Identifying the types and distribution of tree species is the foundation for monitoring tree diversity， and is crucial for forest protection and management and sustainable development of forests. Forest plot-scale hyperspectral images and LiDAR point cloud data scanned by unmanned aerial vehicle （UAV） were used as the data source， and based on the individual tree-scale hyperspectral and point cloud data obtained by the canopy height model， a convolutional neural network （CNN-EGNet） model combined with the attention mechanism of efficient channel attention （ECA） was proposed in this study， aiming at achieve precise tree species identification in mixed coniferous and broadleaf forests in the Maoershan area of Shangzhi City， Then， CNN-EGNet with three traditional CNN models VGG16， VGG19， and GoogLeNet in identification accuracy. Finally， on the basis of the results of the tree species identification， tree species diversity indices （Shannon-Wiener， Simpson， Pielou， Species richness） in the study area were calculated with the 40 m × 40 m window. Results showed that the proposed CNN-EGNet model achieved an overall accuracy （OA） of 89.58% and a Kappa coefficient of 0.8661. Compared with conventional models， the overall accuracy for species identification improved by 9.37%， 5.20%， and 14.58%， and the Kappa coefficients increased by 0.1175， 0.0652， and 0.1896， respectively. The Shannon-Wiener index primarily ranged from 0.8 to 1.4， while the Simpson index predominantly clustered between 0.5 and 0.7. The Pielou index generally fell within the range of 0.7 to 0.95， and the species richness index mostly varied between 3 and 5 species. The tree species diversity indices indicated that the distribution of tree species was uneven， with certain species being dominant while others were relatively scarce. The results of the study can provide technical and data references for the identification of tree species and the protection and management of tree species diversity in the mixed coniferous and broadleaf forests in the Northeast of China， and validate the possibility of identification， monitoring and evaluating the diversity of tree species by combining hyperspectral and LiDAR data from UAVs with convolutional neural networks.

In order to explore the effects of different soil and water conservation engineering measures on soil productivity of slope farmland in black soil area， two kinds of soil and water conservation measures （ terrace and ridge ） in Xingmu small watershed of Dongliao County， Liaoyuan City， Jilin Province in northeast black soil area were taken as the research objects， and the slope farmland without measureswas taken as the control （CK1 and CK2）. The differences of soil productivity of different soil and water conservation measures at different slope positions and the dominant factors affecting soil productivity were compared and analyzed. The results showed that： 1） terraces and ridges significantly improved soil quality. Compared with CK1 and CK2 slope farmland without measures， the contents of total nitrogen， total potassium， available phosphorus， organic matter and clay mass fraction in terraces and ridges increased by 40.25% and 16.16%， 9.14% and 5.57%， 33.27% and 24.50%， 30.25% and 7.94%， 8.47% and 5.03%， respectively， and the sand mass fraction decreased by 7.08% and 12.35 %. 2） The middle slope position was the most sensitive to soil and water conservation measures. Compared with CK1 and CK2， the contents of total nitrogen， total potassium， available phosphorus， organic matter and clay mass fraction in the middle slope position increased by 84.58% and 30.15%， 16% and 8.04%， 48.49% and 38.92%， 38.02% and 11.24%， 18.92% and 9.21%， respectively， and the difference was significant （P<0.05）. 3） The soil productivity index ranged from 0.29 to 0.49， and there were differences in soil productivity index under different measures. Compared with CK1， the soil productivity index of terraced fields increased by 24.05%， 65.85% （P<0.05） and 11.81% at the upper， middle and lower slopes， respectively. Compared with CK2， the soil productivity index of ridges increased by 10.00%， 49.95% （P<0.05） and 35.20% at the upper， middle and lower slopes， respectively. 4） The differences of soil water content， total nitrogen， total potassium， available phosphorus content and organic matter mass fraction under different measures gradually decreased with the increase of soil depth. The depth of 0-10cm soil layer was significantly higher than that of other soil layers， while the change trend of soil bulk density was opposite. 5） Multivariate variance and redundancy analysis showed that the type of measures， slope position， soil layer， type×slope position， type×soil layer had significant effects on soil productivity. Soil total nitrogen mass fraction had the greatest impact on soil productivity index， followed by soil bulk density. The implementation of soil and water conservation measures on slope farmland in black soil area increased soil water content， soil nutrients， clay mass fraction， and reduced bulk density and sand mass fraction， indicating that soil and water conservation measures can effectively improve soil productivity， and terrace measures are better than ridges； among different measures， the change of middle slope position is the most significant， which mainly affects the soil productivity by changing the soil total nitrogen mass fraction and bulk density of middle slope position. It is recommended to give priority to the implementation of terrace projects in the middle slope position， and to use total nitrogen and bulk density as key monitoring indicators to maximize the benefits of soil improvement and productivity improvement of soil and water conservation measures.

In order to solve the problem of poor flower bud growth caused by insufficient light in autumn under facility cultivation conditions， four-year-old blueberry ‘Liberty’ container seedlings were used as materials， and LED lights were used as light sources. Setting different light intensity， light quality （red and blue light ratio） and photoperiod， a three-factor and three-level orthogonal test， and greenhouse natural light was used as a control. The chlorophyll content， gas exchange parameters， and nutrient resorption efficiency of nitrogen （N） and phosphorus （P） in different periods were measured. The weight and morphological indexes （transverse diameter and length） of flower buds were used to study the characteristics of photosynthesis and nutrient resorption of blueberry under different light conditions and their relationship with flower bud quality. The results showed that the photosynthetic capacity of blueberry was different under different light treatments after three weeks of supplementary light. The net photosynthetic rate of blueberry was the highest under low light intensity （300 μmol/（m²·s））， medium or low red and blue light quality ratio （1∶1 or 1∶3）， medium or short photoperiod （7.5 h or 6 h）. The nutrient resorption efficiency of blueberry under different light treatments was different after two weeks of stopping light supplementation. Under the light conditions of medium light intensity （450 μmol/（m²·s））， high red and blue light ratio （3∶1） and short photoperiod （6 h）， the nutrient resorption efficiency of nitrogen （N） and phosphorus （P） was the highest. The quality of flower buds was better under the conditions of high light intensity （600 μmol/（m²·s））， medium or low red and blue ratio （1∶1 or 1∶3）， medium or short photoperiod （7.5 h or 6 h） or medium light intensity （450 μmol/（m²·s））， high red and blue ratio （3∶1） and short photoperiod （6 h）. Different light treatments can affect the quality of flower buds by regulating the photosynthesis and the nutrient resorption efficiency of N and P in late-season blueberries. The effect of nutrient resorption efficiency on flower bud quality was greater than that of net photosynthetic rate， and the effect of N nutrient resorption efficiency on flower bud quality was greater than that of P nutrient resorption efficiency. Based on the effects of photosynthesis and nutrient resorption on flower bud quality， it was concluded that medium light intensity （450 μmol/（m²·s））， low red and blue light ratio （1∶3）， and short photoperiod （6 h） were most beneficial to improve the flower bud quality of blueberries.

Tree height is a key parameter for assessing forest carbon storage， and satellite-borne laser radar technology provides an effective means for large-scale monitoring. The new generation of ice， cloud and land elevation satellite-2 （ICESat-2） equipped with the advanced topographic laser altimeter system （ATLAS） generates a lot of noise in the process of receiving signals， and the terrain is a key factor affecting the denoising results. To address this problem， a ground slope adaptive density clustering denoising algorithm is proposed to complete the photon cloud data denoising. Iterative median filtering and dynamic residual threshold method are used to classify photon clouds and then extract tree height. The canopy height model （CHM） obtained from airborne laser radar data is used as verification data. The reliability of extracting tree height from ICESat-2/ATLAS global geolocated photon data （ATL03） is analyzed and evaluated from three aspects： strong and weak beams， slope， and vegetation coverage. The results show that， 1） The recall rate （R）， precision rate （P） and harmonic mean （F） of the proposed denoising algorithm are better than those of the differential progressive gaussian adaptive denoising algorithm （DRAGANN）. 2） The accuracy of extracting tree height from nighttime strong beam data is the best， with a mean absolute error （MAE） of 2.49 m and a root mean square error （RMSE） of 3.03 m. 3） As the slope increases， the accuracy of tree height extraction gradually decreases， and the RMSE increases from 2.25 m to 6.52 m. 4） As the vegetation coverage increases， the accuracy of tree height extraction gradually decreases， and the RMSE increases from 3.06 m to 4.53 m. The results show that it is feasible to extract tree height using ATL03 photon cloud data， which can provide effective data support for studying forest growth conditions in forest areas.

The excessive consumption of fossil fuels has precipitated a global energy and environmental crisis， propelling the development of green renewable energy materials into a research hotspot. Wood， owing to its multiscale structural designability and renewability， holds significant application potential across various environmental energy harvesting and conversion domains. This paper systematically integrates the functional development and application progress of wood-based energy materials. By examining the mechanisms， structural regulation strategies， and key performance characteristics for harnessing diverse natural energies—including solar thermal， hydropower， mechanical energy， and thermal radiation—it outlines material design approaches for different application scenarios. Through summarizing multi-energy coupling mechanisms， interfacial regulation pathways， and cross-scale structural construction methods， the paper identifies the inherent advantages of wood-based materials in achieving multi-energy conversion. Building upon this foundation， the paper explores the application potential of wood-based energy materials in distributed energy supply， water resource utilization， environmental thermal management， and electrical signal processing. The review concludes by proposing key future development directions for wood-based energy materials， including structural durability， functional integration， and scalable manufacturing， providing guidance for their further application within green energy systems.

To address the challenge of Sentinel-2 data in distinguishing spectrally similar tree species， this study established a multi-dimensional feature fusion classification system based on the google earth engine （GEE） cloud platform， with Tieling City， Liaoning Province as the experimental area. By integrating Sentinel-2 multi-temporal data and employing multi-dimensional feature statistical methods， we extracted spectral and vegetation index features including quantiles， extremes， and standard deviations， combined with topographic， textural， phenological， and harmonic features， forming a total of 120 features across six categories. Multiple feature combination schemes were designed and implemented through a hierarchical classification strategy using the random forest algorithm， ultimately achieving fine classification of seven dominant tree species： Pinus tabuliformis， Pinus sylvestris var. mongolica， Larix gmelinii， Populus， fruit trees， Quercus mongolica， and Robinia pseudoacacia. The results demonstrated that multi-dimensional temporal statistical features effectively captured subtle interspecies differences. Variations in water content between Pinus sylvestris var. mongolica and Pinus tabuliformis were successfully characterized through multiple vegetation indices. Topographic and textural features played decisive roles in distinguishing deciduous species. The classification overall accuracy reached 94.7% for evergreen species and 88.1% for deciduous species， with all six feature combination schemes achieving overall accuracy exceeding 77.9%. This study confirms that the integration of multi-dimensional feature statistical methods with the GEE platform fully exploits the multi-band advantages of Sentinel-2 data， significantly enhancing large-scale tree species classification capabilities through temporal feature analysis. It provides a cost-effective solution for dynamic monitoring of forest resources at large-scale， with the cloud-based processing framework demonstrating potential for application expansion to broader geographical regions.

As an important carrier of the national new energy strategy， the leakage of gas pressure pipelines in forest areas not only causes direct economic losses， but also may lead to secondary disasters such as soil pollution， vegetation destruction and even forest fires due to the sensitivity of forest ecosystems. The current ultrasonic sound source-based localization methods suffer from challenges such as high sidelobe artifact interference and insufficient localization accuracy caused by wide main lobe beamwidth in multi-leakage source scenarios. Moreover， the complex terrain， dense vegetation coverage， and environmental noise in forest areas further compromise the applicability of conventional detection techniques. In this paper， an adaptive inverse convolution beamforming algorithm is proposed to achieve high-precision leakage localization by optimising the weight matrix and the inverse convolution iteration strategy. Firstly， the initial weight matrix is constructed based on the minimum variance distortionless response （MVDR） criterion， and the weights are adjusted with adaptive iteration to enhance the focusing ability of the target signal while suppressing the interference of the sidelobes. Secondly， the main lobe width is compressed through Gauss-Seidel deconvolutional iteration， thereby enhancing resolution. To validate the algorithm's performance， this study establishes a pressure pipeline model with a diameter of 150 mm and operating pressure of 0.8 MPa to simulate ultrasonic signals from 0.5 mm and 0.7 mm leakage orifices， while constructing an experimental system for comparative analysis. Results demonstrate that compared with conventional deconvolution beamforming， the proposed algorithm reduces localization errors by 0.06 m for Source 1 （0.7 mm orifice） and 0.05 m for Source 2 （0.5 mm orifice） under signal-to-noise ratio （SNR） conditions ranging from -10 dB to 20 dB， while effectively eliminating artifact interference. The experiments further validate the method's robustness and computational efficiency advantages under low SNR conditions （-10 dB to 20 dB）. This study provides a high-precision solution for non-contact detection of minor pressure pipeline leaks in forest environments， characterized by strong anti-interference capability and superior environmental adaptability. The findings hold significant implications for ensuring energy transportation safety and ecological conservation.

In order to explore the impact mechanism of vegetation restoration on soil aggregate structure and organic carbon dynamics in industrial and mining wastelands， Pinus sylvestris var. mongolica plantations with different planting years （18， 20， 23， 32 years） on industrial and mining wastelands in Qingling Forest Farm， Hegang City were studied. By combining field sampling and indoor analysis， the particle size distribution of soil aggregates， organic carbon mass fraction， and main soil chemical factors （pH， total nitrogen， and total phosphorus， etc.） in the 0-20 cm and 20-40 cm soil layers were systematically measured. The research results indicated that： 1） With the increase of vegetation restoration years， the structure of soil aggregates underwent significant changes. Among them， the proportion of large aggregates larger than 2 mm showed a significant increase trend （an increase of 50.76%）， while the proportion of micro aggregates smaller than 0.25 mm decreased significantly （a decrease of 44.24%）. The mean weight diameter （MWD） and geometric mean diameter （GMD） increased by 17.90% and 18.42%， respectively， indicating that the stability of soil aggregates was significantly enhanced with increasing forest age. 2） Through Mantel analysis， it was found that forest age， soil depth， and chemical factors had a significant impact on soil organic carbon （SOC） accumulation （P<0.01）. At the same time， forest age and chemical factors had a significant impact on aggregate stability （P<0.05）. The trend of changes in surface soil （0-20 cm） and deep soil （20-40 cm） was basically consistent， but the response of deep soil to environmental factors was relatively lagging behind. 3） Random forest analysis showed that ammonia nitrogen （ \mathrm{N} {\mathrm{H}}_{\mathrm{4}}^{+}- N）， pH， total phosphorus （TP）， and nitrate nitrogen （ \mathrm{N} {\mathrm{O}}_{\mathrm{3}}^{-}-N） had the highest explanatory power for soil aggregate stability. In summary， long-term vegetation restoration can effectively improve the soil structure of industrial and mining wastelands， promote the accumulation and sequestration of soil organic carbon， and provide new ideas for the restoration and reconstruction of degraded ecosystems.

Land is an indispensable part of human life. The analysis of land use status is helpful to deeply understand the relationship between environmental conditions and economic development， and to achieve a more reasonable land use model. Predicting future land use will help improve the sustainable management of land resources and provide a scientific basis for assessing carbon potential. Taking Heilongjiang Province as the research area， the current situation of land use in Heilongjiang Province from 2000 to 2020 was analyzed， and the patch-generating land use simulation （PLUS） model coupled with the long short-term memory （LSTM） model was adopted to simulate and predict the land use situation in Heilongjiang Province in 2030. The results showed that： 1） The Kappa coefficient for verifying the PLUS-LSTM model was 0.878. The relative simulation errors of the six land types （cultivated land， forest land， grassland， water area， construction land， and unused land） were all less than 15%. Compared with the traditional model， it had higher accuracy and can be used to simulate the land use situation in Heilongjiang Province in 2030. 2） Compared with 2020， the area of forest land， grassland， water area， and construction land in Heilongjiang Province would increase in 2030. Among them， the change rate of construction land was the highest， 8.57%； the area of forest land increased by 2 584.26 km²， mainly in the central region； the expansion of grassland was mainly in the southwest. The area of cultivated land and unused land decreased， and the unused land changed the most， with a change rate of 29.68%.

Soil nitrogen （N） and phosphorus （P） are critical for tree growth. Investigating the variation characteristics of N and P fractions across soil profiles and their primary influencing factors following the transformation from pure plantations to multi-layered， uneven-aged plantations provides insights into the mechanisms by which structural regulation of pure forest stands impacts soil quality. This study focused on pure Cunninghamia lanceolata plantations and multi-layered uneven-aged mixed C. lanceolata and Phoebe bournei plantations （hereafter referred to as mixed C. lanceolata and P. bournei plantations）. We examined the variations in N and P fractions， other soil properties， and root traits across different soil layers （0-10 cm， >10-30 cm， >30-50 cm） in these two stands， as well as their interrelationships. The results revealed that after the transition from pure C. lanceolata plantations to mixed C. lanceolata and P. bournei plantations， the contents of most N and P fractions in the 0-10 cm soil layer significantly increased. Specifically， labile， moderately labile， and stable P fractions， along with total P contents， increased by 38.2%， 31.6%， 15.4%， and 25.1%， respectively， compared to the pure C. lanceolata plantations， and inorganic， organic， and total N contents increased by 42.1%， 35.8%， and 35.9%， respectively. In the >10-30 cm soil layer， the contents of moderately labile organic P， inorganic N， acid-hydrolysable organic N， total N， and total P were significantly higher in the mixed C. lanceolata and P. bournei plantations than pure C. lanceolata plantations. However， no significant differences in N and P fractions contents were observed between the two stands in the >30-50 cm soil layer. The contents of N and P fractions of the two stands decreased with increasing soil depth. Furthermore， the transformation of stand structure affects root distribution and traits. The mixed C. lanceolata and P. bournei plantations exhibited higher root biomass， root length density， and specific root length compared to pure C. lanceolata plantations. The root biomass and root length density at 0-10 cm increased by 124.66% and 269.23%， respectively， compared to the pure C. lanceolata plantations. In pure C. lanceolata plantations， root biomass and root length density initially increased and then decreased with soil depth. In contrast， due to the surface aggregation of P. bournei roots in the mixed C. lanceolata and P. bournei plantation， the root biomass and root length density of C. lanceolata gradually increased with soil depth. The variations in soil nitrogen and phosphorus components across different stand types and soil depths were primarily associated with root distribution， soil organic carbon， and soil microbial characteristics. The findings highlight that structural optimization of C. lanceolata plantations in subtropical regions significantly influences soil fertility.

To identify superior poplar varieties adapted to different soil moisture conditions， this study evaluated nine major cultivated poplar varieties in Heilongjiang Province—namely， Populus cathayana （JDQY）， Populus deltiodes×P.cathayana ZHF2， （Populus×euramericana）×（P.simonii×P.nigra）（2111）， Populus euramericana ‘N3016’×P.ussuriensis ‘HQ-1’ （HQY）， P.alba L×P.berolinensis Dippel （YZY）， 1019， Populus ‘Heifang-3’ （QSY）， 406， and Populus deltoides×Populus simonii ‘LongFeng-2’ （LF2）. By applying two soil moisture gradients （mild drought， HL is 14%- 18%； moderate drought， HM is 6%-10%）， we systematically measured 14 morphological and physiological-biochemical indicators， including apparent morphology， leaf water content， relative chlorophyll content， ion metabolism， and antioxidant enzyme activities. Based on principal component analysis and membership function evaluation， the drought resistance rankings were as follows： （1） under mild drought， JDQY， HQY， QSY， YZY， LF2， 2111， 406， ZHF2， 1019； （2） under moderate drought， JDQY， HQY， 2111， YZY， LF2， QSY， ZHF2， 406， 1019. Research indicated that， JDQY maintained osmotic balance by by significantly accumulating K⁺ and Ca²⁺， while HQY responded rapidly to oxidative stress via elevated superoxide dismutase （SOD） activity， both exhibited broad-spectrum drought resistance under two types of stress. This study reveals inter-varietal differences in drought resistance and underlying physiological mechanisms among the nine poplar varieties， identifying JDQY and HQY as drought-tolerant candidates. The findings provide a theoretical basis for stress-resilient afforestation and precision tree species selection in cold， drought-prone regions.

The aim of this research was to address the issues of poor terrain adaptability and low cutting efficiency of the Acanthopanax senticosu brush cutting equipment. Design an in-forest circular saw type Acanthopanax senticosu brush cutter mounted on the front of a tractor. Through structural design， it was clarified that the entire machine consisted of four core mechanisms： support adjustment， transmission，brush cutting and feeding. The support adjustment mechanism and feeding mechanism were developed， and the motion trajectory and cutting force of the circular saw blade in the cutting mechanism were analyzed to validate that its power met the requirements. Field experiments were conducted with the rotational speed of the circular saw， traveling speed， and cutting height as influencing factors， and the experimental data were processed using data processing software. The results indicated that traveling speed had the greatest impact on the cutting efficiency of the brush cutter， followed by the rotational speed of the circular saw， while cutting height had the least effect. Using Design-Expert13 software， a regression model for cut rate was constructed and parameters were optimized. It was obtained that the influence degrees of each factor on the cut rate from large to small were the traveling speed， the rotational speed of the circular saw， and the cutting height. When the rotational speed of the circular saw was 1 925 r/min， the traveling speed was 4.2 km/h， and the cutting height was 39.6 cm， the cut rate reached 99.4%， resulting in the best cutting performance. This equipment can meet the efficient brush cutting needs of Acanthopanax senticosus in complex forest terrain， providing technical support for mechanized pruning in large-scale Acanthopanax senticosus planting.

Used 15-year-old Pinus koraiensis on three slope aspects （sunny slope， semi-sunny slope， and shady slope） in Wangjiagou Management Area， Maoshan Experimental Forest Farm of Northeast Forestry University， as materials to investigate the responses of the functional traits and photosynthetic characteristics of Pinus koraiensis needles to the differences in environmental factors on different slope aspects. The results showed that the soil moisture content of the shady slope was 5% higher than that of the sunny slope， and the photosynthetically active radiation of the sunny slope was 3 times that of the semi-sunny slope and 10 times that of the shady slope. The specific leaf area of Pinus koraiensis needles on shady slopes was about 1.29 times that of sunny slopes. Pinus koraiensis growing on the shady slope obtained more light energy by expanding the specific leaf area. The stomatal density， water content and NSC content of the coniferous leaves on the sunny slope were significantly higher than those on the semi-sunny slope and the shady slope. The photosynthetic capacity of the one-year coniferous leaves was higher than that of the two-year coniferous leaves， and the photosynthetic characteristics on the sunny slope were higher than those on the semi-sunny slope and the shady slope. The environmental conditions on the sunny slope were more conducive to the growth and development of Pinus koraiensis. The main environmental factors affecting the photosynthetic characteristics of Pinus koraiensis needles were land surface temperature and photosynthetically active radiation， and the main environmental factors affecting the functional traits of Pinus koraiensis needles were photosynthetically active radiation. Pinus koraiensis of different slopes adapted to environmental changes by adjusting their own needle morphology， photosynthetic characteristics and nutrient distribution to form a unique adaptation strategy. The environmental factors that affect the growth and development of Pinus koraiensis trees are not single， but the result of the coupling of multiple environmental factors.

The apperance quality of Pu′er Dragon Ball tea plays a decisive role in its market value； however， conventional inspection approaches fail to simultaneously satisfy the demands of real-time efficiency， accuracy， and edge-level deployment. In response， we propose SHM-YOLO， a lightweight object detection framework. Extending YOLOv11， the model employs ShuffleNetV2 （denoted as S in SHM） as the backbone， integrating point wise group convolution with channel shuffling to minimize computational cost. Through the integration of a hierarchical scale feature pyramid network （HS-FPN， denoted as H in SHM） that combines channel attention with dimensional matching， the model strengthens the effectiveness of multi-scale feature fusion. At the same time， the multi-scale attention block （MAB， denoted as M in SHM） is utilized to optimize the C3K2 structure， enabling more effective image detail extraction. To improve bounding-box regression， the model combines Inner-IoU with SIoU loss， which expedites convergence and augments localization precision. Experimental validation on a self-developed dataset for Pu′er Dragon Ball tea appearance quality confirms that SHM-YOLO reaches 97.2% mAP@50， 92.7% precision （P）， 93.6% recall （R）， and 303 fps， with merely 0.969×10⁶parameters and 2.3 MB storage consumption. Compared to YOLOv11n， the model achieves higher accuracy while markedly decreasing floating-point computation （by 62.5%） and memory consumption （by 47.6%）， highlighting its excellent lightweight characteristics and strong suitability for industrial deployment.

With increasing demand for ecological garden maintenance， pruning equipment faces challenges in efficiency， stability， and environmental performance. This study presents a high-efficiency pruning device driven by a unidirectional motor. By optimizing the transmission system and employing a customized lead screw-nut structure， the motor can complete both cutting and resetting with a single-direction rotation， avoiding the need for motor reversal in conventional devices. Finite element analysis verified the static and dynamic performance of the cutting components and transmission system， ensuring sufficient strength and stiffness for long-term operation. Calculations indicated an upper-bound required thrust of approximately 650 N （design baseline） for the motor-lead screw under the worst-case static condition. Under the specified dynamic simulation condition， the peak thrust was about 380 N， meeting the cutting and reset requirements with sufficient margin. Through a series of cutting experiments， the working performance of the equipment under different branch diameters was evaluated. Results demonstrated that the device outperforms traditional manual tools and standard electric pruners in efficiency and energy consumption， with strong continuous operation and energy-saving characteristics. The equipment is well-suited for ecological garden maintenance， meeting the high-efficiency requirements of ecological garden operations.

In Dehua pear cultivation， manual picking is inefficient， with labor costs exceeding 15% per mu. Traditional mechanical picking， due to the rigidity of its equipment， often results in a fruit breakage rate exceeding 20%， making it difficult to adapt to the thin skin （0.2-0.3 mm） and crispy flesh of the pear. To address this industry pain point， this paper designs an integrated harvesting robot system using a ‘bionic end effector+YOLOv11 visual positioning’ approach. The core of the system consists of a six-degree-of-freedom robotic arm， a binocular depth camera， and an electrically driven， separate end effector. The actuator utilizes a three-finger flexible gripping and shearing mechanism， balancing non-destructive grasping with precise stalk severing. The visual system， based on YOLOv11， introduces the C2PSA attention module to enhance the distinction between fruit and leaf features， and combines it with a binocular camera for three-dimensional positioning. Experiments based on samples from a pear orchard in Dehua， Fujian， show that the recall rate for the ‘pear’ category remained above 0.85 at a confidence level≥0.7， with an optimal F1 value of 0.83 （confidence level 0.565） and a stable mAP50 of 0.87. Field tests also demonstrate that the system achieved four times the efficiency of manual picking， while keeping the fruit breakage rate below 5%. This solution provides technical support for automated Dehua pear harvesting， and its design principles are valuable for the development of harvesting equipment for fragile fruits such as peaches and strawberries.

To investigate the effects of nitrogen and phosphorus additions and water treatments on the growth and photosynthetic characteristics of Fraxinus mandshurica seedlings， this study used one-year-old seedlings of F. mandshurica as the research subject. A randomized block design was utilized with three water gradients： drought （DR， volumetric water content 13%）， control （CK， volumetric water content 26%）， and water addition （W， volumetric water content 39%）， and four fertilization gradients： N0P0 （0 g/plant N， 0 g/plant P）， N1P1 （0.5 g/plant N， 0.25 g/plant P）， N2P2 （1 g/plant N， 0.5 g/plant P）， and N3P3 （1.5 g/plant N， 0.75 g/plant P）. The growth， biomass allocation， and photosynthetic characteristics of F. mandshurica seedlings under different water and fertilizer treatments were analyzed to elucidate their physiological responses to these conditions. The results showed that water availability had a more significant impact on the growth of F. mandshurica seedlings compared to nitrogen and phosphorus fertilization. When soil water content was 39% and fertilization reached N2P2， the seedling height growth， base diameter growth， total biomass， and net photosynthetic rate achieved their maximum values. Specifically， these parameters were 82.59%， 39.83%， 74.61%， and 64.03% higher， respectively， compared to the control （soil water content 26%+N0P0）. Compared with water addition treament， drought significantly reduced indicators such as seedling height growth， base diameter growth， total biomass， and net photosynthetic rate of F. mandshurica seedlings. Therefore， appropriate fertilization can enhance the growth of F. mandshurica seedlings under adequate water conditions. These findings provide a theoretical basis for the cultivation of F. mandshurica seedlings.

To investigate the density response characteristics and driving mechanisms of plant diversity and biomass in typical forest stands of the Loess Plateau， this study focused on Robinia pseudoacacia and Pinus tabuliformis plantations， establishing five density gradients for each （The density gradient Ⅰ of Robinia pseudoacacia forests was 1 200-1 500 stems/hm²， density gradient Ⅱ was 1 500-1 800 stems/hm²， density gradient Ⅲ was 1 800-2 100 stems/hm²， density gradient Ⅳ was 2 100-2 400 stems/hm²， and density gradient Ⅴ was 2 400-2 700 stems/hm². The density gradient I of Pinus tabuliformis forests was 1 000-1 500 stems/hm²， density gradient Ⅱ was 1 500-2 000 stems/hm²， density gradient Ⅲ was 2 000-2 500 stems/hm²， density gradient Ⅳ was 2 500-3 000 stems/hm²， and density gradient Ⅴ was 3 000- 3 500 stems/hm²）. Through field surveys， diversity index calculations， and biomass measurements， we employed one-way ANOVA， two-way ANOVA， and Pearson correlation analysis to identify influencing factors. The results showed， （1） Forest type significantly affected plant diversity and biomass， with shrub diversity and tree biomass significantly higher in Pinus tabuliformis stands， while herbaceous diversity was greater in Robinia pseudoacacia stands. （2） In Robinia pseudoacacia stands， shrub and herb diversity indices exhibited bimodal curves with density， peaking at 1 500- 1 800 stems/hm²， whereas in Pinus tabuliformis stands， shrub diversity peaked at 1 500-2 000 stems/hm²， and herb diversity was significantly higher at low densities （1 000-1 500 stems/hm²）. Aboveground and total biomass of Robinia pseudoacacia reached maxima at 1 800-2 100 stems/hm²， while Pinus tabuliformis biomass showed weaker density dependence. （3） Correlation analysis revealed that shrub diversity indices in Robinia pseudoacacia stands were negatively correlated with tree height， and herb evenness indices were negatively correlated with slope. For Pinus tabuliformis stands， diversity indices of shrubs and herbs were significantly correlated with crown width， and positively correlated with diameter at breast height （DBH） and tree height. （4） Pinus tabuliformis demonstrated greater competitive advantages in Loess Plateau ecological restoration. Moderate densities （Robinia pseudoacacia： 1 800-2 100 stems/hm²； Pinus tabuliformis： 1 500-2 000 stems/hm²） synergistically enhanced plant diversity and biomass， with DBH， crown width， and topographic factors identified as key regulators. These findings provide a scientific basis for optimizing stand density and improving ecological functions in the Grain for Green Project on the Loess Plateau.

To conduct a preliminary evaluation and select superior clonal lines of Larix olgensis， this study used 68 clonal lines from the Larix olgensis collection area at the Qingshan National Larch Seed Orchard in Linkou County， Mudanjiang City， Heilongjiang Province， as materials， measured their growth and wood quality traits were measured， and analyzed them using variation analysis， variance analysis， correlation analysis， and other methods for comprehensive evaluation. The results of the variance analysis showed that the differences in growth and wood quality traits among the clonal lines were highly significant. The variation analysis of growth and wood quality traits indicated that the coefficients of variation ranged from 2.55% to 19.71%， with volume exhibiting the largest coefficient of variation and total cellulose having the smallest. The repeatability values ranged from 0.602 to 0.924， with tree height showing the highest repeatability， followed by basic density and total cellulose. Correlation analysis revealed that diameter at breast height （DBH） was significantly positively correlated with tree height， and the east-west crown width was highly positively correlated with the north-south crown width. Volume， DBH， and tree height all exhibited significant positive correlations， with a stronger correlation between volume and DBH， indicating that volume was more influenced by DBH. Furthermore， correlation analysis revealed no significant relationship between growth traits and wood quality traits. Using the Breeding multi-trait evaluation method， superior clonal lines were selected based on both growth and wood quality traits， with a selection rate of 15%. The superior clonal lines selected for growth traits were 241， 456， 96， 475， 179， 161， 83， 759， 339， and 43， while those selected for wood quality traits were 357， 223， 161， 7， 249， 741， 657， 14， 475， and 113. The clonal lines 475 and 161 exhibited excellent performance in both growth and wood quality traits. The selected superior clonal lines can provide valuable material for the future breeding of superior varieties.

Exploring the effects of different rejuvenating treatment measures on the nutritional composition， secondary metabolites and photosynthetic characteristics of Populus simonii×P. nigra leaves， and providing a theoretical basis for the establishment of the rejuvenating system of P. simonii×P. nigra in forestry practice in the future. The 40-year-old superior clones of P. simonii×P. nigra were rejuvenated and cut into seedlings （the mother tree cutting seedlings were used as the control group） by two methods of buried stems and buried roots. The differences in water content， total flavonoid content， and light saturation point among the three cutting seedlings were measured and analyzed. The results showed that the rejuvenating treatment could significantly increase the water content and nutrient element mass fraction of P. simonii×P. nigra leaves. Among them， the relative water content （81.600%） and total potassium mass fraction （2.332%） of the leaves of the buried stem cutting seedlings were the highest. The contents of secondary metabolites such as total phenolics （1.633%） and total flavonoids （6.214%） in the leaves of buried root cutting seedlings were significantly higher than those in the other two leaves. The light saturation point （1 585.093， 1 730.273 μmol/（m²·s）） and CO₂ saturation point （1 132.690、1 123.560 μmol/mol） of the leaves of the buried root and stem cutting seedlings were higher than those of the mother tree， which improved the photosynthetic capacity of P. simonii×P. nigra to a certain extent. It can be seen that rejuvenating treatment will have a positive impact on the nutritional composition and photosynthetic characteristics of P. simonii×P. nigra leaves.

Forest roads are prone to developing defects such as cracks and potholes due to their natural environmental conditions and the heavy vehicle loads they carry， resulting in poor road conditions and high maintenance costs. To address the challenges of inaccurate target detection bounding boxes， significant scale variations of pavement distresses under UAV perspectives， and insufficient lighting conditions， a bimodal asphalt pavement distress detection method （bimodal integrated road getection YOLOv8， BIRD-YOLOv8） was proposed. It employed an intermediate fusion strategy combining visible and infrared images. The DynaSpectra fusion module （DSFM）， constructed by serially connecting adaptive fine-grained channel attention （FCAttention） and linear deformable convolution （LDConv）， replaced the C2f structure in BIRD-YOLOv8's backbone network， enhancing feature extraction capability for distress areas. Normalized Wasserstein distance loss （NWDLoss） was introduced to replace CIoU， strengthening the model's detection ability for small-scale distresses. Experimental results showed that the improved algorithm achieved an mAP of 83.3%， with AP values for transverse cracks， longitudinal cracks， alligator cracks， and potholes reaching 88%， 91.3%， 90.5%， and 63.5%， respectively， laying a foundation for the identification and maintenance of pavement distresses in forest roads.

In the hot-press molding process of reconstituted bamboo， the coupling of multiple process parameters， reliance on empirical parameter adjustment， and high trial-and-error costs make it necessary to establish a quantitative mapping relationship between process parameters and quality indicators， and further realize reverse prediction of parameters for target performance. To address this issue， a support vector regression（SVR） method optimized by a multi-strategy （MS） traffic jam optimization（TJO） algorithm， namely MS-TJO-SVR， is proposed to develop a bidirectional prediction model for the process parameters and quality indicators of reconstituted bamboo. In the forward prediction， density， moisture content， adhesive content， and pressure holding time are used as input process parameters， while modulus of rupture， horizontal shear strength， water absorption width swelling rate， and water absorption thickness swelling rate are used as output quality indicators. In the reverse prediction， the quality indicators are used as inputs to predict the corresponding process parameters. By jointly optimizing the key hyperparameters of SVR， MS-TJO enhances the model’s ability to characterize nonlinear relationships and improves prediction stability. The results indicate that MS-TJO-SVR achieves high fitting accuracy and low prediction error in both forward and reverse prediction tasks， and outperforms traditional SVR and other optimized SVR methods in overall performance. This study provides an effective modeling tool and methodological reference for process parameter optimization and quality prediction in the hot-press molding of reconstituted bamboo.

In order to study the relationship between the growth and development of native plants and the accumulated temperature of the environment under the compound heavy metals stress， a pot experiment was conducted to analyze the response of the leaf expansion process of six plants to the active accumulated temperature （A _AT） and daily temperature difference accumulated temperature （D _AT） under the stress of seven mixed heavy metals by setting three concentration gradients. A Logistic growth curve with accumulated temperature as the independent variable was established. The results showed that the leaf area constants of six plants under different concentrations of heavy metals stress were significantly different from those of the control group. There were significant differences between the leaf area growth of the six plants in the control group and the leaf area growth of the plants under different concentrations of heavy metal stress. The relative leaf area of the six plants in the control group， A _AT and D _AT were in accordance with the Logistic growth curve， but the overall fitting degree （R ²） of theese model in the heavy metals stress groups was poor and the prediction accuracy was low. In particular， the R ² of the relative leaf area-A _AT fitting model of the low concentration treatment was only 0.265 4 for Ficus tikoua， indicating that the heavy metal stress significantly interfered with the normal growth process of the plant. The leaf area-accumulated temperature Logistic growth model can better reflect the growth difference of leaf area under environmental stress during the leaf expansion period of plants. The research results can provide scientific reference for the screening and cultivation of ecological restoration seedlings in mining areas.

Acanthopanax senticosus is a valuable medicinal and edible plant， with significant potential for leaf utilization. To comprehensively investigate the phenotypic diversity of A. senticosus in Heilongjiang Province， 281 natural individuals from the 10 provenances were transplanted into a common nursery garden under uniform cultivation. Two relative phenotypic traits and nine phenotypic traits were measured and calculated， followed by variation， variance， correlation， and cluster analyses. Key findings include： （1） Significant differences （P<0.01） were observed in 11 leaf phenotypic traits both among and within populations， with inter-population variation exceeding intra-population variation. Traits such as petiole hair density （PH）， prickles on petiole （PP）， prickle on petiole length （PPL）， leaf vein hair density （LVH）， prickle vein density （PV）， and prickle on leaf vein length （PVL） exhibited extremely high coefficients of variation （CV）， indicating substantial variability. （2） The average diversity index of A. senticosus reached 1.615， with four phenotypic traits exceeding 1.9. Eight traits showed repeatability above 0.7， reflecting rich leaf phenotypic diversity and relatively stable genetic inheritance of A. senticosus. （3） Leaf phenotypic traits were strongly correlated， among which the prickle-related characteristics of petioles and veins were interrelated and vary together. Leaf phenotypic traits also correlated with geographic and climatic factors， with longitude identified as the primary environmental driver. （4） Cluster analysis classified the 10 provenances into three distinct groups with varying proportions， highlighting population-specific divergence. This study establishes a foundation for elucidating the genetic mechanisms of leaf traits， informs conservation strategies， germplasm collection， and breeding programs for A. senticosus， and provides a theoretical framework for developing reliable phenotypic identification methods.

To effectively utilize forest point cloud data for estimating forest carbon sinks， it is crucial to achieve accurate and efficient separation of leaf and branch point clouds. Forest point clouds are characterized by strong overlap， complex features， and massive data volume. Existing research methods face challenges such as high computational costs， reliance on tree geometric and spectral features， and weak cross-species generalization capabilities. To address these challenges， this paper proposes the use of the PoinTramba method， which employs Transformer and Mamba to model intra-group and inter-group relationships， respectively. Based on the PoinTramba framework， we further improve its grouping strategy and adjust the BIO strategy to adapt to the characteristics of forest point clouds， enabling it to handle the heterogeneous data of forest point clouds while enhancing cross-species generalization. Experimental results show that the proposed method achieves 94.20% OA （overall accuracy）， 85.53% mIoU （mean intersection over union）， and 87.37% mAcc （mean accuracy） on the test set， demonstrating significant improvements compared to Transformer-based methods. Additionally， the practical effects of the grouping strategy and BIO strategy adopted in this paper are analyzed， showing certain advantages over the basic PoinTramba method and other improvement measures.

Asphalt pavements often deteriorate under traffic loads and environmental factors， which shortens their service life and jeopardizes road safety. Plant fibers， which are lightweight， high-strength， abundant， and low-cost， serve as sustainable reinforcements in asphalt mixtures. They improve mechanical performance while reducing cracking and aggregate displacement. This paper introduces the classification and physical properties of plant fibers， summarizes surface treatment methods to address their hydrophilicity and improve fiber-asphalt interfacial bonding， and reviews the rheological properties at high and low temperature， fatigue resistance of plant fiber-reinforced asphalt binders， as well as the preparation techniques and pavement performance of asphalt mixtures. Future research should integrate intelligent mix design optimization， multi-scale interfacial mechanism studies， and standardized engineering systems to synergistically advance the high performance， functionalization， and green sustainable development of plant fiber-reinforced asphalt pavement materials.

This study focuses on the effects of extractives in Dipteryx odorata wood from South America on the color and dimensional stability of wood， offering a theoretical foundation for the efficient utilization of Dipteryx odorata wood. Employing the CIE 1976 L ^* a ^* b ^* color space system， colorimetric parameters of diverse wood colors of Dipteryx odorata wood were quantitatively analyzed. Fourier transform infrared spectrometer （FTIR） and gas chromatography-mass spectrometry （GC-MS） techniques were used to characterize the chemical composition of extractives， analyzing the influence of the composition of extractives on Dipteryx odorata wood color. The existence positions of extractives within wood were observed via microstructure observation. Additionally， the impact of extractives on dimensional stability was analyzed by testing wood's wet swelling rate and equilibrium moisture content under various constant humidities. Results demonstrated that the color differences in Dipteryx odorata wood was mainly influenced by the types and content of chromogenic substances in the extractives， especially phenols， flavonoids and analogs， heterocyclic compounds， and terpenes/resin acids， that the higher the content of the extractives， the higher the a ^* value and the lower the L ^* value. Ray cells were one of the main locations of extractives in Dipteryx odorata wood， affected radial moisture transport， thus having a significant impact on the radial dimensional stability of wood. The findings provide technical support for regulating Dipteryx odorata wood color and enhancing its overall properties， and laying a theoretical foundation for the high-value utilization of Dipteryx odorata wood.

By comparing the response of root and overall seedling growth of Fraxinus mandshurica （deep-rooted seedlings） and Larix gmelinii （shallow-rooted seedlings） to root space requirements and root restrictions， and this turns to clarify the relationship between the root type， root development and root growth space of seedlings. This study took the current-year F. mandshurica and L. gmelinii container seedlings as the research objects， and set up five root growth space （4 cm×20 cm，6 cm×20 cm，8 cm×20 cm，10 cm×20 cm，12 cm×20 cm） comparative experiments. Differences in seedling growth and root development were analyzed at the end of growing season. The seedling height， ground diameter， leaf area， total biomass， lateral root length， root volume， root amplitude， the proportion of 80°-90° lateral roots and lateral-root meristem length of F. mandshurica seedlings increased gradually with the increase of root growth space， while these indexes of L. gmelinii seedlings all increased first and then decreased with the increase of root growth space. With the enhancement of root restriction， the main root length of F. mandshurica seedlings gradually increased， the length of the main root in the minimum root growth space was about 1.5 times that in the maximum root growth space， the diameter of lateral roots at angles <60° for L. gmelinii seedlings gradually increased. Deep-rooted and shallow-rooted seedlings had different root restriction responses and root growth space requirements. When the root growth space was restricted， deep-rooted seedlings mainly resisted the influence of root restriction by increasing the length of the main root， while shallow-rooted seedlings enhanced their resistance mainly by increasing the diameter of lateral roots with smaller branch angles. The reduction of root growth space has a greater impact on the growth of deep-rooted seedlings， and the smaller the root growth space， the weaker the growth ability of seedlings； for shallow-rooted seedlings， moderate reduction of root growth space actually promotes root development and seedling growth.

To predict the micro and macro failure behavior of wood under load， a prediction approach of wood damage precursors based on the b-value （describe the frequency distribution characteristics of acoustic emission signals） of the acoustic emission （AE） signal and the critical slowing down （CSD） characteristics was proposed. Firstly， to analyze the impact of load application rate on the wood failure process， two loading rates of 2 mm/min and 5 mm/min were employed in the wood three-point bending experiment， and AE signals released during the damage process of wood specimens were collected at a sampling rate of 500 kHz. Subsequently， an improved b-value calculation approach based on the classification of AE signal amplitude was proposed， and the AE signals generated during the experiment were divided into three levels， and the process of crack generation and development inside the wood specimen under load was described accordingly. Finally， in accordance with the principle of CSD， a prediction method of wood fracture precursors based on the length correlation coefficient and variance of AE signal was presented. The results demonstrated that the combination of different levels of b-values can reflect the fracture features and the generation of microcracks in the damage process of the specimen. CSD occurred when wood cracked at both the micro and macro levels， which was manifested in the increase of the correlation coefficient and variance of AE signal. At different loading rates， when the load reached approximately 80% of the ultimate yield strength， all the specimens displayed a significant CSD phenomenon.

Single-tree skeleton extraction is a critical step in 3D tree modeling， holding significant importance for precision forestry and forest resource management. Backpack LiDAR scanning （BLS）， as an emerging mobile measurement technology， offers advantages in flexibility and portability. However， its point cloud data suffers from uneven distribution and noise interference， which affect the accuracy of skeleton extraction. To address these issues， this study focuses on Cunninghamia lanceolata in the State-owned Gaofeng Forest Farm of Guangxi Zhuang Autonomous Region and proposes a hierarchical progressive skeleton extraction method based on key path detection using BLS data. This approach integrates geometric constraints and hierarchical analysis to achieve precise localization of branch axes， while employing perpendicular bisector intersection calculations to construct a continuous and topologically complete single-tree skeleton. Terrestrial laser scanning （TLS） data was used as validation data， and preprocessing techniques such as voxel filtering and local elevation normalization are applied to enhance BLS data quality. The results indicate that the proposed method exhibited high performance in branch classification. F ₁-scores ranged from 0.771 to 0.788， with precision ranging from 93.33% to 100%， and recall ranging from 66.67% to 90.63%. Furthermore， the comparative analysis of branch angle estimations based on BLS and TLS data yields a coefficient of determination （R²） of 0.84 and a root mean square error （R_MSE） of 7.22°. This study provides a high-precision technical framework for single-tree 3D modeling， laying a data foundation for forest resource management and ecological simulation.

Aiming at the problems of low harvesting efficiency and high risk of tree damage caused by the fixed excitation force of the traditional walnut harvester's vibration mechanism and its difficulty in adapting to different diameter grades of fruit trees， a vibration mechanism with controllable excitation force was designed. This mechanism adopted two motors to independently drive two sets of fan-shaped eccentric blocks in combination， generating three adjustable theoretical excitation forces. The excitation force was dynamically matched with the tree trunk diameter through real-time detection by a depth camera. A three-dimensional model of the vibration mechanism was established in SolidWorks， and the eccentric blocks were designed through parametric calculation. The performance of the mechanism was verified by finite element analysis and the automatic dynamic analysis of mechanical systems （ADAMS）. The tree trunk diameter was identified by combining the point cloud data of the depth camera with the least squares cylindrical fitting algorithm based on random sample consensus （RANSAC）. The results showed that the actual excitation forces of the three gears obtained by ADAMS software simulation were 12.40， 18.82， and 31.22 kN， with a relative error of no more than 0.80% compared with the theoretical values. ANSYS （analysis system） software analysis of the eccentric blocks showed that the modal frequency range was 755.36 to 3，983.60 Hz， effectively avoiding the working frequency of 16 Hz. The relative error tail mean of the tree trunk diameter identification algorithm verified in the field was 1.63%. The field simulation system realized the closed-loop control of “diameter grade-excitation force-amplitude”. This vibration mechanism can accurately generate three target excitation forces， has a reliable structure， and can adaptively adjust the excitation force through the input of tree trunk diameter， providing a new idea for the intelligent upgrade of forest fruit harvesting equipment.

To address the low detection accuracy of existing methods under complex wood texture conditions， this paper proposes an improved YOLOv8s-based approach for wood defect detection. First， an efficient multi-scale attention （EMA） mechanism is embedded into the backbone network to enhance the model’s contextual perception capability in complex texture scenarios. Second， the neck network is redesigned as a re-parameterized generalized feature pyramid network （RepGFPN） to strengthen cross-scale feature fusion. Third， the loss function is replaced with SCYLLA-IoU （SIoU） to improve bounding box regression precision. Finally， the inverted residual mobile block （iRMB） is integrated into the C2f module， improving the model’s ability to capture fine-grained defects. Experimental results demonstrate that the proposed method outperforms the baseline by 5.09% in precision， 3.13% in recall， 3.72% in mAP@0.5， and 2.63% in mAP@0.5：0.95， while achieving a real-time inference speed of 120 frames per second. These findings indicate that the proposed enhancements significantly improve the model’s robustness and generalization capability， leading to superior and more stable performance in complex wood defect detection tasks.

The climate in Northeast China is cold， and low-temperature events can severely affect the growth of trees. Fraxinus mandshurica is a precious timber tree species in Northeast China. Little was known about its fiber anatomical characteristics in responses to low temperatures and its adaptation strategies. The progeny test forest of Fraxinus mandshurica in Xiao Xing'an Mountains was selected as research object. Dendrochronology and wood anatomy methods were used to investigate the relationship between the fiber anatomical characteristics of Fraxinus mandshurica and the key climate factors， aiming to clarify the effect of low temperatures on wood fiber anatomical characteristics. The results showed that the fiber anatomical characteristics of Fraxinus mandshurica in Xiaoxing'an Mountains exhibited significant interannual variability. There was a highly significant positive correlation between the fiber cell number （FN）， the total fiber cell area （TFA） and the ring width （RW）. All of them showed a trend of ‘first increase and then decrease’ during the young forest stage. The fiber density （FD） and square of the total-to-bound ratio （（T/B）²） showed a decreasing trend year by year. The mean fiber cell area （MFA） and the overall mean thickness of all fiber cell walls （CWTall） showed an increasing trend annually. The fiber anatomical characteristics of Fraxinus mandshurica were greatly affected by temperature， especially the minimum temperature during the growing season. Low-temperature events suppressed the radial growth of Fraxinus mandshurica. The ring width， the fiber cell number， the mean fiber cells area， the total fiber cell area， and the mean percentage of fiber cell area within xylem （RFTA） were significantly decreased by 32.6%， 20.3%， 22.4%， 45.9% and 9.4%， respectively， compared with non low-temperature years， while the fiber density increased by 11.6%. In response to low-temperature events， Fraxinus mandshurica adopted a relatively conservative survival strategy of reducing the fiber cell number and increasing the fiber density. In response to low-temperature events， Fraxinus mandshurica adopts a relatively conservative survival strategy by reducing the number of wood fiber cells and increasing fiber cell density. These findings provide a scientific basis for the breeding of cold-resistant Fraxinus mandshurica in the Xiao Xing'an Mountains region.

In forest harvesting operations， transportation routing and task scheduling are highly coupled. Traditional single-objective optimization approaches often fail to balance operational efficiency with ecological impact. This study proposes a multi-objective scheduling method integrating ecological disturbance control for coordinated optimization of harvesting and transportation. A multi-objective model is developed to minimize total transportation distance， makespan， and surface disturbance. Road-level disturbance coefficients and regional sensitivity factors are prominently introduced for refined assessment of surface disturbance. An improved non-dominated sorting genetic algorithm Ⅱ （NSGA-Ⅱ） is employed， utilizing specific encoding and evolutionary strategies for multi-objective optimization. Simulation results show that the proposed method reduces transportation distance by approximately 14.3% and surface disturbance by nearly 32% compared to greedy and ACO-based algorithms， while maintaining comparable completion times. The multi-objective compromise solution demonstrates superior to single objective extreme solutions in terms of the balance of three indicators. The method effectively reduces environmental disturbance without significantly sacrificing efficiency， achieves efficient coordination between scheduling optimization of forest harvesting tasks and ecological control， and is suitable for smart forestry scheduling scenarios under ecological constraints and showcasing strong potential for practical application.

Taking cold-formed thin-walled steel and straw board as the main materials， a new type of box-section straw-cold-formed thin-walled steel combined beam was constructed by using the connection method of straw board wrapped with cold-formed thin-walled steel. The finite element software ABAQUS was used to establish 7 groups of 25 combined beam models to analyze the effects of connection method， straw board thickness， steel thickness， steel height， web bolt rows， web bolt spacing and beam span on the flexural performance of the combined beams， and to explore the damage modes and damage mechanisms of the combined beams in the case of flexural damage. The results showed that the lower flange of the straw board reached the tensile strength before the upper flange in the finite element simulation loading process， which led to bending cracks and bending damage in the mid-span region of the combined beam， and the lower flange of the straw board underwent localized compression bending and conformal shear damage in the process of load increase. Each parameter had a different degree of influence on the flexural performance of the combined beams， among which the thickness of the straw board， the thickness of the steel and the height of the steel had the most significant influence on the flexural performance. The optimum combination parameters of the combined beams were as follows， with high bending load capacity and small deflection deformation， using adhesive connection， 48 mm thickness of straw board， 2.5 mm thickness of steel， and 200 mm height of steel. The web bolts were arranged in two rows with a spacing of 150 mm， and the overall span was 1800 mm， which was verified to be in line with the expectation by the test. The combination of cold-formed thin-walled steel and straw board can effectively utilize their respective advantages and significantly improve the flexural load capacity of the combined beam.

The water adaptation strategy of extremely small population Taxus cuspidata is the basis of its response to climate change， and studies based on isotopes are still blank. By measuring the δD and δ¹⁸O values of plant xylowater and soil water， direct correlation method and multiple linear mixing model （Iso-Source） were used to analyze the water sources and water use strategies of Taxus cuspidata. The results showed that： the fluctuation range of soil water content in 0-40 cm soil layer of Taxus cuspidata was 32.69% to 54.29%， and the trend was first increased and then decreased； the fluctuation range of soil water content in 40-100 cm soil layer was 22.55% to 25.73%， and the fluctuation range was small. The soil water content in the 0-40 cm soil layer was higher than that in the 40-100 cm soil layer. The δD and δ¹⁸O values of soil water from 0 to 100 cm of Taxus cuspidata were -84.26‰ to -52.17‰ and -11.69‰ to -7.49‰， respectively， and decreased gradually with the increase of soil depth. The δD and δ¹⁸O values of soil water changed sharply in 0-40 cm soil layer， but changed little in 40-100 cm soil layer. The main water source of Taxus cuspidata was 0-40 cm soil water， and the utilization rate was 89.55%， and the utilization rate of 40-70 cm and 70- 100 cm soil water was 5.15% and 5.30%， respectively. This study reflects the water adaptation of Taxus cuspidata under the background of global warming， and the results can provide scientific basis for assessing the stability of Taxus cuspidata ecosystem under future climate change.

In order to study the influence of the structural parameters of the integrated blade for mowing and root removal and looseing on its operational performance， this paper establishes a three-dimensional discrete element simulation model based on discrete element method （EDEM） software to simulate the soil breaking effect of the blade in the process of soil grubbing operation. The influence of forward speed， rotation speed and blade inclination angle on soil breaking rate is analyzed through one-factor test， and Box-Behnken orthogonal test combined with response surface analysis is used to establish the prediction model of soil breaking rate and optimize the parameters. The results of orthogonal test show that the blade inclination angle is the main factor affecting the soil breaking rate， followed by the rotation speed， and the interaction of the three factors is also significant. Under the optimal combination of parameters， with a forward speed of 1.39 m/s， a rotational speed of 107 rad/s， and a blade inclination angle of 4.7°， the soil breaking rate reached 78.3%. The results of the study provide a theoretical basis and practical guidance for the structural design and parameter configuration of mountain mowing and root removal and loosening devices.

Aiming at the problems of multi-scale defects， complex background interference and small target leakage in wood surface defect detection， a multiscale feature aggregation wood defect detection algorithm PADDL-YOLO is proposed based on YOLOV11n （PADDL is a multiscale feature aggregation algorithm consisting of PMSFA （partial multi-scale feature aggregation）， ADown （average pooling down sampling）， DySample （dynamic upsampler）， DPB （dynamic position bias） and LSDECD （lightweight shared detail-enhanced convolutional detection））. First， a partial multi-scale feature aggregation （PMSFA） module is designed to enhance multiscale defect feature extraction. Second， the use of average pooling down sampling （ADown） with a dynamic upsampler （DySample） reduces information loss while reducing computational complexity and improves the model's ability to detect small targets. Then， the dynamic position bias （DPB） module is introduced into the attention mechanism to enhance the C2PSA module， strengthening spatial position awareness and significantly improving defect localization accuracy. Additionally， a lightweight shared detail-enhanced convolutional detection （LSDECD） is designed， employing detail-enhanced convolution to reinforce edge feature capture. Experiments demonstrate that PADDL-YOLO achieves outstanding performance in wood defect detection， with a precision of 91.5%， recall of 91.4%， and mAP@0.5 of 95.0%， representing improvements of 5.2%， 4.9%， and 3.8%， respectively， over the baseline model YOLOV11n. Meanwhile， the model's parameter count is reduced by 25.6%， and computational efficiency is significantly enhanced， providing an effective solution for high-precision real-time detection.

Natural wood end surfaces exhibit irregular textures and defect features， making end surface recognition and localization a challenging problem. To enhance detection accuracy while reducing model parameters and improving computational efficiency for mobile deployment， this study proposes an improved end-to-end deep learning model tailored for log detection by enhancing the YOLO11 architecture. Firstly， the PP-LCNet backbone is adopted to replace the original YOLO11 backbone， effectively reducing the number of parameters， expanding the receptive field， and improving large target detection precision. Secondly， a parameter-free attention mechanism， SimAM， is integrated into the neck network to adaptively emphasize critical features and suppress redundant information， thereby enhancing small target recognition capabilities. Finally， the normalized Wasserstein distance （NWD） loss function is introduced， which is more suitable for measuring similarity between extremely small targets， further improves the accuracy and precision of wood end surface identification. Experimental results demonstrate that the improved model achieves higher end surface recognition accuracy compared to the baseline model， the improved model improves 2.65% and 5.29% on the mAP@0.5 and mAP@0.95 metrics， and FLOPs are decreased by 15.15%. It has good application value in the field of log volume measurement.

The construction and quality improvement of water conservation forests are important research contents in forest management. This paper compares the differences in water conservation functions among four forest types in the eastern mountainous areas of Heilongjiang Province through the study of litter and soil hydrological effects： Korean pine-broadleaved forest （KH）， spruce-fir forest （YLS）， larch forest （LYS）， and Mongolian oak forest （MGL）. The results indicate that the total thickness of the litter layer for the four forest types in the mountainous areas of eastern Heilongjiang Province varies from 3.08 to 7.78 cm， and the litter stock volume ranges from 7.65 to 11.08 t/hm². The effective water retention capacity of the undecomposed litter layer is 2.58-15.41 t/hm²， with an effective retention rate of 56.02%-216.67%. The effective water retention capacity of the semi-decomposed layer is 0.95-9.88 t/hm²， and the effective retention rate reaches 62.78%-281.29%. The water conservation capacity of the undecomposed layer is higher. The immersion time of the litter has a logarithmic relationship with the litter's water holding capacity and a power function relationship with the water absorption rate. For the four forest types， the soil bulk density increases with the increase of soil depth， while the soil porosity decreases. The soil saturated water holding capacity in the 0-20 cm soil layer of KH， YLS， LYS， and MGL ranges from 973.41 to 1，085.08 t/hm². The total water holding capacity of the four forest types， from largest to smallest is KH， LYS， MGL， and YLS. The soil layer plays a key role in the water conservation function of forests， with a water holding capacity of 97.41%-98.15% of the total water holding capacity. The research results provide a theoretical basis for the quality improvement and transformation of water conservation forests in this region.