were kept below 10.15%, indicating robust predictability. Mature wood exhibited greater strength compared to juvenile wood. The approach of using juvenile wood properties to predict those of mature wood is validated. This method provides a feasible avenue for the early prediction of wood properties in slash pine. Full article

Wood basic density (WD) plays a crucial role in estimating forest biomass; moreover, improving wood-density estimates is needed to reduce uncertainties in the estimates of tropical forest biomass and carbon stocks. Understanding variations in this density along the tree trunk and its impact on biomass estimates is underexplored in the literature. In this study, the vertical variability of WD was assessed along the stems of large trees that had a diameter at breast height (DBH) ≥ 50 cm from a dense ombrophilous forest on terra firme (unflooded uplands) in Acre, Brazil. A total of 224 trees were sampled, including 20 species, classified by wood type. The average WD along the stem was determined by the ratio of oven-dry mass to saturated volume. Five models were tested, including linear and nonlinear ones, to fit equations for WD, selecting the best model. The variation among species was notable, ranging from 0.288 g cm⁻³ (Ceiba pentandra, L., Gaertn) to 0.825 g cm⁻³ (Handroanthus serratifolius, Vahl., S. Grose), with an average of 0.560 g cm⁻³ (±0.164, standard deviation). Significant variation was observed among individuals, such as in Schizolobium parahyba var. amazonicum (H. ex D.), which ranged from 0.305 to 0.655 g cm⁻³. WD was classified as low (≤0.40 g cm⁻³), medium (0.41–0.60 g cm⁻³), and high (≥0.61 g cm⁻³). The variability in WD along the stem differs by wood type. In trees with low-density wood, density shows irregular variation but tends to increase along the stem, whereas it decreases in species with medium- and high-density wood. The variation in WD along the stem can lead to underestimations or overestimations, not only in individual trees and species but also in total stocks when estimating forest biomass. Not considering this systematic bias results in significant errors, especially in extrapolations to vast areas, such as the Amazon. Full article

The growth of tropical wet forests has a significant relationship with the climate; aspects such as temperature and precipitation affect the species; however, few studies have characterized the stem growth rate of tropical tree species. This study’s objective was to characterize the effects of climatic variation on the interannual stem growth rate of eight species in tropical wet forest. Six trees per species were selected (n = 48 trees), and a dendrometer was installed to measure diametric growth bi-monthly between 2015 and 2018 (3 years), complemented with environmental measurements, to determine their growth equations from environmental variables and, finally, to define the relationship between the wood density and the stem growth rate. The results showed an average stem growth from 0.45 to 4.35 mm year⁻¹, and 40 to 70% growth occurred in the months with the highest rainfall. Also, species with higher wood densities were found to have lower stem growth rates. Finally, the analysis of stem growth rate showed a significant relationship in all species between the variables of temperature and precipitation (R² adj 0.88 to 0.96). Our results suggest that species with greater stem growth rates in wet tropical forests are more susceptible to climate changes, which may affect their dynamics in the face of potential drought scenarios and heat waves associated with climate change. Full article

Forest and its dynamics are of great significance for accurately estimating regional carbon sequestration, emissions and carbon sink capacity. In this work, an efficient framework that integrates remote sensing, deep learning and statistical modeling was proposed to extract forest change information and then derive forest carbon storage dynamics during the period 2017 to 2020 in Jiangning District, Nanjing, Eastern China. Firstly, the panchromatic band and multi-spectral bands of GF-1 images were fused by using four different methods; Secondly, an improved Mask-RCNN integrated with Swin Transformer was devised to extract forest distribution information in 2020. Finally, by using the substitution strategy of space for time in the 2017 Forest Management and Planning Inventory (FMPI) data, local carbon density allometric growth equations were fitted by coniferous forest and broad-leaved forest types and compared, and the optimal fitting was accordingly determined, followed by the measurements of forest-change-induced carbon storage dynamics. The results indicated that the improved Mask-RCNN synergizing with the Swin Transformer gained an overall accuracy of 93.9% when mapping the local forest types. The carbon storage of forest standing woods was calculated at 1,449,400 tons in 2020, increased by 14.59% relative to that of 2017. This analysis provides a technical reference for monitoring forest change and lays a data foundation for local agencies to formulate forest management policies in the process of achieving dual-carbon goals. Full article