地基LiDAR林木点云估算枝干材积

靳双娜; 张吴明; 蔡尚书; 邵杰; 程顺; 谢东辉; 阎广建

doi:10.11834/jrs.20210537

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地基LiDAR林木点云估算枝干材积
Stem and branch volume estimation using terrestrial laser scanning data
2023年27卷第7期页码：1653-1666
纸质出版日期： 2023-07-07 ，
DOI： 10.11834/jrs.20210537

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靳双娜，张吴明，蔡尚书，邵杰，程顺，谢东辉，阎广建.2023.地基LiDAR林木点云估算枝干材积.遥感学报，27（7）： 1653-1666

Jin S N，Zhang W M，Cai S S，Shao J，Cheng S，Xie D H and Yan G J. 2023. Stem and branch volume estimation using terrestrial laser scanning data. National Remote Sensing Bulletin， 27（7）：1653-1666
靳双娜，张吴明，蔡尚书，邵杰，程顺，谢东辉，阎广建.2023.地基LiDAR林木点云估算枝干材积.遥感学报，27（7）： 1653-1666 DOI： 10.11834/jrs.20210537.

Jin S N，Zhang W M，Cai S S，Shao J，Cheng S，Xie D H and Yan G J. 2023. Stem and branch volume estimation using terrestrial laser scanning data. National Remote Sensing Bulletin， 27（7）：1653-1666 DOI： 10.11834/jrs.20210537.

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株/ha、625株/ha和925株/ha）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。

Abstract

Tree volume is an important parameter in forest inventory. The reconstruction of the Quantitative Structure Model of Trees (TreeQSM) method based on ground-based LiDAR point clouds can achieve nondestructive acquisition of forest volume. It can also solve the time-consuming and labor-intensive problem of traditional forest in situ investigation. However

the reference volume of the felled timber is difficult to obtain. Thus

the ability of the TreeQSM volume estimation has not been studied at the stem and different branch orders. Moreover

TreeQSM is only applied to the ground-based LiDAR point cloud collected at the tree level but not at the plot level. Therefore

this study proposes to assess the stem and branch volume estimation of TreeQSM from the point cloud collected from the tree and plot levels.

In this study

we evaluate the stem and branch volume estimated by TreeQSM using TLS point cloud at the tree and plot levels:(1) Estimating the volume of stem and branch at different orders based on TLS scanning at the tree-level.(2) Estimating and comparing the volume of stem and branch at different orders based on TLS point cloud at the tree and plot levels.(3) Exploring the influence of stand density on the estimation of stem and branch volumes using the TLS point cloud at the plot level.

The experimental results showed that the stem and first-order branch volume can be effectively estimated from the point cloud collected from the tree and plot levels. However

the volume estimation of the secondary branch has obvious deviations. At the plot level

the accuracy of the stem and whole tree volume is equivalent to that of the tree level. The deviations are approximately 5% and 10%. However

the first-order branch volume estimation deviation is slightly large

approximately 10% and 15% at the tree and plot levels

respectively. In addition

the stand density is negatively correlated with the accuracy of volume estimation at the plot level. In the low 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 approximately 15%. In addition

the estimation deviations of the total volume in the plot are 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. These deviations are all approximately 10%. Thus

it can well estimate the tree stem

first-order branch

and whole tree volumes at the plot level in forests with a low stand density.

关键词

蓄积量枝干材积定量结构模型（QSM）地基激光雷达样方尺度

Keywords

volumestem and branch volumeQuantitative Structure Model (QSM)TLSplot level

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