Forest and Grassland Resources Research >
Estimating key attributes of an ancient-tree group using LiDAR-derived individual-tree segmentation data
Received date: 2025-08-18
Revised date: 2025-10-20
Online published: 2026-04-17
To explore the feasibility of applying remote sensing survey methods in the 3rd National Census of Ancient and Famous Trees,this study used field survey data from one ancient-tree-group subcompartment and unmanned aerial vehicle(UAV)-borne LiDAR individual-tree segmentation data in the Northeast Tiger and Leopard National Park.The analogy method and large-tree-based approach were estimated to be the main characteristics of ancient tree groups,including the total number of ancient trees,the mean diameter at breast height(DBH),mean age,mean height,and mean crown width.Three schemes for selecting ‘large trees’ were designed,and for each scheme,seven sets of ‘large trees’ numbers were set,thus forming 21 combinations for comparison and analysis.The results showed:1)Based on the individual tree segmentation data of LiDAR,the three core factors(the number of ancient trees,mean age,and mean DBH)were estimated directly using the analogy method,with relative errors being within 10%;2)Based on overall consideration of tree height and crown width,‘large trees’ were first selected at a quantity 4 times the number of ancient trees observed in the field,subsequent analogy-based estimation reduced the relative errors of the three core factors to within±3%.These findings demonstrate that surveying ancient tree groups is feasible based on LiDAR from UAV.Selecting the appropriate number of "large trees" firstly based on individual tree segmentation data and then using the analogy method for estimation would minimize the errors to the lowest level.
Key words: LiDAR; tree segmentation; ancient tree; coefficient of correction
MENG Haiding , ZHENG Chen , ZENG Weisheng , SUN Zhichao , YU Shiyong , SUN Xiangnan , PANG Junli . Estimating key attributes of an ancient-tree group using LiDAR-derived individual-tree segmentation data[J]. Forest and Grassland Resources Research, 2025 , 0(5) : 114 -120 . DOI: 10.13466/j.cnki.lczyyj.2025.05.00
| [1] | 全国绿化委员会办公室. 关于开展第三次全国古树名木资源普查的通知[R/OL].(2025-07-03)[2025-10-10].https://25520199.s21i.faiusr.com/61/ABUIABA9GAAgk9H2xAYolOTujwY.pdf. |
| [2] | 中华人民共和国国务院. 古树名木保护条例[M]. 北京: 中国法治出版社, 2025. |
| [3] | 刘清旺, 李世明, 李增元, 等. 无人机激光雷达与摄影测量林业应用研究进展[J]. 林业科学, 2017, 53(7):134-148. |
| [4] | 刘会玲, 张晓丽, 张莹, 等. 机载激光雷达单木识别研究进展[J]. 激光与光电子学进展, 2018, 55(8):34-42. |
| [5] | 李平昊, 申鑫, 代劲松, 等. 机载激光雷达人工林单木分割方法比较和精度分析[J]. 林业科学, 2018, 54(12):127-136. |
| [6] | 刘浩然, 范伟伟, 徐永胜, 等. 基于无人机激光雷达点云数据的单木分割研究[J]. 中南林业科技大学学报, 2022, 42(1):45-53. |
| [7] | 朱泊东, 罗洪斌, 金京, 等. 高郁闭度人工林无人机激光雷达单木分割方法优化[J]. 林业科学, 2022, 58(9):48-59. |
| [8] | 邢艳秋, 尤号田, 霍达, 等. 小光斑激光雷达数据估测森林树高研究进展[J]. 世界林业研究, 2014, 27(2):29-34. |
| [9] | 王娟, 张超, 陈巧, 等. 结合无人机可见光和激光雷达数据的杉木树冠信息提取[J]. 西南林业大学学报(自然科学), 2022, 42(1):133-141. |
| [10] | 周理想, 曹明兰, 郎博, 等. 基于无人机激光雷达点云的树干提取[J]. 中南林业科技大学学报, 2024, 44(11):22-28. |
| [11] | 郭佳昌, 杨斌, 张晗钰, 等. 应用云西林场两种人工林激光雷达数据估测单木蓄积量[J]. 东北林业大学学报, 2024, 52(9):69-74. |
| [12] | 李远航, 笪志祥, 闫烨琛. 基于无人机载激光雷达点云数据的人工侧柏林单木分割研究[J]. 西北林学院学报, 2023, 38(6):171-179. |
| [13] | 黄冰倩, 曹霸, 岳彩荣, 等. 基于机载激光雷达技术的山区针叶林单木分割方法研究[J]. 中南林业调查规划, 2024(3):34-39. |
| [14] | 胡中洋, 陕亮, 陈翔宇, 等. CHM与DSM相结合的无人机激光雷达单木分割[J]. 林业科学, 2024, 60(8):14-24. |
| [15] | 张燕妮, 张学霞, 张建军, 等. 不同林分密度时激光雷达点云数据单木分割及参数提取[J]. 东北林业大学学报, 2024, 52(7):36-43. |
| [16] | 贾越, 夏永华, 赵昌福, 等. 机载激光点云密度对单木分割精度的影响[J]. 兰州大学学报(自然科学版), 2025, 61(2):215-221. |
| [17] | 闫兆杰, 苏香玲, 王振锡, 等. 基于不同点云密度LiDAR数据的天山云杉单木树高提取[J]. 新疆农业科学, 2025, 62(4):917-928. |
| [18] | 温雪香, 孙乡楠, 曾伟生. 吉林省10个主要树种(组)单木胸径生长模型研建[J]. 林草资源研究, 2025(4):122-128. |
| [19] | GILL S J, BIGING G S, MURPHY E C. Modeling conifer tree crown radius and estimating canopy cover[J]. Forest Ecology and Management, 2000, 126:405-416. |
| [20] | BECHTOLD W A. Largest crown width models for 53 species in US[J]. Western Journal of Applied Forestry, 2004, 19(4):245-251. |
| [21] | COOMBES A, MARTIN J, SLATER D. Defining the allometry of stem and crown diameter of urban trees[J]. Urban Forestry & Urban Greening, 2019, 44:126421. |
| [22] | 邹文涛, 曾伟生, 温雪香. 我国8个松属树种林木树冠因子与胸径树高回归建模[J]. 林草资源研究, 2025(2):141-150. |
/
| 〈 |
|
〉 |