Accurate individual tree species classification is essential for forest inventory, management, and conservation. However, existing methods relying primarily on single-source remote sensing data (e.g., spectral, LiDAR, or RGB) often suffer from insufficient feature representation and noise interference, particularly in subtropical forests with high species diversity, leading to increased classification errors. To address these challenges, we proposed the Multi-source Tree Species Classification Fusion Network (MTSCFNet), a novel deep learning framework that integrates RGB imagery, LiDAR-derived feature maps, and GF-2 satellite data through a modified UNet backbone, which incorporates a three-branch encoder and a Triple Branch Feature Fusion (TBFF) module within a middle fusion strategy. We evaluated the MTSCFNet in Chinese-fir mixed forests located in the Shanxia Forest Farm, Jiangxi Province, China. The results showed that: (1) MTSCFNet outperformed four baseline models, achieving Macro F1 (0.78 ± 0.01), Micro F1 (0.93 ± 0.01), Weighted F1 (0.93 ± 0.01), a Matthews correlation coefficient (MCC) (0.89 ± 0.01), Cohen’s ĸ (0.89 ± 0.01), and mIoU (0.69 ± 0.01), with respective improvements of 4.05% in Macro F1, 1.89% in Micro F1, 0.09% in Weighted F1, 1.67% in MCC, 1.64% in Cohen’s ĸ, and 5.92% in Mean IoU over the second best model, SwinUNet; (2) Compared to the best two-source combinations (R + S, R + L), MTSCFNet achieved up to 1.50%, 3.28%, 3.42%, 6.72%, 6.76%, and 3.51% higher Macro F1, Micro F1, Weighted F1, MCC, Cohen’s ĸ, and mIoU, and up to 8.11%, 2.63%, 2.88%, 5.01%, 4.99%, and 11.48% improvements over single-source inputs, while also exhibiting the lowest variability, indicating strong robustness; (3) Under different fusion strategies, MTSCFNet with middle fusion surpassed early and late fusion by up to 15.31%, 3.74%, 3.99%, 7.66%, 7.76%, 22.33% and 24.13%, 5.76%, 6.20%, 11.48%, 11.57%, 32.96% in Macro F1, Micro F1, Weighted F1, MCC, Cohen’s ĸ, and mIoU, respectively, validating the effectiveness of feature-level multi-modal integration; (4) In cross-region transfer experiments, MTSCFNet demonstrated strong spatial generalizability, achieving average scores of 0.78 (Macro F1),0.87 (Micro F1), 0.86 (Weighted F1), 0.59 (MCC), 0.59 (Cohen’s ĸ), and 0.68 (mIoU), and outperformed SwinUNet by up to 38.80%, 9.40%, 18.58%, 22.48%, 26.17%, and 33.00% in Macro F1, Micro F1, Weighted F1, MCC, Cohen’s ĸ, and mIoU across varying forest densities. Overall, MTSCFNet offers a robust, accurate, and transferable solution for tree species classification in complex subtropical forest environments. Graphical abstract

Current visual methods of forest dynamic growth mostly focus on the plot or stand level, which cannot express the morphological and structural characteristics of individual trees, as well as their statistical linkages, and causes each tree in the stand to grow at the same rate. In addition, these visual growth models still have some space for improvement in terms of prediction accuracy and multirelational data mining. In this article, uneven-aged Chinese fir (Cunninghamia lanceolata) plantations were chosen as our study subject and proposed a novel method of forest dynamic growth visualization modeling by incorporating spatial structure parameters and using convolutional neural network technique (FDGVM-CNN-SSP) to explore the effect of spatial structure on the morphological growth and to develop a prediction growth model of Chinese fir plantations by introducing a convolutional neural network (CNN) model. The results show that: first, spatial structural parameters C and U have a certain contribution to the forest growth, and C and U can explain 21.5%, 15.2%, and 9.3% of the variance in DBH, H, and CW growth models, respectively; second, CNN model outperformed machine learning algorithms SVR, MARS, Cubist, RF, and XGBoost in terms of prediction performance; third, based on FDGVM-CNN-SSP, we simulated Chinese fir plantations at individual tree level and stand level from 2018 to 2022 and found that DBH and H's fitting performance in measured and predicted data was highly consistent with R-2 and root-mean-square error (RMSE) of 86.8%, 2.06 cm in DBH and 79.2%, 1.11 m in H, but CW's R-2 and RMSE of 72.2%, 0.65 m caused crowding (C) inconsistency.

Diameter at breast height (DBH) is a critical metric for quantifying forest resources, and obtaining accurate, efficient measurements of DBH is crucial for effective forest management and inventory. A backpack LiDAR system (BLS) can provide high-resolution representations of forest trunk structures, making it a promising tool for DBH measurement. However, in practical applications, deep learning-based tree trunk detection and DBH estimation using BLS still faces numerous challenges, such as complex forest BLS data, low proportions of target point clouds leading to imbalanced class segmentation accuracy in deep learning models, and low fitting accuracy and robustness of trunk point cloud DBH methods. To address these issues, this study proposed a novel framework for BLS stratified-coupled tree trunk detection and DBH estimation in forests (BSTDF). This framework employed a stratified coupling approach to create a tree trunk detection deep learning dataset, introduced a weighted cross-entropy focal-loss function module (WCF) and a cosine annealing cyclic learning strategy (CACL) to enhance the WCF-CACL-RandLA-Net model for extracting trunk point clouds, and applied a (least squares adaptive random sample consensus) LSA-RANSAC cylindrical fitting method for DBH estimation. The findings reveal that the dataset based on the stratified-coupled approach effectively reduces the amount of data for deep learning tree trunk detection. To compare the accuracy of BSTDF, synchronous control experiments were conducted using the RandLA-Net model and the RANSAC algorithm. To benchmark the accuracy of BSTDF, we conducted synchronized control experiments utilizing a variety of mainstream tree trunk detection models and DBH fitting methodologies. Especially when juxtaposed with the RandLA-Net model, the WCF-CACL-RandLA-Net model employed by BSTDF demonstrated a 6% increase in trunk segmentation accuracy and a 3% improvement in the F-1 score with the same training sample volume. This effectively mitigated class imbalance issues encountered during the segmentation process. Simultaneously, when compared to RANSAC, the LSA-RANCAC method adopted by BSTDF reduced the RMSE by 1.08 cm and boosted R-2 by 14%, effectively tackling the inadequacies of RANSAC's filling. The optimal acquisition distance for BLS data is 20 m, at which BSTDF's overall tree trunk detection rate (ER) reaches 90.03%, with DBH estimation precision indicating an RMSE of 4.41 cm and R-2 of 0.87. This study demonstrated the effectiveness of BSTDF in forest DBH estimation, offering a more efficient solution for forest resource monitoring and quantification, and possessing immense potential to replace field forest measurements.

[目的]针对森林碰撞检测研究中存在碰撞检测对象冗余、碰撞响应模式单一性等问题,研究突破碰撞检测算法时间复杂度高、响应模式缺乏与环境因子交互的技术瓶颈,实现虚拟森林场景快速碰撞检测与真实响应.[方法]以亚热带林业实验中心山下林场的典型林分杉木(Cunninghamia lanceolata)人工纯林为研究对象,选择碰撞检测总消耗的时间t1、包围盒交叉测试时间t2、包围盒构建时间t3、包围盒更新时间t4作为评价指标,比较单一包围盒树(axis-aligned bounding box,AABB)、混合包围盒树体(mixed bounding volume tree,MBVT)算法及引入最近4株树搜索法的MBVT优化算法的碰撞检测效率,分析不同规模大小(20、50、100、200、400、600、800、1 000)及林分株行距(1 m×1 m、2 m×2 m、3 m×3 m、4 m×4 m)双因子对碰撞检测效率的影响机制,最后模拟验证考虑光照因子碰撞响应策略的可行性.[结果]①相对MBVT算法,基于最近4株树搜索法的混合包围盒层次树MBVT优化算法耗时缩短了 13.75 ms,约为原MBVT算法耗时的29％,能有效减少层次包围盒(bounding volume hierarchies,BVHs)交叉测试时间消耗t2与BVHs构建消耗时间t3,而更新时间t4无明显差异.相对单一包围盒层次树AABB,MBVT算法与基于最近4株树搜索法的MBVT优化算法,分别缩短了 124.93、138.68 ms,约为单一包围盒层次树法AABB总耗时的73％、81％.②种群规模大小与BVHs碰撞检测总消耗时间t1、BVHs交叉测试时间t2及构建时间t3 均表现为正相关性;株行距大小与碰撞检测总耗时间t1与相交测试时间t2呈负相关性,与BVHs构建时间t3无明显关联性;随种群规模增大,株行距减小,总碰撞时间消耗t1、BVHs交叉测试时间t2呈增加趋势,而BVHs构建时间t3 几乎没有变化.③相比传统的碰撞响应模式,提出的顾及光照因子碰撞响应模式,考虑了植物的趋光性生长特征,模拟的杉木林虚拟场景更为真实.测试虚拟场景的帧率为8.6帧/s,准确度为100％,能很好地实现相邻树木之间碰撞检测及其响应.[结论]引入最近4株树搜索法的MBVT优化碰撞算法,优化混合包围盒层次树法MBVT的碰撞检测对象数量,减少了 BVHs交叉测试和构建消耗时间,从而有效提高杉木林虚拟场景的碰撞检测效率;顾及树木生长趋光性的相邻树木碰撞响应算法,通过兰伯特光照模型Lambert Model计算碰撞点周围的光照强度,结合碰撞响应函数完成树木碰撞后可能发生的趋光生长情景模拟,有效解决了森林碰撞响应缺乏与环境因子交互的问题,提高了杉木林虚拟场景的真实感.

Currently, 3D tree modeling in a highly heterogeneous forest environment remains a significant challenge for the modeler. Previous research has only focused on morphological characteristics and parameters, overlooking the impact of micro-environmental factors (e.g., spatial-structural diversification and habitat heterogeneity) and providing less structural information about the individual tree and decreasing the applicability and authenticity of 3D tree models in a virtual forest. In this paper, we chose a mixed-forest conversion of Chinese fir (Cunninghamia lanceolata) plantations in a subtropical region of China as our study subject and proposed a novel 3D tree-modeling method based on a structural unit (TMSU). Our approach modified traditional rule-based tree modeling (RTM) by introducing a nonlinear mixed-effect model (NLME) to study the coupling response between the spatial structures and morphological characteristics (e.g., tree height (H), height-to-crown base (HCB), and crown width (CW)) of three dominant trees (e.g., Cunninghamia lanceolata (SM), Machilus pauhoi (BHN), and Schima superba (MH)) and develop a prediction model of the morphological characteristic by incorporating forest-based structural parameters. The results showed that: (1) The NLME model in TMSU was found to better fit the data and predict the morphological characteristics than the OLS model in RTM. As compared to the RTM morphological model, the prediction accuracy of the TMSU model of morphological features was improved by 10.4%, 3.02%, and 17.8%, for SM’s H, HCB, and CW, respectively; 6.5%, 7.6%, and 8.9% for BHN’s H, HCB, and CW, respectively; and 13.3%, 15.7%, and 13.4% for MH’s H, HCB, and CW, respectively. (2) The spatial-structural parameters of crowding (Ci), mingling (Mi), and dominance (Ui) had a significant impact on the morphological characteristics of SM, BHN, and MH in TMSU. The degree of crowding, for example, had a positive relationship with tree height, height-to-crown base, and crown width in SM, BHN, and MH; under the same crowding conditions, mingling was positively correlated with tree crown width in SM, and dominance was positively correlated with tree height but negatively correlated with height-to-crown base in BHN; under the same crowding and mingling, dominance was positively correlated with height-to-crown base in MH. (3) Using 25 scenes based on the value class of Ci,Mi for SM, 25 scenes based on the value class of Ci,Ui for BHN, and 125 scenes based on the value class of Ci,Mi,Ui for MH, we generated the model libraries for the three dominating species based on TMSU. As a result, our TSMU method outperformed the traditional 3D tree-modeling method RTM in a complex and highly heterogeneous spatial structure of a forest stand, and it provided more information concerning the spatial structure based on the neighborhood relationships than the simple morphological characteristics; a higher morphological prediction accuracy with fewer parameters; and the relationship between the spatial-structural parameters and the morphological characteristics of a reference tree.

在应对气候变化与实现碳中和目标以及社会发展对森林生态系统要求更加多样化的时代背景下,混交林的重要性在世界各国已达成共识.通常认为,相对于纯林,混交林能够增加生物多样性、提高生产力、增加碳储量,具有更高的生态韧性(抵抗力与恢复力).但混交林是否必然优于纯林,且混交林与生物多样性、生产力、碳汇、生态韧性以及混交效应的关系一直是国内外学者探讨与研究的问题.混交林绝不是简单的树种组合与排列,其结构与生态过程相对复杂.文中通过文献梳理,总结分析混交林与生物多样性、生产力、碳储量、生态韧性以及混交林经营管理5个方面的国内外研究成果,发现关于混交林与生态功能和生产力的关系研究结果多样,总的结论是,若要得到混交林的积极效应,不能单纯增加树种多样性,更重要的是要考虑树种特性、生态互补、林分结构以及立地和环境的影响.我国混交林研究主要是通过造林实验对比分析生长效果,选择较优混交造林技术.文中最后提出我国混交林研究发展建议,旨在促进对混交林的科学和全面认识.

[目的]通过分析实测枝下高分布方向与空间竞争强度的关系,解决基于传统林学研究调查数据所构建的林木三维模型对不同方向枝下高分布差异难以直观表达,林木三维模型多态性表现不足的问题.[方法]以江西省新余市分宜县亚热带林业实验中心山下林场8块杉木临时样地为数据源,以已有枝下高模型为理论基础,将空间分析方法缓冲区构建与林分空间结构单元构建结合,构建对林木造成直接影响的水平空间结构参数与垂直空间结构参数,分析空间结构参数与枝下高相关性,并以此计算各方向空间竞争强度,建立空间竞争强度与实测枝下高的分布关系,再按照枝下高模型求解剩余方向枝下高,最终按照实测数据与分析计算结果加载分枝、主干模型,构建林木三维模型.[结果]所选模型变量包括林木属性与空间结构参数,原始模型决定系数为0.720,消除树高影响的调整后实测枝下高与水平空间结构参数相关系数为0.410、与垂直空间结构参数相关系数为0.782,且均呈正相关;将各自相关系数为权重计算对应方向空间竞争强度,将最小竞争强度方向空间结构参数代入模型,拟合结果决定系数为0.790,相比原始模型拟合精度有所提高;将实测枝下高分配到竞争强度最小的方向,根据模型可对其他方向枝下高进行估算.[结论]以杉木为例,通过空间竞争强度判别枝下高分布,在提高已有数据利用率、减小外业工作强度的基础上,可直观表现林木不同方向枝下高分布的差异性,增强了林木三维模型的多态性表达.

For the difficulty of tree polymorphism 3D modeling in the stand, the paper explored a 3D forest-tree-modeling approach based on loading trunk model and branch models. The approach is combined with the characteristics of tree branch structure that calculate the branch matching points of the intersection between the branch model and the crown curve to construct the tree branch structure. In addition, branch models are adjusted to eliminate the overlapping of branch models when the adjacent trees had overlapping crowns. The 3D model of forest-tree was constructed in accordance with the growth law and morphological characteristics of forest-tree. The results showed that this approach can use a small amount of measurement data to simulate forest-tree crown of sample plot or stand.

文中从联邦与州2个层级介绍美国与林地权属相关的主管机构及职责以及不同权属变更的形式、对象、条件及资金管理.研究发现,联邦主管机构主要为内政部的土地管理局、国家公园管理局、鱼类与野生动物保护局,农业部的林务局以及国防和能源部,各州主管机构主要有其自然资源部的自然资源委员会或林业委员会等;林地权属变更的形式主要是出售和交换,变更对象要求任何林地不得出售、交换或捐赠给非美国公民或不受任何州或联邦法律约束的公司;联邦与不同的州关于权属变更的规定详细程度存在差异,而内容却存在一定相似性;联邦与部分州政府建立相关基金或账户,以储存出售或交换土地所获收入并用于购买土地等.

当前,我国绝对贫困已经整体消除,进入了乡村振兴时代.如何建立长效机制,激发落后地区的内生动力,确保脱贫成果巩固提升不反弹、脱贫户持续增收不返贫,是当前亟待解决的重大现实问题.基于全国十省(区)402户脱贫户的调查数据,实证分析科技对农户的收入效应,利用有序多分类Logistic回归模型实证分析脱贫户参与科技助农积极性的影响因素.研究结论表明:科技对农户收入存在显著的正向影响,且技术培训影响最明显;落后地区农户参与科技助农的积极性,受到其土地资源禀赋、收入结构、社会资本、农产品销售等因素影响;土地资源越丰富、立地条件越好、农产品销售越容易、亩产值越高,越倾向于利用科技成果获得最大化的土地产出和农业收入.基于上述结论,提出了针对性的政策措施.

森林旅游与农户收入的关系一直备受政府关注.大多数研究发现,森林旅游能增加农户的就业机会,拓宽农户增收渠道,从而增加农户收入.然而,也有研究表明,发展森林旅游容易引起当地通货膨胀,给社区农户带来更大经济压力.同时,因森林旅游产生的经济效益不公平分配问题,会严重影响当地社会的稳定性,不利于可持续发展.现有文献为进一步研究森林旅游与农户收入的关系提供了指导,但存在样本选择偏差导致内生性问题,以及忽略农户收入的不同构成等.因此,未来的研究应该从以下2个方面进行:1)研究森林旅游对农户的经营性收入、工资性收入、财产性收入和转移性收入的影响;2)选择合适的研究模型及增加样本数量,以避免样本选择偏差导致的内生性问题,使结果更加准确和可靠.

In our study, we have explored the influence of panoramic images and ordinary images on the performance of Siberian crane detection, and compared the detection accuracy under different networks based on YOLOv5, to get fine and high-quality datasets and select the proper model for Serbian crane detection. The results show that (i) Training datasets from the internet and ordinary field photos can achieve a better detection performance than other training datasets, and Training datasets from panoramic images only show low accuracy due to Siberian crane's alertness and mosaic data enhancement method adopted in YOLOv5, which reduced the size of a small target. (ii) when the iteration times reach 40000, the YOLOv5 model can completely converge, and the mAP value reached 81.4%, total loss value 0.0357; (iii) With increasing the width and depth of layer in YOLOv5, the value of mAP show a growth trend, however the FPS show an opposite trend; (iv) through verification, we found that the model can also have an effectively performance of detection in the complex environments, such as multi-objective small objects and occlusions, the color similarity between target and background, different dynamic activities including flying, falling, foraging, playing, etc.

—In view of the complexity and diversity of forest management operation types, and the poor interaction and lack of natural and realistic interaction experience, three kinds of interaction methods for forestry practitioners were developed. [Method] through the network questionnaire survey, the content of forest management visualization method concerned by forestry related workers was obtained, and the interaction mode that forestry practitioners are interested in was selected. Based on the appropriate platform, the forest management operation interaction method was developed to visually simulate the operation process of replanting, pruning and logging. The results of the questionnaire survey showed that the three most interesting interaction modes of forestry practitioners were voice interaction (71.67%), mouse and keyboard interaction (66.67%) and body action interaction (53.89%); the interaction accuracy of voice based forest management operation interaction method was low, and the average number of operations needed to complete cutting, pruning and replanting operations was more than 3 times, which was very important in forestry The recognition accuracy of professional terms is low (86%), but the interactive experience is natural and realistic; the success rate of UI based forest management interaction method is 96%, but it needs to use the mouse to click the forest model in the virtual scene for many times, so the interaction is not natural and realistic; the success rate of body action based forest management interaction method is 90%, and each body action is not natural All of them can be correctly mapped to the forest management operation method, and the interaction is more natural and realistic.

对国内外城市森林景观经营研究现状分析,并通过秦皇岛市海滨林场城市森林景观经营实践,来阐述该经营模式的效果,为推动城市森林景观经营的发展提供理论与实践参考.

基于混交林的相关研究成果,对混交林与纯林的生态与经济优势进行比较分析的结果表明:⑴混交林因具有垂直成层结构及树冠可塑性,从而能更有效利用林冠空间;⑵混交林枯枝落叶层因各种枯枝落叶混交,进而加快无机养分的循环;⑶某些特定混交树种由于具有固氮作用以及不同的根系分布模式、 生长节律及资源利用方式,从而能够获取更多生长所需的资源;⑷混交林具有更强的抗霜害及病虫害能力;⑸在一定条件下,某些林地上的混交林表现出经济效益的优势;⑹混交林不仅能产生可观的经济收入,而且还具有降低金融风险的优势;⑺除木材经济效益外,混交林还具有诸多边际效益等;⑻混交林具有长期经济收益优势,而且在维护林地长期生产力、 高水平的生态服务和功能方面明显优于纯林.总体来说,与纯林相比,混交林具有明显的生态与经济优势,能提供更为多元化、 优质的生态服务.

[Objective]To investigate the differences in the composition and spatial structure of bud bank of Nitrar-ia tangutorum seedling under the different nitrogen addition gradients,and nutrient limitation and nitrogen utiliza-tion,and to reveal the linkage between the quality of roots and cuttings and the bud bank,and finally elucidate the adaptation strategies of bud bank for the nitrogen availability.[Method]The bud bank traits of N.tangutorum seedlings under different nitrogen addition were studied using pot experiments.The N addition levels consist of 0, 12,24,36,48 and 60 mmol·L-1 .[Result]The nitrogen addition significantly increased the number of buds and vegetative shoots in bud bank,and significantly reduced the number of dormant buds and dormant shoots;mean-while,the nitrogen addition promoted the bud production of secondary shoots and tertiary shoots.With the increas-@ing of nitrogen addition,the relative position of vegetative shoots in bud bank had a tendency to move from base to tip.There was a quadratic nonlinear positive correlation between nitrogen balance index (NBI)and budding inten-sity,branching intensity,bud production of secondary and tertiary shoots of N.tangutorum seedling;The N con-tent,accumulation amount (NAA)and the morphological traits underground were positively related to the number of buds and vegetative shoots,and negatively related to the number of dormant buds and dormant shoots.[Conclu-sion]Among the six nitrogen addition gradients,most of the indicators reached their maximum values in either 36 mmol·L-1 or 48 mmol·L-1 N addition levels,which were the optimum N additions for N.tangutorum seedlings. Nitrogen addition has a significantly influence on the bud bank size and spatial distribution of N.tangutorum seed-lings,which also reflects a response to the changing nutrient availability.

党的十九大报告明确提出实施乡村振兴战略,以解决“三农”问题及寻找农村发展新动力.为保障乡村振兴战略的顺利实施,本文从国外乡村振兴发展的历程、规划与政策措施、主要模式、组织方式、投融资渠道,以及林业在乡村振兴发展中的主要作用等6个方面进行分析,从而为中国乡村振兴战略的规划与落实提供参考建议.

荒漠植物构型是植物与环境相互作用、相互适应的结果,其与功能的相互作用决定了荒漠植被的发展与演替.系统研究了半日花(Helianthemum songaricum)的分枝率、分枝角度、分枝长度和枝茎比等构型特征,对比研究了不同土壤类型和不同坡位间、半日花的构型特征响应及其适应机理.结果表明:覆沙地半日花的总体分枝率为0.49±0.03,石砾地半日花总体分枝率0.56±0.03;逐步分枝率SBR2∶3和SBR3∶4呈现覆沙地半日花大于石砾地半日花,SBR1∶2则表现相反;半日花1～4级分枝角度集中在30°～42°,并且1级到4级呈增大趋势;覆沙地的枝茎比RBD2∶1、RBD3∶2、RBD4∶3分别为0.57±0.05、0.59±0.05、0.51±0.05,石砾地分别为0.68±0.06、0.72±0.06、0.50±0.03,其中枝茎比RBD2∶1、RBD3∶2、RBD4∶3都呈现先增大后减小趋势;覆沙地半日花的1～3级枝长均大于石砾地半日花,4级枝长小于石砾地半日花,且覆沙地和石砾地半日花的1～4级分枝长度呈依次减小的趋势;同一土壤条件下,不同坡位半日花的枝系构型特征不存在显著性差异.覆沙地半日花枝系长度、粗度、以及枝条数均大于石砾地半日花,能够获得更多空间资源,在植物竞争和演替过程中,其构型特征具有一定的优势.

[Objective]To understand the effect of sand burial on the phenotypic plasticity of Nitraria tangutorum, [Method]The cutting shoots of N.tangutorum were treated by sand burial under different depths (0,5,10,and 15 cm)and then to analyze the data collected.[Result](1 )With the increase of sand burial depth,the plant height,15 cm stem diameter,leaf area,the amounts of leaf and adventitious root decreased,and the length or di-ameter of adventitious root increased at first and then decreased.It was found that when the sand burial depths were 0,5,10,and 15 cm,the heights of the plant were respectively 62.82,55.90,52.38,and 49.24 cm,the 15 cm stem diameters were respectively 2.79,2.48,2.39,and 2.07 mm,the leaf areas were 477.81,214.38,247.90, and 112.91 cm2,the amounts of adventitious root were respectively 6.40,3.80,2.80,and 3.40,the lengths of adventitious root were respectively 10.19,11.54,13.92,7.62 cm,the diameter of adventitious root were respec-tively 1.51,1.95,1.65,and 1.19 mm.(2)With the increase of sand burial depth,the above-ground biomass and the total biomass decreased,and the length,diameter of adventitious root and the below-ground biomass in-creased at first and then decreased,the biomass of above-ground/the below-ground biomass first decreased then in-creased.It was found that when the sand burial depths were 0,5,10,and 15cm,the branch biomasses were re-spectively 6.29,4.20,3.09,and 2.75 g,the leaf biomasses were respectively 3.93,2.52,3.31,and 1.28 g, the total biomasses were respectively 10.81,7.53,7.41,and 4.30 g,the below-ground biomasses were respec-tively 0.59,0.81,0.59,and 0.28 g,the biomass of above-ground/the below-ground biomass respectively is 17.32、8.30、10.85、14.39.(3)The daily averages of net photosynthetic rate,transpiration rate and stomatal con-ductance of N.tangutorum increased gradually.It was found that when the sand burial depths were 0,5,10,and 15 cm,the daily averages of net photosynthetic rate were respectively 8.06,9.39,9.72,and 11.25 μmol·m-2· s-1,the daily averages of transpiration rate were respectively 5.56,6.70,6.77,and 7.61 mmol·m-2·s-1,the daily averages of stomatal conductance were respectively 0.28,0.31,0.31,and 0.36 mol·m-2·s-1.(4)Com-pared with the CK,the difference of chlorophyll contents of N.tangutorum was not significant.When the depth was 15 cm,the chlorophyll contents of N.tangutorum increased significantly,but the differences of the ratio of the chlo-rophyll a and Chlorophyll b were not significant.[Conclusion]The phenotypic plasticity of morphology,biomass al-location and photosynthesis of N.tangutorum were greater under different sand burial depth,and when the sand burial depth was 15 cm,the differences of photosynthetic parameters of N.tangutorum responded to different sand burial depth were significant.