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.

In order to realize the high-value and large-scale utilization of metallurgical solid waste materials， the soil curing agent was synergistically prepared by using metallurgical solid waste materials （blast furnace slag， fly ash， calcium carbide slag and gypsum）， and the effects of different solid waste materials on the mechanical properties of the cured soil were investigated by orthogonal ratio optimization experiments and the optimal parameter formulations were obtained. The mechanical properties （unconfined compressive strength， splitting strength， delayed molding） and weathering properties （water stability， freeze-thaw cycle） of the solidified soil with solid waste-based curing agent （SWC） were systematically studied and compared with PO 42.5 silicate cement （OPC）. The results showed that： calcium carbide slag played an important role in the curing agent system， and the appropriate gypsum dosage had a positive effect on the strength growth； the strength of cured soil increased with the increase of SWC dosage and the age of curing， and the 7 day unconfined compressive strength of the cured soil at 4% dosage was more than 1 MPa， and the splitting strength was basically comparable to that of the OPC cured soil； the SWC cured soil showed a longer allowable delay in construction， and there was no trend of decreasing strength within 12 h after mixing； the water stability coefficient was more than 90% when the dosage was more than 5%， and the anti-freezing coefficient under the same dosage was increased by 3%-5% compared with that of cement， so it had good water stability and anti-freezing property； X-ray Diffraction （XRD） and Scanning Electron Microscope （SEM） analyses showed that SWC cured soils generate expansionary hydration products， calcite alumina （AFt） and hydrated calcium silicate （C-S-H）， during the hydration process， and that calcite crystals played a significant role in improving the cleavage strength of cured soils and increasing the resistance to cracking and deformation.

Pine wilt disease， as a world quarantine disease， seriously endangers pine plants and threatens forest ecological security. However， there are currently no effective measures to control and eliminate it. To suit people's needs for supplying diverse ecological services to the forest， high-intensity （60% logging intensity） and low-intensity （15% logging intensity） thinning measures were used in Pinus massoniana forests to harvest wood infected with pine wilt disease. The alterations in bacterial and fungal composition in the soil of P. massoniana forests impacted by the disease were analyzed using high-throughput sequencing. The objective was to investigate the alterations in the organization of the soil microbial community in P. massoniana forests impacted by thinning-induced pine wilt disease and assess the effects on the soil microenvironment. The soil bacterial and fungal populations of P. massoniana woods were investigated using the high-throughput sequencing technique. The findings demonstrated that the bacterial and fungal populations in the soil varied significantly among the three stands. The high thinning measure forest group （H-CK） had the greatest Shannon diversity index and Chao1 index， and the diversity and richness of the soil microbial community in the H-CK group were considerably higher than those in the low thinning measure forest group （L-CK） and control group （CK）. Actinobacteriota was the marker bacterial community and Leotiomycetes was the marker fungus community in the L-CK group. In the H-CK group， Chloroflexi was the marker bacterial community and Dothideomycotes was the marker fungal community. The high-intensity thinning H-CK group's soil organic matter content rose by 13% as compared to the control group， according to an analysis of soil physical and chemical properties. The H-CK group's total nitrogen content increased to 1.12 g/kg and its alkaline nitrogen content was 64.15 mg/kg as soil organic matter buildup increased. The forest's soil fertility was enhanced as a result， with a significant increase in both the total and accessible nitrogen content of the soil （P<0.05）. Steadily impoving forest quality， strengthening the disease resistance and boosting the ecological function of the current， pure Pinus massoniana forest that has been harmed by pine wilt disease by progressively transforming it into a multilayer， unevenly aged needle broad-leaved mixed forest.

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.

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.

Helan Mountain is located in the arid and semi-arid region of northwest China. Systematic analysis of the composition characteristics of soil fungal communities in this area is of great significance for clarifying the fungal diversity and community functions in forest ecosystems of arid and semi-arid regions. The Illumina Miseq high-throughput sequencing technology was used to analyze the fungal diversity， community composition， and their responses to changes of soil physicochemical properties in three main constructive tree species—Picea crassifolia， Pinus tabuliformis and Betula platyphylla in Helan Mountain. The results revealed that a total of 276， 353 and 433 fungal OTUs were obtained from rhizosphere soil with 60 shared OTUs. All fungal OTUs belonged to 17 phyla， 42 classes， 107 orders， 210 families and 326 genera. At the phylum level， Basidiomycota and Ascomycota were dominant， with relative abundances of 58.7% and 35.7%， respectively. At the genus level， the top five dominant genera were Sebacina（6.7%）， Tomentela（6.5%）， Inocybe（6.1%）， Geminibasidium（3.7%） and Penicillium（2.9%）. Significant differences were observed in the composition of dominant fungi community at the genus level among different tree species. The genera with the highest relative abundance in the rhizosphere soil are Geminibasidium（10.8%）， Inocybe（16.9%）， and Sebacina（9.0%） respectively. RDA was used to analysis the driving factors of fungal community composition difference in the rhizosphere soil of the main constructive tree species in Helan Mountain. Soil total nitrogen content （TN） and soil water content （SWC） exert significant influences on the composition of fungal communities， accounting for explanation of 41.6% and 39.1%， respectively. This study enriches the biological information of fungal resources in the rhizosphere soil of the main tree species in Helan Mountain， and can provide theoretical basis for the excavation and research of fungal resources in forest ecosystems in arid and semi-arid areas.

To improve the accuracy of non-destructive estimation of volume of trees with complex trunk shapes， this study uses terrestrial laser scanning （TLS） point cloud data and developes a taper-binary volume equation based on the optimal taper model for plantation Mongolian oak. The study was conducted in a Mongolian oak plantation located at the Urban Forestry Demonstration Base in Harbin. Complete point cloud data were collected via TLS， and tree trunk structural parameters were extracted after processes such as clipping， elevation normalization， filtering， individual tree segmentation， and leaf-branch separation. Based on the trunk shape characteristics of Mongolian oak， six taper equation models were tested （Biging（1984）， Amidon（1984）， Meng Xianyu（1982）， Kozak（2004）-Ⅱ， Zeng Weisheng et al.（1997）， Max and Burkhart（1976））， and the best-fitting model was selected through nonlinear regression to construct the taper-binary volume equation. Results showed that the accuracy of individual tree identification was 95.22%， with the coefficient of determination （R²） for extracted tree height and diameter at breast height （DBH） being 0.97 and 0.98， respectively， when compared to field measurements. The optimal taper model achieved an R² of 0.99 and a root mean square error （RMSE） of 0.38 cm. Residual analysis of the Mongolian oak taper-binary volume equation from this study with the existing volume equation showed the reliability of its estimation results. It can provide important technical support for estimating the volume of trees with complex trunk shapes using TLS point cloud data.

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）.