Efficient and accurate tree species identification is critical for the realization of smart forestry. Traditional field survey methods are low efficiency and high cost， while machine learning-based tree species identification approaches often rely on extensive feature extraction and prior knowledge. To address these issues， a tree species identification algorithm based on improved YOLOv10 for UAV imagery is proposed in this paper. The improved architecture integrates lightweight network design and attention mechanisms to enable efficient edge device deployment， providing technical support for digital forest resource management. A UAV imagery dataset was developed for five common tree species （Larix gmelinii， Phellodendron amurense， Juglans mandshurica， Ulmus pumila， and Fraxinus mandshurica） in Northeast China. The backbone network was reconstructed using lightweight convolution （Ghost） for computational complexity reduction. The convolutional block attention module （CBAM） was introduced in the fusion layer to strengthen fine-grained feature extraction through channel and spatial dual dimensional feature calibration. Multi-scale feature fusion was optimized through bidirectional cross-scale connections （BiFPN）， while bounding box regression efficiency was improved using a structured intersection over union （SIoU） loss function. Final deployment validation was conducted on the Jetson Nano embedded platform. The improved YOLOv10 model achieved 91.5% precision and 77.5% mAP@0.5 on the validation set， showing improvements of 4.5% and 3.8% compared to the baseline model， respectively. In practical deployment， the model achieved an inference speed of 43.5 FPS， 35.5% faster than the baseline model， with mAP@0.5 of 75.7%. Results showed that， the improved YOLOv10 algorithm successfully balances identification accuracy and real-time performance in complex forest environments through lightweight architecture and multi-scale feature optimization. The solution demonstrates particular effectiveness in scenarios with dense canopy overlap and variable illumination， offering an embeddable solution for UAV forestry surveys.

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.

To address the issue that drivers cannot perceive road sense through the steering wheel in a steer-by-wire （SBW） system， a road sense simulation motor is employed to provide feedback on road conditions， enabling the driver to perceive road sense and effectively control the vehicle. This study establishes a dynamic model of the SBW system， utilizing the magic formula tire model to describe lateral force， calculate individual wheel slip angles， and determine the self-aligning torque of the wheels. Assist torque， limit torque， friction torque， and damping torque， are designed to obtain the road sense feedback torque of the SBW system. A super-twisting algorithm （STA） approach is implemented to track the current corresponding to the feedback torque， simulation and experimental tests are conducted to analyze experimental results. The findings indicate that the self-aligning torque calculated using the magic formula tire model is highly accurate. The designed road sense simulation control algorithm meets the requirements of light steering at low speeds and clear， stable road sense at high speeds. Moreover， the robustness of the STA surpasses that of the proportional integration differentiation （PID） control. Compared with conventional sliding mode control， the proposed method effectively eliminates chattering effects.

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 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.

The complexity and inaccuracy of extracting waveform features from spaceborne full waveform LiDAR data affect the accuracy of forest aboveground biomass （AGB） estimation. To address this problem， this study combined Global Ecosystem Dynamics Investigation （GEDI） LiDAR waveform data with GF-7 stereo imagery data in the Simao region of Yunnan as an example. The digital surface model （DSM） generated by multi-angle stereo geometry was used to accurately locate the starting point of the GEDI waveform and optimize the waveform features. Multiple stepwise regression methods were used to construct biomass estimation models for coniferous， broadleaf and mixed forests at the footprint scale. These models were then extrapolated to the regional scale using a random forest algorithm. The results showed that the biomass estimation accuracy at the footprint scale was significantly improved after optimizing the waveform features. The root mean square error （RMSE） for the coniferous forest was 20.11 Mg/hm²， the coefficient of determination （R²） was 0.89， and the accuracy （ACC） was 82.87%. The RMSE of broadleaf forest was 22.07 Mg/hm² with R² of 0.89 and ACC of 81.77%. The RMSE of the mixed forest was 24.51 Mg/hm² with R² of 0.88 and ACC of 80.54%. Finally， based on the optimized GEDI biomass footprints， the regional forest AGB distribution map was successfully generated at 25 m resolution.

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.

There are various types of robot grasping methods. In the wood fork production process， the length of the wood fork bundle is relatively long， with a flat side and small height， making traditional robot hands less effective for grasping. Based on the characteristics of the wood fork bundles， a linear parallel clamping method at the end is more suitable for grasping. To address this issue， this paper proposes a Jansen linkage-based linear parallel clamping robot hand （Jansen fingers）， which aims to achieve linear motion of the end effector along a straight trajectory during clamping. The Jansen fingers utilize the Jansen linkage mechanism to achieve linear motion at the end， while a four-bar linkage mechanism ensures the stability and reliability of the finger's end during the clamping process. Theoretical analysis and simulation results show that the Jansen fingers can achieve stable linear clamping， meeting the requirements of the wood fork production process.

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.

To solve the problem of poor low-temperature cracking resistance for traditional high modulus asphalt， desulfurization rubber powder （DRP） was used as the main modifier， and composite modification technology was used to to prepare the desulfurization rubber powder-polyphosphate （DRP-PPA） and desulfurization rubber powder-rock asphalt （DRP-ROCK） composite modified high modulus asphalt. Firstly， the viscoelastic mechanical properties of the composite modified high modulus asphalt were tested by traditional physical properties tests’ methods and rheological properties test methods； meanwhile， the modification mechanism and thermal stability were explored by Fourier transform infrared spectroscopy （FTIR） and differential scanning calorimeter （DSC） tests. On this basis， high modulus asphalt mixture specimens were prepared， and their road performance were evaluated through domestic high-temperature rutting test， low-temperature small beam bending test， and four points bending fatigue test， and compared with the technical performance of traditional PR and HM high modulus mixture. Tests results showed that， the two types of composite modified high modulus asphalt binders had excellent high-temperature properties， which can meet the performance requirements of the traditional high modulus asphalt binders. Furthermore， low-temperature performance and fatigue resistance of the composite modified high modulus asphalt were superior to the traditional high modulus asphalt， among which the road properties of DRP-PPA high modulus asphalt were the best. FTIR test results showed that， the modification process of the above modifiers on asphalt was mainly based on physical modification， supplemented by chemical modification. And DSC test results revealed that addition of PPA and rock asphalt both significantly improved the thermal stability of modified asphalt. The performance test results of the asphalt mixture indicated that the low-temperature performance and fatigue resistance of the composite modified high modulus asphalt mixture were better than those of PR and HM high modulus asphalt mixture. And their high-temperature rutting resistance was slightly lower than those of traditional high modulus asphalt mixture， but it still met the relevant technical standards.

In forest and grassland fire scenarios， the diversity of open flame forms and the complexity of the environment may lead to false or missed detection. Therefore， an improved YOLOv8n fire detection algorithm （YOLOv8n-CSA） is proposed for forest and grassland fires. CSA （channel-spatial attention） is the channel spatial attention module， and a group shuffle convolution （GSConv） module is introduced to replace the third layer standard convolution module （Conv） in the original YOLOv8n， reducing model computation and improving feature extraction ability. And introducing the Slim Neck structure in the head further reduces the computational complexity of the model. Simultaneously design a channel spatial attention module （CSA） integrated into the Backbone section to enhance the expressive power of the input feature map.This module combines channel attention， channel shuffle， and spatial attention mechanisms to capture global dependencies within feature maps. Based on a forest and grassland fire dataset， and without utilizing pretrained models， the proposed fire detection network achieves a 3.7% increase in precision， a 1.51% improvement in recall， a 3.24% enhancement in mAP50， and a 5.62% reduction in GFLOPs compared to the baseline YOLOv8n model. Experimental results demonstrate that the proposed algorithm not only reduces computational cost but also enhances the detection performance of fire-related features.

Accurately grasping the spatial distribution of forest cover is crucial for the protection， restoration and sustainable use of forest ecosystems. However， it is no longer possible to efficiently and accurately obtain the changes in complex forest cover at the county scale by relying on low spatial resolution remote sensing images combined with traditional computer classification models. Therefore， this study took the complex forests in Tangyuan County， Jiamusi， Heilongjiang Province as the research object， used the medium spatial resolution satellite remote sensing images of Sentinel-1 and Sentinel-2， and constructed a machine learning model optimized by particle swarm optimization （PSO） to detect the changes in forest cover at the county scale. The K-fold cross validation was used to evaluate the accuracy of the forest cover detection results. The results showed that the support vector machine and random forest machine learning models optimized by particle swarm algorithm had improved the accuracy of forest cover change detection compared with their own models without parameter optimization. The support vector machine model increased by 6.52%， and the random forest model increased by 4.65%. Compared with the current mainstream ESA COVER WORD land cover product， the random forest model optimized by particle swarm algorithm had the highest accuracy， with an overall accuracy of 0.92. The optimized random forest model was also more precise in detecting forest cover changes. By classifying medium spatial resolution remote sensing images through the random forest model of the particle swarm optimization algorithm， we can quickly and accurately grasp the spatial distribution of forest cover at the county scale， and provide data and technical support for the protection， restoration and sustainable utilization of forest ecosystems.

Forest carbon storage is a critical component of global carbon cycle research and plays a significant role in addressing climate change. This study focused on the northern slope of the Zhangguangcai Mountains in Heilongjiang Province. By combining ground observation data with Landsat TM （thematic mapper）/OLI （operational land imager） data， multiple machine learning models were applied， along with the bootstrap aggregating ensemble learning algorithm， to simulate forest carbon storage. The results showed that from 1990 to 2022， the forest carbon storage in the study area exhibited a significant increasing trend， with an annual average carbon storage of （80.77±0.27） Mg C/hm². The spatial distribution demonstrated notable heterogeneity， with high carbon storage areas concentrated in flat and semi-mountainous regions. Additionally， the mean growing-season temperature was found to have a highly significant positive correlation with forest carbon storage （P<0.01）， indicating that temperature was the primary climatic factor influencing carbon storage changes. This study provides a novel approach for forest carbon storage accurate simulation carbon sink management.

Forest fires are characterized by significant danger， widespread impact， and challenges in both reproducing the fire field and allowing personnel to approach it. Virtual reality （VR） technology offers distinct advantages in simulating the spread of forest fires and providing training for firefighting personnel. This paper presents the design and implementation of a virtual simulation system for forest fire fighting， discussing the key principles and technologies behind its design. These include computer simulation， wireless infrared tracking motion capture， and digital visualization technologies. The paper provides a detailed introduction to the system's overall design， as well as its hardware and software configuration. It also analyzes the main functions of the system， such as understanding and operating fire extinguishers， simulating the spread of forest fires， and facilitating command and decision-making processes. The development and application of this system can significantly enhance the operational proficiency and decision-making capabilities of forest firefighting personnel. Moreover， it offers strong technical support for forest fire prevention， control， and disaster management， ultimately helping to reduce the loss of life， property， and forest resources due to forest fires.

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.

This study estimated the net ecosystem productivity （NEP） of forests in Northeast China based on the MODIS MOD17A3GF dataset， aiming to explore the spatiotemporal coupling relationship between NEP and extreme climate events. By integrating temperature and precipitation data and the extreme climate index calculated by RClimDex， the spatiotemporal variation characteristics of NEP and ten climate factors from 2000 to 2020 were analyzed， and the influence of each climate factor on NEP was evaluated using GeoDetector from two dimensions： factor detection and interaction detection. The results showed that： 1） In the past 21 years， the annual average NEP of forests in Northeast China had shown a slow but continuous upward trend. From 2000 to 2010， the annual average NEP increased by 30.94 gC·m^-2·year^-1， and the increase slowed down from 2010 to 2020， reaching only 9.16 gC·m^-2·year^-1. Spatially， the main growth areas were concentrated in the Greater and Lesser Khingan Mountains. 2） Extreme climate events showed a trend of ‘less cold events， more warm events， and more humid events’， which was specifically manifested in the decrease of the cold persistence index （CSDI）， the increase of the warm persistence index （WSDI）， the significant increase of annual precipitation， the decrease of the number of continuous dryness index （CDD）， and the alleviation of drought in some regions. 3） The annual average temperature， annual precipitation and the number of frost days were the dominant factors of the spatial distribution of NEP （q>0.2）， followed by the continuous dryness index， continuous wet days and the warm persistence index. The daily temperature difference and the number of heavy precipitation days had weaker explanatory power. The interaction of any two climate factors generally had a stronger explanatory power on NEP than the single factor effect. The interaction between annual precipitation and the number of frost days， annual precipitation and annual average temperature， and annual precipitation and the warm persistence index showed high q values in most years. This study reveals the response of forest NEP in Northeast China to climate （especially extreme climate）， emphasizes the importance of evaluating the coupling effects of extreme climate and forest NEP under the context of climate change， and provides a theoretical support for carbon budget regulation and climate adaptation management of forest ecosystems in Northeast China.

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.

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%.

Taking the Northeast China as the research object， this paper investigated the degradation degree and spatial distribution of permafrost over the years. Key meteorological elements were collected， and a multiple linear regression model was used to calibrate the ground surface temperature data. Based on the temperature at the top of permafrost （TTOP） model， and using ANUSPILN software for interpolation， the spatial and temporal distribution of permafrost in Northeast China was analyzed. The results showed that the areas of permafrost in the 1970 s， 1980 s， 1990 s， 2000 s， 2010 s were about 3.99×10⁵， 3.41×10⁵， 2.31×10⁵， 1.80×10⁵， 1.59×10⁵ km²， respectively. During the period of 1970 s to 2010 s， the permafrost area in Northeast China decreased significantly by about 2.40×10⁵ km²， with a decrease of 60.08 %. The proportion of permafrost area in Northeast China decreased from 27.66% to 11.04%， while the proportion of seasonally permafrost area increased from 72.34% to 88.96%. The difference between the model results and the actual borehole data was only 0.05 ℃， and the model results using the corrected ground temperature data were higher than the existing research results.

Forest stand ingrowth is a critical component of the dynamic growth process of forest stands， essential for maintaining biodiversity and community structure stability in forest resources. Based on data from 61 plots established at the Maoer Mountain Experimental Forest Farm， this study considered factors such as stand characteristics and biodiversity. Through Kendall-Tau-b correlation coefficient analysis and selection of the most suitable variables considering multicollinearity among variables， models for ingrowth were constructed using Poisson， negative binomial （NB）， zero-inflated， and Hurdle models. The contribution rate of variables was analyzed using hierarchical partitioning to identify key factors influencing the ingrowth model. The results showed that stand density （K）， arithmetic mean diameter at breast height （d）， Simpson's index， and mean stand height （MH） were significant factors affecting the number of ingrowth trees per hectare （Nn）. Comparing models using AIC， BIC， and Loglike criteria， it was found that ZINB and HNB significantly outperformed other models. The Vuong test further revealed that the negative binomial models and their composite models （ZINB， HNB） performed better than Poisson models and their composites （ZIP， HP） in fitting the ingrowth quantity of natural forests in Maoer Mountain， with the ZINB model slightly outperforming the HNB model. Therefore， the ZINB model was the optimal model for fitting the ingrowth quantity of natural forest stands in Maoer Mountain， a conclusion also supported by ten-fold cross-validation. Additionally， hierarchical partitioning analysis indicated that the Simpson's index and mean stand height （MH） contributed most to the count and zero parts， respectively， of the optimal ingrowth model （ZINB）. The natural forest ingrowth model constructed by this research has a certain statistical reliability and can be used for ingrowth prediction in the Maoer Mountain area， providing a scientific basis for local natural forest regeneration management.

Spatial structure units of Pinus sylvestris plantations stands were established using Voronoi diagram and n=4 methods， with subsequent comparision of their colculated stand spatial structure parameters. Both methods were employed to calculate the comprehensive index （Q） of single-tree spatial structure， enabling forest stand optimization and comparison. The goal was to evaluate the advantages of the Voronoi diagram method in calculating forest structure parameters and optimizing stand structure in Pinus sylvestris plantations. Fixed plots of 22-， 31-， and 43-year-old Pinus sylvestris plantations in Mengjiagang Forest Farm， Jiamusi City， Heilongjiang Province， were selected for this study. Forest spatial structure units were determined using both the Voronoi method and the n=4 method. For each sample plot， the size ratio （U）， angular scale （W）， competition index （C _I）， and openness （K） were calculated. A significance test was conducted to assess the differences between the results obtained from the two methods. Based on these parameters， a comprehensive index （Q） of individual tree spatial structure was constructed to guide stand thinning and simulate the effects of stand spatial structure optimization under different thinning intensities （10%， 20%， and 30%）.The results revealed significant differences between the Voronoi method and the n=4 method in calculating W， C _I， and K for Pinus sylvestris plantations（P<0.01）. Using the Q values derived from the Voronoi method， thinning was conducted at intensities of 10%， 20%， and 30%. After thinning， the average DBH of the stand increased by 0.27 cm， 1.03 cm， and 1.47 cm， respectively. Concurrently， U decreased by 7.17%， 24.80%， and 38.93%； W decreased by 27.98%， 55.65%， and 69.35%； C _I decreased by 19.62%， 35.74%， and 47.78%； and K increased by 6.37%， 16.67%， and 28.92%. The Q value increased by 84.91%， 248.12%， and 530.87%， respectively. The simulated changes under the three thinning intensities demonstrated significant improvements in stand parameters and overall spatial structure optimization， with the most pronounced effects observed at the 30% thinning intensity. The Voronoi method is an effective approach for constructing forest spatial units， calculating stand spatial structure parameters， and optimizing stand structure in Pinus sylvestris plantations. This method provides a robust framework for enhancing forest management practices and achieving sustainable stand optimization.

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.

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.

In order to minimize the self-weight of the structure， it is common practice in engineering projects to utilize concrete slabs with reduced cross-sectional dimensions. To enhance the flexural strength and resistance to cracking of concrete， this study selected well-anchored end-hooked steel fibers （HSF）， polypropylene fibers （PPF） that can significantly mitigate plastic shrinkage and reduce the number and width of cracks， fly ash （FA） that can improve the fluidity and long-term strength of concrete， and silica fume （SF） with high volcanic ash activity as materials to improve the mechanical properties of concrete. This research employed the orthogonal test method to examine the influence of these four material factors on the mechanical properties of concrete when they interact simultaneously. The significance order of each factor’s influence and the optiimal dosage combination were determined through range analysis. On this basis， the single-factor test was employed to verify and supplement the findings of the orthogonal test. The results from both tests were matched and the optimal dosage combinations for the four factors was obtained. The findings of the study indicated that the dosage of HSF had a significant impact on the compressive strength， flexural strength， and split tensile strength of the concrete. The single-factor test analysis further identified that the optimummixing of HSF and PPF were 0.36% and 0.15% by volume， respectively. These proportions led to an enhancement in compressive strength by 30.26% and an increase in splitting tensile strength by 12.79%， in comparison to 0% by volume. The mass fraction of optiumu admixture of FA and SF were 5% and 7.5%， respectively. They can imcrease the compressive strength of concrete by 4.17% and 18.19%， respectively， compared with 0% mass fraction. Scanning electron microscopy （SEM） was conducted on the optimal dosage group to investigate the correlation between mechanical properties and hydration products. The results indicated that the hydration products in this group exhibited greater density， thereby facilitating the enhanced bridging role of the PPF.

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.

To investigate the mechanical characteristics of Caragana korshinskii stem sawing and determine the optimal sawing parameters for supporting the subsequent optimization and improvement of shearing equipment， a self-designed branch sawing test platform was used in this study. Single-factor experiments were conducted to examine the effects of stem diameter， cutting speed， feeding speed， cutting angle， and saw blade tooth count on the peak cutting force and cutting power of the branches. Building upon the results of the single-factor experiments， a Box-Behnken central composite experimental design was employed. Selecting cutting speed， feeding speed， cutting angle， and saw blade tooth count as experimental factors， a multi factor experiment was carried out with peak cutting force and cutting power of branches as target values， and a regression model was established. The experimental results showed that the peak cutting force increased with the increase of stem diameter， while it exhibited an initial increase followed by a decrease with the increase of cutting speed， feeding speed， cutting angle， and saw blade tooth count. Similarly， the cutting power increased with the increase of stem diameter， but initially decreased and then increased with the increase of cutting speed， feeding speed， cutting angle， and saw blade tooth count. Multi-objective optimization analysis of the regression model yielded the optimal combination of parameters： cutting speed of 45.7 m/s， feeding speed of 0.32 m/s， cutting angle of 8.3°， and tooth count of 104. Under these conditions， the peak cutting force was 7.02 N and the cutting power was 181.57 W. The deviation between the predicted value and the actual value of the peak cutting force and cutting power was 1.9% and 2.2%， respectively. The results of this experiment provide valuable reference for the design of efficient and low-energy consumption Caragana korshinskii shearing equipment.

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.

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.

The mixed broad-leaved forest at Maoer Mountain is characterized by its complex stand structure， rich species diversity， and intricate interactions between tree species， all of which influence height to crown base. Therefore， accurately constructing a height to crown base model can provide important reference value for guiding forest management and improving the accuracy of growth prediction. In this study， 28 representative sample plots of mixed broad-leaved forests in Maoer Mountain area were selected， and 18 tree species widely distributed in the sample plots were divided into groups according to the variability coefficient of tree species diameter， average tree species diameter， average height-diameter ratio， proportion of tree species， classification of soft broad-leaved and hard broad-leaved species， and shade tolerance of tree species， thereby solving the problem of tree species complexity and the insufficiency of sample size of a single tree species in model construction. In this study， Wykoff model was adopted as the basic model. At the same time， factors such as stand， competition and species diversity were considered to express the mixed degree and competition of stand， and a generalized model of height to crown base was established. At the same time， the difference of height to crown base among plots and tree species groups was considered to build a model of subbranch height mixed effect. The influence of sampling on the result was analyzed by using the method of ‘remaining one’. The results showed that the common tree species in Maoer Mountain could be divided into four tree species groups. Besides DBH and tree height， the average height of dominant trees， the sum of the section area of intra-species larger than that of the target trees and the Shannon index had significant effects on height to crown base. Considering the mixed effects of plots and tree species groups， the basic and generalized subbranch height models had high fitting accuracy R ² of 0.638 and 0.627， and RMSE of 1.880 and 1.909， respectively. In addition， when one sample was randomly selected from each tree group in each plot to correct the mixed model， compared with the fixed effect model， the MAE and MAPE of the generalized height to crown base model decreased by 7.51% and 13.51%， respectively， showing better prediction effect without wasting manpower， material and financial resources. This study analyzed the effects of various tree species and ecological functions on the growth of height to crown base in Maoer Mountain， and provided some reference for predicting the height to crown base of different tree species in broad-leaved mixed forest in Maoer Mountain.

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.

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.

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.

Quantitative assessment of long-term carbon sequestration capacity in the forest ecosystem of Heihe City， Heilongjiang Province， analyzing forest fire disturbances impacts on carbon sink dynamics to inform China’s ‘Dual Carbon’ goals. Based on dynamic monitoring data （2005， 2010， 2015） from 1 649 forest sample plots in Heihe City， combined with the Canadian Carbon Budget Model （CBM-CFS3） the carbon storage and carbon sink capacity of the forest ecosystem across multiple levels （aboveground， belowground， litter， deadwood， and soil carbon pools） during 2005， 2010， 2015 were evaluated on the basis of localized improvement of model parameters， and the impact of fire disturbances was also analyzed. Results indicated that across the measurement year （2005， 2010， 2015）， the total ecosystem carbon density of Heihe's forests increased from 207.15 t C/hm² to 218.63 t C/hm²， with a carbon sink of 531.54 t C. The frequency of forest fires decreased annually， and carbon-containing gas emissions in 2015 dropped by 60.3% compared to 2005. Using 2005 carbon sequestration patterns under fire disturbance as the baseline scenario， low-intensity fire disturbances slightly enhanced the carbon sequestration capacity of the forest ecosystem， while moderate and severe fire disturbances reduced the carbon sequestration rate by 23.9% and 38.0%， respectively. The forest ecosystem played a positive role in carbon sequestration during this period. Strengthening fire monitoring and prevention can effectively enhance carbon sequestration capacity， ensuring the stability and sustainable development of the regional ecological environment.

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.

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.

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.