Search Results (1,656)

In the context of climate change and the increasing ecological importance of pedunculate oak (Quercus robur L.) in European forests, sustainable regeneration strategies are essential for ensuring long-term forest resilience. This study investigates how different conditions of regeneration sites, namely areas under pine canopies, gaps (openings within the pine stand), inter-gap area (open zone surrounding the pine gaps), and clear-cut area (zone where trees were completely removed), affect the early growth performance of artificially regenerated oak stands in Central Europe. Seedling height, root collar diameter, sturdiness quotient (SQ), and light availability (via hemispherical photography) were assessed. The most favorable growth occurred in gaps and under-canopy sites, where light intensity ranged from 44% to 57%, and seedlings reached mean heights of 148.7 cm and 143.4 cm, respectively. In contrast, seedlings in clear-cut and inter-gap areas exhibited lower growth and higher SQ values, suggesting lower seedling stability. In these areas, the average seedling height was 127.2 cm in clear-cut opening and 137.9 cm in inter-gap area. These sites also had the highest light intensity, amounting to 100% and 89.85% of total incident radiation, respectively. Growth performance was also affected by cardinal direction, except within gaps. This study highlights the importance of microsite selection in oak regeneration and demonstrates how optimizing light conditions can enhance reforestation success and climate resilience. These findings contribute to sustainable forest management practices aimed at supporting adaptive strategies in temperate ecosystems facing climate change. Full article

Coniferous forests in Canada play a vital role in carbon sequestration, wildlife conservation, climate change mitigation, and long-term sustainability. Traditional methods for classifying and segmenting coniferous trees have primarily relied on the direct use of spectral or LiDAR-based data. In 2024, we introduced a novel data representation method, pseudo tree crown (PTC), which provides a pseudo-3D pixel-value view that enhances the informational richness of images and significantly improves classification performance. While our original implementation was successfully tested on urban and deciduous trees, this study extends the application of PTC to Canadian conifer species, including jack pine, Douglas fir, spruce, and aspen. We address key challenges such as snow-covered backgrounds and evaluate the impact of training dataset size on classification results. Classification was performed using Random Forest, PyTorch (ResNet50), and YOLO versions v10, v11, and v12. The results demonstrate that PTC can substantially improve individual tree classification accuracy by up to 13%, reaching the high 90% range. Full article

The manuscript explores the valorization of forest logging residues, collected during forest management operations between summer 2023 and spring 2025 in mixed deciduous and coniferous forests, as a raw material for producing valuable bioactive products. These products offer a sustainable alternative to synthetic pesticides and fertilizers. Seven batches of biomass, comprising understory trees and branches from deciduous (mainly aspen, birch, and grey alder) and coniferous (mainly Scots pine) species, were collected during different seasons, crushed, and extracted using an ethanol–water solution. The yield of hydrophilic extracts containing proanthocyanidins (PACs) ranged from 18 to 25% per dry biomass. The highest PACs concentration (42% of extract dry mass) was found in small branches with a high bark content. The extracts and PACs at concentrations of 6.25‒12.50 mg mL⁻¹ showed fungicidal activity against several pathogenic fungi, including Botrytis cinerea Pers., Mycosphaerella sp. Johanson, Heterobasidion annosum (Fr.) Bref., and Heterobasidion parviporum Niemelä & Korhonen. Residual biomass after extraction, enriched with sea buckthorn berry pomace and a siliceous complex, was characterized and evaluated for its impact on the growth of Scots pine seedlings and selected agricultural crops. Results from forest and agricultural field trials in 2023–2025 confirmed a positive effect of the fertilizer on crop yield and quality at a low application rate (40 kg ha⁻¹ per crop). Fertilizer increased the yield of radish, dill, potatoes, and wheat by up to 44% (highest for potatoes and dill) compared to the reference, confirming its agronomic value. Full article

Pine wilt disease (PWD) is a serious forest disease caused by pine wood nematode (PWN). To examine the relationship between coniferous endophytes and PWD resistance, this study investigated endophytic bacterial and fungal communities in five conifer species: two Japanese black pine populations (Pinus thunbergii from Qingdao University, PQ, and Fushan Forest Park, PF), Chinese arborvitae (Platycladus orientalis, PO), cedar (Cedrus deodara, CD), and Masson pine (Pinus massoniana, PM). Results showed a strong correlation between endophytic microbial diversity and PWD resistance. PO with high PWD resistance hosted the most unique bacterial species, while PM with low PWD resistance had the fewest unique bacteria and significantly lower ACE and Shannon indices. At the bacterial genus level, dominant genera in resistant conifers often showed high nematocidal activity, whereas those in susceptible plants boosted nematode reproduction. PQ featured the unique dominant genus Pantoea, and PO’s unique Acinetobacter and the shared genus Bacillus (with CD) both displayed high toxicity to PWNs. In contrast, PF’s Pseudomonas and PM’s Stenotrophomonas significantly promoted nematode reproduction. Fungal community analysis revealed that the unique endophytic fungi in PQ are more abundant than those in PF, and the Shannon index of its endophytic fungi is comparable to that of CD and significantly higher than that of PF. PF’s dominant fungal genus Pestalotiopsis might facilitate nematode invasion, and its fungal Shannon index is significantly lower than PQ’s. Eight bacterial strains were isolated from these five conifer plants, with six highly nematocidal strains originating from PQ, CD, and PO. This study offers evidence that endophytic microbial communities critically influence PWD resistance, offering a microbial basis for developing resistant conifer cultivars through microbiome engineering. Full article

Crown fire hazard assessment and behavior prediction in dwarf pine (Pinus pumila) forests are dictated by the amount of canopy fuel available, topography, and weather. In this study, we collected data on CFL (available canopy fuel load), CBD (canopy bulk density), and CBH (canopy base height) through the destructive sampling of dwarf pine trees in the Greater Khingan Mountains of Northeast China. Allometric equations were developed for estimating the canopy’s available biomass, CFL, and CBD to support the assessment of canopy fuel. Three burning scenarios were designed to investigate the impact of various environmental parameters on fire behavior. Our findings indicated that the average CFL of a dwarf pine was 0.36 kg·m⁻², while the average CBD was measured at 0.17 kg·m⁻³. The vertical variation trends of both CFL and CBD exhibited consistency, with values increasing progressively from the bottom to the top of the tree crown. Fire behavior simulations indicated that the low CBH of dwarf pine trees increased the likelihood of crown fires. Various factors, including wind speed, slope, and CBH, exerted considerable influence on fire behavior, with wind speed emerging as the most critical determinant. Silvicultural treatments, such as thinning and pruning, may effectively reduce fuel loads and elevate the canopy base height, thereby decreasing both the probability and intensity of crown fires. Full article

In recent years, China has invested substantial funds in ecological restoration, achieving significant accomplishments. The forest coverage rate in the Chengde Bashang area, located in the transitional zone between the monsoon and non-monsoon regions, has now reached 82%. However, the area has also encountered a series of environmental issues, including lake shrinkage, soil salinization, and large-scale die-offs of planted forests. Whether the forests in this region can achieve sustainable development in the future, and whether ecological restoration should prioritize tree planting or grass cultivation, are critical questions that require attention. By studying the historical vegetation dynamics in afforested areas, we can better understand the relationship between climatic environmental changes and vegetation, providing baseline data for future ecological restoration. This study utilized AMS ¹⁴C dates to establish a chronological framework for the core and employed pollen to investigate vegetation dynamics over the past 5000 years in the artificial Larix Mill. forest area. The vegetation and environmental history of this core can be divided into three zones: Zone 1 (5100–4100 a B.P.): vegetation was dominated by pine and spores, with low herbaceous pollen content. Zone 2 (4100–1400 a B.P.): vegetation was primarily herbaceous. Zone 3 (1400 a B.P.–present): arboreal pollen content increased slightly, but herbaceous plants remained dominant. This period included the warm–dry Medieval Warm Period (1400–900 a B.P.), the cold–humid Little Ice Age (900–300 a B.P.), and the recent 300 years of anthropogenic disturbance. Notably, the large-scale afforestation efforts in recent decades are clearly reflected in the profile. A comparative analysis of records from the monsoon–non-monsoon transition zone reveals that, except for Angulinao Lake, other records were dominated by herbaceous vegetation over the past 2000 years. Additionally, the Mu Us Sandy Land, Hunshandake Sandy Land, Hulunbuir Sandy Land, and Horqin Sandy Land in China have experienced aeolian sand accumulation over the same period. Given the anticipated warming–desiccation trend, phytoremediation strategies should favor xerophytic shrubs and herbaceous over monospecific forest plantations. Full article

Longleaf pine forests are economically important habitats that stabilize and enrich the soil and store carbon over long periods. When mixed with oaks, these forests provide an abundance of lichen habitats. The tree canopy lichens promote greater moisture capture and retention and encourage canopy insects. Ground lichens limit some vascular plant germination and growth, promoting a more open and healthy pine community. There is a longstanding mutualistic relationship between longleaf pine habitat and lichens. Longleaf pine habitat has a long history of natural summer burning, which promotes a diverse understory and limits tree densities. Lichen diversity exceeds vascular plant diversity in many mature longleaf pine habitats, yet information on the impacts of prescribed fire on lichen species in these habitats is limited. We assessed lichen diversity and abundance before and after a prescribed ground fire in a longleaf pine/wiregrass habitat near Ocala, Florida. Pre-burn, we found greater lichen abundance and diversity on hardwoods, primarily oak species, than on pines. Post-burn, lichen abundance on hardwoods dropped overall by 28%. Lichen abundance on conifers dropped overall by 94%. Ground lichen species were basically eliminated, with a 99.5% loss. Our study provides insights into retaining lichen diversity after a prescribed burn. Hardwood trees, whether alive or standing dead, help retain lichen biodiversity after burning, whereas conifer trees do not support as many species. Landscapes may need to be actively managed by raking pine needle litter away from ground lichen beds, moistening the ground, or removing some lichen material before the burn and returning it to the site post-fire. Based on these results, we suggest retaining some oaks and conducting burns in a mosaic pattern that retains unburned areas. This will allow for lichens to recover between burns, significantly enhancing biodiversity and the ecological health of these longleaf pine communities. Full article

Pine wilt disease endangers the ecological stability of China’s coniferous woodlands. In a specific region, the number of dead pine trees has exhibited a consistent year-on-year increase, highlighting the urgent need for efficient and sustainable monitoring strategies. However, UAV-based remote sensing methods currently face challenges in complex environments, including insufficient feature-capture capabilities, interference from visually similar objects, and limited localization accuracy. This study developed a remote sensing workflow leveraging high-resolution UAV imagery to oversee pine trees affected with pine wilt disease. An enhanced YOLOv5 detection model was employed to identify symptomatic trees. To strengthen feature extraction capabilities—particularly for color and texture traits indicative of infection—different types of attention mechanisms, for instance SE, CBAM, ECA, and CA, were integrated as part of the model. Furthermore, a BiFPN structure was incorporated to enhance the fusion of features across multiple scales, and the EIoU loss function was adopted to boost the accuracy of bounding box prediction, ultimately enhancing detection precision. Experimental results show that the enhanced SEBiE-YOLOv5 framework achieved a precision of 89.4%, with an AP of 86.1% and an F1-score of 83.1%. UAV-based monitoring conducted during the spring and autumn of 2023 identified 616 dead trees, with field verification accuracy ranging from 88.91% to 92.42% and localization errors within 1–10 m. These findings validate the method’s high accuracy and spatial precision in complex mountainous forest environments. By integrating attention mechanisms, BiFPN, and the EIoU loss function, the proposed SEBiE-YOLOv5 model substantially enhances the recognition accuracy of key features in infected trees as well as their localization performance, and offers a practical and computationally efficient approach for the long-term surveillance of pine wilt disease in challenging terrain. Full article

Grafting constitutes a crucial approach for the preservation of pine clones. Slash pine is commonly used as the rootstock for grafting Masson pine scions in Guangxi. In this context, the fresh oleoresin samples of Masson pine, slash pine, and grafted pine (with Masson pine as scion and slash pine as rootstock) were analyzed by gas chromatography–mass spectrometry and gas chromatography, and the key chemical components (α-pinene, β-pinene, longifolene, and isopimaric acid) that can quickly and accurately distinguish the oleoresin of Masson pine and slash pine were found and identified. According to the changes in the relative content of key compounds of oleoresin in scion and rootstock, it was found that the oleoresin of rootstock and scion could interact. Further research showed that the mutual influence of oleoresin between rootstock and scion was persistent, and the influence of rootstock on oleoresin at the scion was affected by height. However, the height effect included a large individual differences, which were not significantly related to the grafting height, tree height, diameter at breast height, etc., but may have been related to the differences in synthesis speed of oleoresin between rootstocks and scions. This work reveals the possible mechanism of mutual influence and secretion of oleoresin in grafted pine trees, laying a foundation for the study of the characteristics of oleoresin from pines grafted by different types, with great significance for the breeding of pine with high yield of oleoresin, and the production and application of special compounds containing oleoresin. Full article

Aerospace engines and hypersonic vehicles, among other high-temperature components, often operate in environments characterized by temperatures exceeding 1000 °C and high-speed airflow impacts, resulting in severe thermal erosion conditions. Coaxial thermocouples (CTs), with their unique self-eroding characteristic, are particularly well suited for use in such extreme environments. However, fabricating high-temperature electrical insulation layers for coaxial thermocouples remains challenging. Inspired by the self-healing mechanism of pine trees, we designed a composite electrical insulation layer with a similar self-healing function. This composite layer exhibits excellent high-temperature insulation properties (insulation resistance of 14.5 kΩ at 1200 °C). Applied as the insulation layer in K-type coaxial thermocouples via dip-coating, the thermocouples were tested for temperature and heat flux. Temperature tests showed an accuracy of 1.72% in the range of 200–1200 °C, a drift rate better than 0.474%/h at 1200 °C, and hysteresis better than 0.246%. The temperature response time was 1.08 ms. Heat flux tests demonstrated a measurable range of 0–41.32 MW/m² with an accuracy better than 6.511% and a heat flux response time of 7.6 ms. In simulated extreme environments, the K-type coaxial thermocouple withstood 70 s of 900 °C flame impact and 50 cycles of high-power laser thermal shock. Full article

Accurate classification of individual tree types is a key component in forest inventory, biodiversity monitoring, and ecological modeling. This study evaluates and compares multiple Machine Learning (ML) and Deep Learning (DL) approaches for tree type classification based on Airborne Laser Scanning (ALS) data. A mixed-species forest in southeastern Austria, Europe, served as the test site, with spruce, pine, and a grouped class of broadleaf species as target categories. To examine the impact of data representation, ALS point clouds were transformed into four distinct structures: 1D feature vectors, 2D raster profiles, 3D voxel grids, and unstructured 3D point clouds. A comprehensive dataset, combining field measurements and manually annotated aerial data, was used to train and validate 45 ML and DL models. Results show that DL models based on 3D point clouds achieved the highest overall accuracy (up to 88.1%), followed by multi-view 2D raster and voxel-based methods. Traditional ML models performed well on 1D data but struggled with high-dimensional inputs. Spruce trees were classified most reliably, while confusion between pine and broadleaf species remained challenging across methods. The study highlights the importance of selecting suitable data structures and model types for operational tree classification and outlines potential directions for improving accuracy through multimodal and temporal data fusion. Full article

Under cool and moist climates, timely implementation of ditch network maintenance (DNM) is crucial for sustaining productivity of drained forests, thus reducing operational costs, while mitigating environmental risks. This underscores the need to understand tree growth responses to DNM. This study evaluated the effects of DNM on tree radial increment in sites with both organic and mineral drained soils, focusing on regionally commercially important species: Scots pine (Pinus sylvestris), Norway spruce (Picea abies), and silver birch (Betula pendula). Responses of relative growth changes over eight years post-DNM to site and tree characteristics were assessed using a linear mixed-effects model. Species- and site-specific growth responses to DNM were indicated by significant interactions between tree species, site type, and distance from the drainage ditch. While growth responses were generally neutral (non-significant), variability among sites and species suggests that both organic and mineral soils might be prone to site-level moisture depletion near drainage infrastructure in the post-DNM period. The effect of stand age and density suggested higher responsiveness of older and less dense stands, hence positive effects of thinning to resilience of stands to DNM. These findings highlight the importance of adapting DNM strategies to local site conditions and stand characteristics to minimize drought-related growth limitations. Full article

Strip mining creates widespread degraded landscapes that have low soil pH, high bulk density, impacted hydrologic processes, and an accumulation of heavy metals that limit revegetation efforts. To improve soil conditions and restoration success, soil amendments paired with native trees provide a potential solution. However, limited empirical studies have been conducted on the success of soil amendments to facilitate shortleaf pine (Pinus echinata Mill.) growth in the southeastern US. To fill this knowledge gap, a field trial was established on a reclaimed coal-mined site. Shortleaf pine seedlings were planted in a complete randomized block design with two soil amendment treatments: activated carbon and mycorrhizal inoculation, applied at a rate of 3.36 g/m² and 42.5 g per tree, respectively. Soil treatment did not impact tree survival which concluded with a 69 ± 3% (mean ± standard error) survival rate. Activated carbon increased soil electrical conductivity (p = 0.037) and the mycorrhizal amendment led to increased soil Ca content (p = 0.004). After the first growing season, trees in the mycorrhizal-amended soil were 12% shorter (p = 0.016) than trees in the activated carbon treatment. While soil amendment resulted in minimal improvements to soil parameters, shortleaf pine was found to be an effective species choice for post-mined site reforestation. Full article

Pine wood has a natural, rustic, and environmentally friendly style and is used in a large number of applications in the furniture industry. However, its soft and porous texture makes it susceptible to bacteria, mould, and other micro-organisms. Pine wood was selected as the test substrate, and tea tree essential oil@chitosan (TTO@CS) microcapsules with emulsifier concentrations of 4%, 5%, and 6% were added to the waterborne topcoat at a content of 1%–9% (in 2% intervals) to investigate their effect on the surface coating properties of pine wood. With the increase in microcapsule content, there was an overall increase in colour difference and light loss rate of pine wood surface coating, and the reflectance showed an increase and then decrease. The overall performance of the pine wood surface coatings containing 7% of 13# microcapsules was found to be excellent: the antimicrobial activity of the coatings was 62.58% for Escherichia coli and 61.29% for Staphylococcus aureus after 48 h, and the antimicrobial activity of the coatings was 40.14% for Escherichia coli and 38.89% for Staphylococcus aureus after 4 months. The colour difference in the coating was 2.37, and the light loss was 63.71%. The reflectance value was found to be 0.6860, while the hardness was determined to be 2H and the adhesion class was categorised as one. The impact resistance class was determined to be three, while the roughness was measured at 1.320 μm. The waterborne coating on the surface of pine wood was modified by microencapsulation technology with the objective of enhancing the antimicrobial properties of pine wood and expanding its scope of application. Full article

The pine wood nematode is responsible for pine wilt disease, which poses a significant threat to forest ecosystems worldwide. If not quickly detected and removed, the disease spreads rapidly. Advancements in UAV and image detection technologies are crucial for disease monitoring, enabling efficient and automated identification of pine wilt disease. However, challenges persist in the detection of pine wilt disease, including complex UAV imagery backgrounds, difficulty extracting subtle features, and prediction frame bias. In this study, we develop a specialized UAV remote sensing pine forest ARen dataset and introduce a novel pine wilt disease detection model, SLMW-Net. Firstly, the Self-Learning Feature Extraction Module (SFEM) is proposed, combining a convolutional operation and a learnable normalization layer, which effectively solves the problem of difficult feature extraction from pine trees in complex backgrounds and reduces the interference of irrelevant regions. Secondly, the MicroFeature Attention Mechanism (MFAM) is designed to enhance the capture of tiny features of pine trees infected by initial nematode diseases by combining Grouped Attention and Gated Feed-Forward. Then, Weighted and Linearly Scaled IoU Loss (WLIoU Loss) is introduced, which combines weight adjustment and linear stretch truncation to improve the learning strategy, enhance the model performance and generalization ability. SLMW-Net is trained on the self-built ARen dataset and compared with seven existing methods. The experimental results show that SLMW-Net outperforms all other methods, achieving an mAP@0.5 of 86.7% and an mAP@0.5:0.95 of 40.1%. Compared to the backbone model, the mAP@0.5 increased from 83.9% to 86.7%. Therefore, the proposed SLMW-Net has demonstrated strong capabilities to address three major challenges related to pine wilt disease detection, helping to protect forest health and maintain ecological balance. Full article