Forests, Volume 14, Issue 7 (July 2023) – 232 articles

The aim of the research was to create a universal system for monitoring and evaluating the operating parameters of the haulage vehicles used for the haulage of wood with self-maintenance. The article presents partial results from the entire research. Data for research into the operational reliability of IVECO, SCANIA, and TATRA vehicles were obtained from the real-world operating conditions of two companies dealing with the mining/transportation process. Information from the operating conditions was obtained according to the test plan [n, R, t], according to which n objects were simultaneously tested, and the objects that were damaged during the tests were replaced with new ones; the tests ended after the test time t for each of the n positions. Based on the results and statistical analyses, it can be said that the best operational reliability is achieved by IVECO, followed by SCANIA, and only then by TATRA. The resolution of the above conclusions in operating conditions will contribute to the efficiency of the operation of the investigated facilities and the extension of the technical life of the means of transportation. Full article

Wood material is the foundation of wood structure architecture, and its production technology and equipment technology decide the development and upgrading of modern wood structure architecture. Aiming at the problems of low automation degree, low material utilization rate, low production efficiency and high labor costs in the process of traditional wood processing, we explore the integration and innovation of the traditional wood processing industry and modern industrial Internet information technology on the basis of studying the properties of Betula (Betula costata) solid wood materials, wood comprehensive utilization rate, wood structure component development and processing technology requirements, and form an intelligent, automatic and industrial production mode for building materials. Through technology and methods such as mechanical design, automation technology, machine vision, deep learning, optimization algorithm, electronic design automation, computer aided manufacturing, etc., the key technologies of intelligent automatic optimization of wood materials were studied, and intelligent automatic production lines of Betula species identification, log optimization sawing, solid timber longitudinal multiblade sawing, sawn timber quality detection and solid timber optimizing cross-cuts are built. Based on the machine vision method, features are extracted; a tree species, defect classification and recognition model database is established; an image processing algorithm with high recognition accuracy, as well as fast processing speed and high robustness are studied; non-destructive testing and classification methods of machine vision are optimized; key problems of online rapid classification, detection and optimization of sawing are solved and production quality and processing efficiency are improved. Finally, the timber defect detection accuracy and Betula timber yield are analyzed, and the comprehensive utilization value of optimized sawing timber is compared with the comprehensive utilization value of manually marking sawing timber. The processing cost and efficiency of Betula sawing timber with an intelligent automatic production line are calculated. The test results show that the average detection accuracy of timber defect type, size and location is 89.69%, 89.69%, 92.25% and 82.29%, respectively, and the detection stability is high. By adopting intelligent automatic detection, classification and optimization sawing production line of wood, the comprehensive utilization value of optimized sawing timber is 14.13% higher than that of manual marking sawing timber, and 16,089.29 m³ more building materials can be processed annually. In the process of intelligent automatic wood processing, the intelligent detection system is used to detect defects, improve production performance and production efficiency and reduce labor costs. Compared with the traditional wood processing process, the method studied in this paper is improved to optimize the production line processing performance and processing technology. The research and development of an intelligent automatic production system for solid wood can promote the application and development of an automatic industrial production mode for sawn timber for the wood structure construction industry, deepen the integration of artificial intelligence technology, Internet technology and the whole wood processing industry and lead the upgrading of building materials for wood structures to an intelligent manufacturing production mode. Full article

The accurate estimation of forest area is of paramount importance for carbon sequestration projects, ecotourism and ecological safety. Forest segmentation using remote sensing images is a crucial technique for estimating forest area. However, due to the complex features, such as the size, shape and color of forest plots, traditional segmentation algorithms struggle to achieve accurate segmentation. Therefore, this study proposes a remote sensing image forest segmentation model named SegForest. To enhance the model, we introduce three new modules: multi-feature fusion (MFF), multi-scale multi-decoder (MSMD) and weight-based cross entropy loss function (WBCE) in the decoder. In addition, we propose two new forest remote sensing image segmentation binary datasets: DeepGlobe-Forest and Loveda-Forest. SegForest is compared with multiple advanced segmentation algorithms on these two datasets. On the DeepGlobe-Forest dataset, SegForest achieves a mean intersection over union (mIoU) of 83.39% and a mean accuracy (mAcc) of 91.00%. On the Loveda-Forest dataset, SegForest achieves a mIoU of 73.71% and a mAcc of 85.06%. These metrics outperform other algorithms in the comparative experiments. The experimental results of this paper demonstrate that by incorporating the three proposed modules, the SegForest model has strong performance and generalization ability in forest remote sensing image segmentation tasks. Full article

Burn severity is commonly assessed using Burn Ratios and field measurements to provide land managers with estimates of the degree of burning in an area. However, less commonly studied is the ability of spectral indices and Burn Ratios to estimate field-measured fire effects. Past research has shown low correlations between fire effects and Landsat-derived Burn Ratios, but with the launch of the Sentinel-2 constellation, more spectral bands with finer spatial resolutions have become available. This paper explores the use of several red-edge-based indices and Burn Ratios alongside more ‘traditional’ spectral indices for predicting fire effects, measured from the Maple and Berry fires in Wyoming, USA. The fire effects include ash depth, char depth, post-fire dead lodgepole pine (Pinus contorta; PICO) density/stumps, mean basal diameter, cone density on dead post-fire trees, coarse wood percent cover/volume/mass, percent cover of ghost logs and initial regeneration of post-fire PICO/aspen density. All-possible-models regression was used to determine the best models for estimating each fire effect. Models with satisfactory R² values were constructed for post-fire dead PICO stumps (0.663), coarse wood percent cover (0.691), coarse wood volume (0.833), coarse wood mass (0.838), ash depth (0.636) and percent cover of ghost logs (0.717). Red-edge-based indices were included in all of the satisfactory models, which shows that the red-edge bands may be useful for measuring fire effects. Full article

LiDAR technology has been widely used in forest survey and research, but the high-resolution point cloud data generated by LiDAR equipment also pose challenges in storage and computing. To address this problem, we propose a point cloud simplification method for trees, which considers both higher similarity to the original point cloud and the area of the tree point cloud. The method first determines the optimal search neighborhood using the standard deviation of FPFH information entropy. Based on FPFH information entropy and Poisson disc sampling theory, the point cloud is partitioned and sampled. By optimizing the separation thresholds of significant feature points and less significant feature points using a genetic algorithm with the Hausdorff distance and point cloud area as the objective function, the final simplified point cloud is obtained. Validation with two point cloud data sets shows that the proposed method achieves good retention of the area information of the original point cloud while ensuring point cloud quality. The research provides new approaches and techniques for processing large-scale forest LiDAR scan point clouds, reducing storage and computing requirements. This can improve the efficiency of forest surveys and monitoring. Full article

The subjective and empirical setting of hyperparameters in the random forest (RF) model may lead to decreased model performance. To address this, our study applies the particle swarm optimization (PSO) algorithm to select the optimal parameters of the RF model, with the goal of enhancing model performance. We employ the optimized ensemble model (PSO-RF) to create a fire risk map for Jiushan National Forest Park in Anhui Province, China, thereby filling the research gap in this region’s forest fire studies. Based on collinearity tests and previous research results, we selected eight fire driving factors, including topography, climate, human activities, and vegetation for modeling. Additionally, we compare the logistic regression (LR), support vector machine (SVM), and RF models. Lastly, we select the optimal model to evaluate feature importance and generate the fire risk map. Model evaluation results demonstrate that the PSO-RF model performs best (AUC = 0.908), followed by RF (0.877), SVM (0.876), and LR (0.846). In the fire risk map created by the PSO-RF model, 70.73% of the area belongs to the normal management zone, while 15.23% is classified as a fire alert zone. The feature importance analysis of the PSO-RF model reveals that the NDVI is the key fire driving factor in this study area. Through utilizing the PSO algorithm to optimize the RF model, we have addressed the subjective and empirical problems of the RF model hyperparameter setting, thereby enhancing the model’s accuracy and generalization ability. Full article

Understanding the maturation stress and wood properties of poplar tension wood is critical for improving lumber yields and utilization ratio. In this study, the released longitudinal maturation strains (RLMS), anatomical features, physical and mechanical properties, and nano-mechanical properties of the cell wall were analyzed at different peripheral positions and heights in nine artificially inclined, 12-year-old poplar (Populus × euramericana cv. ‘Zhonglin46’) trees. The correlations between the RLMS and the wood properties were determined. The results showed that there were mixed effects of inclination on wood quality and properties. The upper sides of inclined stems had higher RLMS, proportion of G-layer, bending modulus of elasticity, and indentation modulus of the cell wall but a lower microfibril angle than the lower sides. At heights between 0.7 m and 2.2 m, only the double-wall thickness increased with height; the RLMS and other wood properties such as fiber length and basic density fluctuated or changed little with height. The RLMS were good indicators of wood properties in the tension wood area and at heights between 0.7 m and 1.5 m. The results of this study present opportunities to better understand the interactions and effects of these two phenomena, which both occur quite frequently in poplar stands and can influence the wood quality of valuable assortments. Full article

Populus euphratica is a typical stress-resistant tree species that provides valuable natural genetic resources for breeding salt-tolerant plants. The calcineurin B-like (CBL)-interacting protein kinase (CIPK) network plays an important role in regulating plant responses to abiotic stresses. The aim of this study was to characterize the function of a new CBL member, PeCBL4, in response to abiotic stresses. PeCBL4 was cloned, and sequence analysis was performed. The subcellular localization of PeCBL4 was determined using the fusion expression vector of GFP. Yeast two-hybrid assays and bimolecular fluorescence complementation were performed to identify PeCIPK members that interacted with PeCBL4. PeCBL4 was then transformed into the corresponding Arabidopsis thaliana mutants. Na⁺ and K⁺ content as well as their net fluxes were determined under high salt stress and low K⁺ stress. Phylogenetic tree analysis showed that PeCBL4 was clustered together with PtCBL4 and belonged to the same subgroup as AtCBL4. Subcellular localization indicated that PeCBL4 was expressed on the plasma membrane. Yeast two-hybrid assays and bimolecular fluorescence complementation showed that PeCBL4 interacted with PeCIPK24 and PeCIPK26. In addition, under high salt stress, the Na⁺ efflux capacities of seedlings decreased in sos3 mutants, and transgenic plants of PeCBL4 enhanced efflux capacities. In addition, the overexpression of PeCBL4 negatively influenced the influx capacity of K⁺. PeCBL4 interacts with PeCIPK24 and PeCIPK26 and regulates Na⁺/K⁺ balance under low K⁺ and high salt stress. Full article

Precision forestry is a useful technique to help forest stakeholders with proper sustainable forest management. Modern sensors and technologies, with special reference to the sustainability of forest operations, can be applied on a variety of levels, including the monitoring of forest activities regarding the three pillars (economy, environment, and society). In this review, we summarised the current level of knowledge regarding the use of precision forestry techniques for monitoring forest operations. We concentrated on recent data from the last five years (2019–2023). We demonstrated how an Industry 4.0 strategy for remote and proximal monitoring of working performance can be effective when using CAN-bus and StanForD data collected by modern forest machines. The same information can be effectively used to create maps of soil trafficability and to evaluate the patterns of skid tracks or strip roads built as a result of forest intervention. Similar information can be gathered in the case of small-scale forestry by using GNSS-RF (Global Navigation Satellite Systems—Radio Frequency) or even monitoring systems based on smartwatches or smartphones. LiDAR and Structure for Motion (SfM) photogrammetry are both useful tools for tracking soil rutting and disturbances caused by the passage of forest machinery. SfM offers denser point clouds and a more approachable method, whereas laser scanning can be considerably faster but needs a more experienced operator and better data-processing skills. Finally, in terms of the social component of sustainability, the use of location sharing technologies is strongly advised, based for instance on GNSS—RF to monitor the security of forest workers as they operate. Full article

Many studies have been conducted on organic carbon changes under different land use patterns, but studies and data concerning changes in the molecular composition of soil organic matter (SOM) during land use conversion are scarce. In this work, we studied the chemical composition of SOM on two Robinia pseudoacacia L. plantations and their adjacent croplands in the Loess Plateau using biomarker and nuclear magnetic resonance (NMR) techniques. Experimental data on the molecular composition of SOM showed that the soil microbial biomass carbon content initially decreased and then returned to the original level gradually after afforestation, while the SOM content and stocks increased over time. At the initial stage of afforestation, the content of total solvent extracts did not change significantly but changed slowly over time in the plantations without artificial disturbance. With an increase in restoration time, the concentrations of both the microbial- and plant-derived solvent extracts increased. Moreover, the concentrations of plant-derived solvent extracts were consistently lower than those of microbial-derived solvent extracts. Afforestation also significantly increased the lignin-derived phenol content in the surface soil layer (0–10 cm). However, no obvious change was observed in the lignin-derived phenols of the two adjacent croplands. These results indicate that the accumulation of aboveground litter and underground roots has the strongest effects on the lignin-derived phenol content. In contrast to cropland, the two plantations exhibited a high degree of degradation of lignin-derived phenols in the surface soil, but this remained almost unchanged over time. Moreover, in contrast to 20 years after the establishment of the R. pseudoacacia plantation, the low alkyl/O-alkyl carbon ratio of the 8-year R. pseudoacacia plantation indicated that more easily degradable components accumulated during the initial stage of afforestation. Therefore, the proportion of the unstable carbon pool was relatively high and the SOM content may decline in the early stage of afforestation. These results provide evidence illustrating the detailed changes in the chemical composition of SOM during the ecological restoration process. Full article

The spatial pattern of Weizi settlements features distinct regional characteristics. Moreover, it contains profound wisdom in terms of traditional construction; therefore, studies on its association with the microclimate have important implications for improving the quality of human settlements. In the present study, Guanweizi Village in the Xinyang City of Henan Province was used as an example to analyze and evaluate the thermal comfort of green spaces. The impact of peripheral water bodies on the thermal comfort of outdoor green spaces in the settlement was studied, and the association between the components of outdoor green spaces and physiological equivalent temperature as an indicator of thermal comfort was explored. Further, factors negatively affecting the thermal comfort of green spaces were analyzed through the grid method. Thermal comfort in the Weizi settlement is somewhat correlated with the coverage of water bodies, roads, soil, greening, and buildings. Increasing the water area and creating multi-level greening spaces are effective measures to improve the thermal comfort of green spaces in the settlement. Our findings provide a theoretical basis and a pioneering example for future practices of environment design for human settlements. Full article

The core areas of the urban heat island (CAUHI) are the most concentrated and closely associated with humans, and they are key to managing the urban heat island (UHI). It is widely acknowledged that one of the best ways to reduce the risk of UHI is the creation of urban green spaces (UGSs). However, most of the current studies are based on the grid or block scale to explore the impact of UGS on UHI. The key to mitigating the urban heat environment is to plan urban UGS rationally in the CAUHI and explore the thermal environmental benefits of UGS. This paper provides an assessment model for the thermal environmental advantages of UGS and uses ten UGS metrics as explanatory factors for seasonal land surface temperature (LST). It quantitatively evaluates the potential differences in landscape characteristics between LST and UGS under different seasons, as well as the seasonal impact on CAUHI. This study found the following: (1) The overall distribution pattern of CAUHI shows a characteristic of spreading from the central part to the surrounding area. Most of the extremely significant CAUHI is dispersed in the center and southeastern regions of the city, where there is a much greater density of impermeable surfaces and essentially no distribution of CAUHI on the natural surface represented by forest land and water bodies. (2) Except for the aggregation index (AI), correlation analysis revealed that other metrics were highly connected with LST. Among the metrics used in this study, the largest patch index (LPI) and landscape division index (DIVISION) had the highest significant correlation with LST. Patch density (PD) was strongly negatively correlated with LST, indicating that fragmented and complex UGS patches could promote vegetation cooling. (3) The green environmental benefit index (GEBI) results showed a significant degree of spatial and temporal variability in the extracted CAUHI. This study found higher GEBI values in the larger thermal patches and lower GEBI in the surrounding smaller patches. The highest mean GEBI was found in winter, at 0.6083, and the largest distribution of large high-value patches. This study revealed the geographical and temporal variability of UGS and CAUHI, and with the help of the constructed scientific evaluation model, it offered suggestions for the optimization of urban greenery. Full article

Forest fires have devastating impacts on ecology, the economy, and human life. Therefore, the timely detection and extinguishing of fires are crucial to minimizing the losses caused by these disasters. A novel dual-channel CNN for forest fires is proposed in this paper based on multiple feature enhancement techniques. First, the features’ semantic information and richness are enhanced by repeatedly fusing deep and shallow features extracted from the basic network model and integrating the results of multiple types of pooling layers. Second, an attention mechanism, the convolutional block attention module, is used to focus on the key details of the fused features, making the network more efficient. Finally, two improved single-channel networks are merged to obtain a better-performing dual-channel network. In addition, transfer learning is used to address overfitting and reduce time costs. The experimental results show that the accuracy of the proposed model for fire recognition is 98.90%, with a better performance. The findings from this study can be applied to the early detection of forest fires, assisting forest ecosystem managers in developing timely and scientifically informed defense strategies to minimize the damage caused by fires. Full article

This study aimed to investigate the role of plantation forests and natural secondary forests in controlling soil physicochemical properties and microbial biomass in urban forest ecosystems. (1) Background: Urban forests provide numerous benefits to urban ecosystems, but the interaction between forest stands and soil properties in controlling soil microbial biomass carbon (MBC) and nitrogen (MBN) remains poorly understood. The objective of this study was to examine how different forest types (plantation forests and natural secondary forests) influence soil physicochemical properties and microbial biomass in urban forest ecosystems. (2) Methods: We conducted a study in Shushan Urban Forest Park, Hefei, China, utilizing redundancy analysis and linear regression analyses to identify key environmental factors affecting the microbial distribution and significant correlations between soil properties and microbial biomass. (3) Results: Plantation forests generally had lower pH, water content, and organic carbon and nutrient content than natural forests. Natural forests exhibited higher microbial biomass and nutrient cycling capacity. Soil depth and forest type have significant effects on soil properties and microbial biomass in both growing and dormant seasons, with practical implications for forest management and soil conservation in similar ecosystems. Soil water content (SWC), pH, total nitrogen (TN), total phosphorus (TP), and soil organic carbon (SOC) were identified as key factors affecting microbial carbon and nitrogen distribution during both growing and dormant seasons. Our study provides important insights into the role of forest stands and soil physicochemical properties in controlling soil microbial biomass in urban forest ecosystems. Effective forest management strategies should be developed to promote sustainable and resilient forest ecosystems. Future research should investigate the underlying mechanisms driving these relationships and focus on promoting sustainable and resilient urban forest ecosystems. Full article

Nitrogen, as a crucial limiting nutrient in terrestrial ecosystems, plays a vital role in determining land quality. Heavy metals, as drivers of soil substance transformation, are important indicators for assessing ecosystem function. Currently, the relationship between soil nitrogen and heavy metals in karst desertification areas remains unclear. Therefore, this study focuses on the soil of grassland, forest, and agroforestry ecosystems in a karst desertification area to investigate the relationship between heavy metals and nitrogen distribution using ecological stoichiometry. The findings revealed the following: (i) Total nitrogen (TN) and available nitrogen (AN) exhibited the trend of agroforestry * > forest > grassland, while soil microbial biomass nitrogen (SMBN) showed the trend of forest * > grassland * >> agroforestry; (ii) Chromium (Cr), Ferrum (Fe), Niccolum (Ni), and Plumbum (Pb) showed the trend of agroforestry * > grassland > forest, while Cuprum (Cu) demonstrated the trend of agroforestry > grassland > forest, and Zincum (Zn) exhibited the trend of grassland > forest * >> agroforestry. The Nemerow comprehensive pollution index were 0.77 for grassland, 0.69 for forest, and 0.94 for agroforestry; (iii) The sensitivity of soil nitrogen and heavy metals ranked as grassland > agroforestry > forest. The research findings aim to provide a scientific reference for karst desertification control, ecological protection and restoration, and enhancement of ecosystem function. Full article

As the primary mountain range in Central Asia, the Altay Mountains receive water vapor carried by westerly circulation, resulting in relatively abundant local precipitation and lush pastures in all seasons. Consequently, it has become one of the important transportation routes between Asia and Europe. The exploration of long-term variations in precipitation is meaningful for understanding the ebb and flow of the Asia–Europe steppe trade routes. However, previous dendroclimatological studies of the Altay Mountains focused more on temperature changes than precipitations variations. We carried out a 404-year precipitation reconstruction based on the tree rings of Siberian larch growing on the south slopes of the Altay Mountains, which could explain 45.9% of the variance observed in the February–October precipitation. Our reconstruction demonstrated some severe drought events which could be found in the historical documents, such as the drought in the late Ming Dynasty (1640s) and the Ding-Wu Disaster (1870s). The spatial correlation analysis, cross-wavelet spectrum and wavelet coherency analysis indicated that the precipitation variations in the study area may be related to the ENSO and NAO. This study presents a robust precipitation reconstruction of the southern Altay Mountains, serving as a reference for future research on large-scale climatic forces acting on Altay precipitation. Full article

Competition among free branches in the tree canopy is an important factor influencing branch length growth. Therefore, there is a need to quantify this competition and to understand the impact of the regression technique on the predictive accuracy of the growth of free branch length (GFBL) model in a Larix principis-rupprechtii plantation. This study focused on an L. principis-rupprechtii plantation in Saihanba Mechanized Forest Farm. Five competition indices based on 2176-branch data points from 76 trees were used to quantify the branch competition, and three regression techniques (nonlinear least squares (NLS), nonlinear mixed-effects model (NLME), and nonlinear quantile regression (NQR)) were used to construct the GFBL model including the branch competition index. The results showed that the Chapman–Richards growth function, including the diameter at breast height (DBH) and depth of branch into crown (DINC), was the optimal equation for describing the GFBL in the studied L. principis-rupprechtii plantation. The branch competition index (CI) was found to be optimal for quantifying the branch competition when used with the maximum value parameter (${\mathrm{a}}_0$) of the Chapman–Richards growth function. The three parameter estimation methods were compared, and the NLME, which included the CI, was found to have the highest predictive accuracy. The results of this study can act as a reference for improving the management, assessing the management effectiveness, and enhancing the quality of L. principis-rupprechtii plantations. Full article

Protected areas serve as stepping stones for the preservation of biodiversity, and can provide economic and social benefits to communities. National parks aim to limit human intervention to safeguard natural communities and processes. This study analyzes the impacts of transforming the Křivoklátsko Protected Landscape Area into the proposed Křivoklátsko National Park in the Czech Republic, which is a program promoted by political parties. Using the quintuple helix model, it assesses the change from a sustainable development perspective. The analysis considers economic, social, and environmental aspects, including the impact on the local inhabitants, the economy, forestry, business activities, and regional development. The existing management in the Křivoklátsko region exemplifies sustainable multifunctional forest management. Based on the evaluation, the study finds insufficient arguments for declaring the Křivoklátsko National Park. The study emphasizes the need to balance the social demand for nature protection with the awareness of existing measures and specific area conditions. Nature protection should integrate itself into all human activities within the culturally and historically created landscape, rather than solely pursuing political goals. Participatory forestry management plays a crucial role in landscape transformation. The study highlights the importance of sustainable landscape development and the interactions between the university, government, industry, and civil sector actors with the environment. Full article

Forest aboveground biomass (AGB) is an important research topic in the field of forestry, with implications for carbon cycles and carbon sinks. Malania oleifera Chun et S. K. Lee (M. oleifera) is a valuable plant species that is listed on the National Second-Class Protected Plant checklist and has received global attention for its conservation and resource utilization. To obtain accurate AGB of individual M. oleifera trees in a fast, low-finance-cost and low-labor-cost way, this study first attempted to estimate individual M. oleifera tree AGB by combining the centimeter-level resolution RGB imagery derived from unmanned aerial vehicles (UAVs) and the deep learning model of Mask R-CNN. Firstly, canopy area (CA) was obtained from the 3.5 cm high-resolution UAV-RGB imagery using the Mask R-CNN; secondly, to establish an allometric growth model between the diameter at breast height (DBH) and CA, the correlation analysis of both was conducted; thirdly, the AGB estimation method of individual M. oleifera trees was presented based on an empirical equation. The study showed that: (1) The deep learning model of Mask R-CNN achieved an average segmentation accuracy of 90% in the mixed forests to the extraction of the canopy of M. oleifera trees from UAV-RGB imagery. (2) The correlation between the extracted CA and field-measured DBH reached an R² of 0.755 (n = 96). (3) The t-test method was used to verify the predicted and observed values of the CA-DBH model presented in this study, and the difference in deviation was not significant (p > 0.05). (4) AGB of individual M. oleifera was estimated for the first time. This study provides a reference method for the estimation of individual tree AGB of M. oleifera based on centimeter-level resolution UAV-RGB images and the Mask R-CNN deep learning. Full article

As objects for reforestation, the least studied are carbonate substrates, which have a number of specific features in terms of mineral composition, the exchange of nutrients, and biological activity. The use of biological preparations of a consortium of bacteria of the genus Bacillus and mycorrhizal fungi of the genus Glomus in growing seedlings of Scots pine (Pinus sylvestris L.) on carbonate substrates provides the metabolic products; soluble and microelement salts function as catalysts for chemical reactions of exudates and soil products; and a greater amount of plant heavy metals (HM) Cu, Zn, Cd, and Pb accumulate in the soil. Among HMs, the random factors most strongly determined an accumulation of Cd (the influence rate of random factors h²_x = 34.6%) and Pb (the influence rate of random factors h²_x = 21.7%) in the plants. A trend of all studied HMs higher uptake by the Cretaceous pine (Pinus sylvestris var. cretacea (Kalen.) Kom.) in comparison with the P. sylvestris ecotype is revealed. Against the biological preparation background of Biogor KM and MycoCrop^®, a greater value of the HM’s biological absorption in comparison with the option without biological preparations is noted. This process occurs against a background of a significant increase in the nitrification capacity in the chalk fine-grained substrate (soil aggregates < 1 mm in size), which is an indirect indicator of an increased intensity of microbiological processes. Spearman’s correlation was noted between the coefficient of accumulation of Cu, Zn, Cd, and Pb in the dry matter of Scots pine (P. sylvestris) seedlings and the nitrification capacity of substrate (r_s = 0.610–0.744, p < 0.05), as well as the relationship between the nitrification capacity index of substrate and the coefficient of biological absorption of copper, zinc, and cadmium (r_s = 0.543–0.765, p < 0.05). No relationship was found between the coefficient of biological absorption of lead and other soil chemical property indicators. HM absorption by plants was random. No correlations have been established between an accumulation of HMs and a content of total nitrogen, an absolute value of nitrate nitrogen, a humus content, or a pH. The significance of the work is the possibility of providing reliable reforestation with Scots pine (P. sylvestris) and Cretaceous pine (P. sylvestris var. cretacea) on the chalk outcrops using the biological preparations Biogor KM, MycoCrop^®, and BGT* methodology and ensuring soil phytoremediation from HMs. Full article

The enhancement of the urban forest landscape through vegetation colorization has emerged as a continuous concern for urban managers in southern Chinese cities. However, the understanding of how designers can effectively select the appropriate form and intensity of colorization design to align with users’ aesthetic preferences remains limited. The process of visual perception is closely intertwined with eye movements. Employing visualization techniques, this research aims to investigate the impact of colorization design on aesthetic benefits and eye movements in urban forests, considering four intensities (slight, low, medium, and high) and three forms (aggregate, homogeneous, and random). A total of 183 participants (with an average age of 23.5 ± 2.5 years) were randomly assigned to three groups to assess the aesthetics score, while eye-tracking devices were utilized to record eye movement behaviors. The outcomes indicate that a homogeneous design form and a moderate intensity of landscaping yield higher scenic benefits for urban forests. In the case of canopy landscape, both the form and intensity of landscaping have a significant influence on urban forest aesthetics. The HCI with aggregate form showed the best marginal effect (1.313). In contrast, MCI showed the best marginal effect when the design form was random and homogeneous (1.438, 1.308). Furthermore, although the form and intensity of the colorization design significantly affect eye exploration, the perception of landscape aesthetics does not correlate with eye movements. These findings provide valuable insights for design policies aimed at promoting the urban forest landscape, while also contributing to the enrichment of research in landscape perception studies employing eye-tracking technology. Full article

The quality of container-grown seedlings is influenced by the air and water properties of the substrate. These properties are closely tied to the amount and frequency of water supplied through sprinkler systems in nurseries, as well as the density of the substrate in the container cells. Throughout the entire growing season, this study examined how various parameters of Scots pine, Norway spruce, European beech, and pedunculate oak seedlings cultivated in HIKO V120SS and V265 containers were affected by two factors. Firstly, the study analyzed the impact of increased substrate density when filling the containers. Secondly, it explored the precise dosing of water applied by the sprinkler system, which was determined based on substrate sensors and meteorological conditions surrounding the seedlings. The results revealed that increased substrate compaction led to a long-term reduction in air capacity and an increase in water capacity within pine, spruce, and beech containers. However, oak seedlings were not affected by the increased substrate density. Additionally, the higher density of the compacted substrate positively influenced the growth parameters of pine seedlings but did not affect the other species. As a result, the current substrate compaction level used in the nursery where the measurements were taken appears to be optimal for spruce, beech, and oak seedlings. Furthermore, precise control over the amount of water applied during irrigation allowed for a reduction in water consumption by about 8%. This control also resulted in improved seedling sturdiness quotient and a more developed root system in the case of pine seedlings. However, no significant differences were observed for the other species. Full article

The environmental crisis and the safeguarding of the population's health has led to research into different ways of mitigating harmful gases. Among the emissions that the wood industry has sought to reduce are those of formaldehyde, which is why new green adhesive methods for wood panels have been investigated in recent years. In this research, particleboard with two bio-based wood adhesive (PB-bbwa) formulations. The first PB-bbwa formulation, based on proteins obtained from compounds from the alcoholic beverage industry, and the second PB-bbwa formulation, based on proteins from a mixture of compounds from the alcoholic beverage and food industries, were manufactured and tested to evaluate the physical–mechanical, thermal and formaldehyde emission properties of untreated and UV-treated formulations at a laboratory scale. The results of the physical properties obtained in the PB-bbwa were similar or even better than those of the control PB. Additionally, PB-bbwas improve on the control PB sample’s Janka hardness by least 28%, and a decrease in thermal conductivity in the edgewise position and formaldehyde emissions by 12% and 88%, respectively, in comparison to the control PB. The tests performed evidenced that PB-bbwas showed comparable performance against the control PB made with urea-formaldehyde and satisfied international standard requirements. Full article

In forests, seedling renewal is influenced by many environmental factors, including climate change, seed size, wildfires, and ecological factors. It is unclear how different growth years of seedlings affect Chinese cork oak (Quercus variabilis) root endophyte communities. In this study, we took a holistic approach, using Illumina sequencing, to study the composition and function of bacterial communities associated with root microorganisms in four Q. variabilis seedlings after 1, 2, and 3 years of growth. The bacterial alpha diversity indexes were highest in the second year and lowest in the third year, and age was the decisive factor for the differences found in the root endophytic bacterial communities. Total phosphorus had the greatest effect on bacterial communities. The abundance of beneficial bacteria Streptomyces (8.69%) and Novosphingobium (4.22%) was highest in the second-year samples, and their abundance decreased by 7.96% and 3.61% in the third year, respectively. Higher levels of plant disease inhibition and metabolism (23.80%) were in the roots of second-year Q. variabilis seedlings. The metabolic abundance of carbohydrate was 3.66% lower in the first year and 3.95% lower in the third year compared to the second year. Our results suggest that the structure and function of bacterial communities changed with increasing growth years. Full article

Valuing the nonuse value of a national forest park (NFP) is critically important to obtain a better understanding of its total economic value, beyond focusing solely on the recreation value. This paper estimates the nonuse value of an NFP based on the relationship between the local public’s environmental attitudes and their willingness to pay (WTP). The data collected on the local residents’ environmental attitudes relied on the New Ecological Paradigm (NEP). Residents’ willingness to pay (WTP) for the national forest parkland protection was collected using the contingent valuation method (CVM). The nonuse value WTP was estimated using a bivariate dichotomous choice model. This model analyzed the relationship between the environmental attitude scores and WTP in order to estimate the nonuse value of the NFP of our case study site, Dalian Xijiao National Forest Park (DXNFP) in northeastern China. The results showed that DXNFP provides 20.26 CNY (3.02 USD) in nonuse value per household per year in Dalian city, which can then be translated into 140 CNY (21 USD) million annually in total. Full article

Ehretiaceae is a family in the order Boraginales. It contains more than 150 species. The Ehretiaceae classification has remained elusive and changed over time from subfamily to family, or vice versa. In this paper, we sequenced, characterized, and analyzed the complete chloroplast (cp) genomes of Ehretia cymosa and Ehretia obtusifolia, and their cp genomes were compared to those of related species. The length of the chloroplast genomes of E. cymosa was 156,328 bp, whereas that of E. obtusifolia was 155,961 bp. Each genome contained 114 genes, including 80 protein-coding genes, 4 rRNA genes, and 30 tRNA genes. Repeat analysis revealed that complement, forward, palindromic, and reverse repeats were present in the chloroplast genomes of both species. Simple sequence repeat analysis showed that the chloroplast genomes of E. cymosa and E. obtusifolia comprise 141 and 139 microsatellites, respectively. Phylogenetic analysis based on Bayesian and maximum likelihood analyses divided the order Boraginales into two well-supported clades. The first clade includes a single family (Boraginaceae), and the second clade includes three families (Ehretiaceae, Cordiaceae, and Heliotropiaceae). This study provides valuable genomic resources and insights into the evolutionary relationships within Boraginales. Full article

Vegetation and its spatiotemporal variations play a crucial role in regional ecological security and sustainable development. Examining vegetation dynamics in natural reserves provides valuable insights for optimizing vegetation patterns and management strategies. This study utilizes Landsat remote sensing imagery to investigate changes in vegetation pattern and coverage in the Cangshan mountain of the Cangshan Erhai National Nature Reserve, as well as assesses the effectiveness of conservation efforts. The results indicate the following: (1) The primary vegetation types in the Cangshan mountain include warm-temperate coniferous forests, deciduous broad-leaved forests, bamboo forests, and alpine meadows, exhibiting distinct vertical zonation patterns. The vegetated area expanded by 1146 hectares during the study period. (2) The average fractional of vegetation coverage (FVC) in the Cangshan mountain demonstrated an upward trend (0.82 in 1987 to 0.93 in 2017), with the proportion of highly FVC areas increasing from 59.67% in 1987 to 97.89% in 2017. (3) The vegetation landscape fragmentation in Cangshan mountain and various functional areas shows an increasing trend, while connectivity decreases, and is accompanied by a more intricate shape of the vegetation landscape. While conservation and management efforts have yielded certain results in safeguarding the vegetation in the Cangshan mountain, the degree of vegetation landscape fragmentation has intensified due to climate change and human activities. Thus, it is imperative for management authorities to promptly adjust protective measures within the Cangshan mountain. This study contributes to our understanding of vegetation changes within the Cangshan mountain and provides essential baseline information for optimizing and enhancing vegetation conservation management strategies within the reserve. Full article

Infestations or parasitism by forestry pests can lead to adverse consequences for tree growth, development, and overall tree quality, ultimately resulting in ecological degradation. The identification and localization of forestry pests are of utmost importance for effective pest control within forest ecosystems. To tackle the challenges posed by variations in pest poses and similarities between different classes, this study introduced a novel end-to-end pest detection algorithm that leverages deep convolutional neural networks (CNNs) and a transfer learning technique. The basic architecture of the method is YOLOv5s, and the C2f module is adopted to replace part of the C3 module to obtain richer gradient information. In addition, the DyHead module is applied to improve the size, task, and spatial awareness of the model. To optimize network parameters and enhance pest detection ability, the model is initially trained using an agricultural pest dataset and subsequently fine-tuned with the forestry pest dataset. A comparative analysis was performed between the proposed method and other mainstream target detection approaches, including YOLOv4-Tiny, YOLOv6, YOLOv7, YOLOv8, and Faster RCNN. The experimental results demonstrated impressive performance in detecting 31 types of forestry pests, achieving a detection precision of 98.1%, recall of 97.5%, and [email protected]:.95 of 88.1%. Significantly, our method outperforms all the compared target detection methods, showcasing a minimum improvement of 2.1% in [email protected]:.95. The model has shown robustness and effectiveness in accurately detecting various pests. Full article

Tree growth is under the combined influence of abiotic and biotic factors. Trees with different sizes may respond differently to these factors, implying that tree size heterogeneity may also modulate the overall growth trend. To test this hypothesis, we focused on the radial growth trends of natural subalpine forests on the Tibetan Plateau. We first extended the iterative growth model (IGM) to the tree ring scale (IGMR) to test the applicability of the generalized metabolic growth theory to tree growth. As predicted by the IGMR, the radial growth of trees at the aggregate scale is constrained by a unimodal pattern. Using the IGMR, we reconstructed the historical best growth trajectory (HBGT) of trees within the same community based on the tree with the largest radius and/or longest age in the community. From the average difference between the HBGT and the current radial growth rate of trees with different sizes, we constructed an indicator that can measure the overall variation in tree radial growth. Based on this indicator, we found a negative effect of tree size heterogeneity on the overall variability of tree growth across elevations. Further analysis also revealed that the radial growth rate of trees on the Tibetan Plateau has increased significantly compared to the past, where the growing season average temperature and annual minimum temperature were negatively and positively correlated with tree growth below and above the treeline, respectively. Our study not only confirmed that the overall variability of tree growth depends on tree size heterogeneity but also proposed an indicator that reveals net changes in the tree radial growth rate relative to the past. These theoretical advances are highly beneficial for understanding changes in the extent of subalpine forests. Full article

Climate change is posing new challenges to forestry management practices. Thinning reduces competitive pressure in the forest by repeatedly reducing the tree density of forest stands, thereby increasing the productivity of plantations. Considering the impact of thinning on vegetation and physiological and ecological traits, for this study, we used Norway spruce (Picea abies) data from three sites in the PROFOUND dataset to parameterize the 3-PG model in stages. The calibrated 3-PG model was used to simulate the stand diameter at breast height and the stem, root, and leaf biomass data on a monthly scale. The 3PG-MT-LSTM model uses 3-PG simulation data as the input variable. The model uses a long short-term memory neural network (LSTM) as a shared layer and introduces multi-task learning (MTL). Based on the compatibility rules, the interpretability of the model was further improved. The models were trained using single-site and multi-site data, respectively, and multiple indicators were used to evaluate the model accuracy and generalization ability. Our preliminary results show that, compared with the process model and LSTM algorithm without MTL and compatibility rules, the hybrid model has higher biomass simulation accuracy and shows a more realistic biomass response to environmental driving factors. To illustrate the potential applicability of the model, we applied light (10%), moderate (20%), and heavy thinning (30%) at intervals of 10, 15, 20, 25, 30 years. Then, we used three climate scenarios—SSP1-2.6, SSP2-4.5, and SSP5-8.5—to simulate the growth of Norway spruce. The hybrid model can effectively capture the impact of climate change and artificial management on stand growth. In terms of climate, temperature and solar radiation are the most important factors affecting forest growth, and under warm conditions, the positive significance of forest management is more obvious. In terms of forest management practices, less frequent light-to-moderate thinning can contribute more to the increase in forest carbon sink potential; high-intensity thinning can support large-diameter timber production. In summary, moderate thinning should be carried out every 10 years in the young-aged forest stage. It is also advisable to perform light thinning procedures after the forest has progressed into a middle-aged forest stage. This allows for a better trade-off of the growth relationship between stand yield and diameter at breast height (DBH). The physical constraint-based hybrid modeling approach is a practical and effective tool. It can be used to measure long-term dynamic changes in forest production and then guide management activities such as thinning to achieve sustainable forest management. Full article