Objective To address the domain shift issues caused by different spatiotemporal scenarios and species variations, this study proposes a domain adaptation recognition method for wildlife images based on adversarial learning. The aim of this study is to enhance the generalization performance of unlabeled wildlife species recognition in complex field environment, thereby providing a robust theoretical foundation for the classification of wildlife in open environment. Method An adversarial learning network model was constructed by utilizing wildlife image category information as conditional inputs. Batch spectral penalty constraints and mixup feature alignment methods were employed to mitigate distribution discrepancies among wildlife images across different spatiotemporal scenarios and species variations. This framework established a feature alignment model based on adversarial learning to enhance image recognition performance.Result The proposed method was trained and evaluated on domain adaptation datasets containing 8 and 11 wildlife species, respectively. Compared to baseline methods combining adversarial learning and feature alignment, the average recognition accuracy of the proposed method increased by 3.3% and 14.0%, the precision was improved by 3.3% and 20.6%, the recall rate was improved by 3.5% and 20.5%, and the F1-score was improved by 3.6% and 5.1%, respectively. The wild animal image domain adaptation model based on adversarial learning significantly improved performance for wildlife recognition in cross-domain scenarios. Conclusion The wildlife category information used as conditional inputs in the adversarial learning network can provide multimodal structural information of wildlife images, which enables the proposed method in this study to better understand the inter-image relationships and improve domain adaptation learning capabilities. The better the alignment of image features between the training and testing sets, the better the image recognition performance of the testing set. This study provides novel insights and methods for cross-domain wildlife image recognition research.

Objective In this study, a two-stage remote sensing feature selection method combined with multiple machine learning models was proposed to explore an efficient model for estimating forest aboveground biomass (AGB) using domestic GF-1 satellite data, thereby providing scientific reference for the application of GF-1 satellite data in forestry monitoring.Method A total of 44 remote sensing feature variables, such as spectral features, texture features, and vegetation indices, were first extracted from the GF-1 data. Then a two-stage feature selection strategy was proposed and then applied for feature optimization. In the first stage, the filtered method (Relief) and embedded method (Lasso, RF) were used for initial screening, and in the second stage, the feature subset was further optimized by the wrapper method (RFE) to enhance the model estimation capability. Finally, 5 machine learning models including the K nearest neighbor (KNN), support vector regression (SVR), random forest (RF), gradient boosting (GBRT), gradient boosted regression tree (GBRT) and extreme gradient boosting tree (XGBoost) models were selected and applied for AGB estimation. They were applied for evaluating the match between the different feature selection methods and models and their effects on forest AGB estimation accuracy. According to the results, the optimal strategy was explored for estimating for forest AGB based on GF-1 data.Result 1) The Relief-RFE method achieved the best results in AGB inversion using the XGBoost model with R2 = 0.811, RMSE = 8.45 t·hm^–2, rRMSE = 23.39%. 2) The green light band texture feature (mea-b2) was able to capture spatial distribution information of forest surface cover, such as canopy density, tree distribution, and other spatial structural changes of the forest; The spectral characteristics of the blue light band (b1) were sensitive to changes in chlorophyll and leaf pigment concentrations, and was able to characterize vegetation health status and growth stage information. In the two-stage feature selection of various methods, both of the above two features were selected as core features for forest AGB estimation. 3) Feature selection significantly improved model performance, with XGBoost showing a more pronounced improvement compared to KNN, SVR, RF, and GBRT.Conclusion By comparing the application of different feature selection methods combined with machine learning model in forest AGB estimation, the results have demonstrated the effectiveness of the proposed two-stage hybrid feature selection strategy in forest AGB estimation based on GF-1 data, especially combining it with the XGBoost model, a highly accurate and robust forest AGB estimation strategy can be constructed.

Objective This study aims to address the problems of time-consuming and labor-intensive, susceptible to human error, and expensive equipment in the traditional process of measuring the diameter at breast height (DBH) of standing trees, for which an automatic method for measuring DBH of standing trees based on smartphone and deep learning is proposed.Method First, in order to meet the demand for low-cost automatic measurement of standing tree DBH, a smartphone was used to acquire monocular RGB images of standing trees. Then a standing tree image segmentation algorithm based on GC-U-Net model was proposed to accurately extract the contours of standing trees. Based on the traditional U-Net segmentation model, the model's ability to recognize the features of the trunk of a standing tree was enhanced by integrating the VGG16 and CBAM attention mechanisms. Finally, based on photogrammetric principles, a standing tree diameter measurement model was constructed, and used to quickly and accurately calculate DBH of the standing trees using the segmented image.Result The comparative experimental results showed that the GC-U-Net model improved the mean intersection ratio (mIoU) by 4.38%, the mean pixel accuracy (mPA) by 6.08%, and the recall by 4.85% over the traditional U-Net model. Compared with the PSPNet, SegNet, and Deeplabv3+ segmentation models, the GC-U-Net model also obtained better trunk segmentation, with mIoU being improved by 6.04%, 6.52%, and 11.0%, mPA being improved by 7.93%, 7.36%, and 12.31%, and Recall being improved by 6.88%, 7.83%, and 11.08%, respectively. The average relative error of trunk diameter measurement was only 2.37%, and the goodness-of-fit reached 0.91.Conclusion Compared with the traditional method of measuring DBH, the method in this study can automatically obtain the DBH value of standing trees by only acquiring a monocular image of standing trees from a smartphone, which reduces the cost of measurement equipment. The image acquisition process does not require a reference, which simplifies the complexity of on-site measurements and improves the efficiency of the measurements. At the same time, the proposed GC-U-Net model ensures the measurement accuracy of the standing tree DBH.

Objective This study aims to address domain shifts caused by regional variations in ecological factors, such as climate and soil, for which a single-domain generalization method was proposed based on global-local feature fusion, so as to enhance the generalization performance of unlabeled tree species recognition in complex forest scenes and provide new method support for cross-regional tree classification research. Method The southern part of Baden-Württemberg, Germany, and the western part of Qimen County, Huangshan City, China, were selected as the source domains, while the central part of Thuringia, Germany, and the eastern part of Qimen County, Huangshan City, China, were selected as the target domains. A global-local feature fusion network (hierarchical unified feature network, HUFNet) was constructed for tree species classification. This network consists of a CNN-based encoder layer, a Transformer-based decoder layer, a Global–Local Attention Feature Extraction (GLAFE) mechanism, a Feature Refinement Head (FRH), and an Edge Refinement and Validation (ERV) module. The model was trained on the source domain datasets and then tested on the target domain to validate its generalization ability, achieving cross-domain tree species classification in complex scenarios.Result By comparing multiple source and target domain datasets, the HUFNet model achieved an overall accuracy (OA) of 75.1% and a mean intersection over union (mIoU) of 58.3% for the classification of coniferous and broadleaf tree species on the target domain HainichUAV dataset. Compared to the classification architecture based on self-attention mechanisms, the model improved OA and mIoU by 13.7% and 11.7%, respectively. On the HuangshanEast target domain dataset, the HUFNet model achieved OA of 71.7% and mIoU of 56.8%. Compared to the hybrid architecture using ViT-R50 as the encoder, the OA was improved by 1.2%. Conclusion The HUFNet model proposed in this study achieves significant improvements in cross-regional tree species classification, it not only maintains the high-precision recognition ability, but also shows the powerful cross-domain generalization ability in the target domain, and greatly reduces the time and space complexity of the model. It shows strong application potential in resource-constrained environments. The single-domain generalization approach based on global-local feature fusion provides a novel perspective for cross-domain tree species classification in advancing research.

Objective This study aims to enhance the accuracy of eucalyptus stem volume estimation through remote sensing by ameliorating the detection algorithm to acquire precise age variables, thereby mitigating the saturation issue of eucalyptus stem volume.Method Multi-temporal Landsat-8 and Sentinel-2 data were utilized as the primary data sources. The time series growth curve of eucalyptus was analyzed, and a combination of linear interpolation and dynamic recognition approaches was adopted. The simulated continuous change detection and classification (CCDC) algorithm was employed to identify the felling points, and the least squares method along with significant changes in slope and intercept was used to analyze the felling points and estimate the age of eucalyptus. On this basis, two variable sets were constructed using remote sensing features and eucalyptus age: variable set 1 (comprising band values, vegetation indices, and texture features) and variable set 2 (consisting of variable set 1 and age). Four models, namely multiple linear regression (MLR), k-nearest neighbor (KNN), random forest (RF), and support vector regression (SVR), were utilized to estimate eucalyptus stem volume.Result The recognition accuracy of the simulated CCDC detection algorithm for 100 samples was 82%, and the distance correlation coefficient between age and stem volume was 0.71, which was significantly higher than that of other remote sensing variables. The results of eucalyptus stem volume estimation indicated that the R² of variable set 1 was 0.40, the RMSE (root mean square error) was 41.11 m³·hm^–2, and the rRMSE (relative root mean square error) was 34%. The R² of variable set 2 was 0.83, the RMSE was 22.08 m³·hm^–2, and the rRMSE was 18%. In variable set 1, the model calculation outcomes were weak, with a low R² value, high RMSE, and high rRMSE. However, with the introduction of the age variable in variable set 2, the model calculation results were notably improved, especially in the SVR model, where the R² increased to 0.83, and the RMSE and rRMSE decreased obviously. The introduction of the age variable enhanced the accuracy of the eucalyptus stem volume estimation model.Conclusion The improved simulated CCDC change detection method has augmented the accuracy of age variables, and age is significantly correlated with stem volume. The introduction of the age variable has significantly enhanced the estimation performance of the model, with the estimation accuracy increasing by 16 percentage points.

Objective This study aims to explore impact of the fine wind speed field on the accuracy of forest fire spread, through simulating the fine wind speed based on the mountain microenvironment and its interaction mechanism with wind speed , so as to provide reference for the scientific decision-making of firefighting.Method The 2006 Anning“3.29”fire in Yunnan Province was targeted, the key meteorological driving factors affecting the spread of forest fires was analyzed, and GIS geographic simulation of wind velocity field was performed on a 30 m spatial scale. Based on the theory of cellular automata, the simulation of forest fire spread was realized by combining with Wang Zhengfei’s forest fire spread model modified by Mao Xianmin. The simulation accuracy was evaluated by comparing the meteorological simulation results of historical fire archives, and conventional inverse distance weight and Kriging interpolation methods. Result 1) The wind speed field driving factors analyzed with the mechanism model showed that wind speed field was positively correlated with elevation, while negatively correlated with slope, terrain relief, surface roughness, and surface temperature. The average wind speed field field at 30 m scale constructed using multiple linear regression analysis showed that the maximum wind speed field of the average wind speed field field around the fire occurrence area is 3.70 m·s^-1, and the minimum is 0.28 m·s^-1. 2) Combined with the topography and combustible data around the fire, the fire occurrence process from March 30 to April 3 was simulated. The day-by-day range in the fire history archives was used as a reference, and the accuracy validation results showed that the results based on the refined wind speed field simulation showed high simulation accuracy in different time periods, among which the simulation results on April 1 were optimal with Sørensen coefficient and coincidence accuracy of 0.83 and 93.28%, respectively. The simulation results on March 30 had relatively low accuracy with Sørensen coefficient and coincidence accuracy of 0.65 and 80%, respectively. Compared with the two sets of interpolated wind speed field field simulation results, the overlap accuracy and Sørensen coefficient based on the refined wind speed field simulation results were maximally improved by 6.67%, 11.67%, and 0.11, 0.08, respectively.Conclusion Compared with the conventional inverse distance weight and Kriging interpolation methods, the simulated 30 m-scale wind speed field data performs better in terms of spatial heterogeneity and continuity, and can reflect the spatial pattern of mountain wind speed field in a finer way, thus effectively improving the accuracy of forest fire spread simulation. In this study, the key driving factors affecting the spread of forest fires are refined by spatial modeling using GIS, taking into account the macro-meteorological conditions and micro-surface characteristics, and a more accurate simulation of forest fire spread is achieved.

Objective This study aims to explore the seasonal variation within the growing season of carbon, light, water use efficiencies in a Larix gmelinii var. principis-rupprechtii natural secondary forest, analyze their biophysical influence and the trade-off among carbon, light, water use efficiencies. Method The eddy covariance technique was used to continuously monitor ecosystem carbon and water fluxes in a L. gmelinii var. principis-rupprechtii natural secondary forest in Baihuashan, Beijing. Air temperature, soil temperature and soil water content were continuously monitor. The seasonal variations of ecosystem carbon, light, water use efficiencies were analyzed, along with their biophysical influence, and the trade-off relationships among resource use efficiencies were examined. Result 1) During the growing season, carbon use efficiency was small in June and large in October, ranging from 0.14 to 0.97. Light use efficiency was large in August and small in October, ranging from 0.15 to 2.19 g·MJ^-1. Water use efficiency was small in June and large in October, ranging from 0.74 to 8.00 g·kg^-1. 2) Carbon use efficiency was significantly negatively correlated with soil temperature（P < 0.05）, light use efficiency was significantly positively correlated with soil water content（P < 0.05）, water use efficiency was significantly negatively correlated with soil water content（P < 0.05）. The structural equation model showed that soil temperature had a negative effect on carbon use efficiency, by affecting ecosystem respiration（P < 0.05）. Diffuse radiation had a positive effect on light use efficiency, by affecting gross primary productivity（P < 0.05）. Vapor pressure deficit had a negative effect on water use efficiency, by affecting evapotranspiration（P < 0.05）. 3) Carbon use efficiency was significantly positively correlated with water use efficiency（P < 0.01）, higher light use efficiency was observed when both carbon and water use efficiencies were low. Conclusion The carbon use efficiency and water use efficiency followed the same trend in L. gmelinii var. principis-rupprechtii natural secondary forest ecosystem, both declined in the middle of the growing season, and light use efficiency reached the maximum value in the middle of the growing season. Increasing soil temperature decreased the carbon use efficiency. Increasing soil water content and diffuse radiation promoted the light use efficiency. Increasing soil water content and vapor pressure deficit decreased the water use efficiency. There is a trade-off between ecosystem resource use efficiency, higher light use efficiency occurred when both carbon and water use efficiencies were low. Our findings highlight importance of water condition in the trade-off between resource use efficiency in L. gmelinii var. principis-rupprechtii natural secondary forest ecosystem.

Objective This study investigates the variation in functional traits of Sophora japonica across a precipitation gradient and the climatic drivers underlying this variation, aiming to elucidate its habitat adaptation strategies.Method Ten study sites were selected along the precipitation gradient from the humid zone in southeastern China to the arid zone in northwestern China, and a conspecific populations of S. japonica was used to measure functional traits, including leaf functional traits (stomatal size, stomatal density, vein density, huber value, specific leaf mass, leaf thickness, palisade tissue thickness, spongy tissue thickness, palisade to spongy tissue ratio) and branch functional traits (vessel diameter, vessel density, vessel wall thicknesses, thickness span ratio, wood density). Quantify relationships between climatic factors (mean growing-season temperature, sunshine duration, and mean annual precipitation) and functional trait variation in S. japonica, reveal coupling patterns among traits, and elucidate its habitat adaptation strategies.Result Among the functional traits of S. japonica, the three traits with the higher coefficients of variation were palisade tissue thickness (37.26%), palisade to spongy tissue ratio (32.51%), and vessel density (27.53%), while the three traits with the lower coefficients of variation were vessel diameter (12.07%), wood density (13.32%), and vein density (14.75%). Mean annual precipitation exhibited significant correlations with functional trait variation in S. japonica branches (P<0.05), whereas no significant associations were detected between either mean growing-season temperature or sunshine duration and these trait variations. Specific leaf mass of S. japonica showed a significantly positive correlation with both leaf thickness and palisade tissue thickness (P<0.05), while vessel diameter exhibited a highly and significant negative correlation with vessel density (P<0.01).Conclusion Trait variation and inter-trait coupling reflect the habitat adaptation strategies of S. japonica: the overall variability in leaf functional traits exceeds that of branch functional traits. Among climatic factors, mean annual precipitation had the greatest influence on the variation in functional traits of S. japonica, primarily affecting the variation in its branch functional traits. Compared to S. japonica in humid regions, those in semi-arid/arid regions adapt by developing leaf thickness to enhance water storage capacity and enlarging vessel diameter to improve hydraulic efficiency, thereby optimizing short-term water utilization. In semi arid/arid regions, S. japonica achieves efficient utilization of precipitation pulses and rapid growth through the coordinated functioning of its leaf and branch traits.

Objective The aim of this paper was to elucidate the population density, age structure, quantity dynamics of Ephedra equisetina, Nitraria roborowskii and N. sibirica distributed in great Gobi A strictly protected area (GGSPA) in Mongolia, and predict their future development trend, so as to provide decision-making references for the protection and management of Ursus arctos gobiensis from the perspective of edible plants. Method Sample plots and transect lines were set up in the Atas Inges（AI）, Shar Khuls（SK） and Tsagaan Bogd（TB） oases and their surrounding areas of GGSPA based on the terrain and topography of the distribution of E. equisetina, N. roborowskii and N. sibirica, and the population structure of these three species was investigated. The methods of“space replacing time”and smoothing-out were applied to compile a specific time life tables for the three plant populations, and plot their survival and mortality curves. The population dynamics was analyzed through the quantitative analyses of populations, and finally the development trend was predicted by applying a time series model.Result 1) The population density of E. equisetina and N. roborowskii in AI was the highest, while the population density in SK was the lowest. The population density of N. sibirica in TB was the highest, while the population density in AI was the lowest. 2) At the current stage of succession, the populations of E. equisetina in AI, SK and TB were mainly composed of middle-aged individuals. The AI population showed a growing pattern, while the populations in SK and TB showed a declining pattern. All the populations of N. roborowskii and N. sibirica were mainly composed of young and middle-aged individuals, and showed a growing pattern. However, there was a significant shortage of seedlings in the population of N. roborowskii in SK and the population of N. sibirica in AI. 3) The survival curves of all the populations of E. equisetina and N. roborowskii, as well as the population of N. sibirica in AI showed the Deevey-Ⅱ type, while the survival curves of populations of N. sibirica in SK and TB exhibited the Deevey-Ⅰ type. 4) The mortality rates of all the populations of these three species showed a fluctuating upward trend with age. 5) Dynamic indexes and time series analyses showed that the population structures of E. equisetina in SK and TB were not stable enough, and the population in SK was highly sensitive to external random disturbances. The population structures of N. roborowskii and N. sibirica were stable, but all the populations were highly sensitive to external random disturbances. Conclusion At the current stage of succession, the populations of E. equisetina in SK and TB show a declining type and their population structure is unstable. The population in SK is highly sensitive to external random disturbances. The current population structures of N. roborowskii and N. sibirica are stable, but all the populations are highly sensitive to external random disturbances. The insufficient number of seedlings of the N. roborowskii population in SK and the N. sibirica population in AI would ultimately lead to population decline.

Objective This study aims to explore the relationship between Robinia pseudoacacia (black locust) distribution and environmental variables at the national scale as well as the changes of future adaptation areas, so as to provide data support for afforestation planning and management of R. pseudoacacia.Method The MaxEnt model optimized by the Kuenm package in R language and ArcGIS software were applied to explore the main environmental factors affecting its geographical distribution. With the selected 181 distribution point records of black locust in China and 12 environmental factors, this optimized MaxEnt model was used to predict the potential habitat area and centroid changes of black locust in China under three different climate change scenarios (ssp126, ssp245, ssp585) in four periods, namely contemporary, future 2030s, 2050s, and 2070s. Result The results showed that when the feature combination (FC) = linear + product and the modulation frequency was 0.5 (RM = 0.5), the model had the lowest complexity and higher prediction accuracy. The area under curve (AUC) was 0.880, which was able to be used to predict the suitable growth range of black locust. The mean air temperature in the coldest quarter, precipitation in the warmest quarter, and the altitude were the main environmental factors affecting the potential geographical distribution of black locust, and their adaptation ranges were from –5 to 6.5 ℃, from 335 to 1 825 mm, and from –155 to 1 725 m, respectively. Under contemporary climate conditions, the total suitable area for black locust in China is 262.51 × 10⁴ km², and the highly suitable area is 37.86×10⁴ km². The total suitable area for black locust in all three future climate change scenarios would generally consistent compared with current situation, while the highly suitable area would decrease. However, the highly suitable area in the ssp126 scenario would decrease in 2070s. The centroid analysis results indicated that under future climate change scenarios, the potential total suitable area for black locust in China would shift towards the northeast, and the highly suitable area would shift towards the southwest.Conclusion The optimized MaxEnt model can accurately predict the potential suitable habitats of black locust in China. Temperature, precipitation, and altitude are identified as the dominant environmental variables influencing its distribution. Climate change is expected to reduce the highly suitable habitat area for black locust in the future and cause shifts in its potential distribution.

Objective This study aims to construct additive biomass models for thirteen typical shrubs in Erdos region based on crown area and shrub height variables, and to determine whole-plant integrated carbon content by weighting biomass allocation coefficients, thereby providing foundational support for precise assessment of shrub carbon storage at regional scales. Method Targeting thirteen typical shrub species in Erdos, Inner Mongolia, we measured biomass and carbon content of each organ. Four model types, linear models, logarithmic models, power functions, and theoretical growth models, were employed to construct basic shrub biomass models with crown area, shrub height, and crown volume as independent variables. The optimal model forms for each organ’s biomass were then selected, and additive biomass models were constructed using a multivariate nonlinear joint estimation method with component summation. Finally, weighted regression was applied to eliminate model heteroscedasticity. The integrated carbon content of each shrub species was calculated by weighting the carbon content of each organ according to its biomass proportion.Result For the thirteen shrub species in Erdos, power functions performed best as the basic biomass equations. The constructed additive biomass models achieved high precision, with most models having R² (coefficient of determination) values generally above 0.8 and normalized mean squared error (NMSE) close to 0.1. Among the single-factor predictors, crown area provided higher model accuracy than shrub height. Composite factors combining crown area and shrub height (e.g., crown volume) were the optimal independent variables for most shrub biomass models. The carbon content of organs showed variability, ranging from 28.86% to 46.97%. The carbon content of the same organ varied significantly among different shrub species. The weighted average carbon content of organs for the thirteen shrubs ranged from 34.68% to 42.37%.Conclusion Power function models are the best form for predicting shrub biomass. Additive biomass models using composite indicators of crown area and shrub height as independent variables exhibit high precision and practicality. Carbon content varies among organs and whole plants of different shrub species. Therefore, differences in species-specific carbon content must be considered when estimating shrub carbon storage. The results of this study provide parameters and model support for precise remote sensing monitoring and assessment of shrub carbon storage and carbon sinks in arid and semi-arid regions.

Objective This study aims to investigate the desiccation sensitivity as well as the critical moisture content of the seed and embryonic axis of Quercus acutissim and explore low-temperature preservation techniques for Q. acutissima seeds and ultra-low temperature preservation techniques for the embryonic axis, in order to provide technical guidance for the preservation of recalcitrant seeds such as Q. acutissima. Method Wild Q. acutissim seeds were used as experimental materials, the silica gel weight reduction method was used to rapidly dehydrate the seeds to the target moisture content, and then the germination indexes of seeds and embryonic axes with different moisture contents were determined. The differential scanning calorimetry (DSC) was employed to determine the thermodynamic parameters of embryonic axes and cotyledons with different moisture contents, and the critical moisture content of crystallization in the embryonic axis was evaluated according to the relationship between moisture content and corresponding enthalpy. The low-temperature sealed storage method was used to preserve seeds with different moisture contents, and the vitrification method was used for cryopreservation of the embryonic axis. Result 1) When the initial moisture content of the seeds and embryonic axes decreased from 34.90% and 44.14% to 10.00%, their germination percentage decreased from 93.00% and 90.00% to 5.00% and 52.00%, respectively. 2) The DSC results of Q. acutissim embryonic axis and cotyledon with different moisture contents showed that with the decrease of moisture content of embryonic axis and cotyledon, the crystallization initiation temperature, peak temperature and enthalpy value showed a regular downward trend, which were significantly positively correlated with moisture content. The average enthalpy value of the embryonic axis was higher than that of the cotyledon, and the free moisture content of the embryonic axis was higher than that of the cotyledon. It was preliminarily determined that the theoretical critical moisture content of crystallization in this batch of the embryonic axes was 11.72%. 3) The effects of dehydration on the physiological and biochemical characteristics of Q. acutissima seeds showed that as dehydration progressed, the activities of superoxide dismutase (SOD) and peroxidase (POD), as well as the contents of proline (PRO) and malondialdehyde (MDA) in seeds initially increased and then decreased. 4) Q. acutissim seeds with initial moisture content stored at 4 ℃ for 21 days showed no change in the germination vitality, and the seeds with severe dehydration (10%–20%) were still able to tolerate a certain degree of low temperature damage. The results of cryopreservation of embryonic axis with different moisture contents showed that moisture content had a significant impact on the results of cryopreservation. When pretreated with PVS2 for 30 min before cryopreservation, the embryonic axis with 10.00% and 15.00% moisture content had a 5.00% and 3.00% survival rate, respectively, and the thermodynamic characteristics of the embryonic axis with different moisture content changed. The embryonic axis with 15.00% moisture content regained free moisture. Conclusion The germination rate of Q. acutissim seeds and embryonic axes decreases with the decrease of moisture content, which is a typical characteristics of recalcitrant seeds with the dehydration sensitivity. Dehydration has a significant effect on the physiological and biochemical characteristics of the seeds. The theoretical critical moisture content of crystallization in Q. acutissim embryonic axis is 11.72%. Cryopreservation of Q. acutissimembryonic axis is possible, but the method still needs adjustment to increase the recovery of germination rate, and then is applied to the long-term preservation of the recalcitrant seed.

Objective OsFTL1 in rice is a flowering regulator which can also affect leaf size, plant height and spike structure. At the early stage, 18 FT genes were identified from Phyllostachys edulis (moso bamboo), one of which exhibited the closest genetic relationship with OsFTL1. This study aims to clarify the biological function of this gene in moso bamboo in order to provide a theoretical basis for revealing the mechanism of flowering in moso bamboo. Method Real-time quantitative PCR was used to detect the tissue-specific expression of PheFT1 and its response to photoperiod. The subcellular localization of PheFT1 protein was analyzed by PEG-mediated assay. Agrobacterium-mediated transfer of PheFT1 gene into Arabidopsis thaliana was used to produce overexpression plants and ft mutant complemented plants. The phenotypic differences between overexpression plants and wild-type plants as well the differences between ft mutant complementation plants and ft mutant plants were compared to analyze PheFT1 biological function. Furthermore, yeast two-hybrid and bimolecular fluorescence complementation assays were used to analyze the interacting proteins of PheFT1. Result The results of bioinformatics analysis showed that the CDS of PheFT1 gene was 537 bp in full length, encoding 178 amino acids, belonging to the PEBP protein family. The results of subcellular localization showed that PheFT1 protein was localized in the nucleus and cytoplasm. The real-time quantitative PCR showed that the PheFT1 gene was expressed in root, stem, leaf and lateral bud, with higher expression in lateral bud and stem, and lower expression in leaf. PheFT1 exhibited a strong circadian rhythm under long-day. Ectopic expression showed that the PheFT1 gene induced earlier flowering, thinner stem and shorter plant height in A. thaliana. Moreover, the results of protein interaction showed that PheFT1 was able to interact with PheGF14 and PheFD proteins. Conclusion PheFT1 is a flowering promoting factor in moso bamboo, and also participates in the stem development and height growth. This study provides a reference for further revealing the molecular mechanism of the PheFT1 gene involved in flowering and development in moso bamboo.

Objective Camellia oleifera is a self-incompatible tree species. In this study, the optimal pollination configuration combinations of five early- to mid-maturing C. oleifera varieties recommended in Hunan Province were systematically investigated, in order to provide practical reference and data support for the variety configuration and standard cultivation of high quality and high yield of C. oleifera in Hunan Province.Method Five C. oleifera varieties, including ‘Dezi 1’, ‘Huajin’, ‘Huaxin’, ‘Xianglin 210’, and ‘Xianglin 97’, were used as experimental materials. Artificial pollination experiments were conducted based on observations during the flowering period. Various indicators such as the fruit setting rate, fruit development, economic traits, kernel oil content, and fatty acid content were determined. Principal component analysis was employed to comprehensively evaluate the effects of various pollination combinations of C. oleifera varieties. Result 1) The initial flowering period of ‘Dezi 1’ was on October 14th, which was 3, 9, 12, and 25 days earlier than ‘Huajin’, ‘Huaxin’, ‘Xianglin 210’, and ‘Xianglin 97’, respectively. The peak flowering period for all five varieties was mainly concentrated in November. ‘Huajin’ had the longest peak flowering period, lasting from October 30th to November 26th. However, the initial flowering period and peak flowering period of ‘Xianglin 97’ overlapped with ‘Dezi 1' and ‘Huajin’ for only 9 and 12 days, respectively. 2) The combinations (♀×♂) for the five C. oleifera varieties with highest fruit-setting rate were ‘Dezi 1’בHuajin’, ‘Huajin’בDezi 1’, ‘Huaxin’בHuajin’, ‘Xianglin 210’בDezi 1’, and ‘Xianglin 97’בHuaxin’, and the fruit-setting rate was 44.66%, 43.94%, 31.08%, 16.38%, and 10.05% higher than that of their natural pollination, respectively. Notably, the single fruit weight resulting from 'Huaxin' pollinating 'Xianglin 97' was higher than that of other paternal pollination effects. 3) When ‘Huaxin’ was used to pollinate ‘Huajin’, the oil content of dry kernels and dry seeds of ‘Huajin’ were the highest, significantly higher by 23.57% and 36.89%, respectively, compared to the combination of ‘Huajin’בXianglin 97’ (P<0.05). When ‘Xianglin 210’ and ‘Huajin’ were mutually pollinated, the fruit oil content was highest, both reaching 7.60%. The fruit oil content of ‘Xianglin 97’בHuaxin’ was 15.65% and 14.67% higher (P<0.05) than that of the combinations with ‘Xianglin 97’בDezi 1’ and ‘Xianglin 97’בHuajin’, respectively. 4) (♀×♂) ‘Dezi 1’בHuajin’, ‘Huajin’בHuaxin’, ‘Huaxin’בXianglin 97’, and ‘Xianglin 210’בHuaxin’ were the combinations that produced significantly higher fruit unsaturated fatty acid content than did the other pollination combinations of varieties (P<0.05). Conclusion Through comprehensive comparison of flowering periods, fruit-setting rates, fruit yield, and quality indicators, the optimal pollination variety for ‘Dezi 1’ is ‘Huajin’, followed by ‘Xianglin 210’. The best pollination variety for ‘Huajin’ is ‘Huaxin’, followed by ‘Dezi 1’ and ‘Xianglin 210’. The best pollination variety for ‘Huaxin’ is ‘Huajin’, followed by ‘Xianglin 97’, ‘Xianglin 210’ and ‘Dezi 1’. The best pollination variety for ‘Xianglin 210’ is ‘Xianglin 97’, followed by ‘Huajin’ and ‘Huaxin’. The optimal pollination variety for ‘Xianglin 97’ is ‘Huaxin’, followed by ‘Xianglin 210’. Among them, ‘Huajin’ with ‘Huaxin’, ‘Huaxin’ with ‘Xianglin 97’, ‘Xianglin 210’ with ‘Xianglin 97’, and ‘Dezi 1’ with ‘Xianglin 210’ are suitable for equal proportion configurations.

Objective This study analyzed the spatiotemporal distribution dynamics of lightning-caused fires in the Altai Mountain forest region from 2000 to 2022. By exploring the activity patterns of lightning-caused fires, this research aims to facilitate local lightning-caused fire prediction and prevention. It is of great significance for the protection of state-owned forest assets and the maintenance of forest security.Method Based on the historical data of lightning-caused fires in Altai Mountains forest region in Xinjiang from 2000 to 2022, the temporal distribution characteristics and dynamics of lightning-caused fires were analyzed. By combining the digital elevation model (DEM) of the study area, the spatial information of corresponding lightning-caused fires was extracted by ArcGIS software, and its spatial characteristics and dynamics were analyzed.Result The inter-annual dynamics of lightning-caused fires in the Altai Mountains forest region of Xinjiang from 2000 to 2022 show an increase in active fire years and a decrease in inactive fire years after 2012 compared to before 2012. Active years are characterized by a higher number of lightning-caused fires and more active fire days, while inactive years have fewer fires and active days. The earliest occurrence of lightning-caused fires is in April, and the latest in September, with the majority (95%) concentrated in June to August. July experiences the highest number of fires and the largest burned area, while August has the highest firefighting costs and the greatest number of personnel mobilized. The Habahe area has the highest number of lightning-caused fires and the most personnel mobilized, Altay has the largest burned area, and Burqin has the highest firefighting costs. The distribution of fire numbers with respect to altitude is approximately normal, peaking between 1 800-2 000 m, with the 1 400-1 600 m range having the highest firefighting costs and the most personnel mobilized. Slope terrains have a higher number of lightning-caused fires, greater firefighting difficulty, and larger burned areas, with over 50% of fires occurring on slopes between 0°-20°. The steepest slopes, between 70°-80°, incur the highest firefighting costs. The eastern slopes have the highest number of fires and personnel mobilized, while the southeastern slopes have the largest burned area and the most firefighting costs.Conclusion After 2012, lightning-caused fires became significantly more active. This is evidenced by an increase in the number of active years and an extension of the average active period of lightning-caused fires compared to the period before 2012, although the number of inactive years of lightning-caused fires decreased after 2012, the average active period of lightning-caused fires in these inactive years was significantly shortened. Overall, the inter-annual fluctuations in the activity level of lightning-caused fires became stronger. Lightning-caused fires in specific longitudes, latitudes, altitudes, slopes, and aspects exhibit a clustered distribution. Post-2012, a notably higher occurrence of lightning-caused fires is observed within the range of 48.4°N to 48.7°N and 86.5°E to 87°E. Overall, there is a trend of migration from lower to higher altitudes and from steeper to milder slopes, with a preference for concentration on the eastern slopes.

Objective To address the issues of low walnut harvesting efficiency and low fruit removal rate, a flexible rocker-arm vibration-based walnut harvesting machine is designed, utilizing trunk vibration for harvesting. The study investigates the impact of different vibration parameters on vibration performance. By employing response surface methodology, the vibration acceleration responses under the two-way interaction of parameters are obtained, aiming to optimize the design structure and operational parameters.Method This paper conducts structural design and stress analysis of key components of the harvester to determine the optimal parameters. Simulation analysis and vibration operation tests are performed on the clamping area of the walnut tree and the trunk to assess the acceleration response. Using Design-Expert 13.0 software, vibration frequency, tree diameter, and vibration time are considered as the main factors, with vibration acceleration as the evaluation criterion. The response surface methodology is employed to study the influence of these three parameters on vibration acceleration.Result The ADAMS simulation software indicates that the optimal diameter of the flexible rocker arm is 15 cm, and the optimal clamping force is 2 800 N. By comparing the simulation results with the field harvesting test results, the vibration acceleration errors at the clamping area and trunk of the walnut tree, under different frequencies, are within 5%. The contribution rates of vibration frequency, tree diameter, and vibration time to the vibration acceleration evaluation index are 2.317 8, 1.649 2, and 1.489 4, respectively. When the vibration frequency is 14 Hz and the tree diameter is 15 cm, the vibration acceleration reaches its maximum value of 63.417 m·s^–2. The impact of the three factors on vibration acceleration, in decreasing order, is: vibration frequency, tree diameter, and vibration time.Conclusion This paper focuses on the designed flexible rocker-arm vibration-based walnut harvester, analyzing the structural features of the equipment and the impact of operational parameters on energy transfer. The study provides operational parameter guidelines for efficient walnut harvesting in large-scale standardized orchards, aiming to effectively improve walnut harvesting efficiency and fruit removal rate.

Objective In the context of building a forestry power with strong industrial resilience, the characteristics of the non-equilibrium of forestry economic resilience in China and its influencing factors are explored to provide a scientific basis for the high-quality development of forestry.Method Based on the panel data of 30 provincial units in China from 2011 to 2020, the evaluation index system of forestry economic resilience level is constructed to measure the level of forestry economy resilience, and entropy weight method, spatial autocorrelation analysis, Dagum Gini coefficient decomposition method, Kernel density estimation method, spatial convergence model, and geographic detector are adopted to analysis the non-equilibrium characteristics of and its influencing factors of the forestry economy resilience in China.Result 1) The forestry economic resilience of China from 2011 to 2020 shows an overall growth trend, increasing from 3.19 in 2011 to 4.53 in 2020, with spatial characteristics of high in the southeast and low in the northwest. The spatial agglomeration characteristics mainly present significant high-high agglomeration, high-low agglomeration， and low-high agglomeration. 2) The forestry economic resilience of China gradually expanded from 2011 to 2020, and the Dagum Gini coefficient increased from 0.306 5 in 2011 to 0.325 9 in 2020. 3) The result based on the east-central-west zoning indicates that hypervariable density and inter-regional differences are the main influencing factors on the non-equilibrium of the forestry economic resilience of China. The result based on the Hu Huanyong line zoning shows that intra-regional and inter-regional differences are the main influencing factors of the non-equilibrium of the forestry economic resilience of China. 4) There is no σ-convergence feature, and there are absolute β-convergence and conditional β-convergence features in the forestry economic resilience of China from 2011 to 2020. The result based on the east-central-west zoning indicates the conditional β convergence of the forestry economic resilience of China. The conditional β convergence of each region is faster than the absolute β convergence, and the central region has the quickest rate of conditional β convergence. The result based on the Hu Huanyong line zoning shows the convergence speed of the west region of the Hu Huanyong line is higher than that of China and the eastern region of the Hu Huanyong line. 5) Adaptive capacity, sustainability, industrial multi-collaborative, forestry pesticide use, value of non-timber forest-based economy output, forestry tourism, and leisure industry driven output value are important influencing factors for the forestry economic resilience of China. Conclusion It is necessary to vigorously develop green, ecological, and intelligent forestry, promote the enhancement of the quality and efficiency of the forestry ecological chain, industrial chain, and value chain, improve the mechanism for high-level protection and cultivation of forest resources, improve the ability of forestry to adapt to the risk of uncertainty, scientifically develop new forms of forest tourism, forest recreation and non-timber forest-based economy, realize the integrated development of the three industries of forestry, actively serve the regional economy, and promote synergistic development of the forestry economy and the regional economy.

Objective The objective of paper is to explore the influence of cooperatives’ embeddedness on farmers’“fertilizer and pesticide double reduction” behaviors, analyze the influence mechanism of different functions within cooperatives on farmers’ fertilizer and pesticide reduction and their differences, and give recommendation to reduce farmers’ fertilizer and pesticide application precisely, so as to promote green production behaviors of forestry, change the mode of forestry development, and guarantee the quality and safety of forest products.Method On the basis of existing research, based on the theory of industrial organization and the theory of farmers’ behavior, using the survey data of 688 household samples with non-timber forest product management in 7 case counties (districts) of Zhejiang Province in 2023, the Tobit model was used to analyze the impact of cooperative embedding on fertilizer and pesticide application during farmers’ non-timber forest product operations, to analyze the mechanisms of the three functions of technical support, organizational constraints and premium incentives of cooperatives on the fertilizer and pesticide reduction behaviors of farmer members for non-timber forest products, to reveal the differences of the three paths on the fertilizer and pesticide application of cooperative members, and give recommendations to promote the fertilizer and pesticide reduction of farmer members.Result 1) Participation in forestry cooperatives can significantly reduce the amount of fertilizers and pesticides applied by non-timber forest product farmers, and there are differences in the reduction effects on farmers with different forest land sizes, forestry income dependence and adoption of ecological management techniques. Especially for small-scale farmers and farmers with low dependence on forestry income, participation in cooperatives can effectively reduce the amount of fertilizer applied by these farmers. 2) Relying on the technical support, organizational constraints, and premium incentives provided by cooperatives can effectively promote“fertilizer and pesticide double reduction”. 3) Three different functions within the cooperative had more significant effects on the reduction of fertilizers and pesticides by the core members than by the non-core members, among which the two functions of the cooperative, organizational constraints and premium incentives, had better effect on the promotion of the“fertilizer and pesticide double reduction”behavior of the core members.Conclusion Firstly, we should rely on farmers’ professional cooperative organizations as a platform to promote green production behaviors. Secondly, we should continue to improve the multi-service functions within the cooperatives, so as to motivate farmers to carry out the“fertilizer and pesticide double reduction”behaviors; and lastly, we should give full play to the role of the demonstration of the core members, such as the large-scale households, in the promotion of the double reduction of fertilizer and medicine behaviors. Finally, in the promotion of the“fertilizer and pesticide double reduction”, the role of core members, such as large-scale households, should be given full play, and more farmers, especially small farmers, should be supported and guided to join cooperatives.

A scientific and reasonable pricing mechanism is the key to forestry carbon sink trading. It plays a decisive role in revitalizing the forestry carbon sink market, mitigating global climate change, and realizing the“double carbon”goals. While some countries and regions have actively explored pricing mechanisms for forestry carbon sink products and have accumulated valuable experience, a unified and mature pricing mechanism for forestry carbon sinks is still lacking. In this study, we first systematically review the pricing mechanisms of forestry carbon sink products in typical domestic and international trading platforms. Subsequently, a comparative analysis is carried out across multiple dimensions, including the types of forestry carbon sink products, pricing principles, pricing theoretical models and information levels, pricing constraints, membership management systems, pricing supervision, and pricing settlement. Finally, in light of the current situation and challenges surrounding the pricing practices of forestry carbon sink products in China, seven inspirations are proposed by integrating relevant economic pricing theories and typical pricing experience: 1) Establish the legal market rules and standards for the forestry carbon sink pricing mechanism. 2) Optimize the carbon quota adjustment price mechanism for forestry carbon sink pricing. 3) Adjust the market supply and demand mechanism for forestry carbon sink pricing. 4) Develop the pricing mechanism for high-quality forestry carbon sink products. 5) Build an information trading platform for the forestry carbon sink pricing mechanism. 6) Improve the regulatory system governing the forestry carbon sink pricing mechanism. 7) Cultivate a professional team dedicated to the forestry carbon sink pricing mechanism. The results can provide a scientific basis for improving the pricing mechanism of carbon sink products and promoting the development of the forestry carbon sink market, thereby facilitating the achievement of the“double carbon”goals.

Objective This study aims to explore the effects of artificial dehydration on the viability and germination characteristics of Phoebe bournei seeds, so as to provide basis for understanding the mechanism of seed viability reduction and germination restriction of P. bournei seeds.Method The physiological and biochemical characteristics as well as gene expression of seeds with four different water contents of 40% (no dehydration), 30% (mild dehydration), 20% (moderate dehydration) and 10% (severe dehydration) were analyzed through artificial dehydration treatment, to reveal the effects of different dehydration degrees on P. bournei seed viability and germination rate. Result It was found that the seed viability of P. bournei first increased and then decreased with the increase of dehydration. The germination rate of seeds with mild dehydration (30% water content) was up to 93%, which was even higher than that of non-dehydrated seeds. Severe dehydration (10% water content) led to the increase of malondialdehyde (MDA) content, indicating that the degree of seed membrane lipid peroxidation increased with the increase of dehydration. Peroxidase (POD) activity first increased with increasing dehydration, but decreased significantly when seed water content reached 10%. When P. bournei seeds stored in 25 ℃ and humidity environment, mold rate of seeds with 30% and 40% water content was about 58%, while severe dehydrated seeds were all moldy. The expression analysis of key genes involved in P. bournei seed germination showed that the negative regulatory transcription factor PbABI3 was down-regulated during seed germination. The expression of PbFUS3 and PbLEC2 genes was significantly up-regulated at the early stage of radicle growth in seeds with 30% water content, but not in seeds with 40% water content. Conclusion P. bournei seed does not tolerate dehydration, the viability of seeds increases first and then decreases with increasing dehydration. The germination rate of moderately dehydrated (20% water content) or severely dehydrated (10% water content) seeds decreases significantly. During severe dehydration, the POD activity in the seeds reduces and the seeds are subjected to oxidative damage. The change of seed water content can induce the activity of antioxidant enzyme system in P. bournei, and affect seed germination by regulating the specific expression of key genes PbABI3, PbFUS3 and PbLEC2 involved in the seed germination.