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.

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.

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.

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.

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.

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.

Quickly and accurately obtaining land use information can provide a reference for urban development and ecological environment protection. This study classifies land use types in Yunnan Province based on multi-temporal Landsat image dense time stacking and random forest algorithm on the Google Earth Engine （GEE） platform， analyzes the spatiotemporal change trends of LULC in Yunnan Province， and uses geodetectors to quantitatively evaluate key drivers factor. The results show that， 1） the average overall accuracy and Kappa coefficient of LULC classification in this study are 88.64% and 86.01%， respectively， which is highly accurate and meets the data usage requirements. 2） The land types in Yunnan Province are mainly forest land， cultivated land， grassland， and sparse shrub grass mixed land， accounting for 97.91%-98.38%. Land use transfer mainly involves the conversion of forest land and cultivated land， and the conversion of grassland and sparse shrub grass mixed land into cultivated land. 3） The land use intensity in central and eastern Yunnan in Yunnan Province is generally higher than that in other regions， while the land use intensity in northwest and southwestern Yunnan is lower. 4） There are significant differences in the influence of different driving factors on LULC. Vegetation type， average annual temperature and soil type have a relatively small impact on LULC changes. Elevation， slope， aspect， average annual precipitation， population density， GDP and population urbanization rate， etc.， generally have a high impact on LULC changes， Among them， GDP， population density， and population urbanization rate have a higher impact on LULC changes. The research results can provide data basis and support for subsequent ecological and environmental protection policy formulation and regional sustainable development in Yunnan Province.

In order to improve vehicle′s trafficability in the desert， based on the analysis of the desert soil parameters and the theory of traficability of crawler vehicles on soft ground， a LY352J desert crawler transport vehicle equipped with triangular track shoes was developed. This vehicle can carry out personnel and material transportation， vehicle traction， and winch tration operations， mainly composed of key parts such as triangular track shoes， winch and cab. Through theoretical analysis and calculation， the parameters of each key component andh traction traficability of desert crawler transport vehicle on soft desert ground were determined. Creo software was used to build a virtual prototype， and RecurDyn （Recursive Dynamic） dynamic simulation software was used to simulate the optimal tension selection， straight driving， climbing， and turning of vehicle crawler. The results showed that， under the condition of 45% vehicle weight， the optimal tension of the track was 26.46 kN； under the condition of 26.46 kN track tension （45% vehicle weight）， it could ensure the smooth operation and good performance of the vehicle； the maximum slope obtained by uphill simulation was 30°； in the steering dynamics simulation， the steering stability of the vehicle on the road was better， but it was easy to lose stability when turning， and it would appear lateral inclination. At last， the experiments of straight running， longitudinal climbing and turning of test vehicle were carried out in test site， and it was verified that the vehicle had good trafficability when actually walking in the desert terrain， and can overcome the drawbacks of wheeled transport vehicles sinking in sand， meeting the requirements of current desert terrain transportion operations.

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.

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 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.

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.

Airborne hyperspectral data can reflect the spectral characteristics of tree species， which can be used for precise classification of forest tree species. This study applies different machine-learning classification algorithms to classify tree species in hyperspectral images of unmanned aerial vehicles （UAV）. Firstly， a UAV was used to collect hyperspectral data from the Maor Mountain Experimental Forest Farm in Heilongjiang Province， and a series of preprocessing was completed for the obtained data. Then， three different machine learning classification algorithms， namely， support vector machine （SVM） based on Gaussian kernel， random forest （RF）， and K-nearest neighbor （KNN）， were used to establish the tree species classification models， respectively， based on the full-band hyperspectral data. Meanwhile， tree species classification models were constructed based on the dimension-reduced full-band hyperspectral data using different band selection methods （successive projections algorithm， competitive adaptive reweighted sampling method and uninformative variable elimination method）. Finally， the tree species classification model was constructed by combining different band selection methods and hyperspectral image texture features， and the results of different processing methods were compared. Research shows， the kernel SVM had the highest classification accuracy （87.55%） among the tree species classification models with full-band hyperspectral data. After selecting different bands， the stability of RF is the best among the three classification algorithms， and the accuracy rate was high， while the classification accuracy of the support vector machine based on the Gaussian kernel improved with the increase of feature dimension. The accuracy of the tree species classification model established by extracting texture features based on a grayscale co-occurrence matrix combined with band selection was higher than that of the model established by a single band selection. In particular， the K-nearest neighbor classification algorithm has the greatest improvement， which indicated that modeling with clearly partitioned features can achieve good classification results. This study used different feature selection methods combined with three different machine learning classification algorithms to achieve dominant tree species classification based on hyperspectral data， which provides technical reference for the combination of band selection methods and machine learning algorithms， and it is also of great significance for forest biomass retrieval and carbon storage estimation based on UAV hyperspectral data.

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.

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 order to accurately obtain forest canopy height information， accurately estimate forest aboveground biomass， and evaluate forest carbon sink capacity， this study constructed 30 long time series feature variables based on ground measurements， multi-source remote sensing data， and digital elevation models， combined with machine learning algorithms （ML）， to invert the forest canopy height in Lishui City， Zhejiang Province. The study revealed that terrain factors had no significant impact on the inversion of forest canopy height， while vegetation factors related to the red and green bands were strongly correlated with forest canopy height. Adding long time series feature factors can help improve the accuracy of ML algorithm in inverting forest canopy height. The performance improvement of CNN was particularly significant， achieving an optimal coefficient of determination （R ²） increase of 0.39 and a root mean square error （RMSE in the formula， it is denoted as R _MES） decrease of 4.15. Random forest had the highest inversion accuracy among the four ML algorithms （R ²=0.79， R _MSE=1.65）， greater than support vector machine （R ²=0.65， R _MSE=1.97）， extreme gradient ascent method （R ²=0.76， R _MSE=1.81） and convolutional neural networks （R ²=0.71， R _MSE=1.83）.

Wood and its products are widely used in people’s daily life， including furniture manufacturing， interior decoration， construction and other fields. The total annual output value of the wood industry has exceeded 3 trillion yuan， which is an important basic industry of the national economy. However， there are still problems such as low added value of products， weak innovation ability of enterprise， and decentralized production supply chain in the process of wood industry development. Starting towards the new and green departure， incubating new technologies and materials for functional wood manufacturing， and cultivating new productivity in the wood industry are important directions to boost the wood industry development and accumulatively achieve low-carbon transformation. This paper focuses on the frontier development direction of wood science， discusses the important quality productivity of wood industry， and clarifies the importance of scientific and technological innovation for wood industry development. Furthermore， this paper puts forward four directions for the future development of wood science， including the micro in-depth， the macro expansion， the extreme conditions， and the comprehensiveness， and summarizes the newly representative research results， sorts out the development status and trend of the representative wood new products and functional new materials， and lays the foundation for further proposing the development path of new quality productivity in China's wood industry.

Quantifying the relationship between radial growth of Larix gmelinii （Rupr.） Kuzen of different ages and climatic factors， and provide decision making basis for adaptive management and management of natual Larix gmelinii （Rupr.） Kuzen forests. Therefore， based on the data of 159 Larix gmelinii （Rupr.） Kuzen.from Pangu Forest Farm of Tahe Forestry Bureau in Heilongjiang Province in 2022， this study adopted the method of systematic cluster method to divide the trees into young-age， middle-age and old-age group based on tree age. Then， using dendrochronology， standard chronology was established to analyze and compare the relationship between radial growth of trees of different age groups and climate factors， and multiple stepwise regression method was used to construct the prediction model of single tree radial growth sensitive to climate.The results showed that： 1） The total sample explanatory weight （EPS）， mean sensitivity （MS） and signal-to-noise ratio （SNR） of the （total group， old-age group， middle-age group， and young-age groups） were higher， which indicated that the standard chronology of Larix gmelinii （Rupr.） Kuzen retained abundant climate information. 2） The main limiting factors for radial growth of Larix gmelinii （Rupr.） Kuzen were temperature in August of the current year， temperature from August to October of the previous year， and precipitation in February and July of the current year， and in July and December of the previous year. 3） There were significant differences in the response of Larix gmelinii （Rupr.） Kuzen to climate factors in different age groups. The radial growth of old-age Larix gmelinii （Rupr.） Kuzen was affected not only by the summer temperature of the current year but also by the summer temperature and precipitation of the previous year， showing a significant lag effect， while the young and middle age Larix gmelinii （Rupr.） Kuzen were less affected by environmental factors.

In order to optimize the technical problems of cutting rooting of rare and endangered tree species Euptelea pleiospermum， using 1-year-old semi lignified branches of E. pleiospermum wood as cutting materials， an orthogonal experimental design was adopted to explore the effects of different substrates， exogenous hormones， concentrations， and treatment times on the rooting of E. pleiospermum wood cutting through the calculation of rooting rate， root effect index， and other indicators （Matrix type、hormone type， mass concentration， hormone treatment time were four factors， and each factor had four gradients）. The membership function numerical method was used to comprehensively evaluate the rooting effect of 16 treatment combinations， providing technical reference for the expansion of E. pleiospermum wood. Among the four cutting substrate ratios （100% peat；V （peat）∶V （perlite）=5∶2； V （peat）∶V （vermiculite）=5∶2； V （peat）∶V （perlite）∶V （vermiculite）=5∶2∶2， V represents the volume ratio）， peat： perlite （5∶2） had the best rooting effect， with a rooting rate， average root length， longest root length， average root number， and average root diameter of 56.67%， 6.74 cm， 7.47 cm， 5.73 stripes/ears， and 0.31 cm， respectively. The root effect index reached 1.74， which was much higher than the other three substrate ratios （P<0.05）. The results of the orthogonal experiment showed that the rooting effect of cuttings soaked with ABT-1 at a concentration of 1 g/L for 5 seconds was the best， with a rooting rate of 65.73%， an average root length of 7.38 cm， an average number of 7.59 stripes/ears， a maximum root length of 7.52 cm， an average root diameter of 0.35 cm， and a root effect index of 1.87. Comprehensive analysis showed that the optimal treatment combination for rooting effect was （peat to perlite volume ratio 5∶2 as substrate， with the highest average membership function value in ABT-1 at a medium speed of 1.0 g/L for 5 seconds）， and the lowest treatment combination was （peat to vermiculite volume ratio 5：2 as substrate， soaked in 0.8 g /L indole butyric acid （IBA） for 12 hours.

To meet the requirements of fixed-point monitoring and reconnaissance tasks within forest areas by rotary-wing drones， a bionic perching robotic arm is designed through the study of birds' perching processes and the bionic research of legs and feet. The modular design and kinematics snalysis of the whole machine are carried out. The leg module employs Automatic Dynamic Analysis of Mechanical Systems （ADAMS） for kinematic trajectory simulation， while the claw module uses the D-H（Denavit-Hartenberg） parameter method to obtain the kinematic equation of the toetip. The workspace point cloud distribution of the claw module's toetip is derived through MATLAB simulation. A prototype is fabricated to establish an experimental system， which is then used to verify the activity range of the toe part and the overall perching capability of the machine. This design is simple and easy to control， capable of completing perching behavior in the laboratory stage.

The canopy width is a necessary variable for the growth of desert vegetation. Predicting the crown width of desert plant in different habitats can provide reference for the scientific management of desert vegetation. Taking the species of Haloxylon ammodendron in the Gurbantunggut Desert as the research object， select 6 commonly （M1， M2， M3， M4， M5， M6） used regression models， using tree height and ground diameter as survey factors， establish a canopy model of H. ammodendron in different habitats of highlands， gentle slopes， and flat lands. Perform regression analysis on the model using Origin and Anaconda software， select the optimal model based on four fitting accuracy standards （R²， RMSE，MAE， and MAPE）. The model fitting results indicate that： in the tree height -shrub crown width and shrub ground diameter models， the R² values of the six regression models are all greater than 0.5， the model fitting effect is good. M5 （cubic polynomial model） is the optimal model for tree height crown width and ground diameter crown width in three different habitats. All six models have statistical significance （Sig.<0.001）， which has important reference value for predicting the crown size of H. ammodendron in the Gurbantunggut Desert， and provides a certain scientific basis for maintaining the ecology and vegetation restoration of the region.

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 explore the advantages and complementarity of optical data and synthetic aperture radar （SAR） data in forest type classification， this study focused on the overlapping area of Landsat8 data and ALOS2 data from one scene SAR image in Simao District， Puer City， Yunnan Province， China， and used hierarchical classification technology for forest type classification research. Three feature sets were constructed： optical feature set （spectral + vegetation + texture + terrain features）， SAR feature set （backscattering + polarization decomposition features）， and optical-SAR fusion feature set （spectral + vegetation + texture + terrain + backscattering + polarization decomposition features）. Recursive Feature Elimination （RFE） was employed to perform stratified feature selection on the three feature sets， and random forest （RF） and support vector machine （SVM） were used for forest type classification. The SVM classification with the fusion of optical images and SAR data achieved the best results. The results showed， 1） In the first layer （vegetation/non-vegetation） classification， the overall accuracy was 98.57%， the Kappa coefficient was 0.971. 2） In the second layer （forest/non-forest） classification， the overall accuracy was 92.14%， the Kappa coefficient was 0.826. 3） In the third layer （coniferous/broad-leaved/mixed forest） classification， the overall accuracy was 83.47%， and the Kappa coefficient was 0.743. The fusion data showed an improvement of 6.74% in accuracy compared to optical data feature set classification and 29.24% compared to SAR classification. 4） In the classification of the third layer using fusion data， the influence of different window sizes （3×3， 5×5， 7×7， 9×9） of texture features in optical images was compared， and the highest accuracy was achieved with a 7×7 texture window. Results shows that， the accuracy of forest type classification using multi-source data is higher than that using a single data source.

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.

In order to study the effects of top pruning time and retained stem height on the quality of Pinus koraiensis seedlings， a top pruning test was carried out with 4-year bare root seedlings of Pinus koraiensis as materials， and 3 retained stem heights （20， 25， 30 cm） and 5 top pruning times （April， May， June， July， August） were set. The root collar diameter， biomass， shoot to root ratio， root length， root surface area and root volume of seedlings under different top pruning treatments were measured. The results showed that top pruning treatment in the middle of the growing season （June） could significantly increase the root collar diameter growth of Pinus koraiensis seedlings by 23.68% compared with the control， but the effect of top pruning treatment at other times was not obvious. The top pruning treatment had no effect on the increase of biomass， the decrease of shoot to root ratio and the growth of thick roots， but had a significant effect on the growth of fine roots of Pinus koraiensis seedlings. The length of fine roots， surface area of fine roots and volume of fine roots were increased by 18.27%-84.95%， 9.38%-75.01% and 1.28%-64.68%， respectively， compared with the control. The top pruning in the middle of the growing season （June） increased the root collar diameter growth of Pinus koraiensis seedlings， promoted the growth of fine roots and promoted the quality of Pinus koraiensis seedlings to a certain extent.

Forest mobile robots based on visual navigation face the problem of limited computational power as edge devices and the navigation performance is greatly affected by illumination. To address this， a lightweight trunk detection method is proposed. This method uses visible and thermal image as inputs， minimizing the impact of illumination on navigation performance it also employs a feature extraction module based on Partial Convolution （PConv） and a Partial Efficient Layer Aggregation Network （P-ELAN） to achieve lightweight improvements to the baseline model. During training， the alpha-CioU loss function is used to replace the original CIoU loss function， increasing the accuracy of bounding box regression. The results show that the proposed tree trunk detection method for forest mobile robots reduces the parameter count of the original YOLOv7-tiny model by 31.7%， decreases computation by 33.3%， and improves inference speeds on Graphics Processing Units （GPU） and Central Processing Units （CPU） by 33.3% and 7.8%. The modified model maintains comparable accuracy while being more lightweight， making it an ideal choice for deployment on edge devices such as robots.

Studying the variation patterns of soil microbial biomass and enzyme activity during the vegetation recovery processes of burned areas， providing a scientific basis for reforestation approaches and effectiveness assessment in burned forests. The three distinct types of Larix gmelinii seed tree-Betula platyphylla forests （MB）， understory nursery-Betula platyphylla forests （FB）， and Betula platyphylla forests （BB） in the severely burned areas in Daxing′an Mountains were selected as the research objects， with Larix gmelinii plantation（LL） served as a control. The soil microbial biomass carbon （MBC）， microbial biomass nitrogen （MBN）， and the activities of urease （URE）， acid phosphatase （ACP）， sucrase （SUC）， and catalase （CAT） at depths of 0-5， 5-10， and 10-20 cm were measured. Results showed that： 1） The variations in soil MBC and MBN ranged from 239.16 to 852.09 mg/kg and 37.08 to 114.99 mg/kg， respectively. The order of MBC and MBN content was MB>FB>LL>BB， except for the 10-20 cm soil layer. 2） The activities of ACP， CAT， and URE were highest in MB. In FB， the average activities of ACP， SUC， and CAT were higher than those in LL， while the average activities of URE and ACP in BB were lower than those in LL. 3） Redundancy analysis showed that the interpretation rates of soil enzyme activities reached 46.8%， 24.9% and 4.5% for 0-5 cm SOC， SAP and MBN， and 61.8%， 11.4% and 4.0% for \mathrm{N} {\mathrm{O}}_{\mathrm{3}}^{-}-N， pH and MBC in 5-10 cm soil layer. The interpretation rates of \mathrm{N} {\mathrm{O}}_{\mathrm{3}}^{-}-N， pH and TN in 10-20 cm soil layer were 53.3%， 14.7% and 12.4%， respectively. Retaining trees with reproductive capacity as seed trees after a wildfire and implementing timely nurturing management have a positive promoting effect on soil microbial biomass and enzyme activity， contributing to the recovery of vegetation in burned areas.

Using remote sensing methods to accurately estimate aboveground biomass carbon stock （ABGCS） in forest canopy layers and light saturation value of carbon storage， aiming to replace the cumbersome procedures of traditional large-area surveys， providing references and basis for carbon storage estimation， and improving the efficiency of sustainable forest management. In this study， the ABGCS in Jiayin County， Yichun City， Heilongjiang Province in 2017 was selected as the research object. Landsat 8 OLI remote sensing images and forest resource two-class survey data were used to construct parameter models of stepwise multiple regression model （SMR）， non-parameter models of BP neural network model （BP-NN）， random forest model （RF）， support vector regression model （SVR） to estimate and reverse the spatial distribution of ABGCS in Jiayin County. The research results showed that the estimation accuracy of non-parameter models was significantly higher than that of parameter models. Among them， the fitting accuracy of the three non-parameter models （BP-NN， RF， SVR） was increased by 25.0%， 12.2%， and 7.3%， respectively， compared with the parameter model （SMR）. By comprehensive comparison of the evaluation indexes of the four models in ten-fold cross-validation， the performance of the models was analyzed： BP-NN>RF>SVR>SMR， among which the BP-NN model fitted the largest R ² （0.785） and the smallest RMSE （3.572 t/hm²）， MSE （12.757 t/hm²）， MAE （2.687 t/hm²）. From the perspective of carbon storage residual segmentation test results， all four models exhibited varying degrees of overestimation and underestimation of carbon storage. The BP-NN model had the smallest ME and MRE values in each carbon storage segment， indicating strong generalization ability. The light saturation value of ABGCS was determined to be 63.056 t/hm² using a cubic model， which was close to the predicted ABGCS light saturation value by BP-NN （64.232 t/hm²）. Therefore， the BP-NN model has a relatively ideal effect in estimating ABGCS in Jiayin County， providing important basis for dynamic monitoring and research of forest carbon storage.

This experiment used an indoor culture dish germination method to study the effects of exogenous addition of polyethylene glycol， sodium carbonate， and sodium bicarbonate on the embryos germination and physiological indicators of S. pohuashanensis seedlings. The results showed that polyethylene glycol treatment increased the germination percentage of S. pohuashanensis embryos by 15% to 25%， while sodium carbonate and sodium bicarbonate treatment reduced the germination percentage of S. pohuashanensis embryos by 88% and 85%， respectively. Polyethylene glycol can alleviate the inhibition of saline alkali stress on the germination of S. pohuashanensis embryos， and increase the germination rate of embryos under stress by 1-2 times. Polyethylene glycol can increase the activity of antioxidant enzymes in the embryos of S. pohuashanensis under saline alkali stress， resulting in an increase of 16.45%， 75%， and 80% in enzyme activities such as SOD， POD， and CAT， respectively. Polyethylene glycol reduced MDA in embryos under stress by 41.44%. Research has shown that polyethylene glycol can effectively alleviate the inhibitory effect of salt alkali stress on the embryos of S. pohuashanensis， improve the activity of antioxidant enzymes in S. pohuashanensis embryos， enhance the stress resistance of S. pohuashanensis， and provide scientific basis for establishing PEG priming technology to promote the germination of S. pohuashanensis embryos.

In order to explore the potential of L-band full-polarization SAR data to estimate forest aboveground biomass （AGB）， five polarimetric scattering ratio parameters （R₁， R₂， R₃， R₄， R₅） were constructed based on the canopy-ground scattering component of Unmmaned Aero Vehicle Synthetic Aperture Radar （UAVSAR） data of the AfricaSAR project. Calculating the Radar Vegetation Index （RVI）， and 21 polarimetric decomposition parameters were extracted by four model-based decompositions， including six-component and seven-component decomposition. Finally， all features were merged and the random forest feature importance was used to screen out the optimal feature combination， and random forest （RF）， support vector machine regression （SVR）， K-nearest neighbor regression （KNN） were used to estimate forest AGB of Lope， The Gaboneses Repbulic， Africa， with different feature combinations. The results showed that the polarimetric scattering ratio parameters， bulk scattering （Vol） and RVI had high sensitivity to forest AGB， and the correlation between R₂ and AGB was 0.823， and the optimal feature combination was Vol， polarimetric scattering ratio parameters and RVI. Machine learning models with different feature combinations had shown good performance， the coefficient of determination （R²） of the machine learning model based on the polarimetric decomposition parameters was bigger than 0.800， and the root mean square error （RMSE） was less than 88.000 Mg/hm²， and the best effect was the RF model based on the optimal feature combination， which increased R² by 0.144 and decreased RMSE by 30.327 Mg/hm² compared with the polarimetric decomposition parameters alone. The polarimetric scattering ratio parameter had certain potential in the estimation of forest AGB， the introduction of RVI improved the accuracy of the model， the model-based decomposition was suitable for forest AGB estimation， and the machine learning model based on feature screening can better invert forest AGB， and there was no obvious saturation point when the AGB reached 400 Mg/hm².

In order to explore the adaptability of the roots and leaves of Amorpha fruticosa L. stoichiometric characteristics to nutrients， and to provide a basis for the study of fertilization and restrict modes of leguminous plants. We usedfertilization treatment with three factors （nitrogen （N）， phosphorus （P）， potassium（K））， and three fertilization levels （N1， N2， N3， P1， P2， P3， K1， K2， K3） to measure the carbon （C）， N， P， K content of the roots and leaves of Amorpha fruticosa L. seedlings， and analyze the relationship between elements in the roots and leaves. Our results showed that the leaves had a stronger response than roots under P， K addition， and roots had a stronger response to N addition. Leaf and root N contents， root N/K and root N/P were increased significantly with increasing N addition level， root P/K， leaf P/K and root C/N decreased significantly. Leaf N， P and K content， root P content， root N/K and root P/K increased significantly， leaf N/K and leaf N/P decreased significantly with increasing P application； root and leaf K content increased significantly and root N/K， root P/K， leaf N/K and leaf P/K decreased significantly with the increasing of K application. Leaf C content， root P content， leaf K content and root K content were highly significantly positively correlated， while the relationship between leaf C， N contents and root C， N contents was not significant， while leaf and root N， P， and K contents were significantly negatively correlated with their corresponding C/N， N/P and N/K， respectively. The relationship between the ratio of carbon to nitrogen in leaf and root was insignificant， while the ratio of nitrogen to phosphorus， nitrogen to potassium and phosphorus to potassium in leaf were significantly correlated. The results of membership function analysis showed that in the accumulation of C， N， P and K contents of Amorpha fruticosa L.， N2 was the best treatment. N addition resulted in P limitations to leaves and roots； P addition resulted N limitations to leaves and roots； K addition did not changed the limitation patterns. These results can provide a good basis for rational fertilization and scientific management of Amorpha fruticosa L..

To study the identification of burned areas and post fire vegetation restoration in the Daxing'an Mountains region， based on Landsat TM remote sensing images from 2006 to 2020， Google Earth Engine was used to write code. The research background was the 2006 forest fire in the Nayuan Forest Farm in the Songling District of the Daxing'an Mountains region. The differential normalized burned ratio （dNBR） data was used to identify the burned areas， and the severity was classified into mild， moderate， severe， and extremely severe levels. Based on the Enhanced Vegetation Index （EVI） values of burned areas， methods such as univariate linear regression analysis， Mann-Kendall mutation test for climate diagnosis and Theil-Sen media trend analysis for treud analysis were used to analyze the vegetation restoration characteristics of burned areas from 2006 to 2020， and to explore the process of vegetation restoration in the Daxing'an Mountains region. The results showed that， 1）Based on dNBR， the burned areas in the study area was 2 488.7 hm²， with 23.5%， 9.6%， 35.2%， and 31.7% of the areas affected by mild， moderate， severe， and extremely severe fires， respectively. Severe and extremely severe areas of excessive fire were distributed in the western and eastern parts of the burned area， and the severity of excessive fire gradually decreased from the central to the southern and northern parts. The EVI values decreased by about 30%， 40%， 58%， and 67% compared to before the fire， respectively. 2）The recovery rate of EVI in forest burned areas with different intensities showed extremely severe， severe， moderate， mild. During the vegetation restoration process， the EVI value of the burned areas gradually increased. Mild and moderate burned areas can recover 6-8 years after the fire， while the recovery of severely burned areas required 14 years. 3）During the restoration process of burned areas， there were fewer EVI mutation points in forested areas compared to grasslands， indicating stronger stability of forest ecosystems compared to irrigated grasslands. There were also certain differences in the mutation situation of forest burning sites with different intensities， and the mutation time point in the control area lagged behind the burning sites.

Zhalong National Nature Reserve is the largest and world-renowned wetland in China， and its vegetation changes have a significant impact on the ecological environment of the wetland. This study focuses on the Zhalong Wetland in Qiqihar City. Based on MODIS data， the pixel binary model method and ArcGIS 10.2 software were used to analyze the spatiotemporal changes in vegetation cover of the wetland from 2001 to 2022. The aim is to reveal the trend of vegetation change in Zhalong Wetland， in order to provide reference and guidance for the ecological protection， restoration， and construction of the wetland. The results show that： 1） the vegetation coverage in the eastern region of Zhalong Wetland is better than that in the western region， and the overall spatial variation of vegetation coverage level shows a fluctuating increasing trend； The overall transformation is from low vegetation coverage to high vegetation coverage， with a significant increase in the area of high vegetation coverage， while the area of intermediate， medium， and low vegetation coverage gradually decreases. 2） There is a correlation between vegetation coverage and annual precipitation， average annual temperature， and annual water replenishment in the region. The high vegetation coverage area shows a significant positive correlation with annual precipitation and annual water replenishment （R=0.42； R=0.62）， while the medium high vegetation coverage area shows a significant positive correlation with the annual average temperature of the previous year （R=0.46）， and also has a certain correlation with the annual average temperature of the current year； The high vegetation coverage area also has a certain correlation with the annual precipitation of the previous year， showing a characteristic that the change in vegetation coverage lags behind the change in precipitation.

Taking the marine-continental facies soft silty soil in the Pearl River Delta region as the research object， the changing rules of shear strength and shear strength indexes under different consolidation pressures and degrees of consolidation were investigated through indoor straight shear tests， and a three-dimensional logistic mathematical model of shear strength and corresponding indexes-degree of consolidation-consolidation pressure was proposed. The results showed that： when P≥200 kPa， U≥40%， the growth of soft soil cohesion （c） and internal friction angle （φ） was more obvious； when U=100%， campared to the initial state， the soft soil shear strength （τ） under the action of all levels of consolidation pressure increased by 7.89， 12.73， 13.50， 18.20， 22.38 kPa， respectively； the given three-dimensional mathematical model can directly calculate the shear strength index and shear strength under a certain consolidation pressure and consolidation degree； the research results can more accurately evaluate the overall stability of the soft soil foundation in this area during the step-by-step loading process.

Climate change and forest vegetation affect the distribution of PM_2.5 concentrations， and PM_2.5 as an important air pollutant can also affect forest vegetation growth directly or indirectly. Currently， the technique of inverting daytime PM_2.5 based on optical aerosol thickness （AOD） data is relatively mature， and as a complement to daytime PM_2.5， nighttime PM_2.5 is of great significance for the all-day PM_2.5 monitoring. Based on the radiation transmission theory， the machine learning estimation model of nighttime PM_2.5 concentration in the three northeastern provinces was established with nighttime light brightness， enhanced vegetation index and seven meteorological factors（2 m dewpoint temperature， 2 m temperature， u component of wind speed， v component of wind speed， atmospheric surface pressure， evaporation，precipitation） as input variables， and nighttime PM_2.5 concentration as response variable， aiming to provide a reference for monitoring nighttime PM_2.5 concentration in the three northeastern provinces. The results show that the model constructed based on the integration tree has high estimation accuracy， with a goodness of fit （R ²） of 0.68， a mean absolute error （MAE） of 7.05 µg/m³， and a root mean square error （RMSE） of 11.62 µg/m³. In addition， the model is found to have certain spatial and temporal sensitivity by analyzing the errors between the estimated and true PM_2.5 values at each monitoring station in the three northeastern provinces. It can provide a reference for the forest vegetation conservation work by timely and accurately controlling the distribution of nighttime PM_2.5 concentration.

To reveal the evolution characteristics of vegetation cover in Nanchang City and identify the main climate drivers affecting its change trends， in order to provide guidance for its long-term stable and benign development and active response to the subsequent climate change. In this paper， NDVI monthly data and ten meteorological driving factors such as temperature， precipitation and pressure of Nanchang City from 2000 to 2020 were used to study the importance of different drivers by using single sample K-S test， Friedmann test， Kendall harmony coefficient test and random forest analysis. The results showed that， 1） the NDVI of Nanchang City showed a fluctuating downward trend from 2000 to 2020， the peak appeared in 2000， while the trough occurred in 2010. 2） The overall vegetation cover of the city showed a spatial distribution pattern of high perimeter and low center， with the NDVI values in the north and the west being relatively high and decreasing at a slow rate， and the NDVI values of the central areas of the city such as the East Lake District and the Castle Peak Lake District being relatively low and with an obvious downward trend. 3） The great average value of the vegetation cover appeared in August each year， which was most significantly affected by temperature and least affected by wind direction， with a certain time lag influenced by precipitation， so the study of vegetation cover change should be determined by combining the driving effect of multiple elements.

In view of the uncertain mechanical properties of knot-bearing wood and the difficulty of judging whether it is usable， this article proposes a method to evaluate the usability of wood containing knots by detecting the bending properties of wood containing knots. The common tree Quercus mongolica， which accounts for 15%-20% of the total forest area in Northeast China， is selected as the experimental object. Firstly， the object detection algorithm is used to identify the areas containing knots on the suface of wood， followed by spectral extraction of the identified area and the construction of a quantitative prediction model. Finally， the mechanical properties of wood containing knots are analyzed through deep learning. The experimental results indicate that the SPA-SVM prediction model proposed in this article has excellent predictive ability for the bending properties of wood， with experimental results indicators of R ²=0.96， RMSE=0.58， and RPD=5.09. The prediction model proposed in this article can accurately predict the bending properties of wood containing knots. The predicted results have a small error with the actual values， which meets the experimental requirements and standards. The predicted results can provide a basis for whether the wood can be used.

To address the challenge of accurately extracting phenotypic parameters from in situ root images collected from minirhizotrons amidst background noise interference， this paper proposes a minirhizotron root phenotypic parameter measurement system based on an improved U-Net model. In the U-Net network， optimized ASPP （Atrous Spatial Pyramid Pooling） and ECA （Efficient Channel Attention） modules are employed to increase the receptive field and enhance the ability to capture detailed features， thereby obtaining precise segmentation images. The experimental results show that the mean intersection over union and mean pixel accuracy of the improved U-Net model are 87.07% and 91.85%， which are 2.49% and 2.3% higher compared to the original U-Net， respectively. Comparing with measurements obtained using WinRHIZO root analysis software， the determination coefficients for the root length and area are 0.951 8 and 0.984 9. respectively. The Spearman correlation coefficients are 0.972 5 for the root length and 0.975 7 for root area. This indicates the system′s capability to accurately measure the root length and area.

To explore the feasibility of coal gangue powder applied in micro-surfacing， the coal gangue powder and activated coal gangue powder were selected to replace the filler of micro-surfacing mixture， and the different combination specimen was formed. The Hamburg wheel tracking test， accelerated wear test and shear fatigue test were used to analyze the road performance and interlayer property of micro-surfacing mixture with different fillers， and the significant influence of different factors on long-term performance of micro-surfacing was analyzed. The results showed that the high temperature performance， moisture susceptibility， abrasion resistance， long-term skid resistance of micro-surfacing were improved when the coal gangue powder was selected to replace the mineral powder， and the improvement effect of high temperature activated coal gangue powder was better， and the substitution amount of coal gangau powder had a higher impact on the different properties of micro-surfacing than the coal gangue powder. But the road performance and interlayer property of micro-surfacing were damaged due to the cement was replaced by the coal gangue powder. The long-term performance of micro-surfacing was closely related to the dosage of coal gangue powder， the dosage of coal gangue powder was greater， the long-term performance of micro-surfacing was better. The mineral powder was replaced completely by the coal gangue powder， the high temperature performance， moisture susceptibility， abrasion resistance， long-term skid resistance and shear fatigue life of micro-surfacing were improved by about 26.8% and 36.4%， 33.5% and 40.0%， 33.4% and 45.1%， 4.7% and 6.8%， 9.5% and 10.4%， respectively. The research results will provide reference basis for coal gangue applied in asphalt pavement maintenance technology.