Individual tree detection in the high canopy density <i>Larix principis-rupprechtii</i> forest based on airborne LiDAR

CHEN Siyu; LIU Xianzhao; WANG Yixiang; LIANG Dan

doi:10.11833/j.issn.2095-0756.20210399

Volume 39 Issue 4

Jul. 2022

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Article Navigation > Journal of Zhejiang A&F University > 2022 > 39(4): 800-806

CHEN Siyu, LIU Xianzhao, WANG Yixiang, LIANG Dan. Individual tree detection in the high canopy density Larix principis-rupprechtii forest based on airborne LiDAR[J]. Journal of Zhejiang A&F University, 2022, 39(4): 800-806. doi: 10.11833/j.issn.2095-0756.20210399

Citation:

CHEN Siyu, LIU Xianzhao, WANG Yixiang, LIANG Dan. Individual tree detection in the high canopy density Larix principis-rupprechtii forest based on airborne LiDAR[J]. Journal of Zhejiang A&F University, 2022, 39(4): 800-806. doi: 10.11833/j.issn.2095-0756.20210399

Individual tree detection in the high canopy density Larix principis-rupprechtii forest based on airborne LiDAR

doi: 10.11833/j.issn.2095-0756.20210399

CHEN Siyu^{1, 2, 3
,},
LIU Xianzhao⁴,
WANG Yixiang^{1, 2, 3},
LIANG Dan^{1, 2, 3
,
,}

1.
College of Environment and Resources, Zhejiang A&F University, Hangzhou 311300, Zhejiang, China
2.
State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, Zhejiang, China
3.
Zhejiang Provincial Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration, Zhejiang A&F University, Hangzhou 311300, Zhejiang, China
4.
Key Laboratory of Forest Management and Growth Modeling, National Forestry and Grassland Administration, Research Institute of Forest Resources Information Techniques, Chinese Academy of Forestry, Beijing 100091, China

Received Date: 2021-05-31
Accepted Date: 2022-03-25
Rev Recd Date: 2022-03-25

Available Online: 2022-07-20

Publish Date: 2022-08-20

Abstract

Objective With low identification accuracy of individual trees in Larix principis-rupprechtii forest with high canopy density employing high resolution images, this paper is aimed to confirm the strengths of airborne Laser Detection and Ranging (LiDAR) 3D point cloud data as an alternative with a workable method proposed. Method Based on the preprocessing of point cloud data, an improved Mean Shift with Gaussian kernel function (MSP) position recognition method on the basis of the spatial characteristics of airborne LiDAR point cloud was proposed. The comparison is made with other three commonly used methods: regional growing segmentation algorithm based on point cloud (RGP), local maximum method based on canopy height model (LMC) and multi-scale segmentation method based on CHM (MSC). Result Identification accuracy of the four methods is: MSP (89.30%)＞LMC (85.60%)＞RGP (77.50%)＞MSC (70.00%), and MSC, the proposed method, displayed high average individual tree crown extraction accuracy (90.18%) and relatively low omission error and commission error rate: 8.7% and 8.0% respectively. Conclusion The proposed MSP has good applicability in high crown density L. principis-rupprechtii forest and provides a new way of extracting L. principis-rupprechtii forest structure parameters accurately on the basis of airborne LiDAR point clouds. [Ch, 3 fig. 3 tab. 28 ref.]
- individual tree position detection,
- airborne LiDAR,
- high crown density,
- mean shift,
- Larix principis-rupprechtii

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华北落叶松Larix principis-rupprechtii是中国华北地区第一大树种，具有较高的经济和生态价值，探讨华北落叶松单木参数的提取、林分蓄积量的估算具有重要意义^[1]。高精度的单木识别是获取单木和森林结构参数的重要前提，单木识别包括单木位置识别和树冠分割^[2-4]。相比高分辨率卫星影像和光学影像，机载LiDAR技术不受区域信号影响，分辨率较高，是一种主动遥感技术，能获取林木的水平和垂直结构信息^[5-6]。利用机载LiDAR研究单木识别的方法主要有2类：基于冠层高度模型(CHM)识别方法和基于归一化后的点云识别方法^[3]。其中基于CHM的单木位置识别方法即在灰度图像中探测单木树顶，主要实现算法包括局部最大值法和多尺度分割法。局部最大值法假设树冠顶点高于树冠边缘，通过设置固定尺寸^[7-10]或动态尺寸^[11-12]的搜索窗口从CHM中识别树冠顶点特征，单木位置探测率可达85%。多尺度分割法以像元为单位，根据相似特征逐渐外扩分割，关键在于最优分割尺度的选取^[13-17]。基于归一化后的点云单木位置识别方法以点云的结构信息为基础，主流实现算法包括区域生长法和均值漂移法。区域生长法通过选择合适的种子点，定义距离生长准则，判别不同属类^[18-20]，选择的种子点和制定的距离生长规则不同，取得的单木识别精度也不同。均值漂移法是一种基于密度梯度上升的非参数方法，通过迭代运算求得同簇的数据点^[21]，关键在于漂移均值向量的计算。单木识别时可利用林分树冠的距离和多维特征空间为向量来完成^[22-23]，其在中低郁闭度林分中的单木识别精度较高，但在高郁闭度林分条件下，由于树冠重叠度增加，漂移均值向量计算会有偏差，识别结果不尽合理。为提高识别精度，结合树冠冠型特点，本研究提出基于机载LiDAR点云空间特征的高斯核函数改进的均值漂移单木位置识别方法(MSP)。为验证方法的有效性，对比了基于点云空间特征的区域生长图像分割方法(RGP)、基于CHM的局部最大值单木探测方法(LMC)和基于CHM的多尺度分割方法(MSC)等3种目前应用较为广泛的单木位置识别方法。最后，在单木位置识别结果上，进行单木树冠的提取。

1. 研究区概况

忻州市五寨县羊圈沟林场(38º44′~39º01′N，111º48′~112º03′E)位于山西省中北部，属亚寒带大陆性气候，季节变化明显，海拔为1 350~2 700 m，年均气温为5 ℃，年降水量为453.9 mm；华北落叶松-云杉 Picea asperata天然次生林与华北落叶松人工林带状混交。本研究在华北落叶松人工林带内设置8个20 m×20 m的正方形样地，植株密度为1 875~2 925 株·hm⁻²，郁闭度为0.8~1.0，是典型高郁闭度森林。

4. 讨论与结论

4.1. 讨论

本研究提出的改进均值漂移法对高郁闭度华北落叶松林的单木提取总体精度达91.3%，这与张怡卓等^[25]对中低密度人工林的研究结果(总体精度达89.0%)相似，远高于JING等^[27]对加拿大高郁闭林分的总体精度(69.0%)。相比于其他3种提取方法，MSP法有2个优势。一是在林木树冠重叠的情况下，MSP法直接利用了点云空间特征，避免了生成CHM过程中产生的栅格误差；二是该方法利用统计迭代的方式确定聚类点位，根据访问频率分类，比直接比较邻域特征而进行分类稳健性更好。

本研究发现：MSP法提取单木冠幅的平均提取精度为90.18%，比同等郁闭度林分的树冠提取精度要高出很多^[28]，可满足森林调查需求。约10%的单株冠幅提取精度低于80%，这些点主要位于林分下层。由于上层遮挡，下层点云密度远低于上层，而点云聚类算法和点云密度息息相关，这也是本研究发现冠幅提取值偏小的原因。具体来说，由于上层冠层存在重叠，人工目视解译可以考虑到冠层重叠的影响并准确提取冠幅，而在三维点云图上，树冠界限分类时存在距离阈值类似的情况，影响提取结果；实测中，研究者往往通过东—西、南—北方向来测量冠幅，但是这只是一种理想状态下的测量，在现实中，华北落叶松林木树冠是不规则的，测量因此会存在误差；同时研究区域的坡度、地形等的限制，也会对人工测量造成影响。

4.2. 结论

本研究基于点云单一空间特征，引入高斯核函数简化并改进了均值漂移量的计算过程，同时所提方法不需要先验知识，算法的稳健性高，解决了高郁闭度的华北落叶松林受复杂冠层影响的问题，提高了在该林分内机载LiDAR单木识别的精度，为高郁闭度华北落叶松林单木识别提供了新的方法。

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样地	密度/(株·hm⁻²)	平均树高/m	平均胸径/cm	平均冠幅/m	样地	密度/(株·hm⁻²)	平均树高/m	平均胸径/cm	平均冠幅/m
1	2 075	13.11	13.22	3.19	5	2 100	11.72	13.82	3.82
2	2 925	12.66	12.44	2.86	6	2 475	12.00	12.98	3.43
3	2 500	14.04	15.35	2.99	7	2 275	13.45	14.63	2.92
4	1 875	13.60	13.41	3.97	8	2 125	13.52	13.90	3.34

类别	方法	A_O/%	A_D/%	E_O/%	E_C/%	E_R/%
点云	MSP	91.3±3.7 a	89.3±2.5 a	8.7±3.7 b	8.0±2.5 c	5.8±1.5 b
点云	RGP	81.5±2.6 b	77.5±2.2 b	18.5±2.6 a	16.7±5.5 b	9.8±1.4 a

CHM	LMC	91.0±2.0 a	85.6±2.0 c	9.0±2.0 b	9.3±1.7 c	4.3±1.7 b
CHM	MSC	80.8±2.1 b	70.0±1.5 d	19.2±2.1 a	30.1±5.0 a	5.0±1.4 b
说明：同列不同字母表示差异显著(P＜0.05)

单株冠幅提取精度范围/%	精度范围占比/%	冠幅偏小/%	冠幅偏大/%	平均单株提取精度/%
＜60	0	62.07	37.93	90.18
60~70	1.05
70~80	9.15
80~90	32.68
90~100	57.12