The identification of dominant tree species is an important part of forestry resource surveys. Improving the accuracy of dominant tree species identification has significant practical implications for conducting forest resource surveys and related research. Using the Google Earth Engine （GEE） cloud platform， we obtained Sentinel-2 time series images for the Huodong mining area from January to December 2023. The annual growth trajectory features of dominant tree species were constructed based on the CCDC algorithm and the NDFI index. A dominant tree species hierarchical identification method combining "trajectory features + spectral features + texture features" of long-time series remote sensing images was proposed. A control group of "spectral features + texture features" was set up， and hierarchical classification and random forest classification algorithms were used to identify 7 dominant tree species （Pinus tabuliformis， Quercus wutaishansea， Betula playphylla， Larix principis-rupprechtii， Platycladus orientalis， Populus davidiana， and poplars spp.） in the Huodong mining area. The results showed that： 1） The NDFI index can effectively distinguish between deciduous forests and evergreen forests； 2） The dominant tree species identification based on "trajectory features + spectral features + texture features" performed well， with an overall classification accuracy of 79.6% and a Kappa coefficient of 0.742 in the study area， which was 7.3% higher than the control group.

The moisture content of forest floor litter is a key factor in forest fire occurrences， and its accurate detection is crucial for fire prevention. Near-infrared spectroscopy （NIRS） can directly invert moisture content from spectral data， enabling rapid detection of litter moisture content. However， spectral characteristics differ between fuel types due to variations in light intensity data at different wavelengths， requiring separate detection models for litter from different tree species to match specific light intensity-moisture content inversion relationships. Collecting and labeling spectral data across different forest stands is time-consuming， limiting the practical application of the spectral method. To address this issue， this study proposes a moisture content detection method for forest floor litter based on Bi-LSTM （Bidirectional Long Short-Term Memory） transfer learning. By transferring the trained model parameters to new models， we avoid training models from scratch， thereby improving model learning efficiency and reducing the data required for training. The study demonstrates that the Bi-LSTM method surpasses the traditional inversion approach using LSTM in terms of detection accuracy. Specifically， the mean absolute error （MAE） for Quercus mongolica and Larix gmelinii is reduced by 0.62% and 0.87%， respectively， while the mean squared error （MSE） is reduced by 0.28% and 0.70%， respectively. Moreover， the Bi-LSTM-based transfer learning approach significantly lessens the reliance on labeled NIR spectral data. With a target domain sample size of 300 and a source domain sample size of 1 000， the detection model record an MAE of 3.27%， an MSE of 1.10%， and an R² of 0.918. When compared to models without source domain training， the MAE and MSE show reductions of 2.36% and 1.02%， respectively， and an increase in R² of 0.114. A comparative analysis before and after implementing transfer learning reveals that this methodology offers a novel strategy to diminish the time cost associated with modeling moisture content in spectral litter and to enhance the practical application of spectral detection.

In order to solve the problems of conventional radiative cooling devices limited by the theoretical cooling power limit of 150 W/m² and by the inhibition of radiant power by low-temperature condensate on the radiant surface and the intrinsic water under high humidity conditions， an asymmetric functional structure design based on unidirectional liquid transport proposes a passively cooled wood （REW） with radiative refrigeration and evaporative cooling integrated in series. The wood is delignified by a sodium chlorite solution to enhance its hydrophilicity； then a hydrophobic silica/epoxy solution with high reflectivity and infrared emission properties is coated on the top of the hydrophilic wood to form a hydrophobic radiative cooling layer， while the hydrophilic wood at the bottom serves as an evaporative cooling layer. By virtue of the asymmetric wetting design with unidirectional water transport， low-temperature condensate can be spontaneously transported through the radiation-cooling layer to the evaporative-cooling layer for evaporative cooling， whereas the native water in the evaporative-cooling layer is unable to pass through the radiation-cooling layer to inhibit radiation. As a result， based on the tandem integration of radiant-evaporative cooling， the REW achieves a maximum cooling power of 214 W/m² during daytime， and 172 W/m² even at high humidity of 80%， which is more than 2.8 times higher than that of radiant cooling alone. The potential application of REW in energy-efficient cooling of buildings is demonstrated through building models， providing a universal optimisation strategy for expanding the practical application of passive cooling and new insights into the functional utilisation of wood resources.

Fraxinus mandshurica is one of the precious broadleaf tree species in Northeast China. It has high economic and ecological value.There′s a shortage of F. mandshurica resources and a structural deficiency in resilient cultivars. It′s particularly important to select and breed F. mandshurica with excellent cold resistance in the context of global climate change. The F. mandshurica in Xiaoxing'an Mountains of Heilongjiang Province were selected as the reseach objects. Dendrochronology and wood anatomy were used to compare the radial growth of 52 F. mandshurica families（Families 1-77 in total）. The relationship between the radial growth and xylem anatomical characteristics of F. mandshurica and main climatic factors was clarified. The response of F. mandshurica families to low temperature event was studied. The results showed that there were significant differences in average annual radial growth of F. mandshurica families in Xiaoxing'an Mountains. The radial growth of No. 56， 46 and 38 F. mandshurica families were higher， the values were 4.07 mm， 3.82 mm and 3.71 mm， respectively. The radial growth of F. mandshurica was constrained by temperature and precipitation during the growing season. The radial growth was significantly negatively correlated with the temperature in October of the previous year， positively correlated with the temperature from January to April， and negatively correlated with the precipitation from March to April. Temperature was the primary climatic factor affecting xylem anatomical features of F. mandshurica families in Xiaoxing'an Mountains. Under low temperature stress， the ring width（RW）， mean vessel area （MVA）， total vessel area（TVA） and theoretical hydraulic conductivity（Kh） decreased by 25.5%， 38.2%， 21.8% and 55.1%， compared with non-low temperature years， while vessel density（VD） increased by 64.1%. There was difference in radial growth among F. mandshurica families under low temperature stress. No. 39， 70 and 36 families had stronger resistance to cold， the values of resistance were greater than 1. The recovery of No. 57， 17 and 70families were better， the values of recovery were higher than 1.63. The No.70 F. mandshurica family in Xiaoxing'an Mountains had excellent growth and stronger cold resistance， which could be used as an excellent F. mandshurica family for directional cultivation.

By conducting multi-point regional trials on introduced Kazakhstan birch， analyzing its adaptability， screening out good family lines， and providing a theoretical basis for the selection and application of introduced birch family lines. In this study， we analyzed the genetic variation patterns of tree height， diameter at breast height （DBD）， volume of timber， straightness and other traits in 23 11-year-old Kazakhstani birch family lines and two Chinese birch family lines of the Mao'ershan seed source， which were planted in Daqing， Heilongjiang Province， Shangzhi， Heilongjiang Province， and Jinzhou City， Liaoning Province， and fitted a mixed linear model with heteroscedasticity by using the software package R-language ASReml4.0. Best linear unbiased prediction （BLUP） method was used to obtain the breeding values of each family line at different test locations， and combined with Genotype main effects and genotype × environment interaction （GGE） bisplot plots for comprehensive evaluation and selection of each participant and family line. In the mixed-effects model with location as a fixed effect， it was found that the environmental effects were significant among locations， and growth traits reached significant differences （P<0.05， （Z ratio）>1.5） among locations and among family lines within the same test site； family line No. 17 in the Daqing test site had the highest preservation rate and breeding value， and had better salt tolerance； the GGE biplot based on the BLUP method showed that the introduced birch fast-growing property of No.3 family line was the best， and the stability of No.9 family line was the strongest. Based on the comprehensive ranking of the stability and rapidity of each family line， four excellent family lines， No.20， 9， 7 and 24， were selected according to the 30% selection rate and combined with the genetic gain of the volume of each family line.

The Larix gmelinii forests is one of the most important forest types in Northeast China， playing a crucial role in maintaining the stability of the forest ecosystem in the region. The woody plants in the permanent plot of Larix gmelinii forests in Northeast China were taken as the research object. Through field plot investigation， diversity index and spatial structure characteristics calculation， combined with variance partitioning analysis and redundancy analysis， this study explored the characteristics and influencing factors of tree species diversity in Larix gmelinii forests. The results showed that the average tree species richness of Larix gmelinii forests was 10.75 in Northeast China， Simpson index was 0.72， Shannon-Wiener index was 1.69， and Pielou evenness index was 0.76. The average mingling intensity was 0.57， indicating that the forest stands were moderately mixed. The uniform angle index was 0.54， and the forest stands were clustered. The breast diameter dominance was 0.51， indicating that the forest growth was in a moderate state. The spatial structure characteristics of forest stands and geoclimatic conditions jointly explained 35.9% of the changes in tree species diversity， followed by spatial structure characteristics， with an explanatory rate of 29.2%. Simple term effects showed that spatial structure and geoclimatic indicators such as mingling intensity， latitude， annual mean temperature， and annual mean precipitation were the main influencing factors of tree species diversity changes. The research results will provide theoretical basis and data support for the formulation of management strategies for Larix gmelinii forests in Northeast China.

The optimization of stand spatial structure is a key issue in achieving sustainable forest management. Traditional optimization methods often exhibit low efficiency in handling complex spatial relationships and large-scale data. This study proposed a stand spatial structure optimization method based on Graph Attention Networks （GAT）. An integrated spatial structure evaluation system was established using the entropy-weighted matter-element analysis method， and a graph neural network model was constructed based on stand data from the Tanglin Forest Farm of the Xinqing Forestry bureau in northern Yichun，Heilongjiang Province. The model was applied to perform multi-objective optimization analysis of stand spatial structure. Experimental results showed that at a 25% harvesting intensity， the integrated spatial structure index improved from 4.336 to 7.256. The GAT model demonstrated superior performance in capturing complex spatial relationships and optimizing multi-objective tasks. This study provides an innovative and intelligent approach for optimizing stand spatial structure and managing forests， contributing to the enhancement of forest ecosystem health and stability.

With the popularization of automated production lines， the pressing process of tea cakes has become particularly important for product quality. However， the quality control of Pu'er mini tea cakes produced by automated production lines often falls short of those made by hand. Therefore， the detection of the appearance quality of tea cakes after production by automated lines has become an urgent issue to be addressed. To this end， this study proposes an automated quality detection algorithm for Pu'er tea cakes based on machine vision. The algorithm comprehensively applies various image processing techniques， including Otsu threshold segmentation and Canny edge detection， and introduces multiple algorithm optimization strategies to improve detection accuracy and efficiency. The algorithm can automatically complete the detection and evaluation of the appearance quality of tea cakes and transmit the results in real-time to a Programmable Logic Controller （PLC）. Experimental results show that the algorithm can accurately identify appearance defects of tea cakes， with an average computational accuracy of 95.75%， demonstrating high robustness and reliability. It is suitable for quality control in automated production lines and has a wide range of application prospects， especially in the intelligent transformation of the tea production industry， where it has significant reference value.

This study aims to clarify the spatial distribution characteristics of ridge plant belts on soil water-holding capacity and soil structure in sloping farmland， providing a scientific basis for optimizing ridge plant belt configurations and soil and water conservation measures in Northeast China's black soil region. Sloping farmland with ridge plant belts was selected as the research object （Ridge 1： ridge spacing of 12.5 m； Ridge 2： ridge spacing of 19.5 m）， and sloping farmland was selected as the control. The uniform spatial point sampling method was obtained using basic physical property indicators in the surface layer （0-15 cm）， and to quantify the differences in the spatial distribution characteristics of soil water-holding capacity and soil structure in sloping farmland with different spacing of ridge plant belts. The result showed that， the sloping farmland with ridge construction showed a significant increase in total porosity， capillary porosity， saturated water-holding capacity， field capacity， and capillary water-holding capacity， with a relatively uniform distribution across the slope. In addition， compared to the Ridge 2， the soil of Ridge 1 showed an increase of 0.96-1.11 times in total porosity， 1.21-1.31 times in capillary porosity， 1.03-1.25 times in saturated water-holding capacity， 1.22-1.78 times in field capacity， and 1.33-1.52 times in capillary water-holding capacity， respectively. The soil mechanical stable aggregate mass fraction， MWD （mean weight diameter）， water-stable aggregate mass fraction， and GMD （geometric mean diameter） in the sloping farmland with ridge showed significant improvements across all fields. Compared to the controls， the sloping farmland with ridge increased by 1.01-1.15 times， 0.94-1.61 times， 1-1.17 times， and 1.05-1.55 times， respectively. This indicated that the sloping farmland with ridge effectively improved soil structure compared to the control. Moreover， compared to the sloping farmland with Ridge 2， the soil mechanical stable aggregate mass fraction， MWD， water-stable aggregate mass fraction， and GMD in the sloping farmland with Ridge 1 increased by 1.08-1.14 times， 0.95-1.28 times， 1.07-1.15 times， and 1.14-1.40 times， respectively. Constructing ridges can improve water-holding capacity and soil structure characteristics， with a more significant improvement effect observed in relatively small distances smaller distances between ridges.

Soluble solids content （SSC） is a key indicator for assessing the internal quality of fruits. This study proposes a non-destructive detection method based on hyperspectral image fusion to predict the SSC of blueberries. Three widely used wavelength dimensionality reduction algorithms are employed： Monte Carlo uninformative variable elimination （MC-UVE）， Competitive Adaptive Reweighted Sampling （CARS）， and Successive Projections Algorithm （SPA）， to identify optimal wavelengths. Additionally， a strategy integrating Local Binary Patterns （LBP） and Gray Level Co-occurrence Matrix （GLCM） is proposed for feature extraction. Using spectral features， image features， and fused features， Partial Least Squares （PLS）， Backpropagation Neural Network （BPNN）， and Support Vector Machine （SVM） models are developed for SSC prediction. The results demonstrate that the BPNN model， utilizing spectral features extracted via the CARS algorithm and image features derived from the LBP+GLCM algorithm， yields the highest prediction accuracy. The model's coefficient of determination （R {}_p^{\mathrm{2}}） is 0.926 1， while the Root Mean Square Error of Prediction （RMSEP） is 0.364 1. This study indicates that hyperspectral image fusion technology holds significant potential for the non-destructive prediction of blueberry SSC.

Plant residues are an important source of forest soil carbon pool， and changes in soil carbon flux in woodland are closely related to soil carbon pool and carbon cycle. However， current studies on soil organic carbon stability mainly focus on farmland soil. In order to clarify the influence of exogenous carbon input changes on the stability of soil organic carbon， an indoor constant temperature culture experiment was set to study the litter species （Cherry， YH； Sycamore， WT； Poplar， YS）， additive amount （0， 2%， 4% and 6%）， particle size （2 mm， D； 0.25mm， X） as variables， 18 different treatments and 2 controls were concluded. The changes of soil CO₂ release， soil organic carbon content and mineralization intensity under different factors and their interactions were analyzed. Results showed that different litters had significant effects on soil total CO₂ release， and cherry blossom and poplar were more likely to promote soil total organic carbon mineralization. The highest total CO₂ release rate was observed in YHX6 treatment， and the cumulative total CO₂ release of YHD6 treatment was 4.37 times that of CK1. Compared with CK1， the potential mineralizable organic carbon C _p value of 6% added dose was significantly increased. The dynamic changes of soil total organic carbon accumulation mineralization over time can be fitted by the first-order kinetic equation， and the fitting results showed that exogenous carbon input accelerated soil carbon turnover， while litters in small particle size， 6% addition amount and YH type yielded the highest total soil organic carbon turnover rate. WTX2 significantly decreased total organic carbon mineralization intensity， which was only 1.67%. Organic carbon intensity in soil with small particle size was lower than that of large particle size treatment. Therefore， sycamore leaves in small particle size and added with low addition amount can be applied to increase the stability of soil organic carbon and promote the retention of carbon in soil in regional soil organic carbon management.

The production of plywood consumes a large amount of energy. In order to improve economic efficiency， support carbon peak and carbon neutrality goals， it is necessary to improve energy efficiency and reduce energy consumption. This article took 5 heat exchange logistics in the production process of plywood as the research object， used pinch point technology to analyze the heat exchange network under existing production conditions， and proposed optimization and improvement plans. Aspen Plus was applied to establish a heat exchange network in the plywood production process， calculate the stream flow rate and physical property data of each side line， divide the temperature range， determine the minimum heat transfer temperature difference， and calculate the pinch point temperature. The traditional pinch point method determined the minimum heat transfer temperature difference ∆ T_{\mathrm{m}\mathrm{i}\mathrm{n}} to be 9 ℃. After considering carbon emissions， the minimum heat transfer temperature difference ∆ T_{\mathrm{m}\mathrm{i}\mathrm{n}} was adjusted to 7 ℃， and the average pinch point temperature was 116.5 ℃. The pinch point temperature was used to analyze and diagnose the phenomenon of crossing pinch points in the heat exchange network， accurately located the bottleneck position of the heat exchange network， adjusted the improperly configured cold and hot stream heat exchangers， and achieved the goal of optimizing the entire heat exchange network. After optimization， the usage of cold and hot utilities in the system decreased by 862 465.0 kW and 202 642.0 kW respectively， significantly reducing the energy consumption of the equipment.

In the customization process of passive wooden window manufacturing， reducing material waste during frame cutting is key to cost reduction. This problem is modeled as a one-dimensional cutting stock problem. To address the issue of traditional genetic algorithms where the individual encoding method tends to lead to the destruction of cutting patterns and low exploration efficiency during iterations， a new individual encoding method is proposed to maintain the integrity of cutting patterns throughout the evolutionary process. Additionally， a heuristic strategy and a correction strategy are introduced for individual correction and population evolution. Simulation results show that for different test cases， the average material utilization rate， excluding the last remnants， exceeds 99%， with some improvements in the length of the last remnants compared to other algorithms. For two sets of real production data from enterprises， the proposed algorithm achieves the theoretical lower bound， with average utilization rates （excluding the last remnants） of 99.49% and 99.66%， respectively， outperforming the results of the company's existing software. This demonstrates the algorithm's potential to effectively reduce costs and provide practical solutions in engineering applications.

The effects of ethephon （ETH）， ethephon inhibitor 1-methylcyclopropene （1-MCP） and cysteine （CYS） on the yield and main quality indexes of rubber tree latex were analyzed， and the dosage threshold was calculated. An optimized orthogonal experimental design was used to analyze 14 treatments with three factors （ETH， 1-MCP， CYS） and four levels （four concentrations of each reagent） for applying rubber tree cut surfaces. Key indicators such as rubber latex yield， dry rubber content， molecular weight， initial plasticity value， plasticity retention index and Mooney viscosity were measured， and the correlation between the indicators was analyzed. The results showed that there were significant differences in yield and dry rubber content of 14 treated rubber trees. Correlation analysis showed that the number-average molecular weight was positively correlated with the weight-average molecular weight and Mooney viscosity， and the correlation coefficient was 0.71 and 0.83， respectively， and the correlation coefficient was negative with the polydispersity indexes， and the correlation coefficient was -0.91. Initial plasticity value was positively correlated with Mooney viscosity， and the correlation coefficient was 0.73. The polydispersity indexes was negatively correlated with Mooney viscosity with the correlation coefficient -0.89. The regression equations based on dry rubber content index were established respectively. The maximum concentration of ethephon， 1-MCP and CYS were 0.15%， 1.08% and 0.41 g/L， respectively. The optimized orthogonal test method can effectively calculate the threshold of the regulator and provide theoretical and practical guidance for the subsequent experiments.

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.

Rare and endangered plants have formed a stable association with their associated tree species in the long-term evolution process， but whether this association is related to allelopathy produced by litters is still unknown. Taking the unique rare and endangered plant Davidia involucrata Baill. and its associated species Cornus controversa Hemsl. in China as research objects， the allelopathic effects of water extracts from different types of litter （branch litter， leaf litter， and their mixture litter） at natural concentrations on each other's seedlings were studied. The results revealed that， 1）the water extract from the branch litter of C. controversa significantly promoted the basal diameter and root length growth of D. involucrata seedlings， and increased the contents of chlorophyll a and b， total chlorophyll， leaf mass fraction of N， and P， while the extracts from leaf litter and branch-leaf mixture litter did not show significant promotional effects. 2）The water extract from the branch litter of D. involucrata significantly promoted the basal diameter growth of C. controversa seedlings and increased the contents of chlorophyll a and b， and total chlorophyll， whereas the extracts from leaf litter and branch-leaf mixture litter significantly reduced the contents of chlorophyll a and b， total chlorophyll， leaf mass fraction of N. 3）The allelopathic effect index indicated that different litter types from C. controversa had allelopathic promotional effects on D. involucrata seedlings， the intensity from large to small was branch， branch-leaf mixture， and leaf； the allelopathic effects of different litter types from D. involucrata on C. controversa seedling were varied， showing promotion by branch litter， and inhibition by leaf litter and branch-leaf mixture litter， with the mixture litter exhibiting stronger inhibitory effects. These findings suggest that the water extracts from different types of litter from D. involucrata and C. controversa have distinct allelopathic effects on each other's seedling growth， and the extracts from the two types of branch litter exhibit the greatest allelopathic promotional effects on seedlings.

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.

Due to the decreasing supply of petroleum based plastics resources， the development of biomass resources has attracted extensive attention of researchers recently. The research on environmental friendly antibacterial packaging materials has increasingly become a new trend. Nanocellulose （CNC） derived from biomass has attracted particular attention. In recent years， nanocellulose has been widely used in packaging materials， medical fields， filter media and other fields. It is a good substrate， modifier and additive. Polylactic acid （PLA） is an environmentally friendly and renewable polymer， which can be widely used in the field of food packaging to replace petroleum based plastics. However， its high brittleness and poor antibacterial properties limit the application of PLA in the field of packaging. In this study， two kinds of polylactic acid/carvacrol/nanocelluluse-zinc oxide （PLA/CRV/CNC-ZnO） composite films with antibacterial effect were successfully prepared by solution casting and electrospinning. The effects of CRV and CNC-ZnO hybrid on the properties of the composite films were studied. At the same time， two kinds of composite films were applied to strawberry preservation experiment to explore their preservation effect on strawberry. The PLA/CRV20%/CNC-ZnO3% composite film prepared by solvent casting method and F-PLA/CRV20%/CNC-ZnO3% composite film prepared by electrospinning method were used to freshen strawberries in the same environment， and the fresh-keeping effect of strawberries was evaluated by sensory evaluation， weight loss rate and other tests， and the effects of packaging films prepared by different processes on the fresh-keeping effect of strawberries were compared and analyzed. The results showed that both casting film and spinning film had better preservation effect on strawberry， and the preservation effect of F-PLA/CRV20%/CNC-ZnO3% spinning film on strawberry was better than PLA/CRV20%/CNC-ZnO3% casting film， and the shelf life of strawberry was successfully extended to 10 days at 25 ℃. This study has a certain guiding role in expanding the application of PLA composite film in antibacterial food packaging.

Under the background of new quality productivity， blockchain has brought new opportunities for transformation and upgrading of traditional industries with its transparency and traceability， and also opened up a new path for carbon emission reduction cooperation in wood supply chain. In view of this， this paper constructs an evolutionary game model of China-Russia wood supply chain， and studies the operating evolution law of the four parties-Russian wood suppliers， China wood processing manufacturers， wood products distribution retailers and the government in the carbon emission reduction cooperation mechanism under the blockchain platform. The results show that the initial strategy probability of supply chain members， consumers' low-carbon preference and the increase of manufacturers' carbon emission reduction can effectively encourage the main members of the wood supply chain to actively participate in the carbon emission reduction cooperation mechanism. At the same time， the government reward and punishment mechanism will also affect the final stable result of the game system. The greater the punishment for hitchhiking， the better the game system will reach the ideal state， while excessive subsidies will weaken the possibility of the system reaching the optimal equilibrium. The research results provide theoretical inspiration and reference for carbon emission reduction cooperation in China-Russia wood supply chain.

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.

Xinjiang is an important forest and fruit industry base in China， and the characteristic forest and fruit industry is an important part of regional economic development. In order to prevent fruit tree diseases from restricting the development of forest and fruit industry， a MobileNet-V2 NAM fruit tree leaf classification and disease identification model was designed in this study. It incorporated a lightweight normalization-based attention module to improve the model's sensitivity to feature information and make the model focus on salient features. At the same time， L1 regularization was added to the loss function to penalize the sparsity of the weights and suppress the non-significant weights. The experimental results showed that： in leaf classification， the model performed well in the classification results of self-built， Plant Village， and mixed datasets， with the accuracy rates reaching 97.05%， 98.73%， and 94.91%， respectively， and had good generalization ability. In disease identification， the MobileNet-V2 NAM model achieved a recognition accuracy of 94.55%， which was higher than the AlexNet， VGG16 classic CNN models， and the number of parameters of the model was only 3.56M. MobileNet-V2 NAM has good accuracy while maintaining a low amount of model parameters， provides technical support for embedding deep learning models into mobile devices.

To explore the differences of community characteristics， species diversity and their coupling relationship with environment in different cold temperate coniferous forests in Daxing'an Mountains， and to provide theoretical basis and data support for the scientific management and biodiversity protection of cold temperate coniferous forests in this area. Taking the typical Larix gmelinii forest， Pinus sylvestris var. mongolica forest， Picea jezoensis forest， Picea koraiensis forest， and Pinus pumila var. pumila forest in Daxing'an Mountains as the research objects， the characteristics of tree layer， shrub layer and herb layer were investigated respectively， and the diversity index， evenness index and richness index were calculated. Redundancy analysis （RDA） was used to explore the coupling relationship between community characteristics and species diversity of five populations. The results showed that the plant species in the cold temperate coniferous forest were poor. The overall tree layer tree species type was single. The average DBH and average tree height of Pinus sylvestris var. mongolica were the highest in the five coniferous forest associations. Vaccinium vitis-idaea was the main species in the shrub layer of five coniferous forests. Deyeuxia purpurea， Pyrola asarifolia subsp. incarnata and Maianthemum bifolium were the main species in the herb layer. These five populations had significantly different diversity characteristics and had significant differences in coupling relationships. The diversity index was relatively concentrated on the left side of the second axis， and was positively correlated with tree height， DBH and number of species in each layer， but negatively correlated with altitude， canopy density， shrub layer coverage and herb layer height. Five typical cold temperate coniferous forests in the Daxing 'an Mountains have significantly different community structure and species diversity characteristics.

As the main part of plant photosynthesis， chlorophyll plays an important role in monitoring vegetation growth and evaluating the capacity of carbon sequestration. Remote sensing technology， which is a high efficiency and low cost earth observation technology， can realize the estimation of chlorophyll content by leaf reflection spectrum characteristics. However， the accuracy of chlorophyll content estimation will be reduced due to the influence of leaf water content and leaf cell structure on leaf spectrum. Solar-induced chlorophyll fluorescence （SIF） is a technology of detecting the fluorescence information of chlorophyll excited fluorescence signal and the variation of fluorescence signal is directly related to chlorophyll content， which has great potential in chlorophyll content estimation. In this study， the fluorescence sensitive band of chlorophyll content was determined by sensitivity analysis using the fluorescence radiation transfer model SCOPE （Soil Canopy Observation， Photo-chemistry and Energy fluxes） and the chlorophyll content estimation model was established based on the fluorescence spectrum. Finally， the robustness of the model was verified by the measured data. The results showed that 700 nm and 730 nm were the highest and lowest sensitive bands of chlorophyll content， respectively. The band of 760 nm was the highest correlated band of chlorophyll content. The chlorophyll content estimation model were established based on the indices of fluorescence ratio of these three bands. SIF₇₆₀/SIF₇₀₀ had the best modeling accuracy with the R² of 0.998 1 and root mean square error （RMSE） of 0.043 5 μg/cm². The accuracy of SIF₇₀₀/SIF₇₃₀ and chlorophyll content （Cab） was the lowest in three models with R² and RMSE 0.904 8 and 0.088 6 μg/cm²， respectively. Independent samples of measured data were used to verify the above three estimated methods. SIF₇₆₀/SIF₇₃₀ had the best estimation results， with RMSE of 0.210 8 μg/cm²， followed by SIF₇₀₀/SIF₇₃₀， with RMSE of 0.345 4 μg/cm². But estimated model by using SIF₇₀₀/SIF₇₃₀ showed an overall overestimating results. The estimated results of SIF₇₆₀/SIF₇₀₀ showed a different results compared with measured data with RMSE of 0.743 5 μg/cm². In summary， the ratio vegetation index calculated by SIF₇₆₀/SIF₇₃₀ showed higher accuracy of modeling and excellent robustness of chlorophyll content estimation. Relevant studies provide technical references for the estimation of leaf biochemical parameters by chlorophyll fluorescence remote sensing technology.

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.

Microwave curing technology of concrete can significantly improve the early strength of concrete， but there is no uniform design standard for microwave curing method， resulting in great differences in material properties after curing. To explore the influence of microwave curing methods， this study employed the response surface methodology to systematically analyze the influence of microwave curing method on the compressive strength of cement mortar. The relationship model between key microwave curing parameters and material properties was established， and the optimal microwave curing method was determined when the heating temperature was under 40℃ and the heating power was 1 000 W for 30 min. Chemical components and microstructure impacts were studied using X-ray diffraction （XRD）， mercury intrusion porosimetry （MIP）， and scanning electron microscope （SEM）， and it was revealed that a reasonable microwave curing method can promote the hydration degree of mortar and form a denser structure. The temperature variation in microwave curing process was analyzed by combining experimental data and computer simulation， and it was found that microwave curing temperature had hot spot effect. It is concluded that microwave curing temperature is the most important factor affecting the strength of mortar.

In view of the limitations of existing indicators， in order to better evaluate the high and low temperature performance of warm mix asphalt binder， different amounts of Sasobit and Evotherm 3G warm mix agent were selected to be mixed with 70^# matrix asphalt and SBS modified asphalt to prepare modified asphalt. Two parameters of complex shear modulus G^* and phase angle δ were obtained and analyzed by dynamic shear rheological （DSR） test. The rutting factor G^*/sinδ， improved rutting factor G^* /（sinδ）⁹ index and critical temperatureT_{G\mathrm{*}/\mathrm{s}\mathrm{i}\mathrm{n}\delta }， improved critical temperatureT_{G\mathrm{*}/{(\mathrm{s}\mathrm{i}\mathrm{n}\delta )}^{\mathrm{9}}} index were used to comprehensively evaluate and analyze the high temperature performance of modified asphalt. Two parameters of creep stiffness modulus S and creep rate m were obtained and analyzed by low temperature bending rheological （BBR） test. The k index was established and the creep compliance J（t） index was introduced to evaluate the low temperature performance of modified asphalt. The test results and data analysis showed that： G^* /（sinδ）⁹ was more accurate than G^*/sinδ in evaluating the high temperature performance of warm mix asphalt， and T_{G\mathrm{*}/{(\mathrm{s}\mathrm{i}\mathrm{n}\delta )}^{\mathrm{9}}} was suitable for the high temperature performance evaluation of SBS warm mix asphalt， and there was no obvious difference for 70^# matrix asphalt. The k index can distinguish the difference of low temperature performance between matrix asphalt and modified asphalt， and the J（t） index can well reflect the low temperature creep performance of warm mix asphalt. Finally， the weight analysis of high and low temperature indexes was carried out by analytic hierarchy process （AHP）， and the weight values of T_{G\mathrm{*}/{(\mathrm{s}\mathrm{i}\mathrm{n}\delta )}^{\mathrm{9}}} and J（t） were the largest. It was suggested that T_{G\mathrm{*}/{(\mathrm{s}\mathrm{i}\mathrm{n}\delta )}^{\mathrm{9}}} was used to evaluate the high temperature performance of warm mix asphalt， and J（t） was used to evaluate the low temperature performance of warm mix asphalt.

To solve the problem that the target detection algorithm is prone to leakage and lacks detection accuracy in detecting wood surface defects， this paper proposes an improved YOLOv8 model （YOLOv8-CBW， C， B and W are abbreviations for CondSiLU， BiFPN and Wise-IoU） and constructs a self-made dataset containing various wood defects. By optimizing the original YOLOv8 algorithm and combining CondConv （conditional convolution） with SiLU （sigmoid-weighted linear unit） to form the CondSiLU module instead of the traditional convolution module， the flexibility of feature extraction is improved； the bidirectional feature pyramid network （BiFPN） is introduced to enhance the multi-scale feature fusion capability； and the Wise-IoU （weighted intersection over union） loss function replaces the CIoU （complete intersection over union） to improve the adaptability and generalization performance of the model to low-quality samples. The experimental results show that the improved YOLOv8-CBW model improves the mAP50 （mean average precision at IoU threshold 0.50） and mAP50-95（mean average precision over IoU thresholds from 0.50 to 0.95） by 3.7% and 3.9%， respectively， compared with the YOLOv8 model， and it shows higher precision and stability in complex wood defect detection tasks. The research in this paper provides new ideas for wood defect detection tasks and has good practical application prospects.

Aiming at the bottleneck problem of insufficient adaptability of traditional defect detection methods in automated wood processing industry， research on intelligent detection technology based on deep learning is carried out， and a dataset covering multi-species wood characteristics and typical defect types is proposed. Applying object detection technology to defect detection， using dilation wise residual （DWR） module to optimize C2f module， and proposing task aligned dynamic detection head （TADDH） and feature focusing spread pyramid network （FSPN） to impove YOLOv8 algorithm （DFT-YOLO）. The experimental results showed that a significant improvement in accuracy， reaching 96.8%， which was 7.9 higher than the original model. On the average accuracy of the key evaluation indicators mAP50 and mAP50-95， the impoved model reached 93.8% and 75.2%， respectively， increasing by 6.8% and 17.5%， respectively. While improving the detection accuracy， the number of parameters of the model had decreased by approximately 1/6 （16.2%）. The impoved model can provide a lightweight detection method for wood defects.

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.

In response to the complex diversity of surface defects in plywood veneers and the difficulties in feature extraction， as well as the large number of parameters and computational costs of deep learning-based defect detection algorithms， which makes effective application on devices with lower computing power challenging， a detection model for surface defects （live knots， dead knots， holes， cracks， and notches） in veneers based on an improved YOLOv8n is constructed. To enhance the detection accuracy and lightweight performance of the model， improvements are made to the plywood veneer surface defect detection model. First， a new efficient attention mechanism （coordinate attention， CA） is adopted， which can enhance the accuracy of feature extraction and the network's spatial information perception ability while avoiding excessive computational burden； secondly， a novel structure based on partial convolution （PConv） is proposed——CSPPC （CSP（coross stage partial） pyramid convolution）， it to improve computational efficiency and the fusion capability of multi-scale features； finally， an improved weighted intersection over union loss function——WIoUv3， it is introduced， which enhances the model's localization accuracy and robustness. Experimental results show that the improved YOLOv8 model （CP-YOLOv8） performs excellently in the task of detecting surface defects in plywood veneers， achieving an average precision mean （mAP） of 93.8%， an increase of 0.9% over the original model， while reducing the model's floating-point operations （GFLOPs） and parameter count to 7.2 G and 2.58 M， respectively， a reduction of 0.9 G and 0.42 M， which can fully meet practical application needs and provide an efficient， accurate， and lightweight solution for quality inspection of plywood veneers.

The low-temperature bonding performance at the asphaltmastic -aggregate interface plays a crucial role in determining the low-temperature crack resistance and water stability of asphalt mixtures in cold regions. This study employs a pull-off test coupled with ImageJ software for detailed image analysis of the failure surfaces. It focuses on two key metrics： bonding strength and bonding failure ratio， to evaluate the bond performance of asphalt， asphalt mortar， and asphalt concrete with limestone aggregates under varying low temperatures （-10， -20， and -30 ℃） and in wet conditions. The results reveal that the asphalt-aggregate interface achieves the highest bonding strength at -20 ℃. However， the bonding failure ratio increases as temperatures decrease. Notably， the presence of water can lead to a 49% reduction in low-temperature bonding strength， resulting in a continuous detachment of the interface and an increase in the bonding failure ratio by 44%. Furthermore， an optimal powder-binder ratio can enhance the low-temperature bonding strength of asphalt mortar； specifically， a ratio of 1.2 yields a bonding strength that is 1.44 times greater than that of the asphalt matrix. Consistency in the low-temperature bonding strength results is further corroborated through bending creep stiffness tests. The incorporation of mineral powder and fine aggregates modifies the contact characteristics at the interface， leading to significant alterations in the failure location and bonding failure ratio. Failures in asphalt mortar typically occur within the mortar itself， while those in asphalt concrete predominantly occur at the interface， with the bonding failure ratios of asphalt concrete being substantially higher than those of asphalt mortar.

Hemicellulose acid hydrolysates contain diverse sugars， yet existing separation and purification processes remain complex. This study presents a simplified and efficient method for isolating and extracting crystalline xylose from hemicellulose hydrolysates. By leveraging the preferential selectivity of phenylboronic acid toward xylose， a xylose-boronate ester XDE intermediate was formed. Subsequent transesterification with propylene glycol in ethyl acetate enabled the precipitation of high-purity crystalline xylose. The process achieved an average xylose yield of 75% and purity up to 99.8% over five operational cycles. Notably， this green solvent-based tandem system operated at ambient temperature， eliminating industrial-scale procedures including decolorization， desalination， and recrystallization. The developed methodology demonstrates efficient xylose extraction with minimized energy consumption and environmental impact.

Using rubberwood as raw material， orthogonal experimental design was used to explore the influence of silver-loaded nano titanium dioxide impregnated heat treatment on the mold and corrosion resistance of rubberwood， and compared the mold and corrosion resistance， dry shrinkage and wet swelling properties of the control timber， heat-treated timber， silver-loaded nano titanium dioxide impregnated timber and silver-loaded nano titanium dioxide impregnated heat-treated timber， and the influence mechanism of different modification treatments on the physicochemical properties of the timber was revealed through the means of electron microscopy， XRD（X-ray diffraction）. The results showed that the optimal process of silver-loaded nano titanium dioxide impregnated heat treatment was 160 ℃， 2.5 h， 1.5% silver loading， 1.5 mg/mL modifier concentration， 60 min vacuum impregnation time， at this time， the modified timber had the greatest efficacy in the prevention of Aspergillus niger and Penicillium citrinum， both of them were 100%， and the modified timber had the smallest rate of quality loss under the influence of white-rot fungus and brown-rot fungus， which were 2.36% and 1.9%， respectively. Compared with the control timber， heat-treated timber and silver-loaded nano titanium dioxide impregnated timber， silver-loaded nano titanium dioxide impregnated heat-treated timber had the lowest wet swelling and dry shrinkage， the radial， tangential and volumetric water-absorbing wet swelling rate was reduced by 31.34%-36.36% compared with that of the control timber， the radial， tangential and volumetric moisture-absorbing wet swelling rate was reduced by 41.06%-61.7% compared with that of the control timber， the radial， tangential and volumetric air-drying dry shrinkage rate was reduced by up to 23.28%-32.24% compared with that of the control timber. The silver-loaded nano titanium dioxide impregnated heat treatment can realize the uniform loading of silver-loaded nano titanium dioxide particles in wood， which can comprehensively improve the mold and corrosion resistance and dimensional stability of wood， and realize the excellent wood modification effect.

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.

The amplification circuit represents a pivotal component within the domain of forestry intelligent equipment， with its functionality exerting a direct influence on the efficacy with which weak signals are monitored within forestry monitoring applications. High-performance discrete amplification circuits are complex in structure， and the traditional manual selection of discrete component parameters in analogue circuits is inefficient and difficult to meet the requirements of low noise and high stability in fields such as forest fire monitoring and wood defect detection. The purpose of this paper is to propose an automatic parameter optimization method for discrete components in circuits based on the nondominated sorting genetic algorithm Ⅱ （NSGA-Ⅱ）. Firstly， a parameter optimization model of the amplifier circuit is established， and design indicators are proposed in accordance with the requirements. Next， NSGA-Ⅱ is used to solve the circuit parameters. Finally， the optimization results are verified by comparing NSGA-Ⅱ with manual methods， particle swarm algorithms， vortex search algorithms and genetic algorithms through simulation and physical testing of circuit boards. The experimental findings demonstrate that the NSGA-Ⅱ circuit parameter optimization method proposed in this study exhibits a substantial superiority over manual approaches in terms of circuit performance. In comparison with classical single-objective optimization， it also possesses advantages in terms of convergence speed and optimization stability. This method provides an efficient solution for the design of high-precision amplification circuits for forestry sensors， and it can be expanded to encompass the design optimization of other forestry equipment circuits in the future.

To solve the problems of low efficiency， high labor intensity， and poor working environment in plywood production， a surface defect repair equipment for plywood based on visual detection and automation control technology to improve the surface quality and production efficiency of plywood was designed. The equipment mainly included a visual detection and intelligent control system， a positioning system， an extrusion system， and a repairing mechanism. The visual detection and intelligent control system identified the size and position of defects through visual detection technology and generated corresponding G-code instructions （the most widely used CNC programming language for computer numerical control machines）， which were transmitted to the positioning system and extrusion system. The positioning system used the CoreXY mechanism （dual motor drive （Motors M1 and M2）， timing belt drive， X-shaped structure）， and the system received the instructions and controls the repairing mechanism to determine the defect repair point through the coordination of M1， M2， and Z motors. The putty extrusion machine of the extrusion system was used to realize stable extrusion and even coverage of the putty at the defect site. The test showed that the detection accuracy of the equipment for holes， cracks and other defects reached 97.1% and 70.6% respectively， which can effectively cover the surface defects of the plywood and avoid large areas of coating. The repaired plywood is superior to manual repair in terms of putty residue， usage and repair time， providing an effective solution for the automatic repair of the plywood processing industry.

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.

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.

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.