Search Results (35)

The plywood industry is one of the most significant sub-sectors of the forestry industry and serves as a cornerstone of sustainable construction within a bioeconomy framework. Plywood is a panel composed of multiple layers of wood sheets bonded together. While automation and process monitoring have played a crucial role in improving efficiency, data-driven decision-making remains underutilized in the industrial sector. Many industrial processes continue to rely heavily on the expertise of operators rather than on data analytics. However, advancements in data storage capabilities and the availability of high-speed computing have paved the way for data-driven algorithms that can support real-time decision-making. Due to the biological nature of wood and the numerous variables involved, managing manufacturing operations is inherently complex. The multitude of process variables, and the presence of non-linear physical phenomena make it challenging to develop accurate and robust analytical predictive models. As a result, data-driven approaches—particularly Artificial Intelligence (AI)—have emerged as highly promising modeling techniques. Leveraging industrial data and exploring the application of AI algorithms, particularly Machine Learning (ML), to predict key performance indicators (KPIs) in process plants represent a novel and expansive field of study. The processing of industrial data and the evaluation of AI algorithms best suited for plywood manufacturing remain key areas of research. This study explores the application of supervised Machine Learning (ML) algorithms in monitoring key process variables to enhance quality control in veneers and plywood production. The analysis included Random Forest, XGBoost, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Lasso, and Logistic Regression. An initial dataset comprising 49 variables related to the maceration, peeling, and drying processes was refined to 30 variables using correlation analysis and Lasso variable selection. The final dataset, encompassing 13,690 records, categorized into 9520 low-quality labels and 4170 high-quality labels. The evaluation of classification algorithms revealed significant performance differences; Random Forest reached the highest accuracy of 0.76, closely followed by XGBoost. K-Nearest Neighbors (KNN) demonstrated notable precision, while Support Vector Machine (SVM) exhibited high precision but low recall. Lasso and Logistic Regression showed comparatively lower performance metrics. These results highlight the importance of selecting algorithms tailored to the specific characteristics of the dataset to optimize model effectiveness. The study highlights the critical role of AI-driven insights in improving operational efficiency and product quality in veneer and plywood manufacturing, paving the way for enhanced industrial competitiveness. Full article

This article focuses on the need for digitalization in the forestry and timber sector using information from CT scans of logs. The National Forest Centre (Slovak Republic) operates a unique 3D CT scanner for wooden logs at the Stráž Biotechnology Park. This real-time scanner generates a 3D model of a log, displaying the wood’s internal features/defects. To optimize log-cutting plans effectively, it is necessary to automatically detect and classify these features and defects in real time, leveraging computer vision principles. Artificial intelligence, specifically neural networks, addresses this need by enabling solutions for tasks of this nature. Building a highly efficient neural network for detecting wood features and defects requires creating a database of log scans and training the network on these data. This is a time-intensive process, as it involves manually marking internal features and defects on hundreds of CT scans of various wood types. A functional neural network for detecting internal wood defects represents a significant advancement in sector digitalization, paving the way for further automation and robotization in wood processing. For the forestry sector to remain competitive, efficiently process raw materials, and improve product quality, the effective application of CT scanning technology is essential. This technological innovation aligns the sector more closely with leaders in other fields, such as the automotive, engineering, and metalworking industries. Full article

Effective detection techniques are important for wildlife monitoring and conservation applications and are especially helpful for species that live in complex environments, such as arboreal animals like koalas (Phascolarctos cinereus). The implementation of infrared cameras and drones has demonstrated encouraging outcomes, regardless of whether the detection was performed by human observers or automated algorithms. In the case of koala detection in eucalyptus plantations, there is a risk to spotters during forestry operations. In addition, fatigue and tedium associated with the difficult and repetitive task of checking every tree means automated detection options are particularly desirable. However, obtaining high detection rates with minimal false alarms remains a challenging task, particularly when there is low contrast between the animals and their surroundings. Koalas are also small and often partially or fully occluded by canopy, tree stems, or branches, or the background is highly complex. Biologically inspired vision systems are known for their superior ability in suppressing clutter and enhancing the contrast of dim objects of interest against their surroundings. This paper introduces a biologically inspired detection algorithm to locate koalas in eucalyptus plantations and evaluates its performance against ten other detection techniques, including both image processing and neural-network-based approaches. The nature of koala occlusion by canopy cover in these plantations was also examined using a combination of simulated and real data. The results show that the biologically inspired approach significantly outperformed the competing neural-network- and computer-vision-based approaches by over 27%. The analysis of simulated and real data shows that koala occlusion by tree stems and canopy can have a significant impact on the potential detection of koalas, with koalas being fully occluded in up to 40% of images in which koalas were known to be present. Our analysis shows the koala’s heat signature is more likely to be occluded when it is close to the centre of the image (i.e., it is directly under a drone) and less likely to be occluded off the zenith. This has implications for flight considerations. This paper also describes a new accurate ground-truth dataset of aerial high-dynamic-range infrared imagery containing instances of koala heat signatures. This dataset is made publicly available to support the research community. Full article

The aim of the study is to quantify the impacts of a possible transition to close-to-nature forestry in Slovakia and to compare the expected development of the total volume production, growing stock, merchantable wood increment and harvesting possibilities of forests in Slovakia with current conventional management using the FCarbon forest-growth model and available data from the Information System of Forest Management. The subject of the study was all forest stands available for wood supply (FAWS). The simulations were run in annual iterations using tree input data aggregated over 10-year-wide age classes. The calculation of wood increments was based on available growth models. In the business-as-usual (BAU) scenario, stock losses were based on the actual intensity of wood harvesting in the reference period 2013–2022. In the scenario of the transition to close-to-nature forest management, the losses were specifically modified from the usual harvesting regime at the beginning, to the target harvesting mode in selective forest at the end of the simulated period. With the modelling method used, a gradual increase in forest stocks occurred in both evaluated scenarios in the monitored period, namely by 10% in the case of BAU and by 23% in the case of close-to-nature forest management until 2050. In absolute mining volume, CTNF is by 5–10% lower than BAU management, with the difference gradually decreasing. The results show that the introduction of close-to-nature forest management will temporarily reduce the supply of wood to the market, but this reduction will not be significant and will be compensated by a higher total volume production, and thus also by increased carbon storage in forests. Full article

Karst yellow soil is one of the most important cultivated soils in southwest China. At present, only a few studies have dealt with rainfall erosivity and erosive rainfall thresholds in the karst yellow soil region. This paper utilizes statistical methods to identify erosive rainfall thresholds and slope erosion-prone areas in the Qianzhong region. This analysis is based on long-term experimental data from 10 experimental stations and 69 experimental plots within the region in 2006 to 2022. The findings show the following: The rainfall amount threshold was 12.66 mm for woodland plots, 10.57 mm for grassland plots, 9.94 mm for farmland plots, and 8.93 mm for fallow plots. Soil and water conservation measures in forestry and grassland effectively increase the rainfall amount thresholds. Compared to farmland, the rainfall threshold increased by 27.32% for woodland and 6.32% for grassland. Bare land and farmland are erosion-prone areas in the karst yellow soil region. The erosive rainfall thresholds for farmland plots with slopes of 13°, 15°, 20°, 23°, and 25° were 10.41 mm, 10.28 mm, 9.66 mm, 9.52 mm, and 9.15 mm, respectively. With the increase in the 13–25° slope gradient of farmland, the initial rainfall required for runoff generation leads to a reduction. The wrong selection indices (WSI) of all landcover plots were less than 10%, and the efficiency indices (EFF) were between 80.43% and 90.25%. The relative error index (REI) of the erosive rainfall thresholds for all landcover runoff plots was less than 0.50%, very close to 0, indicating that these thresholds have small errors and high accuracy. This study gained a better understanding of natural rainfall-induced erosion characteristics in the study area, determined rainfall thresholds for distinguishing erosive rainfall events from non-erosive across different landcover types, and reduced the workload of calculating rainfall erosivity while enhancing the accuracy of soil erosion forecasting and simulation in the karst mountain yellow soil area. Full article

The 2020–2021 COVID-19 pandemic has had a tremendous impact on the daily lives of everyone, including local communities and entire societies. Under the influence of this new experience, the importance of the services and benefits provided by forests and other green spaces has increased. A very large role in this aspect was played by media messages promoting the idea of being close to nature as a remedy for malaise and stress, and pushing the thesis that the risk of spreading the virus outdoors is lower than it is indoors. Thanks to media messages, as well as government responses (i.e., lockdown, temporary bans on entering the forest), public attention has been directed toward forests, generating greater interest in forest management and conservation issues, as well as in nature and forestry education. The purpose of our research was to determine how the pandemic affected the frequency of visits to the forest and how it changed the public’s views on the role of forests. The research material consists of the results of a questionnaire survey (online and traditionally way) carried out in Poland from September to October in 2020. A total of 1402 people were surveyed. The results show that nearly 52% of respondents increased their use of forest recreational services during the COVID-19 pandemic. We also found that more than 80% of respondents agreed with statements that the forest is a safer space than, for example, parks or squares, and it is more difficult to contract the virus there. Men were more likely to agree with this statement than women (1.51), respondents without children (1.45), respondents over the age of 31 (1.72), and respondents with more than primary or secondary education (1.37). Also, more than 80% of respondents said that the social functions of the forest (e.g., recreational) had gained importance as a result of the pandemic. The social functions of the forest gained importance primarily among respondents with higher education (2.40), and among respondents who had visited the forest rather infrequently (several times a year) for recreational purposes before the pandemic (1.72). Those with children were more likely to agree with the statement that the economic functions of the forest have lost their importance (1.43), as were those who had formerly visited the forest several times a year (1.53). With regard to the statement “the slowdown of the economy has contributed to the improvement of the environment,” there were no statistically significant differences in the views of respondents in terms of their socio-demographic characteristics. Full article

Climatic changes significantly impact forest ecosystems, inevitably affecting forestry and forest-related industry. Considering that most forests are actively managed, there is a need to define the future risks and set a strategy for forestry and silviculture in a changing world. This review provides insight into the new challenges and opportunities forest management and silviculture face in the coming decades. There is sound recognition of risk factors expected from climate change, yet great uncertainty exists in the predictions of the response of forests to new conditions. Additionally, the stakeholders’ interests in the goods and services offered by forests are changing, and this also needs to be taken into account in future forest management. Undoubtedly, the goal of future forestry and silviculture in the 21st century will be primarily to ensure the continuity and sustainability of the forest. Sustainable use of goods and ecosystem services from forests will be directly related to the continuity and sustainability of the forest in the future. Adaptive forest management aims to promote the adaptive capacity of forests to new conditions resulting from climate change. If adaptation efforts are effective, adaptive forest management should be a kind of risk management. There is no one-fits-all strategy for adaptation to uncertain future conditions. Silviculture in the 21st century is expected to be more conducive to adapting forests to changes. Operational silvicultural activities should focus on ensuring the resilience and adaptation of forests to future environmental conditions. Modern silviculture offers activities that fall within the scope of contemporary close-to-nature silviculture practices. However, some of the currently applied practices will require review and modification to be applicable under new conditions. This review also identifies the need to fill knowledge gaps in order to develop more effective and flexible adaptation strategies to foster sustainable forest development and, thus, sustainable forestry. Full article

It is highly valuable to analyze and assess the landscape ecological risk of nature reserves to prevent and resolve ecological risks, as well as to effectively protect and maintain the sustainable development of nature reserves. Taking the forest landscape of the Lushan National Nature Reserve as its study object, this study performed grid processing for the nature reserve and classified forest landscape types using the Forest Resource Inventory Database in 2019. A landscape ecological index model was constructed to evaluate the ecological risk. Global and local Moran index values were used to reveal the autocorrelations for ecological risk. The geodetector method was used to comprehensively analyze the effects of natural and human factors on ecological risk. The results showed that, in general, the ecological risk level of the nature reserve was relatively low, as the proportion of the lowest-, lower-, and medium-risk areas to the total forestry land area accounted for 91.03%. The ecological risk ranking of each functional zone, from high to low, was in the order of the experimental zone, the buffer zone, and the core zone. The ecological risk levels of different forest landscape types were closely related to their area, spatial distribution, and succession stage, as well as human factors, such as the proximity to roads and settlements, etc. The forest landscape with the highest ecological risk was the Cunninghamia lanceolata (Lamb.) Hook. forest, and the forest landscape with the lowest ecological risk was other forestry land. Ecological risk had a positive spatial correlation and tended to be aggregated in space, demonstrating coupling with the proximity to roads and settlements. The ecological risk was affected by both human and natural factors, among which human factors played a dominant role. The proximity to roads and settlements, the relative humidity, and the temperature were the main driving factors. The interaction of pairwise factors had a stronger influence than that of single factors. Therefore, controlling the intensity of human activities and enhancing the coordination between humans and nature are beneficial for alleviating the ecological risks in the forest landscapes of nature reserves. Full article

Efficient and precise forest surveys are crucial for in-depth understanding of the present state of forest resources and conducting scientific forest management. Close-range photogrammetry (CRP) technology enables the convenient and fast collection of highly overlapping sequential images, facilitating the reconstruction of 3D models of forest scenes, which significantly improves the efficiency of forest surveys and holds great potential for forestry visualization management. However, in practical forestry applications, CRP technology still presents challenges, such as low image quality and low reconstruction rates when dealing with complex undergrowth vegetation or forest terrain scenes. In this study, we utilized an iPad Pro device equipped with high-resolution cameras to collect sequential images of four plots in Gaofeng Forest Farm in Guangxi and Genhe Nature Reserve in Inner Mongolia, China. First, we compared the image enhancement effects of two algorithms: histogram equalization (HE) and median–Gaussian filtering (MG). Then, we proposed a deep learning network model called SA-Pmnet based on self-attention mechanisms for 3D reconstruction of forest scenes. The performance of the SA-Pmnet model was compared with that of the traditional SfM+MVS algorithm and the Patchmatchnet network model. The results show that histogram equalization significantly increases the number of matched feature points in the images and improves the uneven distribution of lighting. The deep learning networks demonstrate better performance in complex environmental forest scenes. The SA-Pmnet network, which employs self-attention mechanisms, improves the 3D reconstruction rate in the four plots to 94%, 92%, 94%, and 96% by capturing more details and achieves higher extraction accuracy of diameter at breast height (DBH) with values of 91.8%, 94.1%, 94.7%, and 91.2% respectively. These findings demonstrate the potential of combining of the image enhancement algorithm with deep learning models based on self-attention mechanisms for 3D reconstruction of forests, providing effective support for forest resource surveys and visualization management. Full article

As global temperatures warm, drought reduces plant yields and is one of the most serious abiotic stresses causing plant losses. The early identification of plant drought is of great significance for making improvement decisions in advance. Chlorophyll is closely related to plant photosynthesis and nutritional status. By tracking the changes in chlorophyll between plant strains, we can identify the impact of drought on a plant’s physiological status, efficiently adjust the plant’s ecosystem adaptability, and achieve optimization of planting management strategies and resource utilization efficiency. Plant three-dimensional reconstruction and three-dimensional character description are current research hot spots in the development of phenomics, which can three-dimensionally reveal the impact of drought on plant structure and physiological phenotypes. This article obtains visible light multi-view images of four poplar varieties before and after drought. Machine learning algorithms were used to establish the regression models between color vegetation indices and chlorophyll content. The model, based on the partial least squares regression (PLSR), reached the best performance, with an R² of 0.711. The SFM-MVS algorithm was used to reconstruct the plant’s three-dimensional point cloud and perform color correction, point cloud noise reduction, and morphological calibration. The trained PLSR chlorophyll prediction model was combined with the point cloud color information, and the point cloud color was re-rendered to achieve three-dimensional digitization of plant chlorophyll content. Experimental research found that under natural growth conditions, the chlorophyll content of poplar trees showed a gradient distribution state with gradually increasing values from top to bottom; after being given a short period of mild drought stress, the chlorophyll content accumulated. Compared with the value before stress, it has improved, but no longer presents a gradient distribution state. At the same time, after severe drought stress, the chlorophyll value decreased as a whole, and the lower leaves began to turn yellow, wilt and fall off; when the stress intensity was consistent with the duration, the effect of drought on the chlorophyll value was 895 < SY-1 < 110 < 3804. This research provides an effective tool for in-depth understanding of the mechanisms and physiological responses of plants to environmental stress. It is of great significance for improving agricultural and forestry production and protecting the ecological environment. It also provides decision-making for solving plant drought problems caused by global climate change. Full article

Fungi constitute a diverse group with highly positive and negative impacts in different environments, having several natural roles and beneficial applications in human life, but also causing several concerns. Fungi can affect human health directly, but also indirectly by being detrimental for animal and plant health, influencing food safety and security. Climate changes are also affecting fungal distribution, prevalence, and their impact on different settings. Searching for sustainable solutions to deal with these issues is challenging due to the complex interactions among fungi and agricultural and forestry plants, animal production, environment, and human and animal health. In this way, the “One Health” approach may be useful to obtain some answers since it recognizes that human health is closely connected to animal and plant health, as well as to the shared environment. This review aims to explore and correlate each of those factors influencing human health in this “One Health” perspective. Thus, the impact of fungi on plants, human, and animal health, and the role of the environment as an influencing factor on these elements are discussed. Full article

Ungulate impacts on forest understory alter tree species composition, with cascading effects on forest functions and resilience against future climate conditions. Indeed, the ungulate browsing pressure on tree seedlings is species-specific and causes contrasted growth reductions that alter tree recruitment rates. Untangling the effects of browsing from the effects of the other factors driving regeneration success is required to guide the forest and ungulate management. In particular, Fagus sylvatica L. strongly dominates temperate Quercus-Fagus forests close to their climax, and it remains unclear if controlling ungulate populations can maintain tree species diversity in naturally regenerated forests. We addressed this question by monitoring 734 pairs of fenced and unfenced 6-m² plots across a broad gradient of Cervus elaphus L. abundance in Belgian Quercus-Fagus forests managed by continuous cover forestry. Seedling height, density, and vegetation cover were monitored from 2016 to 2021. Species diversity and ecological affinity for light, temperature, and atmospheric humidity conditions were computed from these measures. With ungulates, the mean growth of Betula pendula Roth. and Sorbus aucuparia L. was negligible, whereas, without ungulates, their growth was higher than the growth of other species. With ungulates, the growth of Fagus sylvatica L. and Picea abies (L.) H. Karst was higher than other species. Quercus (Quercus petreae (Matt.) Liebl and Quercus robur L.) growth was the lowest in all conditions. Finally, Carpinus betulus L. was heavily browsed but still grew higher than its competitors with ungulates. Ungulate browsing can then severely affect seedling growth and likely reduce the diversity of future recruited trees. In the study area, browsing unfavored the regeneration of the species that are less shade tolerant, more-drought tolerant, and more-heat tolerant. It thus accelerates the natural succession and reduces forest resilience to heat and drought events. Such an observation was found valid over a wide study area encompassing contrasting levels of Cervus elaphus L. abundance. Combining further reductions of ungulate populations with foodscape improvement is likely required to maintain species diversity in these forests. Full article

Close-to-nature forestry is a viable option to manage forests that are resilient to the challenges presented by climate change. The new silvicultural schemes necessitate adapting the operational side, posing challenges to productivity and the environmental effects of harvesting machinery and technologies. This study focused on analysing the disturbance of residual stands and forest soils in stands that were being restructured into multistorey, close-to-nature managed ones using low-impact forest harvesting technologies. Measurements were performed in four stands after logging, divided into 30 sample plots with dimensions of 20 × 20 m. Within the plots, the disturbance of the residual stands and changes to the soil parameters, such as the soil bulk density (g·cm⁻³) and soil penetration resistance (MPa), were measured. The results showed that the intensity of the residual stand disturbance reached between 13% and 23% and was not significantly (p > 0.05) affected by the intensity of the performed harvesting operations. The mean size of the wounds was between 38.99 and 233.05 cm², and wounds were most frequently in the size category of 11–50 cm². Regarding soil disturbance, Spearman’s correlation showed a significant relationship (p ˂ 0.05) between the longitudinal slope and soil bulk density in the rut of the trail. The relative increase in BD showed that the largest increase occurred between the stand (undisturbed) and rut locations (12.5% to 24.77%). Penetration resistance measurements were affected by low moisture content and high coarse fragment content. Subsequently, Spearman’s correlation did not show (p > 0.05) a relationship between the soil bulk density and penetration resistance. Therefore, we can conclude that, from an environmental perspective, the proposed technologies are viable options for foresters who manage close-to-nature forests, and there was less disturbance of residual stands and forest soil caused by harvesting machinery. Full article

The effective implementation of ecological protection policies requires the adequate assessment of temporal and spatial changes in the environment. To understand how ecosystem services can be used to track environmental changes, we carried out a study which focused on assessing the changes in the ecosystem services over time and space in Cili County, which is an important forestry region. The method we used was to evaluate the value of the regional ecological services based on the method for evaluating the value equivalent factor per unit area; then, by introducing multi-source data, the InVEST model was optimized to accurately assess the function of the regional ecosystem services, such as carbon storage, soil conservation, and water production. In addition, the values of the ecosystem services and the function assessment results were compared. Finally, based on the results of the optimized InVEST model, the value of the ecosystem services in the study area was regulated and re-evaluated. After re-evaluation, the total value of the ecosystem services in Cili county between 2000 and 2020 still showed an upward trend, increasing from CNY 26.136 billion to CNY 35.444 billion, with a total increase of CNY 9.308 billion. Compared to before the re-evaluation, the total value of the ecosystem services only increased from CNY 32.243 billion to CNY 32.473 billion, with a total increase of CNY 0.23 billion; the change in the value of the ecosystem services was more obvious, with a stronger spatial heterogeneity. The areas with high ecosystem service value in Cili County are mainly concentrated in the eastern parts, as well as the northwestern and southern parts, while the areas with low value are mainly concentrated in the central part of Cili County. The value of the central, southern, and northwestern parts of Cili County increased significantly. Such changes are closely related to China’s implementation of ecological protection policies in this region since 2000, such as returning farmland to forest and natural forest protection. The evaluation results of the ecosystem services and the method for evaluating the value equivalent factor in this study are more consistent with the changes in the ecosystem services in the study area. The dynamic assessment method of ecosystem service value proposed in this study is helpful in achieving accurate assessments of the regional ecosystem services and thus provides a useful reference for the formulation of more reasonable regional ecological protection policies. Full article

Forest stand structure (the characteristics and interrelationships of live trees) and site conditions (the physical and environmental characteristics of a specific location) have been linked to forest regeneration, nutrient cycling, wildlife habitat, and climate regulation. While the effects of stand structure (i.e., spatial and non-spatial) and site conditions on the single function of Cunninghamia lanceolata and Phoebe bournei (CLPB) mixed forest have been studied in previous studies, the relative importance of stand structure and site conditions in terms of productivity, species diversity, and carbon sequestration remains unresolved. In this study, a structural equation model (SEM) was adopted to analyze the relative importance of stand structure and site conditions for the forest productivity, species diversity, and carbon sequestration of CLPB mixed forest in Jindong Forestry in Hunan Province. Our research demonstrates that site conditions have a greater influence on forest functions than stand structure, and that non-spatial structures have a greater overall impact on forest functions than spatial structures. Specifically, the intensity of the influence of site conditions and non-spatial structure on functions is greatest for productivity, followed by carbon sequestration and species diversity. In contrast, the intensity of the influence of spatial structure on functions is greatest for carbon sequestration, followed by species diversity and productivity. These findings provide valuable insights for the management of CLPB mixed forest in Jindong Forestry and have significant reference value for the close-to-natural forest management (CTNFM) of pure Cunninghamia lanceolata forests. Full article