基于机载LiDAR点云多层聚类的单木信息提取及其精度评价

doi:10.11707/j.1001-7488.20210109

摘要/Abstract

摘要：

目的: 针对已有三维点云数据单木分割方法提取下层林木困难、准确提取林木数量占总体比例偏低导致提取工作有效性不高、提取效果受点云密度和林分结构复杂程度影响等问题，改进单木提取策略和算法，为LiDAR单木提取技术向生产实践应用转化提供支撑。方法: 以机载LiDAR点云数据为基础，提出一种基于分层聚类的三维立体单木分割方法，并对点云分层、分割、单木匹配等环节进行算法改进，实现空间异质性较高林分的单木分割和信息提取。结果: 改进后的算法可在高密度、高空间异质性林分中进行单木分割和信息提取，并能更合理地与地面实测林木信息匹配，可匹配的林木比例最高达88.70%，单木树高、林分平均高精度最高分别达92.38%、99.84%，树高基尼指数、树高变异系数精度最高达89.65%。结论: 通过多水平分层和纵向聚类融合，可提升对于林下层尤其是更新层林木的提取能力；构建提取有效性指标，更加关注成果的适用性；评价指标中加入空间结构精确度指标，可充分发挥LiDAR对空间结构的反演能力。

关键词: 机载LiDAR, 单木分割, 提取有效性, 林分结构

Abstract:

Objective: In individual tree segmentation using three-dimensional point could data, common problems include insufficient detection of the understory trees, the low proportion of true positive detections which decreases the effectiveness of the extracted information, and the detection being sensitive to the point density and the complexity of the forest structure. This study aimed to solve these problems by improving the segmentation algorithm, which was expected to support the implementation of the algorithm in the practice. Method: In this study, we proposed an algorithm for the detection and delineation of individual trees in heterogeneous and dense forests based on multi-slice clustering by using low density point cloud data, and the procedures for slicing, detection, segmentation and matching were also improved. Result: The results showed that the improved algorithm could achieve the detection and delineation of individual trees in heterogeneous and dense forests. The obtained trees could be reasonably matched with field trees, where the proportion of accurately extracted trees was 88.70%. Furthermore, the accuracy of individual tree height and mean height were 92.38% and 99.84%, respectively. The highest accuracy of the forest-structure parameters was 89.65%. Conclusion: In general, the main accomplishments of this study were as follows: 1) through the improvement of multi-slice clustering to the algorithm, we enhanced the detection capacity for understory and regeneration trees. 2) Through the establishment of an effectiveness index, named validity index, we were able to assess the effectiveness of the detection and delineation results. 3) Through the addition of the stand spatial structure parameters, we were able to utilize the prominent capacity of LiDAR in obtaining vertical structure information.

Key words: airborne LiDAR, individual tree segmentation, effectiveness of the information extraction, forest structure

中图分类号:

S757

霍朗宁,张晓丽. 基于机载LiDAR点云多层聚类的单木信息提取及其精度评价[J]. 林业科学, 2021, 57(1): 85-94.

Langning Huo,Xiaoli Zhang. Individual Tree Information Extraction and Accuracy Evaluation Based on Airborne LiDAR Point Cloud by Multilayer Clustering Method[J]. Scientia Silvae Sinicae, 2021, 57(1): 85-94.

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树高最大值 Maximum height/m	树高最小值 Minimum height/m	树高均值 Mean height/m	冠幅加权平均树高 Average height weighted by crown diameter/m	冠幅最大值 Maximum crown diameter/m	冠幅最小值 Minimum crown diameter/m	冠幅均值 Mean crown diameter/m
33.80	2.00	9.29	10.92	9.05	0.50	3.22

郁闭度 Canopy closure	株数密度 Stem density/ hm^-2	树高基尼指数 Gini coefficient	树高变异系数 Coefficient of variation	聚集指数 Aggregation index	最邻近体间平均距离 Mean distance between the nearest neighbors/m
0.70	1 435	0.35	55.20	0.99	1.31

参数名称 Parameter name	LiDAR提取木 Test trees from LiDAR data	实际对照木 Reference trees	精度 Accuracy
单木树高Individual tree height	h_test	h_ref	A_h
单木冠幅Individual tree crown diameter	c_test	c_ref	A_c
林分平均高(冠幅加权平均树高) Mean height of the stands (average height weighted by crown diameter)	H_test	H_ref	A_H
林木数量Number of trees	N_test	N_ref	N_match
树高基尼指数Gini coefficient	Gc_test	Gc_ref	A_Gc
树高变异系数Coefficient of variation	Cv_test	Cv_ref	A_Cv
最邻近体间平均距离 Mean distance between the nearest neighbors	r_test	r_ref	A_r
聚集指数Aggregation index	R_test	R_ref	A_R
树高分布均方根误差RMSE of tree height	RMSE_H
冠幅分布均方根误差RMSE of crown diameter	RMSE_C

分层数 Number of slices	A_k(%)	单木信息精度 Accuracy of individual tree information			林分结构信息精度 Accuracy of forest structure information
分层数 Number of slices	A_k(%)	A_h(%)	A_c(%)	A_H(%)	RMSE_H	RMSE_C	A_Gc(%)	A_Cv(%)
3	82.61	91.85	68.51	91.73	66.88	139.26	64.93	65.38
4	84.86	92.38	66.13	93.40	59.68	132.73	74.94	75.04
5	86.24	91.80	64.52	95.60	48.04	128.54	77.45	77.31
6	87.35	91.73	63.10	97.31	46.17	126.33	80.45	80.25
7	87.69	91.05	63.02	98.29	39.93	121.44	83.05	82.72
8	87.80	91.30	61.12	99.18	43.85	119.26	86.45	86.11
9	88.28	91.12	62.27	99.84	33.94	114.75	87.15	86.56
10	88.70	90.99	61.56	99.37	29.53	111.68	89.65	89.23

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