林业科学 ›› 2025, Vol. 61 ›› Issue (12): 83-93.doi: 10.11707/j.1001-7488.LYKX20250026
• 研究论文 • 上一篇
张沛东1,马天天1,闫飞1,*(
),张晓媛2,王智灏3,刘牧4
收稿日期:2025-01-19
修回日期:2025-04-25
出版日期:2025-12-25
发布日期:2026-01-08
通讯作者:
闫飞
E-mail:yanfei522@bjfu.edu.cn
基金资助:
Peidong Zhang1,Tiantian Ma1,Fei Yan1,*(
),Xiaoyuan Zhang2,Zhihao Wang3,Mu Liu4
Received:2025-01-19
Revised:2025-04-25
Online:2025-12-25
Published:2026-01-08
Contact:
Fei Yan
E-mail:yanfei522@bjfu.edu.cn
摘要:
目的: 利用计算虚拟测量(CVM)方法,通过虚拟森林样地仿真测量虚拟树木个体,以获取现实中难以测量的生物参数。结合虚拟样地(VSP)构建、树木建模与光照模拟技术,定量分析相邻树木冠层遮荫对目标树木的光照影响,探讨叶片在光照模拟中的作用,以优化虚拟样地技术并提高光照分析精度。方法: 构建集成化的多尺度建模技术体系,综合运用Context Capture Center点云重建技术、定量结构模型与体素建模方法,实现从单木器官到群落冠层的跨尺度三维重建。引入基于物理原理的动态光传输模型,并将其与数字地面模型和太阳轨迹模拟算法耦合,构建逼真的虚拟光环境。设计“全日照”与“邻域遮荫”双对比场景,系统分析在邻近树木冠层动态遮荫作用下目标树木光能截获的时空变化规律,有效分离遮荫导致的纯能量损失。结果: 邻木遮荫导致目标树木的太阳辐射损失达35.80%~45.72%,表明本研究提出方法具有较高的可靠性。模型结构对模拟结果影响显著,包含叶片的模型表现出更强的光衰减效应,样木T26的能量损失率依次为CCC模型(45.72%)> Voxel模型(39.26%)> QSM模型(35.80%),证实几何完整性是评估遮荫效应的关键。冠层结构特征同样主导能量损失差异,冠层稀疏的成年欧洲白桦T27(35.80%)损失显著低于冠层密集的幼龄云杉T26 (45.72%),说明幼树更易受遮荫压制。经数据校正后,明确各模型在不同光照梯度下的响应特征,反映出光环境异质性对模拟结果的影响。结论: 本研究集成VSP-CVM方法,可实现以往方法难以实现的对森林邻木遮荫效应的精准量化评估,证实虚拟样地技术能够精准重建林分三维结构并支持毫米级光能分布模拟。该方法不仅可克服传统实测方法的局限性,还能为林分光竞争研究提供新的技术手段。未来,可拓展至多气候带与林分类型验证其普适性,并将光合作用过程模型整合进气象模型框架中,提升对光能利用过程的模拟,为森林精准经营与结构优化提供科学依据。
中图分类号:
张沛东,马天天,闫飞,张晓媛,王智灏,刘牧. 利用虚拟测量评价相邻木带来的辐射损失[J]. 林业科学, 2025, 61(12): 83-93.
Peidong Zhang,Tiantian Ma,Fei Yan,Xiaoyuan Zhang,Zhihao Wang,Mu Liu. Evaluating Radiation Loss Caused by Neighboring Trees Using Computational Virtual Measurement[J]. Scientia Silvae Sinicae, 2025, 61(12): 83-93.
表1
研究区及扫描设备基本概况"
| 项目 Item | 信息 Information | 参数 Parameter |
| 研究样地 Research plot | 面积 | 32×32 |
| Area/m2 | ||
| 样地树木总数 | 51 | |
| Total number of trees in the plot | ||
| 林分密度 | 600 | |
| Stand density/(N·hm?2) | ||
| 扫描设备 Scanning equipment | 扫描仪型号 | HDS6100 |
| Scanner model | ||
| 测量系统 | Leica Geosystems | |
| Measurement system | ||
| 扫描仪波长 | 650~690 | |
| Scanner wavelength/nm | ||
| 视场角 | 360×310 | |
| Field angle/(°) | ||
| 25 m处精度 | ±2 | |
| Accuracy at a distance of 25 meters/mm | ||
| 扫描模式 | High density | |
| Scan patterns | ||
| 水平/垂直角度增量 | 0.036 | |
| Horizontal / vertical angle increment/(°) | ||
| 在25 m距离处的点间距 | 15.7 | |
| Point spacing at a distance of 25 meters/mm |
图4
太阳光分析的10个场景 a:无邻近树木的T27 (Voxel模型)日照分析 Sunlight analysis for T27 (voxel model) without neighboring trees;b:无邻近树木的T26 (Voxel模型)日照分析 Sunlight analysis for T26 (voxel model) without neighboring trees;c:无邻近树木的T26 ( QSM模型)日照分析 Sunlight analysis for T26 (QSM model) without neighboring trees;d: T27 (Voxel模型)与邻近树木的日照分析 Sunlight analysis for T27 (voxel model) with neighboring trees;e:有邻近树木的T26 (Voxel模型)日照分析 Sunlight analysis for T26 (voxel model) with neighboring trees;f:有邻近树木的T26 (QSM模型)日照分析 Sunlight analysis of T26(QSM model)with neighboring trees;g:无邻近树木的T27 (CCC模型)日照分析 Sunlight analysis of T27 (CCC model) without neighboring trees;h:无邻近树木的T26 (CCC模型)日照分析 Sunlight analysis of T26 (CCC model) without neighboring trees;i:有邻近树木的T27 (CCC模型)日照分析 Sunlight analysis of T27 (CCC model) with neighboring trees;j:有邻近树木的T26 (CCC模型)日照分析 Sunlight analysis of T26 (CCC model) with neighboring trees."
表2
VSP中10个场景的日照分析结果"
| 场景 Scenes | 表面面积按辐照时间分类 Surface area classified by irradiation duration/m2 | 网格中三角形数量 Number of triangles in mesh | ||||||||
| 0~1 h | 1~2 h | 2~3 h | 3~4 h | 4~5 h | 5~6 h | 6~7 h | 7~8 h | 8 h | ||
| a | 501.91 | 20.99 | 16.14 | 21.07 | 6.78 | 4.66 | 3.35 | 3.50 | 1.68 | 2 081 198 |
| b | 77.63 | 6.15 | 5.33 | 7.36 | 2.60 | 1.87 | 1.52 | 1.57 | 0.97 | 758 991 |
| c | 1.09 | 0.27 | 0.34 | 0.19 | 0.28 | 0.14 | 0.10 | 0.21 | 0.04 | 560 072 |
| d | 527.98 | 22.70 | 15.75 | 8.59 | 2.46 | 1.10 | 1.08 | 0.35 | 0.08 | 1 440 302 |
| e | 83.60 | 8.59 | 6.48 | 2.78 | 1.54 | 1.16 | 0.82 | 0.01 | 0.00 | 537 462 |
| f | 1.35 | 0.45 | 0.29 | 0.25 | 0.14 | 0.15 | 0.05 | 0.00 | 0.00 | 368 727 |
| g | 599.60 | 131.71 | 90.11 | 56.46 | 43.75 | 24.79 | 20.84 | 11.13 | 0.93 | 573 345 |
| h | 20.09 | 13.03 | 16.28 | 15.93 | 17.66 | 15.08 | 13.15 | 8.00 | 0.26 | 7 761 180 |
| i | 620.31 | 142.17 | 96.11 | 46.61 | 35.56 | 18.16 | 14.62 | 5.74 | 0.04 | 385 326 |
| j | 21.60 | 14.81 | 17.37 | 16.28 | 16.16 | 13.82 | 13.81 | 9.05 | 0.00 | 5 656 708 |
表3
受邻近树木遮蔽下的光照条件与无遮蔽条件下的差异"
| 场景 Scenes | 表面面积按辐照时间分类 Surface area classified by irradiation duration/m2 | 三角形的损失率 Loss rate of triangles(%) | ||||||||
| 0~1 h | 1~2 h | 2~3 h | 3~4 h | 4~5 h | 5~6 h | 6~7 h | 7~8 h | 8 h | ||
| d~a | 26.07 | 1.71 | ?0.38 | ?12.49 | ?4.32 | ?3.56 | ?2.27 | ?3.15 | ?1.60 | 30.79 |
| e~d | 5.98 | 2.44 | 1.14 | ?4.57 | ?1.05 | ?0.71 | ?0.69 | ?1.57 | ?0.97 | 29.19 |
| f~c | 0.26 | 0.18 | ?0.06 | 0.06 | ?0.14 | 0.00 | ?0.04 | ?0.21 | ?0.04 | 34.16 |
| i~g | 20.71 | 10.46 | 6.00 | ?9.85 | ?8.19 | ?6.63 | ?6.22 | ?5.39 | ?0.89 | 32.79 |
| j~h | 1.51 | 1.78 | 1.09 | 0.35 | ?1.50 | ?1.26 | ?0.66 | ?1.05 | ?0.26 | 27.11 |
表4
受邻近树木遮蔽分析与无遮蔽条件日照分析之间的面积转移"
| 树木 Tree | 按辐照持续时间分类的表面积转移率 Surface area transfer rates by duration of irradiation(%) | ||||||||
| 0~1 h | 1~2 h | 2~3 h | 3~4 h | 4~5 h | 5~6 h | 6~7 h | 7~8 h | 8 h | |
| T27 voxel | 93.86 | 6.16 | ?1.37 | ?44.97 | ?15.55 | ?12.82 | ?8.17 | ?11.34 | ?5.76 |
| T26 voxel | 62.55 | 25.52 | 11.92 | ?47.80 | ?10.98 | ?7.43 | ?7.22 | ?16.42 | ?10.15 |
| T26 QSM | 52.53 | 36.36 | ?12.12 | 12.12 | ?28.28 | 0.00 | ?8.08 | ?42.42 | ?8.08 |
| T27 CCC | 55.72 | 28.14 | 16.14 | ?26.50 | ?22.03 | ?17.84 | ?16.73 | ?14.50 | ?2.40 |
| T26 CCC | 31.92 | 37.63 | 23.14 | 7.40 | ?31.71 | ?26.64 | ?13.95 | ?22.20 | ?5.50 |
表5
评估单棵树木因邻近遮荫造成的能量损失"
| 树木 Trees | T27体素模型 T27 Voxel | T26体素模型 T26 Voxel | T26 QSM模型 T26 QSM | T27 CCC模型 T27 CCC | T26 CCC模型 T26 CCC | |
| 表面类型分类 (按照辐射持续时间) Surface area classification based on by irradiation duration/(kW·h) | 0~1 h | 1.434 | 0.329 | 0.014 | 2.28 | 0.17 |
| 1~2 h | 0.282 | 0.403 | 0.030 | 1.73 | 0.29 | |
| 2~3 h | ?0.105 | 0.314 | ?0.017 | 1.65 | 0.30 | |
| 3~4 h | ?4.809 | ?1.759 | 0.023 | ?3.79 | 0.13 | |
| 4~5 h | ?2.138 | ?0.520 | ?0.069 | ?4.05 | ?0.74 | |
| 5~6 h | ?2.154 | ?0.430 | 0.000 | ?4.01 | ?0.56 | |
| 6~7 h | ?1.623 | ?0.493 | ?0.029 | ?4.45 | ?0.47 | |
| 7~8 h | ?2.599 | ?1.295 | ?0.173 | ?4.45 | ?0.57 | |
| 8 h | ?1.408 | ?0.854 | ?0.035 | ?0.78 | ?0.23 | |
| 能量损失 Loss of energy/(kW·h) | ?13.12 | ?4.31 | ?0.26 | 15.87 | 2.57 | |
| 原始能量 Original energy/(kW·h) | 28.95 | 10.97 | 0.71 | 37.17 | 5.62 | |
| 损失率 Loss rate (%) | 45.32 | 39.26 | 35.80 | 42.70 | 45.72 | |
| 崔佳佳, 铁 牛. 大兴安岭北部森林群落结构及植物多样性特征研究. 西北林学院学报, 2021, 36 (2): 24- 30. | |
| Cui J J, Tie N. Forest community structure and plant diversity characteristics in northern greater Khingan Mountains. Journal of Northwest Forestry University, 2021, 36 (2): 24- 30. | |
| 陈文盛, 丁慧慧, 李江荣. 森林小气候特征研究进展. 湖南生态科学学报, 2022, 9 (3): 89- 95. | |
| Chen W S, Ding H H, Li J R, et al. Research progress on microclimate characteristics of different forest habitats. Journal of Hunan Ecological Science, 2022, 9 (3): 89- 95. | |
| 王智超, 马天天, 邵亚奎, 等. 面向未来的中国智慧林业: 观测仪器体系的演进与发展趋势. 林业科学, 2024, 60 (4): 1- 15. | |
| Wang Z C, Ma T T, Shao Y K, et al. Future oriented smart forestry in China: evolution and development trends of observation instrument systems. Scientia Silvae Sinicae, 2024, 60 (4): 1- 15. | |
| 徐自为, 刘绍民, 车 涛, 等. 黑河流域地表过程综合观测网的运行、维护与数据质量控制. 资源科学, 2020, 42 (10): 1975- 1986. | |
| Xu Z W, Liu S M, Che T, et al. Operation and maintenance and data quality control of the Heihe integrated observatory network. Resources Science, 2020, 42 (10): 1975- 1986. | |
| 张 宇, 张怀清, 安 锋, 等. 2024. 基于计算机模拟模型的林木冠层太阳短波辐射定量分析方法. 林业科学, 60(4): 16-30. | |
| Zhang Y, Zhang H Q, An F, et al. 2024. A quantitative analysis method of solar shortwave radiation within forest canopy based on a computer simulation model. Scientia Silvae Sinicae, 60(4): 1–15. [in Chinese] | |
| 赵 宽, 周葆华, 马万征, 等. 不同环境胁迫对根系分泌有机酸的影响研究进展. 土壤, 2016, 48 (2): 235- 240. | |
| Zhao K, Zhou B H, Ma W Z, et al. The influence of different environmental stresses on root-exuded organic acids: a review. Soils, 2016, 48 (2): 235- 240. | |
| 赵鹏武, 管立娟, 刘兵兵. 等. 我国半干旱区东段森林动态研究现状及展望. 世界林业研究, 2021, 34 (2): 74- 79. | |
| Zhao P W, Guan L J, Liu B B, et al. Current research and prospect of forest dynamics in eastern section of semi-arid area in China. World Forestry Research, 2021, 34 (2): 74- 79. | |
|
Aalto I, Aalto J, Hancock S, et al. Quantifying the impact of management on the three-dimensional structure of boreal forests. Forest Ecology and Management, 2023, 535, 120885.
doi: 10.1016/j.foreco.2023.120885 |
|
|
Abegg M, Bösch R, Kükenbrink D, et al. Tree volume estimation with terrestrial laser scanning: testing for bias in a 3D virtual environment. Agricultural and Forest Meteorology, 2023, 331, 109348.
doi: 10.1016/j.agrformet.2023.109348 |
|
|
Babst F, Bouriaud O, Poulter B, et al. Twentieth century redistribution in climatic drivers of global tree growth. Science Advances, 2019, 5 (1): 4313.
doi: 10.1126/sciadv.aat4313 |
|
| Bienert A, Hess C, Maas H G, et al. 2014. A voxel-based technique to estimate the volume of trees from terrestrial laser scanner data. ISPRS Journal of Photogrammetry and Remote Sensing, XL–5: 101–106. | |
| Cannon C H, Borchetta C, Anderson D L, et al. 2021. Extending our scientific reach in arboreal ecosystems for research and management. Frontiers in Forests and Global Change, 4: 712165. | |
| Colaizzi P D, Evett S R, Howell T A, et al. 2012. Radiation model for row crops: I. geometric view factors and parameter optimization. Agronomy Journal, 104(2): 225–240. | |
|
De Pauw K, Sanczuk P, Meeussen C, et al. Forest understorey communities respond strongly to light in interaction with forest structure, but not to microclimate warming. New Phytologist, 2022, 233 (1): 219- 235.
doi: 10.1111/nph.17803 |
|
| Dou H, Niu G. 2020. Plant responses to light. Plant Factory, 153−166. | |
|
Duan Y, Yang C, Chen H, et al. Low-complexity point cloud denoising for LiDAR by PCA-based dimension reduction. Optics Communications, 2021, 482, 126567.
doi: 10.1016/j.optcom.2020.126567 |
|
|
Forrester D I. Linking forest growth with stand structure: tree size inequality, tree growth or resource partitioning and the asymmetry of competition. Forest Ecology and Management, 2019, 447, 139- 157.
doi: 10.1016/j.foreco.2019.05.053 |
|
| Garlick K, Drew R E, Rajaniemi T K. Root responses to neighbors depend on neighbor identity and resource distribution. Plant and Soil, 2021, 467 (1): 227- 237. | |
| Gao W, Larjavaara M, 2024. Wind disturbance in forests: a bibliometric analysis and systematic review. Forest Ecology and Management, 564: 122001. | |
| Gendron F, Messier C, Comeau P G, 2001. Temporal variations in the understorey photosynthetic photon flux density of a deciduous stand: the effects of canopy development, solar elevation, and sky conditions. Agricultural and Forest Meteorology, 106(1): 23–40. | |
|
Giday K, Aerts R, Muys B, et al. The effect of shade levels on the survival and growth of planted trees in dry afromontane forest: implications for restoration success. Journal of Arid Environments, 2019, 170, 103992.
doi: 10.1016/j.jaridenv.2019.103992 |
|
| Hackenberg J, Morhart C, Sheppard J, et al. 2014. Highly accurate tree models derived from terrestrial laser scan data: a method description. Forests, 5(5): 1069–1105. | |
| Hosoi F, Omasa K. 2006. Voxel-based 3-D modeling of individual trees for estimating leaf area density using high-resolution portable scanning LiDAR. IEEE Transactions on Geoscience and Remote Sensing, 44(12): 3610–3618. | |
| Kothari S, Montgomery R A, Cavender-Bares J, 2021. Physiological responses to light explain competition and facilitation in a tree diversity experiment. Journal of Ecology, 109(5): 2000–2018. | |
|
Liang X, Hyyppä J, Kaartinen H, et al. International benchmarking of terrestrial laser scanning approaches for forest inventories. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 144, 137- 179.
doi: 10.1016/j.isprsjprs.2018.06.021 |
|
|
Liang X, Kankare V, Hyyppä J, et al. Terrestrial laser scanning in forest inventories. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 115, 63- 77.
doi: 10.1016/j.isprsjprs.2016.01.006 |
|
|
Luo C, Wang Z, Sauer T J, et al. Portable canopy chamber measurements of evapotranspiration in corn, soybean, and reconstructed prairie. Agricultural Water Management, 2018, 198, 1- 9.
doi: 10.1016/j.agwat.2017.11.024 |
|
| Luo W, Liang J, Cazzolla Gatti R, et al. 2019. Parameterization of biodiversity–productivity relationship and its scale dependency using georeferenced tree-level data. Journal of Ecology, 107(3): 1106–1119. | |
|
Martínez Cano I, Shevliakova E, Malyshev S, et al. Allometric constraints and competition enable the simulation of size structure and carbon fluxes in a dynamic vegetation model of tropical forests (LM3PPA-TV). Global Change Biology, 2020, 26 (8): 4478- 4494.
doi: 10.1111/gcb.15188 |
|
|
Matsuo T, Martínez Ramos M, Bongers F, et al. Forest structure drives changes in light heterogeneity during tropical secondary forest succession. Journal of Ecology, 2021, 109 (8): 2871- 2884.
doi: 10.1111/1365-2745.13680 |
|
|
Neudam L C, Fuchs J M, Mjema E, et al. Simulation of silvicultural treatments based on real 3D forest data from mobile laser scanning point clouds. Forests and People, 2023, 11, 100372.
doi: 10.1016/j.tfp.2023.100372 |
|
|
Patacca M, Lindner M, Lucas-Borja M E, et al. Significant increase in natural disturbance impacts on European forests since 1950. Global Change Biology, 2023, 29 (5): 1359- 1376.
doi: 10.1111/gcb.16531 |
|
|
Piato K, Lefort F, Subía C, et al. Effects of shade trees on Robusta coffee growth, yield and quality: a meta-analysis. Agronomy for Sustainable Development, 2020, 40 (6): 38.
doi: 10.1007/s13593-020-00642-3 |
|
| Picard N, 2021. The role of spatial competitive interactions between trees in shaping forest patterns. Theoretical Population Biology, 142: 36–45. | |
|
Qi Y, Coops N C, Daniels L D, et al. Comparing tree attributes derived from quantitative structure models based on drone and mobile laser scanning point clouds across varying canopy cover conditions. ISPRS Journal of Photogrammetry and Remote Sensing, 2022, 192, 49- 65.
doi: 10.1016/j.isprsjprs.2022.07.021 |
|
| Raumonen P, Kaasalainen M, Åkerblom M, et al. 2013. Fast automatic precision tree models from terrestrial laser scanner data. Remote Sensing. 5(2): 491–520. | |
| Ross C W, Loudermilk E L, Skowronski N, et al. 2022. LiDAR voxel-size optimization for canopy gap estimation. Remote Sensing, 14(5): 1054. | |
| Sarkar D, Chapman C A, 2021. The smart forest conundrum: contextualizing pitfalls of sensors and AI in conservation science for tropical forests. Tropical Conservation Science, 14: 19400829211014740. | |
| Song Q, Xiao H, Xiao X, et al. A new canopy photosynthesis and transpiration measurement system (CAPTS) for canopy gas exchange research. Agricultural and Forest Meteorology, 2016, 217, 101- 107. | |
| Song Q, Zhu X G, 2018. Measuring canopy gas exchange using CAnopy photosynthesis and transpiration systems (CAPTS). In Photosynthesis: Methods and Protocols, PP: 69–81 | |
| Sun J, Wang P, Li R, et al. 2022. Fast tree skeleton extraction using voxel thinning based on tree point cloud. Remote Sensing, 14(11): 2558. | |
|
Tao F, Xiao B, Qi Q, et al. Digital twin modeling. Journal of Manufacturing Systems, 2022, 64, 372- 389.
doi: 10.1016/j.jmsy.2022.06.015 |
|
|
Thammanu S, Marod D, Han H, et al. The influence of environmental factors on species composition and distribution in a community forest in northern Thailand. Journal of Forestry Research, 2021, 32 (2): 649- 662.
doi: 10.1007/s11676-020-01239-y |
|
|
Torresan C, Benito Garzón M, O’Grady M, et al. A new generation of sensors and monitoring tools to support climate-smart forestry practices. Canadian Journal of Forest Research, 2021, 51 (12): 1751- 1765.
doi: 10.1139/cjfr-2020-0295 |
|
| Tripathi S, Bhadouria R, Srivastava P, et al, 2020. Effects of light availability on leaf attributes and seedling growth of four tree species in tropical dry forest. Ecological Processes, 9(1): 2. | |
| Urraca R, Martinez-de-Pison E, Sanz-Garcia A, et al. 2017. Estimation methods for global solar radiation: case study evaluation of five different approaches in central Spain. Renewable and Sustainable Energy Reviews, 77: 1098–1113. | |
| Vanhove W, Vanhoudt N, Van Damme P, 2016. Effect of shade tree planting and soil management on rehabilitation success of a 22-year-old degraded cocoa (Theobroma cacao L.) plantation. Agriculture, Ecosystems & Environment, 219: 14–25. | |
| Wang D, 2020. Unsupervised semantic and instance segmentation of forest point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 165: 86–97. | |
| Wang Z C, Lu X, An F, et al. 2022a. Integrating real tree skeleton reconstruction based on partial computational virtual measurement (CVM) with actual forest scenario rendering: a solid step forward for the realization of the digital twins of trees and forests. Remote Sensing, 14(23): 6041. | |
| Wang Q W, Robson T M, Pieristè M, et al. 2022b. Canopy structure and phenology modulate the impacts of solar radiation on C and N dynamics during litter decomposition in a temperate forest. Science of The Total Environment, 820: 153185. | |
| Wang Y, He C, Liu B, et al. Single wood parameters extraction and DBH model construction based on UAV tilt photography technology. Journal of Southwest Forestry University, 2022, 42 (1): 166- 173. | |
| Wang Z C, Zhang X, Zheng J, et al. 2021a. Design of a generic virtual measurement workflow for processing archived point cloud of trees and its implementation of light condition measurements on stems. Remote Sensing, 13(14): 2801. | |
| Wang Z C, Shen Y J, Zhang X Y, et al. 2021b. Processing point clouds using simulated physical processes as replacements of conventional mathematically based procedures: a theoretical virtual measurement for stem volume. Remote Sensing, 13(22): 4627. | |
|
Wang Z C, Zhang X, Zhang X, et al. Exploring a new physical scenario of virtual water molecules in the application of measuring virtual trees using computational virtual measurement. Forests, 2024, 15 (5): 880.
doi: 10.3390/f15050880 |
|
|
Weng E S, Malyshev S, Lichstein J W, et al. Scaling from individual trees to forests in an earth system modeling framework using a mathematically tractable model of height-structured competition. Biogeosciences, 2015, 12 (9): 2655- 2694.
doi: 10.5194/bg-12-2655-2015 |
|
|
Yazdi H, Shu Q, Rötzer T, et al. A multilayered urban tree dataset of point clouds, quantitative structure and graph models. Scientific Data, 2024, 11 (1): 28.
doi: 10.1038/s41597-023-02873-x |
|
| Zhang B, DeAngelis D L, 2020. An overview of agent-based models in plant biology and ecology. Annals of Botany, 126(4): 539–557. | |
|
Zuleta D, Krishna Moorthy S M, Arellano G, et al. Vertical distribution of trunk and crown volume in tropical trees. Forest Ecology and Management, 2022, 508, 120056.
doi: 10.1016/j.foreco.2022.120056 |
| [1] | 崔泽宇,张怀清,刘洋,张京,杨廷栋,傅汝饶. 树木三维建模与可视化模拟技术进展与应用[J]. 林业科学, 2025, 61(6): 1-12. |
| [2] | 杨阳,王海洋,马立辉. 濒危植物树枫杜鹃的结实及种子萌发特性[J]. 林业科学, 2020, 56(10): 173-183. |
| 阅读次数 | ||||||
|
全文 |
|
|||||
|
摘要 |
|
|||||