Search Results (86)

Accurate estimation of individual tree stem volume is essential for forest resource assessment and the implementation of sustainable forest management. In South Korea, traditional regression models based on non-destructive and easily measurable field variables such as diameter at breast height (DBH) and total height (TH) have been widely used to construct stem volume tables. However, these models often fail to adequately capture the nonlinear taper of tree stems. In this study, we evaluated and compared the predictive performance of traditional regression models and two machine learning algorithms—Random Forest (RF) and Extreme Gradient Boosting (XGBoost)—using stem profile data from 1000 destructively sampled Chamaecyparis obtusa trees collected across 318 sites nationwide. To ensure compatibility with existing national stem volume tables, all models used only DBH and TH as input variables. The results showed that all three models achieved high predictive accuracy (R² > 0.997), with XGBoost yielding the lowest RMSE (0.0164 m³) and MAE (0.0126 m³). Although differences in performance among the models were marginal, the machine learning approaches demonstrated flexible and generalizable alternatives to conventional models, providing a practical foundation for large-scale forest inventory and the advancement of digital forest management systems. Full article

Virtual power plants (VPPs) enable the efficient participation of distributed renewable energy resources in electricity markets by aggregating them. However, the profitability of VPPs is challenged by market volatility and regulatory constraints, such as price caps and imbalance penalties. This study examines the joint impact of varying price cap levels and imbalance penalty structures on the bidding strategies and revenues of VPPs. A stochastic optimization model was developed, where a three-stage scenario tree was utilized to capture the uncertainty in electricity prices and renewable generation output. Simulations were performed under various market conditions using real-world price and generation data from the Korean electricity market. The analysis reveals that higher price cap coefficients lead to greater revenue and more segmented bidding strategies, especially under asymmetric penalty structures. Segment-wise analysis of bid price–quantity pairs shows that over-bidding is preferred under upward-only penalty schemes, while under-bidding is preferred under downward-only ones. Notably, revenue improvement tapers off beyond a price cap coefficient of 0.8, which indicates that there exists an optimal threshold for regulatory design. The findings of this study suggest the need for coordination between price caps and imbalance penalties to maintain market efficiency while supporting renewable energy integration. The proposed framework also offers practical insights for market operators and policymakers seeking to balance profitability, adaptability, and stability in VPP-integrated electricity markets. Full article

Accurate estimation of stem profiles is critical for forest management, timber yield prediction, and ecological modeling. However, traditional taper equations often fail to capture species-specific growth variability and exhibit significant biases, particularly in the upper stem regions. Machine learning regression models were applied to estimate Quercus mongolica stem profiles across South Korea, and performance was compared with that of a traditional taper equation. A total of 2503 sample trees were used to train and validate Random Forest (RF), XGBoost (XGB), Artificial Neural Network (ANN), and Support Vector Regression (SVR) models. Predictive performance was evaluated using root mean square error, mean absolute error, and coefficient of determination metrics, and performance differences were validated statistically. The ANN model exhibited the highest predictive accuracy and stability across all diameter classes, maintaining smooth and consistent stem profiles even in the upper stem regions where the traditional taper model exhibited significant errors. RF and XGB models had moderate performance but exhibited localized fluctuations, whereas the Kozak taper equation tended to overestimate basal diameters and underestimate crown-top diameters. Machine learning models, particularly ANN, offer a robust alternative to fixed-form taper equations, contributing substantially to forest resource inventory, carbon stock assessment, and climate-adaptive forest management planning. Full article

During the natural growth of trees, a large number of branches are formed, with a negative impact on timber quality. Therefore, pruning is an essential measure in forest cultivation. In this work, the effect of pruning on poplar timber quality was evaluated. This study used an artificial forest of Populus deltoides “Nanlin 3804”, established in 2014, as the research object. Pruning was carried out in March 2018 and March 2020 with a pruning intensity of one-third, and a control group was also set up. In December 2023, the growth of 11-year-old poplars under different treatments was investigated and analyzed, and sample trees were cut down for a wood property analysis. The results showed that pruning did not have a significant effect on the growth of the diameter at breast height, the tree height, or the volume. However, pruning could significantly facilitate the forming of higher-quality timber with smaller knots. Compared to unpruned wood, the ring width decreased 1–2 years after pruning, while it turned out to be greater than that of the control 3 years after pruning. Moreover, pruning can reduce the degree of trunk tapering. The fiber aspect ratio two years after pruning was greater than that of the control. The distribution frequency of fiber lengths of between 1500 μm and 1900 μm and that of fiber widths of between 32 μm and 38 μm were higher than that of the control. However, pruning had little effect on their density and oven-dried shrinkage. In addition, compared to the control, the bending strength and the modulus of elasticity increased by approximately 11%–14%, the impact toughness decreased by approximately 5%, and the compressive strength increased by approximately 6%. Pruning proved to be a successful method to improve the timber quality. Full article

Understanding precipitation changes over a long period of time can provide valuable insights into global climate change. Taking the forest–steppe ecotone of North China as the research area, based on the tree ring width index of Carya cathayensis Sarg (Carya cathayensis), the relationship between tree growth and climate factors is analyzed, and the annual precipitation is reconstructed from data from the nearest five weather stations from AD 1540 to 2019. The results show that the growth of trees was affected by the changes in precipitation. The precipitation was divided into three dry periods and three wet periods over 480 years, based on wavelet analysis. There were 328 years of precipitation within the mean plus or minus one standard deviation (SD) (accounting for 68.3% of 480 years), indicating that relatively stable climate conditions exist in the study area, which has become one of the main agricultural areas in China. Each period lasted 2–7 years according to the multi-taper method, indicating that precipitation change was closely related to the El Niño–Southern Oscillation (ENSO) on a short time scale and affected by the Atlantic Multidecadal Oscillation (AMO) on a medium time scale during the period of 60–80 years based on wavelet analysis. Full article

The stem taper function is essential in predicting diameter outside bark (DOB) variations along the tree height, contributing to volume estimation, harvest planning, and precision forestry. Traditional taper models, such as the Kozak function, offer interpretability but often fail to capture nonlinear growth dynamics and regional variability, particularly in the upper stem segments. This study aimed to evaluate and compare the prediction accuracy of conventional and machine learning-based taper models using Pinus densiflora, a representative conifer species in Korea. Field data from two ecologically distinct regions (Gangwon and Central Korea) were used to build and test four models: the Kozak taper function, random forest, extreme gradient boosting, and an artificial neural network (ANN). Model performance was assessed using the RMSE, R², and MAE, along with stem profile visualizations for representative trees. The results showed that the ANN consistently achieved the highest prediction accuracy across both regions, particularly at an upper crown zone relative height (RH) > 0.8, while maintaining smooth and stable taper curves. In contrast, the Kozak model tended to underestimate the diameter of the upper stem. This study demonstrates that machine learning models, particularly ANNs, can effectively enhance the taper prediction precision and serve as practical tools for data-driven forest management and the implementation of precision forestry in Korea. Full article

Stem volume is a critical factor in managing and evaluating forest resources. At present, stem volume is commonly estimated indirectly by constructing a taper model that utilizes sampling, diameter at breast height (DBH), and tree height. However, these estimates are constrained by errors arising from spatial and stand environment variations as well as uncertainties in height measurements. To address these issues, this study aimed to accurately estimate stem volume using light detection and ranging (LiDAR) technology, a key tool in modern precision forestry. LiDAR data were used to build comprehensive three-dimensional models of forests with multi-platform LiDAR systems. This approach allowed for precise measurements of tree heights and stem diameters at various heights, effectively mitigating the limitations of earlier measurement methods. Based on these data, a Kozak taper curve was developed at the individual tree level using LiDAR-derived tree height and diameter estimates. Integrating this curve with LiDAR data enabled a hybrid approach to estimating stem volume, facilitating the calculation of diameters at points not directly identifiable from LiDAR data alone. The proposed method was implemented and evaluated for two economically significant tree species in Korea: Pinus koraiensis and Larix kaempferi. The RMSE comparison between the taper curve-based approach and the hybrid volume estimation method showed that, for Pinus koraiensis, the RMSE was 0.11 m³ using the taper curve-based approach and 0.07 m³ for the hybrid method, while for Larix kaempferi, the RMSE was 0.13 m³ and 0.05 m³, respectively. The estimation error of the hybrid method was approximately half that of the taper curve-based approach. Consequently, the hybrid volume estimation method, which integrates LiDAR and the taper model, overcomes the limitations of conventional taper curve-based approaches and contributes to improving the accuracy of forest resource monitoring. Full article

Technological developments have allowed helicopter airborne laser scanning (HALS) to produce high-density point clouds below the forest canopy. We present a tree stem classification method that combines linear shape detection and model-based clustering, using four discrete methods to estimate stem diameter. Stem horizontal size was estimated every 25 cm below the living crown, and a cubic spline was used to estimate where there were gaps. Individual stem diameter at breast height (DBH) was estimated for 77% of field-measured trees. The root mean square error (RMSE) of DBH estimates was 7–12 cm using stem circle fitting. Adapting the approach to use an existing stem taper model reduced the RMSE of estimates (<1 cm). In contrast, estimates that were produced from a previously existing DBH estimation method (PREV) could be achieved for 100% of stems (DBH RMSE 6 cm), but only after location-specific error was corrected. The stem classification method required comparatively little development of statistical models to provide estimates, which ultimately had a similar level of accuracy (RMSE < 1 cm) to PREV. HALS datasets can measure broad-scale forest plantations and reduce field efforts and should be considered an important tool for aiding in inventory creation and decision-making within forest management. Full article

Teak [Tectona grandis L.f.] has a wide distribution range in tropical countries and is Nepal’s second most planted commercial tree species. This study aimed to develop a robust and reliable taper equation for Teak species in Nepal. To achieve this, 15 parametric taper equations were fitted and evaluated using the diameter and height data of 100 trees sampled from two stands of the Sagarnath Plantation projects, Nepal. The data set was split into training (90%) and testing (10%) sets based on the trees’ ID, and model fitting was conducted in two phases. In the first phase, nonlinear models were fitted to the training data using 10-fold cross-validation, and the performance was evaluated based on fit and validation statistics. The top five models were further analyzed in the second phase using a mixed effects framework to account for variance and correlation structures. The modified Bi model performed best under a fixed effects modeling framework (R² = 0.96, RMSE = 1.83 cm). However, the Sharma and Zhang model performed the best under a mixed-effects modeling framework (R² = 0.97, RMSE = 1.54 cm). Therefore, we suggest using the modified Bi under fixed effects and variable exponent equation of Sharma and Zhang under mixed-effects modeling as a taper equation for Teak. The Sharma and Zhang’s equation is recommended for its high accuracy and better performance over previously recommended variable exponents equations. Full article

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. Full article

The ascomycete family Nectriaceae includes soil-borne saprobes, plant pathogens and human pathogens, biodegraders, and biocontrol agents for industrial and commercial applications. Cinnamomum camphora is a native tree species that is widely planted in southern China for landscaping purposes. During a routine survey of Eucalyptus diseases in southern China, disease spots were frequently observed on the leaves of Ci. camphora trees planted close to Eucalyptus. The asexual fungal structures on the leaf spots presented morphological characteristics typical of the Nectriaceae. The aim of this study is to identify these fungi and determine their pathogenic effect on Ci. camphora. Of the isolates obtained from 13 sites in the Fujian and Guangdong Provinces, 54 isolates were identified based on the DNA phylogeny of the tef1, tub2, cmdA, and his3 regions and morphological features. Two isolates were identified as Calonectria crousiana, and fifty-two isolates were described as a new genus, including a single species. These fungi were named Recticladiella inexpectata gen. et sp. nov. The identification of the new genus was based on strong DNA base differences in each of the four sequenced gene regions. The conidiophores of this fungus had several avesiculate stipe extensions tapering toward a straight, occasionally slightly curved terminal cell, distinguishing it from other phylogenetically close Nectriaceae genera. The results indicate that R. inexpectata is distributed in wide geographic regions in southern China. Inoculation showed that R. inexpectata and Ca. crousiana caused lesions on the leaves of Ci. camphora seedlings within 6 days of inoculation, indicating that they are pathogenic to native Ci. camphora in China. Full article

Estimation of tree volume typically focuses on excurrent forms, with less attention given to decurrent forms. Species with a decurrent form, particularly hardwoods, lack a dominant stem and have large diameter branches that can be included in the merchantable wood volume. We developed a preliminary two-equation system comprising a taper equation and a merchantable whole-tree volume (stem and branches) equation for Quercus rubra L. growing in Navarra (Northern Spain). The equation system includes the diameter at breast height and total tree height as independent variables, along with merchantable height—the height up to which the stem maintains a well-defined excurrent form—as an additional variable. After estimating the stem volume, the branch volume is estimated by subtracting the stem volume from the merchantable whole-tree volume. A second order continuous autoregressive error structure was used to correct for autocorrelation between residuals from the fitted taper equation. The equations explained 90% of the observed variability in diameter and 86% of the observed variability in merchantable whole-tree volume. Both equations have been implemented in the Cubica Navarra 3.0 software for use as a system of equations. These equations are considered preliminary and will be refitted or validated as additional data becomes available from new locations. Full article

Two new species of free-living marine nematodes, Metachromadora parobscura sp. nov. and Molgolaimus longicaudatus sp. nov., from mangrove wetlands of Beihai, Guangxi province in China, are described. Metachromadora parobscura sp. nov. is characterized by eight longitudinal rows of somatic setae arranged from the posterior part of the body, loop-shaped amphidial foveae with an open top and double contours, pharynx with bipartite cuticularized internal cavity, spicules with well-developed capitulum, gubernaculum canoe-shaped, without apophysis, 6–8 precloacal tubular supplements, and a short, conical tail with two ventral protuberances. It could be easily distinguished from the known species by spicule length and numbers of precloacal supplements. Molgolaimus longicaudatus sp. nov. is characterized by short cephalic setae, relatively small amphidial fovea, slender spicules ventrally bent with pronounced hooked capitulum and tapered distal end, two poriform precloacal supplements, and a relatively long conico-cylindrical tail. It differs from other species by the shape of spicules and long tail. Nearly full-length SSU sequences (1542–1592 bp) of the two species were provided, and phylogenetic trees based on maximum likelihood analyses supported the taxonomic position of the two new species. The combined use of traditional morphology-based taxonomy and molecular approaches has been proven to be a good choice for identification of free-living nematodes. Full article

The nematode family Aphelenchoididiae is considered fungal-feeding, predatory, or root hair feeders. Some members of this family are universally present in agricultural landscapes and are an integral part of soil health and conservation studies. In the present soil nematode biodiversity survey, we detected four species of the genera Aphelenchus, Aphelenchoides, and Robustodorus. Because fungal-feeding nematodes from southern Alberta have not previously been reported, we conducted a detailed morphological and molecular investigation, identifying these species as Aphelenchus avenae, Aphelenchoides limberi, Aphelenchoides prairiensis n. sp. and Robustodorus paramegadorus n. sp. The first two species we document as new records from southern Alberta, whereas A. prairiensis n. sp. and R. paramegadorus n. sp. we describe in detail as new taxa. Briefly, A. prairiensis n. sp. is an amphimictic species having 4 lateral lines; hemispherical anteriorly flattened lip region; delicate stylet and swelling-like stylet knobs; excretory pore at the posterior edge of nerve ring. Female tail conical, gradually tapering towards a truncated end with single mucro. Spicule 23.0 (20.0–25.0) µm long having elongated rounded condylus, small, blunt conical rostrum, and lamina that gradually tapers towards the rounded distal end; three pairs of caudal papillae were present on the male tail. Robustodorus paramegadorus n. sp., is a parthenogenetic species with 3 lines in the lateral fields; lip region rounded, anteriorly flattened; stylet robust, with knobs rounded to bean-shaped; excretory pore located posterior to nerve ring; reproductive components were quite indiscernible with a short 24.0 (18.0–27.0) µm post-vulval uterine sac; tail conical, ending with pointed to wedge-shaped tip. We performed molecular characterizations for each species and constructed phylogenetic trees to study the phylogenetic relationship of these aphelenchid species. The discovery of A. prairiensis n. sp. and R. paramegadorus n. sp. indicates that soil nematode diversity is relatively unexplored in southern Alberta. The findings of this study will significantly enhance the identification processes and may contribute towards future soil health and biodiversity efforts. Full article

Each year, numerous tornadoes occur in forested regions of the United States. Due to the substantial number of fallen trees and accessibility issues, many of these tornadoes remain poorly documented and evaluated. The process of documenting tree damage to assess tornado intensity is known as the treefall method, an established and reliable technique for estimating near-surface wind speed. Consequently, the demand for documenting fallen trees has increased in recent years. However, the treefall method proves to be extremely expensive and time-consuming, requiring a laborious assessment of each treefall instance. This research proposes a novel approach to evaluating treefall in large, forested regions using deep learning-based automated detection and advanced image processing techniques. The developed treefall method relies on high-resolution aerial imagery from a damaged forest and involves three main steps: (1) instance segmentation detection, (2) estimating tree taper and predicting fallen tree directions, and (3) obtaining subsampled treefall vector results indicating the predominant flow direction in geospatial coordinates. To demonstrate the method’s effectiveness, the algorithm was applied to a tornado track rated EF-4, which occurred on 10 December 2021, cutting through the Land Between the Lakes National Recreation Area in Kentucky. Upon observation of the predicted results, the model is demonstrated to accurately predict the predominant treefall angles. This deep-learning-based treefall algorithm has the potential to speed up data processing and facilitate the application of treefall methods in tornado evaluation. Full article