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遥感技术与应用  2020, Vol. 35 Issue (5): 1136-1145    DOI: 10.11873/j.issn.1004-0323.2020.5.1136
遥感应用     
基于机载LiDAR点云数据森林郁闭度估测
赵勋1(),岳彩荣1(),李春干2,谷雷1,张国飞1
1.西南林业大学 林学院,云南 昆明 650224
2.广西大学 林学院,广西 南宁 530004
Estimation of Forest Canopy Density based on Airborne LiDAR Point Cloud Data
Xun Zhao1(),Cairong Yue1(),Chungan Li2,Lei Gu1,Guofei Zhang1
1.College of Forestry,Southwest Forestry University,Kunming 650224,China
2.College of Forestry,Guangxi University,Nanning 530004,China
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摘要:

郁闭度是反映森林数量和质量的重要参数,是森林调查的重要因子之一。以广西壮族自治区高峰林场试验区获取的机载LiDAR点云数据为基础,基于二维冠层高度模型(Canopy Height Model,CHM)和三维点云开展了森林郁闭度估测研究。使用实地调查的105块样地作为验证参考数据对郁闭度估测结果进行了精度评价,结果表明:基于二维CHM估测郁闭度与实测值之间的R2=0.388,RMSE=0.17;而基于三维点云估测郁闭度采用了2种方法:第一种方法采用归一化后2 m以上高度植被点云密度与归一化后所有点云密度比值估测郁闭度,估测结果与实测值之间的R2=0.467,RMSE=0.13。第二种方法采用归一化后2 m以上高度第一次回波植被点云密度与归一化后第一次回波所有点云密度比值估测郁闭度,估测结果与实测值之间的R2=0.478,RMSE=0.12;基于三维点云的2种方法估测林分郁闭度的精度皆优于基于二维CHM的方法,基于三维点云估测林分郁闭度方法中,第二种方法的精度优于第一种方法。

关键词: 机载LiDAR点云数据冠层高度模型高峰林场郁闭度    
Abstract:

Canopy density is an important parameter reflecting the quantity and quality of forest, and also one of the important factors of forest survey. This paper is based on the airborne LiDAR point cloud data obtained from the experimental area of Gaofeng Forest Farm in Guangxi Zhuang autonomous region, the canopy height model (CHM) and three-dimensional point cloud were used to estimate forest canopy density. The accuracy of canopy density estimation results were evaluated by using 105 field samples as reference data. The results showed that R2=0.388 and RMSE=0.17 between canopy density estimation and measured values based on canopy height model(CHM).Two methods are used to estimate canopy density based on three-dimensional point cloud: In the first method, canopy density was estimated by the ratio of point cloud density of vegetation at a height of more than 2 meters after normalization to density of all point clouds after normalization, and R2=0.467 and RMSE=0.13 between the estimated results and measured values. In the second method, the density of vegetation point cloud in the first echo at a height of more than 2 meters after normalization and the density ratio of all point cloud in the first echo after normalization were used to estimate canopy density. R2=0.478 and RMSE=0.12 between the estimated results and the measured values. The accuracy of the two methods based on three-dimensional point cloud is better than that based on Canopy Height Model (CHM). Among the methods based on three-dimensional point cloud, the accuracy of the second method is better than that of the first method.

Key words: Airborne LiDAR point cloud data    CHM    Gaofeng Forest Farm    Canopy density
收稿日期: 2019-08-05 出版日期: 2020-11-26
ZTFLH:  S771.8  
基金资助: 亚太森林网络“大湄公河次区域森林可持续发展遥感监测”(APFNET/2018P1?CAF);云南省教育厅项目(2018JS330);国家自然科学基金项目(31260156)
通讯作者: 岳彩荣     E-mail: 617277977@qq.com;cryue@163.com
作者简介: 赵勋(1993-),男,云南保山人,硕士研究生,主要从事资源环境遥感研究。E?maill:617277977@qq.com
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引用本文:

赵勋,岳彩荣,李春干,谷雷,张国飞. 基于机载LiDAR点云数据森林郁闭度估测[J]. 遥感技术与应用, 2020, 35(5): 1136-1145.

Xun Zhao,Cairong Yue,Chungan Li,Lei Gu,Guofei Zhang. Estimation of Forest Canopy Density based on Airborne LiDAR Point Cloud Data. Remote Sensing Technology and Application, 2020, 35(5): 1136-1145.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2020.5.1136        http://www.rsta.ac.cn/CN/Y2020/V35/I5/1136

图1  高峰林场试验区位置示意图
图2  生成CHM模型过程
图3  点云数据归一化
图4  提取冠层点
图5  郁闭度结果图
图6  郁闭度精度评价比较
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