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引用本文:

DOI:

10.11834/jrs.20210537

收稿日期:

2020-11-28

修改日期:

2021-03-31

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地基LiDAR林木点云估算枝干材积研究
靳双娜1, 张吴明2, 蔡尚书1, 邵杰2, 程顺3, 谢东辉1, 阎广建1
1.北京师范大学 遥感科学国家重点实验室;2.中山大学 测绘科学与技术学院;3.塞罕坝机械林场
摘要:

材积是森林清查工作的一个重要参数,基于地基激光雷达点云的树木定量结构模型(QSM)重建方法能够实现林木材积的非破坏性获取,解决了传统森林原位调查方式耗时耗力的问题。但由于伐木材积真值的获取较难实现,使得量化结构模型方法的材积获取能力在树干及各级树枝水平上尚未开展研究,且仅应用于单木尺度地基激光雷达点云中,缺乏基于样方尺度扫描点云进行材积获取的探究。因此本文分别在单木及样方尺度完成QSM重建方法在树干及各级枝材积估算结果评估。实验结果表明,基于单木及样方尺度地基激光雷达点云均能有效地获取树干和一级枝的材积,而次级枝的材积估算存在明显的偏差:样方扫描尺度点云的树干及全树材积估算精度与单木尺度相当,估算偏差均为5%及10%左右,而一级枝材积估算偏差略大,其中单木尺度一级枝估算偏差在10%左右,样方尺度一级枝材积估算偏差在15%左右;此外,林分密度与样方尺度枝干材积估算精度呈负相关关系,在较低林分密度(425、625和925株/公顷)的样方中树干材积估算误差均在5%以内,一级枝材积估算误差在15%左右,另外受树干及一级枝材积低估与各次级枝材积高估的部分中和效应影响,样方内总蓄积量估算偏差均在10%左右,因此在较低林分密度的森林中,样方尺度扫描数据能够很好地估算树干、一级枝及全树材积。

Stem and branch volume estimation using terrestrial laser scanning data
Abstract:

Objective: Tree volume is an important parameter in forests inventory. The reconstruction of Quantitative Structure Model of Trees (QSM) method based on ground-based LiDAR point clouds can achieve non-destructive acquisition of forest volume and solve the time-consuming and labor-intensive problem of traditional forest in-situ investigation. However, because it is difficult to obtain the reference volume of the felled timber, the ability of the TreeQSM volume estimation has not been studied at stem and different branch orders, and it is only applied to the ground-based LiDAR point cloud collected at tree-level but not at plot-level. Therefore, this paper proposed to assess the stem and branch volume estimation of TreeQSM from point cloud collected from both tree-level and plot-level. Method: In this paper, we evaluate the stem and branch volume estimated by TreeQSM using TLS point cloud at tree and plot-level: 1) Estimating the volume of stem and branch at different orders based on TLS scanning at tree-level; 2) Estimation and comparison of the volume of stem and branch at different orders based on TLS point cloud at tree and plot-level; 3) Exploring the influence of stand density on the estimation of stem and branch volume using TLS point cloud at plot-level. Result and conclusion: The experimental results showed that stem and first-order branch volume can be effectively estimated from point cloud collected from both tree and plot-level, while the volume estimation of secondary branch has obvious deviations: at plot-level, the accuracy of the stem and whole tree volume is equivalent to that of the tree- level, and the deviations are about 5% and 10%, while the first-order branch volume estimation deviation is slightly larger, of which first-order branch volume estimation deviation is about 10% at tree level, but 15% at plot-level; in addition, there is a negative correlation between the stand density and the accuracy of volume estimation at plot-level. In the lower forest density (425, 625 and 925 plants/ha), the stem volume estimation error is within 5%, and the first-order branch volume estimation error is about 15%. In addition, affected by the partial neutralization effect of the underestimation of the stem and the first-order branch volume and the overestimation of the secondary branch volume, the estimation deviations of the total volume in plot are all about 10%. So, in the forests with lower stand density, it can well estimate the volume of tree stem, first-order branch and whole tree volume at plot-level.

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