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遥感技术与应用  2021, Vol. 36 Issue (3): 511-520    DOI: 10.11873/j.issn.1004-0323.2021.3.0511
森林遥感专栏     
基于无人机激光雷达的森林冠层高度分析
边瑞(),年雁云(),勾晓华,贺泽宇,田行宜
兰州大学 资源环境学院 西部环境教育部重点实验室,甘肃 兰州 730000
Analysis of Forest Canopy Height based on UAV LiDAR: A Case Study of Picea crassifolia in the East and Central of the Qilian Mountains
Rui Bian(),Yanyun Nian(),Xiaohua Gou,Zeyu He,Xingyi Tian
Key Laboratory of Western China's Environmental Systems(Ministry of Education),College of Earth and Environmental Sciences,Lanzhou University,Lanzhou,China,730000
 全文: PDF(6252 KB)   HTML
摘要:

快速准确获取森林结构参数对森林资源调查管理及全球碳汇研究具有重要意义。以祁连山东、中部青海云杉林为研究对象,利用16个无人机激光雷达(LiDAR)点云数据、正射影像数据结合实地样方观测数据,提取样方内青海云杉的单木树高并准确验证树木分割精度;结合实测数据和地形数据,依据统计指标验证提取树高精度并分析原因;基于点云数据提取的各样方树高分析祁连山青海云杉冠层高度在空间上的变化。结果表明:在祁连山山地森林,冠层高度平均值估算精度最高,R2为0.93,RMSE为1.39 m(P<0.05);地形影响基于点云数据的树高提取,坡度较小的青海云杉树高提取效果更好;从东到西,青海云杉平均树高呈下降趋势;随着海拔高度上升,青海云杉的平均树高先上升后下降,这与祁连山东西水热条件差异和不同海拔树木年龄分布有关。

关键词: 山地森林无人机激光雷达森林冠层高度空间分布    
Abstract:

Rapid and accurate acquisition of forest structural parameters has been significant for forest resource investigation. In this study, photogrammetric and field-based tree height measurement of the Picea crassifolia were validated in the east and central of the Qilian Mountains. The individual segmentation algorithm using Canopy Height Model was applied to identify the position and height of the Picea crassifolia within each plot. The extraction accuracy of the average tree height was recognized the highest among the four indexes of maximum value, minimum value, mean value and standard deviation, with Root Mean Square Error (RMSE) values of 1.39 m and R2 values of 0.93(P<0.05). Tree heights extracted from LiDAR data of Picea crassifolia were used to analyze the spatial distribution of tree height in the Qilian Mountains. There was a downward trend of the average forest canopy height from east to west in the Qilian Mountains. As the altitude rises, the forest canopy height showed a “unimodal” change, which peaked at change an altitude of 2 900 m. This study shown that UAV photogrammetric tree height measurements was a viable option for intensive forest monitoring plots. Additionally, it was shown that underestimated evident in field-based and UAV laser scanning tree height measurements could potentially lead to misinterpretation of results when field-based measurements are used as validation.

Key words: Mountain forest    UAV LiDAR    Forest canopy height    Spatial distribution
收稿日期: 2020-07-28 出版日期: 2021-07-22
ZTFLH:  TP79  
基金资助: “第二次青藏科学考察”项目(2019QZKK0301)
通讯作者: 年雁云     E-mail: bianr18@lzu.edu.cn;yynian@lzu.edu.cn
作者简介: 边瑞(1995-),女,山西忻州人,硕士研究生,主要从事遥感与无人机遥感等相关研究。E?mail:bianr18@lzu.edu.cn
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引用本文:

边瑞,年雁云,勾晓华,贺泽宇,田行宜. 基于无人机激光雷达的森林冠层高度分析[J]. 遥感技术与应用, 2021, 36(3): 511-520.

Rui Bian,Yanyun Nian,Xiaohua Gou,Zeyu He,Xingyi Tian. Analysis of Forest Canopy Height based on UAV LiDAR: A Case Study of Picea crassifolia in the East and Central of the Qilian Mountains. Remote Sensing Technology and Application, 2021, 36(3): 511-520.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2021.3.0511        http://www.rsta.ac.cn/CN/Y2021/V36/I3/511

图1  研究区样地分布及使用仪器
图2  激光雷达点云数据树高提取流程
图3  样方单木提取
图4  基于LiDAR的树高统计分析
类别地面点植被点合计制图精度
总体精度95.80%
地面点40 6378 15048 78783.29%
植被点1 000168 175169 17599.41%
合计41 637176 325217 962
用户精度97.60%95.38%
表1  分类混淆矩阵
图5  单木分割精度评价
图6  实测树高与估测树高比较
图7  样方树高提取精度评价及坡度分析
图8  基于不同统计指标的冠层高度空间变化
图9  同一海拔高度平均冠层高度的空间变化
图10  不同海拔梯度上冠层高度变化
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