Search Results (54)

This investigation examines diffusion processes for predicting whole-stand volume, incorporating the variability and uncertainty inherent in regional, operational, and environmental factors. The distribution and spatial organization of trees within a specified forest region, alongside dynamic fluctuations and intricate uncertainties, are modeled by a set of nonsymmetric stochastic differential equations of a sigmoidal nature. The study introduces a three-dimensional system of stochastic differential equations (SDEs) with mixed-effect parameters, designed to quantify the dynamics of the three-dimensional distribution of tree-size components—namely diameter (diameter at breast height), potentially occupied area, and height—with respect to the age of a tree. This research significantly contributes by translating the analysis of tree size variables, specifically height, occupied area, and diameter, into stochastic processes. This transformation facilitates the representation of stand volume changes over time. Crucially, the estimation of model parameters is based exclusively on measurements of tree diameter, occupied area, and height, avoiding the need for direct tree volume assessments. The newly developed model has proven capable of accurately predicting, tracking, and elucidating the dynamics of stand volume yield and growth as trees mature. An empirical dataset composed of mixed-species, uneven-aged permanent experimental plots in Lithuania serves to substantiate the theoretical findings. According to the dataset under examination, the model-based estimates of stand volume per hectare in this region exhibited satisfactory goodness-of-fit statistics. Specifically, the root mean square error (and corresponding relative root mean square error) for the living trees of mixed, pine, spruce, and birch tree species were 68.814 m³ (20.4%), 20.778 m³ (7.8%), 32.776 m³ (37.3%), and 4.825 m³ (26.3%), respectively. The model is executed within Maple, a symbolic algebra system. Full article

Forest measurements are essential for monitoring stand dynamics and long-term trends. Errors in tree measurement can seriously affect the outcomes of a forest inventory. This study investigates measurement errors from successive measurements on permanent concentric circular plots based on data from 74 plots in Dinaric uneven-aged mixed fir–beech stands. Tree data errors were detected and corrected. Diameter increment was calculated as a difference in DBH from two successive inventories, and linear regression models were developed based on original and corrected data. Measurement errors were identified in 2.57% of trees, some having a substantial impact on tree volume. Volume discrepancies between original and corrected data were generally minor, where 93.2% of plots in the first and 70.3% in the second inventory required no corrections and volume differences in the overall levels were negligible and statistically non-significant: 0.30 m³/ha in the first inventory (p = 0.550) and 0.05 m³/ha in the second (p = 0.974). Although diameter increment models with original and corrected data differed significantly, model choice resulted in minimal impact on volume increment. Since omitting erroneous measurement data would lead to volume underestimation, data correction is preferable. However, when modeling tree increment, excluding incorrect or doubtful data remains a practical and acceptable approach. Full article

The diversity of forest tree life strategies is fundamental to species coexistence in mixed stands. Growth rate is one of the most important elements of a species’ life strategy. This aspect has been relatively well recognised in even-aged stands. However, the situation is different in uneven-aged stands, particularly in multi-species stands comprising species with different light demands. In this study, we aimed to compare stem basal area increment (BAI) in regard to five species forming multi-species, uneven-aged deciduous forests in Central Europe as an important element of their growth strategy. Particular attention was paid to the relationship between this feature and tree height and competitive status. These relationships were analysed using a linear mixed model. The BAI was positively correlated with tree height, while a negative correlation was observed between BAI and increasing competitive level. However, the observed variations in the trends of these relationships were not associated with the light demands of the compared species. In general, the majority of the studied species demonstrated similar growth dynamics. This may suggest that the role of this trait in shaping species coexistence is modulated by other life-history strategy components and by specific growth conditions. An exception to this is the most light-demanding species, black alder (Alnus glutinosa (L.) Gaertner), which, contrary to expectations, exhibits a lower basal area increment under uneven-aged conditions. Full article

The role of forest community configurations in multiple ecosystem functions remains poorly understood due to the absence of quantifiable metrics for evaluating these configurations. This limitation hinders our ability to use forests to enhance urban well-being effectively. This study integrates both observation and experimentation to elucidate the effects of community configurations on the multifunctionality of forests. We examine seven ecosystem functions in Changchun’s urban forests: carbon sequestration, rainwater interception, temperature reduction, humidity increase, particulate matter reduction, noise reduction, and water conservation. Assortment indices, derived from traditional diversity metrics and relative importance values, reveal a negative correlation with multifunctionality. This suggests that improving forest multifunctionality requires a strategically planned species composition rather than simply increasing diversity. Furthermore, the creation of comprehensive configuration indices for evaluating intraspecific configurations has confirmed their beneficial impact on multifunctionality. Our results highlight the significance of intraspecific structural configurations and advocate for using mixed-species plantings in urban forestry practices. We propose practical management strategies to enhance urban forest multifunctionality, including selecting tree species for their functional benefits, implementing uneven-aged plantings, and integrating both shade-tolerant and sun-loving species. Together, our findings underscore the essential role of community configuration in sustaining multifunctionality and strongly support the management of urban forests. Full article

Canopy gaps and their associated processes play an important role in shaping forest structure and dynamics. Understanding the information about canopy gaps allows forest managers to assess the potential for regeneration and plan interventions to enhance regeneration success. Traditional field surveys for canopy gaps are time consuming and often inaccurate. In this study, canopy gaps were detected using unmanned aerial vehicle (UAV) imagery of two sub-compartments of an uneven-aged mixed forest in northern Japan. We compared the performance of U-Net and ResU-Net (U-Net combined with ResNet101) deep learning models using RGB, canopy height model (CHM), and fused RGB-CHM data from UAV imagery. Our results showed that the ResU-Net model, particularly when pre-trained on ImageNet (ResU-Net_2), achieved the highest F1-scores—0.77 in Sub-compartment 42B and 0.79 in Sub-compartment 16AB—outperforming the U-Net model (0.52 and 0.63) and the non-pre-trained ResU-Net model (ResU-Net_1) (0.70 and 0.72). ResU-Net_2 also achieved superior overall accuracy values of 0.96 and 0.97, outperforming previous methods that used UAV datasets with varying methodologies for canopy gap detection. These findings underscore the effectiveness of the ResU-Net_2 model in detecting canopy gaps in uneven-aged mixed forests. Furthermore, when these trained models were applied as transfer models to detect gaps specifically caused by selection harvesting using pre- and post-UAV imagery, they showed considerable potential, achieving moderate F1-scores of 0.54 and 0.56, even with a limited training dataset. Overall, our study demonstrates that combining UAV imagery with deep learning techniques, particularly pre-trained models, significantly improves canopy gap detection accuracy and provides valuable insights for forest management and future research. Full article

To maintain the ecosystem resilience to large-scale disturbances in managed forests, it is essential to adhere to the principles of close-to-nature silviculture, adapt practices to the traits of natural forest types, and utilize natural processes, including natural regeneration. This study examines the natural regeneration patterns in silver fir (Abies alba Mill.)-dominated forests, analyzing how the stand structure—tree size diversity, species composition, and stand density—affects the regeneration. We analyze the data from four sites in Poland, Germany, and Italy, employing generalized linear and zero-inflated models to evaluate the impact of the management strategies (even- vs. uneven-aged) and forester-controlled stand characteristics (structural diversity, broadleaf species admixture, and stand density) on the probability of regeneration, its density, and the developmental stages (seedling, small sapling, and tall sapling) across a climatic gradient. Our results indicate a significantly higher probability of regeneration in uneven-aged stands, particularly in areas with lower temperatures and lower overall regeneration density. The tree size diversity in the uneven-aged stands favors advancement from juveniles to more developed stages (seedling to sapling) in places with higher aridity. A denser stand layer (higher stand total basal area) leads to a lower density of natural regeneration for all the present species, except silver fir if considered separately, signifying that, by regulating the stand growing stock, we can selectively promote silver fir. A higher admixture of broadleaf species generally decreases the regeneration density across all the species, except in a water-rich site in the Bavarian Alps, where it had a strong positive impact. These findings underscore the complex interactions of forest ecosystems and provide a better understanding required for promoting silver fir regeneration, which is essential for a close-to-nature silviculture under climate change. Full article

High-value timber species with economic and ecological importance are usually distributed at very low densities, such that accurate knowledge of the location of these trees within a forest is critical for forest management practices. Recent technological developments integrating unmanned aerial vehicle (UAV) imagery and deep learning provide an efficient method for mapping forest attributes. In this study, we explored the applicability of high-resolution UAV imagery and a deep learning algorithm to predict the distribution of high-value deciduous broadleaf tree crowns of Japanese oak (Quercus crispula) in an uneven-aged mixed forest in Hokkaido, northern Japan. UAV images were collected in September and October 2022 before and after the color change of the leaves of Japanese oak to identify the optimal timing of UAV image collection. RGB information extracted from the UAV images was analyzed using a ResU-Net model (U-Net model with a Residual Network 101 (ResNet101), pre-trained on large ImageNet datasets, as backbone). Our results, confirmed using validation data, showed that reliable F1 scores (>0.80) could be obtained with both UAV datasets. According to the overlay analyses of the segmentation results and all the annotated ground truth data, the best performance was that of the model with the October UAV dataset (F1 score of 0.95). Our case study highlights a potential methodology to offer a transferable approach to the management of high-value timber species in other regions. Full article

Assessments of synergies and trade-offs between climate change mitigation and forest biodiversity conservation have focused on set-aside areas. We evaluated a more comprehensive portfolio of silvicultural management adaptations to climate change and conservation measures exemplary for managed European beech forests. Based on the available literature, we assessed a range of common silvicultural management and conservation measures for their effects on carbon sequestration in forest and wood products and for substituting more carbon-intensive products. We complemented this review with carbon sequestration simulations for a typical mountainous beech forest region in Austria. We propose three priority actions to enhance the synergies between climate change mitigation and biodiversity. First, actively increase the proportion of European beech in secondary Norway spruce forests, even though beech will not be unaffected by expected water supply limitations. Secondly, optimize the benefits of shelterwood systems and promote uneven-aged forestry, and thirdly, enhance mixed tree species. Targeted conservation measures (deadwood, habitat trees, and old forest patches) increase the total C storage but decrease the annual C sequestration in forests, particularly in wood products. The establishment of a beech wood market with an extended product portfolio to reduce the use of fuelwood is essential for sustainable climate change mitigation. Since there are limitations in the production of saw timber quality beech wood on low fertility sites, C accumulation, and biodiversity can be emphasized in these areas. Full article

Uneven-aged mixed forests have been recognized as important contributors to biodiversity conservation, ecological stability, carbon sequestration, the provisioning of ecosystem services, and sustainable timber production. Recently, numerous studies have demonstrated the applicability of integrating remote sensing datasets with machine learning for forest management purposes, such as forest type classification and the identification of individual trees. However, studies focusing on the integration of unmanned aerial vehicle (UAV) datasets with machine learning for mapping of tree species groups in uneven-aged mixed forests remain limited. Thus, this study explored the feasibility of integrating UAV imagery with semantic segmentation-based machine learning classification algorithms to describe conifer and broadleaf species canopies in uneven-aged mixed forests. The study was conducted in two sub-compartments of the University of Tokyo Hokkaido Forest in northern Japan. We analyzed UAV images using the semantic-segmentation based U-Net and random forest (RF) classification models. The results indicate that the integration of UAV imagery with the U-Net model generated reliable conifer and broadleaf canopy cover classification maps in both sub-compartments, while the RF model often failed to distinguish conifer crowns. Moreover, our findings demonstrate the potential of this method to detect dominant tree species groups in uneven-aged mixed forests. Full article

There is increasing interest in optimizing stand structure through forest management. The forest structure influences growth and maintains the structure, promoting sustainability. Structure-based forest management (SBFM), which is based on the spatial relationships between a reference tree and its four nearest neighbors, considers the best spatial structure for the stand and promotes the development towards a healthy and stable state by selectively thinning specific trees. This management method is a scientific approach for sustainable forest management, and appropriate harvesting is the core principle of uneven-aged forest management. However, the application of this approach in the management of uneven-aged mixed stands is a challenge because their dynamics are more difficult to elucidate than those of planted or pure stands. This study presented a stand spatial structure optimization model with a transition matrix growth model for selecting suitable timber harvest during uneven-aged mixed-forest management optimization. The model was developed using three neighborhood-based structural indices (species mingling, diametric differentiation, and horizontal spatial pattern) and diameter diversity indices. The approach was applied to four broadleaf stands in the Maoershan Forest Farm of the Heilongjiang Province. The results demonstrate that optimizing the stand spatial structure with a transition matrix growth model improved the objective function values (F-index) by 23.8%, 12.8%, 14.6%, and 28.3%, and the optimal removal of trees from the stands ranged from 24.3% to 25.5%. The stand structure in the next cycle (after 5 years) was closer to the uneven-mixed state. The main conclusion of this study is that optimizing the stand spatial structure with a transition matrix growth model can improve the speed and accuracy of tree selection for harvesting in unevenly mixed forests, thus helping regulate stable and diverse forest growth. Full article

Temperate mixed forests in Mexico are considered highly important ecosystems because of their high levels of biodiversity and capacity to store carbon. The aim of this study was to evaluate temporal and between-forest soil respiration (CO₂ efflux) variability, and to assess the effect of vegetation diversity metrics on soil CO₂ fluxes in mixed-uneven-aged forests in Durango, Northwestern Mexico. Soil CO₂ efflux, soil moisture, and soil temperature were measured in three temperate forest types. A generalized linear model (GLM) was fitted to analyze the relationship between soil CO₂ fluxes and stand variables, diversity metrics, soil moisture, and soil temperature. Furthermore, a two-way analysis of variance was used to assess the effect of forest type, month of the year, and their interaction on soil respiration. Annual average, minimum, and maximum soil CO₂ efflux rate values were 3.81 (±2.94), 2.28 (±1.47), and 7.97 (±2.94) µmol m⁻² s⁻¹, respectively. Soil respiration was positively related to species richness, aboveground biomass, and quadratic mean diameter; however, forest type did not contribute to understanding the dynamics of soil CO₂ fluxes. The results highlight the importance of seasonality, species diversity and aboveground biomass stocks to preserve the ecosystem processes driving soil respiration in temperate forests. Full article

To explore the effects of competition, site, and climate on the growth of Chinese fir individual tree diameter at breast height (DBH) and tree height (H), a regionally compatible individual tree growth model under the combined influence of environment and competition was constructed. Using continuous forest inventory (CFI) sample plot data from Fujian Province between 1993 and 2018, we constructed an individual tree DBH model and an H model based on re-parameterization (RP), BP neural network (BP), and random forest (RF), which compared the accuracy of the different modeling methods. The results showed that the inclusion of competition and environmental factors could improve the prediction accuracy of the model. Among the site factors, slope position (PW) had the most significant effect, followed by elevation (HB) and slope aspect (PX). Among the climate factors, the highest contribution was made by degree-days above 18 °C (DD18), followed by mean annual precipitation (MAP) and Hargreaves reference evaporation (Eref). The comparison results of the three modeling methods show that the RF model has the best fitting effect. The R² of the individual DBH model based on RF is 0.849, RMSE is 1.691 cm, and MAE is 1.267 cm. The R² of the individual H model based on RF is 0.845, RMSE is 1.267 m, and MAE is 1.153 m. The model constructed in this study has the advantages of environmental sensitivity, statistical reliability, and prediction efficiency. The results can provide theoretical support for management decision-making and harvest prediction of mixed uneven-aged forest. Full article

The age, size and morphology of trees, including crown dimensions, can influence crown defoliation. In Croatia, the selection management of silver fir (Abies alba Mill.) forests involves pure or mixed stands, either of which can be affected by various disturbances, resulting in unbalanced stand structures. The aim of this study was to estimate the probability of trees transitioning from one defoliation class to the next, examine the influence of tree attributes on that process and analyze the changes in survival over time. The study was conducted over a 18-year period (1990–2007) on two sites with contrasting stand structures: a uniform stand with a dominant share of silver fir (Site A) and an uneven-aged mixed beech–fir stand (Site B). Logistic regression was used to model tree transitions between defoliation classes. Uniform stand structure increased the likelihood of silver fir trees transitioning to a higher defoliation class, with limited dependence on the tree crown position. In contrast, suppressed and central trees in uneven-aged stands were more likely to transition to a higher defoliation due to greater competition between them. Diameter at breast height (DBH) was found to be a significant predictor of tree transition to higher defoliation classes, with a linear trend of increasing probability with increasing DBH. Crown position and crown length were also found to be significant predictors of changing defoliation class, with observed differences between sites occurring due to differences in stand structure. To ensure a balanced stand structure and enhance tree vitality, careful consideration of easily measurable tree elements such as DBH, crown length, and tree crown position is imperative when selecting trees for felling. Full article

The negative influence of timber harvesting on the forest environment is reflected through damage to the residual trees, regeneration, and forest soil. Considering that skidding, a popular extraction method, can cause substantial and severe damage to the remaining stand, the aim of this research was to determine damage to residual trees during skidding by an LKT 81T cable skidder, including oxen bunching. The research was conducted in eastern Bosnia and Herzegovina, in an uneven-aged mixed fir (Abies alba Mill.) and spruce (Picea abies L.) forest with pine (Pinus sylvestris L.) on limestone soils. Tree felling was conducted using a Husqvarna 372 XP chainsaw. Extraction operations caused damage to 6.31% of the residual trees in the stand. The most damage was “removed bark” (65.34%) and occurred on the lower parts of the tree, the butt end (55.11%) and root collar (32.39%). The average size of the damage was 197.08 cm². A statistically significant correlation was found between the damage position and the diameter at the breast height (p < 0.05) and the damage position and damage size (p < 0.01) by Spearman correlation analysis. The conducted analysis by the chi-squared test showed that there is a statistically significant difference in the proportion of damage for trees with different distances to the nearest skid road (p = 0.0487), but the share of damaged trees did not decrease by increasing the distance from the skid road. Full article

Fire modifies vegetation dynamics in terrestrial ecosystems. Abundant literature has studied the post-fire effects with satellite sensors; however, relatively fewer studies have used unmanned aerial vehicles (UAVs) to assess the dynamics of greenness prior to and immediately following prescribed fires. Using multispectral sensors mounted on UAVs, we documented the results of the normalized difference vegetation index (NDVI) as a proxy for pre- and post-fire greenness in a natural forest stand in northern Mexico. Using spectral reflectance techniques and the statistical analyses of Kruskal–Wallis and pairwise Wilcoxon rank-sum tests, statistically significant differences were found in the NDVI values, measured before and after controlled burning (p < 0.05). The results showed an increase in post-fire “greenness” from 0.57 to 0.65. This was interpreted as an immediate change in vegetation activity in the canopy, which could be attributable as a stimulus to heat stress. Complementary spectral indices also reinforce our findings; we recognize that further research is required, for instance, to address the timing of image capture. Our findings demonstrate the potential and some of the challenges associated with the use of UAVs to monitor prescribed fires, while also suggesting the need for more detailed physiological and phenological studies. High spatial and spectral resolution maps of greenness represent a valuable starting point for subsequent temporal monitoring and contribute to the knowledge of fire effects at fine spatial resolutions. Full article