Breast Height Research Articles

Effect of dioecy on the etructure of a population of Araucaria angustifolia (Bert.) O. Kuntze

Araucaria angustifolia (Bert.) O. Kuntze is a dioecious tree, due to the distinction between the reproductive functions of males and females, the species may have secondary sex characteristics accumulated throughout its life in variables such as diameter and height. Therefore, the present study aimed to characterize the diameter and height structure of male and female trees of the A. angustifolia species using probabilistic distribution models as well as to test the sex ratio trend throughout their distributions. A census was carried out in a ​​98-ha area located in the Campos Gerais region, in the municipality of Palmeira, state of Parana, Brazil. In the study area, a total of 621 trees were recorded, of which 51.53 % were male and 48.47 % were female with a sex ratio of 1.06, which did not differ statistically from a hypothetical 1:1 ratio, by means of a Chi-square test. The Weber distribution was the one that showed the best fit for the diameter at breast height (DBH) of both sexes and the normal distribution showed the best fit for the height. In this study, it was observed that male trees have presented a greater reproductive effort, so they tend to be more susceptible to environmental disturbances. As A. angustifolia is a species with limitations, due to forest fragmentation and its low natural regeneration, its unequal reproductive behavior may not favor one of the sexes, nor alter the balance of the Mendelian proportion, but could compromise seed production.

Thinning Improves Large Diameter Timber Cultivation but Undermines Ecosystem Multifunctionality in the Short Term

Implementing thinning practices can enhance the growth of plantation forests and improve soil health. Nevertheless, the impacts of thinning applications on soil quality, large-diameter timber production of Castanopsis hystrix, and ecosystem multifunctionality are poorly understood. Therefore, we chose two sample plots, unthinned (control) and thinned, to investigate productivity and ecosystem multifunctionality after thinning for six years. Results revealed that thinning significantly reduced the soil’s bulk density, enhanced large-diameter timber growth, and undermined ecosystem multifunctionality in the short term compared to control (unthinning) treatment. Compared to the control, the thinning treatment considerably enhanced the soil organic carbon (0–30 cm soil layer) and tree diameter at breast height (20–30 cm), and enhanced shrub leaf nitrogen (N), shrub root N, herb aboveground N, Gram-positive bacteria (0–10 cm soil layer), and Gram-positive bacteria (20–30 cm soil layer) contents by 29.61%, 65.29%, 44.61%, 274.35%, and 323.44%, respectively. Furthermore, the thinning application could improve the N and P resorption efficiency more than control. Furthermore, compared with control, thinning treatment maximized decomposition and nutrient cycling function by 11.81% and 143.40%, respectively. Moreover, total PLFA content significantly impacts carbon stocks, wood production, and water regulation functions. In conclusion, this study underscores the considerable potential of thinning in augmenting large-diameter timber production by stimulating the positive effects of forest stands. These findings provide valuable insights for ecosystem multifunctionality elevation and the judicious application of thinning to improve forestry productivity, facilitating sustainable development in the forestry sector.

Evaluating Annual Growth Dynamics Across Forest Types Using Permanent Sample Plots in Yellapur Forest Division, Karnataka, India

Annual tree increment requires repeated assessments on permanent sample plots, which are crucial for evaluating changes in tree size and stem volume. These plots enable long-term monitoring to estimate timber production and analyse growth dynamics. This study examines the current annual increment of growth parameters like tree height, GBH, basal area, and volume in dry, moist deciduous, and semi-evergreen forests within the Yellapur Forest Division, Uttara Kannada, Karnataka. The Permanent sample plots were utilized to assess growth patterns across various forest types. Total of nine one-hectare permanent plots were established across the forest types with three plots laid in each forest type and divided into subplots of size 33.33m× 33.33m and data were collected for tree parameters including girth at breast height and tree height using digital laser hypsometer. Measurements were conducted over two consecutive years i.e., in 2022 and repeated in 2023 to calculate CAI. The results revealed significant variation among forest types. Moist deciduous forests exhibited the highest CAI in height (0.45 m/year) and GBH (1.84 cm/year), while dry deciduous forests showed lower increments due to water stress and leafless periods. Semi-evergreen forests, although with dense canopy competition, demonstrated moderate growth rates. The basal area increment was highest in moist deciduous forests (0.93 m²/ha/year), reflecting favourable moisture and nutrient conditions. The tree volume increased significantly in moist deciduous forests (22.80 m³/ha/year). Variations in basal area and volume suggest that environmental conditions and anthropogenic activities, such as litter collection, impacted growth across forest types. These insights are critical for developing sustainable forest management practices and mitigating the impact of anthropogenic activities on forest ecosystems. This research underscores the importance of monitoring permanent plots to understand forest dynamics and supports sustainable forest management strategies.

Crecimiento del “shihuahuaco” Dipteryx ferrea (Ducke) Ducke en plantaciones bajo diferentes espaciamientos y tipos de suelo en Ucayali, Amazonía peruana

The results of experimental trials with Dipteryx ferrea (Ducke) Ducke, a forest species commonly known as "shihuahuaco", established under four different forest and agroforestry systems at the Alexander von Humboldt Experimental Center, Ucayali, at an altitude of 225 meters above sea level, are presented. These systems were differentiated by spatial arrangement, initial spacing, soil type, and were 11 years old. Measurements of total height, diameter at breast height (DBH), basal area, and timber volume of the plantations were obtained. The results demonstrate the growth potential, survival, and productivity of D. ferrea in both pure and mixed open-field plantations. System 1, on Plinthic acrisol soil, flat physiography, and 5 × 5 m spacing, showed the highest average DBH, total height, and dominant height, with values of 20.23 cm, 13.9 m, and 16.8 m, respectively. These results are statistically superior (p < 0.05) to those of other analyzed plantations and indicate promising performance. In terms of productivity, system 3, on Plinthic gleysol soil, flat physiography, and 2 × 2 m spacing, exhibited the highest values of basal area and roundwood volume, with 38.40 m2 /ha and 243.3 m3 /ha, respectively, statistically superior to the other systems, but also heavily influenced by the high plantation density. These findings suggest encouraging prospects for establishing D. ferrea plantations in forestry settings.

Carbon Sequestration Potential of Cashew (Anacardium occidentale L.) Plantations Across Climatic Gradients in Togo

The current study seeks to estimate the carbon sequestration capability of a cashew plantation in Togo as a function of climate gradient. The research was conducted in the Guinean and Sudanian climatic zones, on farmers' cashew plantations. The study was run from March to October 2023. This study was carried out in ten (10) cashew farms (five per climatic zone) which have ten years old. Cashew trees were classed based on their diameter at breast height (0-5 cm, 5-10 cm, 10-15 cm, and > 15 cm). To measure carbon stock in the biomass, allometric equations were utilized, and soil carbon stock was assessed using laboratory analysis. The Guinean zone has a higher soil carbon stock (86.89 ± 4.06 t C/ha) compared to the Sudanian zone (80.23 ± 3.78 t). The same trend can be observed in the various cashew tree components (trunk, branches and leaves). In general, the tree trunk had 79% of the carbon supply, compared to 19% and 2% in the branches and leaves, respectively. Carbon sequestration in the soil varies depending on the climatic zone and the soil depth (0-25 cm; 25-50 cm). Cashew-based cropping systems might be deemed carbon-efficient.

Optimizing Thermal Comfort in Urban Squares of Hot-Humid Regions: A Case Study Considering Tree Growth, Species, and Planting Intervals

The worsening urban thermal environment has become a critical challenge in many cities. Trees, as vital components of urban green spaces, provide multiple ecosystem services, especially in improving the microclimate. However, limited studies address how morphological changes during tree growth influence their cooling benefits. This study combined the tree growth model with ENVI-met to simulate 27 scenarios in a subtropical urban square, considering three planting intervals, three urban tree species, and three growth stages to evaluate their daytime thermal impacts. The key findings include: (1) Tree size and planting intervals are more important than tree quantity in enhancing thermal comfort. (2) Reducing intervals by 2 m enhances cooling effects but minimally affects PET (physiological equivalent temperature). (3) Increasing DBH (diameter at breast height) significantly improves cooling. For every 10 cm increase in DBH, Michelia alba, Mangifera indica, and Ficus microcarpa L. f. reduced solar radiation by 19.54, 18.09, and 34.50 W/m2, and mean radiant temperature by 0.61 °C, 0.68 °C, and 1.35 °C, respectively, while decreasing PET by 0.23 °C, 0.23 °C, and 0.46 °C. These findings provide empirical evidence and practical recommendations for designing comfortable open spaces in subtropical cities.

Response of Understory Plant Diversity to Edge Effects in Plantation Forests on the Loess Plateau

The majority of the world’s forests are located at landscape edges and are highly fragmented; the plantations on the Loess Plateau are no exception, experiencing pronounced edge effects. However, edge effects are often overlooked in assessments of carbon storage and biodiversity, and the extent and impact of these effects on Loess Plateau plantations remain inadequately understood. The objective of this study is to reveal how edge effects influence biodiversity and species composition and to examine their long-term impacts on ecosystem structure and function. Furthermore, it aims to explore the mechanisms underlying edge effects in plantation systems. Examining these effects is essential for guiding forest management practices and formulating effective biodiversity conservation strategies, thereby providing scientific insights to support the ecological restoration and sustainable management of plantations. In this study, we classified 44 sample plots into four groups according to their distances from the plantation edges to compare and analyze species composition. Additionally, we evaluated the intensity and range of edge effects on stand structure, species diversity, and carbon storage. The Shannon index of understory vegetation was used as the dependent variable, with canopy cover, edge distance, and stand density as independent variables. We used multiple linear regression to examine the effects of these factors on the Shannon index of understory vegetation (shrubs, herbs, and trees). The key findings were as follows: (1) Tree height did not exhibit edge effects across any distance range, while the Shannon index, species richness, and carbon storage showed edge effects within 54 m from the edge. Diameter at breast height (DBH), stand density, and canopy cover exhibited edge effects within 0–83 m from the edge. (2) The significance values for edge distance and canopy cover in the linear regression with the Shannon index were 0.99 and 0.51, respectively, showing no significant correlation. In contrast, stand density had a significant positive effect on the Shannon index (p = 0.03). (3) Notable differences in understory species composition were observed between the outermost and innermost groups of the plantation. Climatic conditions on the Loess Plateau exert a dominant influence on understory plants, altering species composition and abundance. High stand density appeared to moderate the microclimate, contributing to a higher understory Shannon index, but reducing carbon storage. Our findings suggest that the edge effects of plantation forests on the Loess Plateau exert varying degrees of influence on different indicators. Management decisions should be guided by the specific silvicultural objectives, whether the manager’s goals are to optimize biomass accumulation, enhance species recovery, or achieve a balance between these two goals.

Quantification of the Influencing Factors of Stand Productivity of Subtropical Natural Broadleaved Forests in Eastern China Using an Explainable Machine Learning Framework

Natural broadleaf forests (NBFs) are the most abundant zonal vegetation type in subtropical regions. Understanding the mechanisms influencing stand productivity in NBFs is important for developing “nature-based” solutions for climate change mitigation. However, minimal research has captured the effects of nonlinearities and feature interactions that often have nonlinear impacts on stand productivity and influencing factors. To address this research gap, we used continuous forest inventory data, and a machine learning model for stand productivity of NBFs was constructed. Subsequently, through leveraging the interpretable machine learning framework of the SHapley Additive explanation (SHAP) and partial dependence plot, we determined global and local explanations of the influencing factors of stand productivity. Our findings indicate the following: (1) The Autogluon model performed the strongest based on R2, RMSE, and rRMSE metrics. (2) The basal area (BA), neighborhood comparison of diameter at breast height (NC), and stand age (AGE) were the key influencing factors. Stand productivity increased with increasing BA and decreased with increasing NC and AGE. BA was maintained above 15 m2ha−1 and NC was maintained below 0.45, which represent favorable conditions for NBFs to maintain optimal growth. (3) SHAP interaction values were calculated to determine the effects of the five major interactions on stand productivity. Our study provides a reference for the sustainable management of NBFs, thereby highlighting the important role of forests in mitigating climate change.

Comparative Study on Growth Characteristics and Early Selection Efficiency of Hybrid Offspring of Populus deltoides 'DD-109' and P. maximowiczii in Liaoning, China.

Poplar is an important tree species for timber supply and ecological protection in northern China. Cultivating and selecting high-quality varieties and germplasm resources suitable for cultivation are key factors in enhancing the quality and productivity of poplar plantations in the arid and semi-arid northern regions with shorter growing seasons. This study conducted a field cultivation experiment on 10 progeny clones from the direct cross (D × M) of imported Populus deltoides 'DD-109' with Populus maximowiczii and 7 progeny clones from the reciprocal cross (M × D) using one-year-old rooted cuttings planted at a 4 m × 8 m spacing. Based on 17 years of annual growth observations, the study systematically compared growth characteristics, age of quantitative maturity, path relationships between traits, and early selection efficiency in the hybrid offspring. The results indicated that the D × M population had superior diameter at breast height (DBH), tree height (H), and volume (V) compared to the M × D population, while the height-to-diameter ratio (HDR) was lower. The growth rate of the 17 clones peaked from 10 to 14 years, with annual volume growth increments (PAIs) higher than mean annual volume increments (MAIs) during the early growth stages; the quantitative maturity age ranged between 12 and 16 years. The D × M population generally reached quantitative maturity earlier than the M × D population, with the fastest clone maturing in 12 years. Four clones (DM-9-17, DM-9-18, DM-9-14, and MD-61) showed values for V, DBH, H, and HDR above the hybrid group average. Path analysis demonstrated that DBH had the most significant direct and indirect effects on V, suggesting it as the best predictor for V. Using DBH as a reference, correlation and early selection efficiency analysis showed a strong relationship between growth characteristics at planting years 4-5 and later-stage performance, indicating this as the optimal period for early selection. These findings contribute to evaluating the production potential of P. deltoides 'DD-109' and P. maximowiczii germplasm in northern China and provide valuable guidance for selecting poplar clones suitable for local cultivation, accelerating breeding processes, and informing management planning for poplar plantations.

Shifts in the ecological drivers influence the response of tree and soil carbon dynamics in central Himalayan forests.

Understanding and regulating global carbon relies crucially on comprehending the components and services of forest ecosystems. In particular, interactions that govern carbon storage in trees, soil, and microbes, driven by factors like vegetation structure, function, and soil characteristics, remain poorly understood, especially in the central Himalayas. To address this gap, we investigated carbon storage in tree aboveground biomass, root biomass, and soil across different vegetation types. We also examined how vegetation parameters {vegetation diversity (H'), diameter at breast height (DBH), basal area (BA), and biomass}, and soil characteristics {bulk density (BD), moisture (Mo), pH, and total nitrogen (N)} might influence forest carbon storage. Our study, based on 14 plots (0.1ha each) spanning four distinct vegetation types {Sal forest (SF, 3), Chir-pine forest (PF, 4), Nepalese-alder forest (AF, 3), and Banj-oak forest (OF, 4)} in the central Himalaya, revealed several key insights. Tree carbon storage ranged from ∼79 to 261MgC ha-1, accounting for 41-65% of forest carbon storage, while soil carbon storage ranged from ∼28 to 69MgC ha-1, contributing 35-58%. These values varied with vegetation types and were influenced by the vegetation and soil characteristics associated with each forest type. Important contributors to tree and soil carbon storage included soil Mo, N, and vegetation structural diversity (H', BA), explaining 8-64 % of the variation. Path analysis indicated that increased vegetation diversity, soil properties, and conservative traits (fine roots and leaves) strongly influence tree and soil carbon storage. The study highlights the potential complex system to optimizing carbon storage in natural forest ecosystems, offering valuable insight for managing carbon sinks. Further research is needed to fully understand ecosystem responses to carbon storage across different forest habitats and different spatial-temporal scales.

Woody species diversity, structure and community distribution along environmental gradients of Seqela Dry Afromontane forest in Northwestern Ethiopia.

Dry evergreen Afromontane forests are severely threatened due to the expansion of agriculture and overgrazing by livestock. The objective of this study was to investigate the composition of woody species, structure, regeneration status and plant communities in Seqela forest, as well as the relationship between plant community types and environmental variables. Systematic sampling was used to collect vegetation and environmental data from 52 (20 m x 20 m) (400 m2) plots. Density, Diameter at Breast Height (DBH), basal area, frequency, and importance value index (IVI) of woody species were computed to characterize the vegetation structure of the forest. Agglomerative hierarchical cluster analysis and Canonical Correspondence Analysis (CCA) with R software were used to identify plant communities and analyse the relationship between plant community types and environmental variables, respectively. A total of 68 woody plant species belonging to 63 genera and 44 families were identified. The Shannon diversity index and evenness values of the study area were 2.12 and 0.92, respectively. The total basal area and density of woody species were 27.4 m2 ha-1 and 1079.3 individual ha-1, respectively. The most frequent woody species in the Seqela forest included Albizia gummifera (51.92%), Croton macrostachyus (44.23%), Olinia rochetiana and Teclea nobilis (36.54%). Additionally, the most dominant species, as indicated by their importance value index (IVI), were Erythrina brucei (IVI = 11.24), Prunus africana (IVI=8.68), and Croton macrostachyus (IVI=7.38). Four plant community types were identified: Albizia gummifera - Ekebergia capensis, Prunus africana - Croton macrostachyus, Vachellia abyssinica - Dombeya torrida and Schefflera abyssinica - Teclea nobilis. The CCA results showed that the variation of species distribution and plant community formation were significantly (P < 0.05) related to altitude, organic matter, aspect, slope and soil available phosphorus. The regeneration status assessment of the forest revealed a good regeneration status, which was linked to diverse and abundant seed bank in the soil can ensure a continuous supply of seeds for regeneration; therefore, it is recommended to implement periodic soil seed bank assessments to monitor seed diversity and abundance and inform targeted conservation actions.

Hierarchical Optimization Segmentation and Parameter Extraction of Street Trees Based on Topology Checking and Boundary Analysis from LiDAR Point Clouds.

Roadside tree segmentation and parameter extraction play an essential role in completing the virtual simulation of road scenes. Point cloud data of roadside trees collected by LiDAR provide important data support for achieving assisted autonomous driving. Due to the interference from trees and other ground objects in street scenes caused by mobile laser scanning, there may be a small number of missing points in the roadside tree point cloud, which makes it familiar for under-segmentation and over-segmentation phenomena to occur in the roadside tree segmentation process. In addition, existing methods have difficulties in meeting measurement requirements for segmentation accuracy in the individual tree segmentation process. In response to the above issues, this paper proposes a roadside tree segmentation algorithm, which first completes the scene pre-segmentation through unsupervised clustering. Then, the over-segmentation and under-segmentation situations that occur during the segmentation process are processed and optimized through projection topology checking and tree adaptive voxel bound analysis. Finally, the overall high-precision segmentation of roadside trees is completed, and relevant parameters such as tree height, diameter at breast height, and crown area are extracted. At the same time, the proposed method was tested using roadside tree scenes. The experimental results show that our methods can effectively recognize all trees in the scene, with an average individual tree segmentation accuracy of 99.07%, and parameter extraction accuracy greater than 90%.

Free Pasture of Exotic Goats Reduces Diversity and Negatively Affects Body Condition in Scorpions (Arachnida: Scorpiones) Assemblage From Brazilian Seasonal Dry Tropical Forest.

Extensive grazing carried out freely by exotic goats represents an important source of anthropogenic degradation in seasonally dry tropical forests of Brazil. The presence of these herbivores may negatively impact the local fauna through the reduction of habitat complexity. In this study, we investigate the effect of goat farming in scorpion assemblage from Brazilian seasonally dry tropical forest. Scorpions were sampled in 36 areas (18 disturbed and 18 undisturbed) distributed in three sites in the seasonally dry tropical forest of Brazil. At each location, we recorded a set of local variables such as litter depth, diameter at breast height of trees, vegetation density (trees and shrubs), and detritus (stones and fallen logs). In total, 396 scorpions were collected, distributed across six species and two families. Our results showed that scorpion diversity was negatively affected, with species richness and abundance reduced in areas influenced by goats. Furthermore, in these sites, the composition of the species was also affected, with habitat-generalist species favoring habitat-specialist species. Finally, sites with free pasture of goats presented scorpions with reduced body condition (body mass and size) than sites without goats. Furthermore, habitat degradation caused by free pasture of goats negatively affects the assemblage of scorpions in terms of their diversity and body condition the seasonally dry tropical forest of Brazil.

How much carbon is stored in the stem bark of Austrian trees?

With 48% forest land cover, Austria is one of the most densely forested countries in Europe and the forest area is still increasing by about 4000 hectares each year. The Austrian National Forest Inventory (NFI) provides the basic forest data such as volume stock or the carbon inventory. It is currently common that the carbon stored in the bark is recorded as part of the total tree carbon stock (including the stump) and no explicit data is reported for bark carbon. Tree bark is an important, yet currently unknown, carbon storage, plays a vital role during the life of a tree and is important for the decomposition process following tree death. The purpose of this paper is to determine the amount of carbon stored in the bark of a tree using a taper curve, a bark percentage model, bark density, and a bark fissure index. We applied this to the latest data from the Austrian NFI and calculated the bark carbon and its proportion versus the total tree carbon. We utilized a three-dimensional approach to quantify the bark carbon (excluding branches, leaves and roots), considering longitudinal variations in bark along the stem and developed a simple model to estimate the bark carbon based on tree species and diameter. The results show that 13% of the total tree carbon is stored as bark. The bark carbon of the tree increases exponentially with tree diameter at breast height for all analysed tree species and supports the fact that the bark of Austrian forests play an important role in the overall carbon budget. Our study is, to our knowledge, the first assessment of bark carbon using the large-scale forest inventory data such as the Austrian NFI. Thus it can be easily adapted for use in other European national forest data sets.

Equations to characterize diameter at breast height growth of Pinus montezumae based on increment cores

A set of equations to characterize the growth and increments in diameter at breast height (DBH) as a function of age (A) for Pinus montezumae in Puebla, Mexico was generated. From radial increment cores collected in the Forest Management Unit 2103 “Teziutlan” 294 pairs of DBH-A observations were processed. Six growth models were fitted. The Hossfeld IV model was selected (R2adj of 0.8328, RMSE of 4.6649 cm and bias of 0.0525 cm). The technical turn was 27.5 years, which corresponds to a maximum mean annual increment of 0.86 cm year-1 and a DBH of 23.8 cm.

Interspecific competition and survival pressures in endangered Barringtoniaracemosa populations of Mainland China

This study evaluates the growth, survival pressures, and community dynamics of Barringtonia racemosa (L.) Spreng. populations in Jiulong Mountain and Suixi County, Guangdong Province. Six distinct plant communities were identified, with human disturbances significantly disrupting natural succession processes. The population in Jiulong Mountain, particularly within the Talipariti tiliaceum-B. racemosa community (JLS-T), experienced higher survival pressures compared to Suixi County. Interspecific competition varied, with species like Derris trifoliata, T. tiliaceum, and invasives such as Ipomoea cairica and Mikania micrantha exerting substantial pressure on B. racemosa. Analysis of 234 B. racemosa individuals revealed significant correlations between diameter at breast height (DBH), plant height, and age structure distribution, with a linear relationship between DBH and height underscoring their relevance in understanding wood volume, biomass, and stand structure. Survival pressures were inversely related to DBH, indicating reduced competition as trees matured. Growth patterns exhibited an age-dependent plateau in height, potentially influenced by environmental and anthropogenic factors. Management strategies should prioritize the growth of individuals with DBH less than 5 cm (age classes I ~ II). These findings underscore the need for targeted conservation efforts to protect B. racemosa communities and sustain wetland ecosystems.

Describing and Modelling Stem Form of Tropical Tree Species with Form Factor: A Comprehensive Review

The concept of tree or stem form has been central to forest research for over a century, playing a vital role in accurately assessing tree growth, volume, and biomass. The form factor is an essential component for expressing the shape of a tree, enabling more accurate volume estimation, which is vital for sustainable forest management and planning. Despite its simplicity, flexibility, and advantages in volume estimation, the form factor has received less attention compared to other measures like taper equations and form quotient. This review summarizes the concept, theories, and measures of stem form, and describes the factors influencing its variation. It focuses on the form factor, exploring its types, parameterization, and models in the context of various tropical species and geographic conditions. The review also discusses the use of the form factor in volume estimation and the issues with using default or generic values. The reviewed studies show that tree stem form and form factor variations are influenced by multiple site, tree, and stand characteristics, including site quality, soil type, climate conditions, tree species, age, crown metrics, genetic factors, stand density, and silviculture. The breast height form factor is the most adopted among the three common types of form factors due to its comparative benefits. Of the five most tested form factor functions for predicting tree form factors, Pollanschütz’s function is generally considered the best. However, its performance is often not significantly different from other models. This review identifies the “Hohenadl” method and mixed-effects modelling as overlooked yet potentially valuable approaches for form factor modelling. Using the form factor, especially by diameter or age classes, can enhance tree volume estimation, surpassing volume equations. However, relying on default or generic form factors can lead to volume and biomass estimation errors of up to 17–35%, underscoring the need to limit variation sources in form factor modelling and application. Further recommendations are provided for improving the statistical techniques involved in developing form factor functions.

Resilience of the Miombo Woodland to Different Fire Frequencies in the LevasFlor Forest Concession, Central Mozambique

Fires play a significant role in shaping the Miombo woodlands. Understanding how fire affects the Miombo region’s resilience is crucial for ensuring its sustainability. This study evaluated plant composition and structure across different fire frequencies in the Miombo woodlands of the LevasFlor Forest Concession (LFC), central Mozambique. Fire frequency clusters-high (HFF), moderate (MFF), and low (LFF)-were identified using a 21-year remote-sensing dataset. In each cluster, 90 random sampling plots were established (30 per cluster). In each plot, the diameter at breast height (DBH) and total height of the saplings and trees were measured. Subplots were used to count and identify seedlings, herbs, climbers, and grasses. Plant species richness, evenness,—diversity, the importance value index (IVI), and similarity were computed to assess plant composition. For the structure, stem density, biomass, basal area, diameter, and height were assessed. A total of 124 plant species-including trees, saplings, seedlings, herbs, climbers, and grasses-were identified across the three clusters. The Bray-Curtis Dissimilarity Index, tested with an ANOSIM similarity test, revealed significant differences in plant species composition among clusters (p &lt; 0.0003), with an overall average dissimilarity of 71.98%. In the HFF cluster, fire-tolerant species were among the five species with the highest IVI, while fire-sensitive species predominated in the LFF. Additionally, the Kruskal-Wallis test indicated significant differences in seedling stem density (p &lt; 0.005) between the LFF and other clusters. However, overall, the composition and structure attributes suggested that current fire regime does not significantly compromise the plant species resilience of the Miombo woodlands in the LFC. Still, it is essential to concentrate management and conservation efforts on seedlings of some key Miombo species, such as Brachystegia spiciformis, whose ecology is particularly affected by fire.

Quantitative Genetic Aspects of Accuracy of Tree Biomass Measurement Using LiDAR

The growing focus on the role of forests in carbon sequestration highlights the importance of accurately and efficiently measuring biophysical traits, such as diameter at breast height (DBH) and tree height. Understanding genetic contributions to trait variation is crucial for enhancing carbon storage through the genetic improvement of forest trees. Light detection and ranging (LiDAR) has been used to estimate DBH and tree height; however, few studies have explored the heritability of these traits or assessed the accuracy of biomass increment selection based on them. Therefore, this study aimed to leverage LiDAR to measure DBH and tree height, estimate tree heritability, and evaluate the accuracy of timber volume selection based on these traits, using 60-year-old larch as the study material. Unmanned aerial vehicle laser scanning (ULS) and backpack laser scanning (BLS) were compared against hand-measured values. The accuracy of DBH estimations using BLS resulted in a root mean square error (RMSE) of 2.7 cm and a coefficient of determination of 0.67. Conversely, the accuracy achieved with ULS was 4.0 cm in RMSE and a 0.24 coefficient of determination. The heritability of DBH was higher with BLS than with ULS and even exceeded that of hand measurements. Comparisons of timber volume selection accuracy based on the measured traits demonstrated comparable performance between BLS and ULS. These findings underscore the potential of using LiDAR remote sensing to quantitatively measure forest tree biomass and facilitate their genetic improvement of carbon-sequestration ability based on these measurements.

Improving European Black Pine Stem Volume Prediction Using Machine Learning Models with Easily Accessible Field Measurements

Reliable prediction of tree stem volume is crucial for effective forest management and ecological assessment. Traditionally, regression models have been applied to estimate forest biometric variables, yet they often fall short when handling the complex, non-linear patterns typical of biological data, potentially introducing biases and errors. Tree stem volume, a critical metric in forest biometrics, is generally estimated through easily measured parameters such as diameter at breast height (d) and total tree height (h). This study investigates advanced machine learning (ML) techniques—Extreme Gradient Boosting (XGBoost), epsilon-Support Vector Regression (ε-SVR), and Random Forest regression (RFr)—to predict the stem volume of European black pine (Pinus nigra) on Mount Olympus, Greece, using basic field measurements. Machine learning (ML) approaches demonstrated substantial improvements in prediction accuracy compared to traditional non-linear regression-based models (RMs). Notably, XGBoost significantly enhanced predictive performance by reducing the Furnival index (FI) by as much as 42.3% (from 1.1859 to 0.1056) and 21.3% (from 0.1475 to 0.1161) in the test and fitting datasets, respectively, for the single-entry model. For the double-entry model, XGBoost achieved FI reductions of 40.5% (from 0.1136 to 0.0676) and 41.3% (from 0.1219 to 0.0715) in the test and fitting datasets, respectively. These findings highlight the potential of ML models to improve the accuracy of forest inventory predictions, thereby supporting more effective and data-driven forest management strategies.