Research on TLS single tree detection method based on point cloud slicing combined with clustering algorithm

doi:10.12302/j.issn.1000-2006.202206035

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2024, Vol. 48 ›› Issue (4): 113-122.doi: 10.12302/j.issn.1000-2006.202206035

Special Issue: 专题报道Ⅲ：智慧林业之森林可视化研究

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Research on TLS single tree detection method based on point cloud slicing combined with clustering algorithm

YI Jing¹(), MA Kaisen¹^,², XIANG Jianping³, TANG Jie¹, JIANG Fugen¹, CHEN Song¹, SUN Hua¹^,^*()

1. Research Center of Forestry Remote Sensing & Information Engineering, Central South University of Forestry and Technology, Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security for Hunan Province, Key Laboratory of National Forestry and Grassland Administration on Forest Resources Management and Monitoring in Southern China, Changsha 410004,China
2. National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science & Technology, Xiangtan 411201, China
3. Lutou Experimental Forest Farm, Central South University of Forestry and Technology, Yueyang 414000, China

Received:2022-06-20 Revised:2022-08-29 Online:2024-07-30 Published:2024-08-05
Contact: SUN Hua E-mail:yijing@csuft.edu.cn;sunhua@csuft.edu.cn

Abstract

Abstract:

【Objective】To solve the problem that a canopy height model (CHM) and normalized point cloud (NPC) directly generated by terrestrial laser scanning (TLS) are not capable of detecting individual trees in complex stands, this study introduced the method of point cloud slicing combined with clustering to improve the detection accuracy.【Method】In this study, six sample plots in a plantation with different stand densities in Guangxi Zhuang Autonomous Region, China, were used as the research objects. First, the NPC data of a sample plot obtained by TLS were used to extract point cloud slices at a height of 1.3 m, and then the density-based spatial clustering of applications with noise (DBSCAN) and mean shift algorithms were used to cluster the tree trunk point clouds in the slices. The accuracy was verified by the field survey data, and the detection results were compared with those of the local maximum algorithm based on a CHM, and a point cloud segmentation algorithm based on an NPC. The applicability and parameter sensitivity of the different detection methods were evaluated and analyzed.【Result】Satisfactory detection results were obtained by all methods, and the optimal detection accuracy F-score was ≥ 0.86 for each sample plot. The individual tree detection method using point cloud slicing combined with a clustering algorithm produced better results. The clustering threshold epsilon neighborhood (Eps) value of the DBSCAN algorithm and the clustering radius r of the mean shift algorithm significantly affected the individual tree detection rate, with the maximum Eps depending on the maximum stand spacing and optimum results when r was close to the maximum individual tree diameter at breast height.【Conclusion】Individual tree detection based on point cloud slicing combined with a clustering algorithm can increase the detection rate of understory trees lower forest, effectively improve the accuracy of single tree detection in dense stands, and provide a reference for the selection of single tree detection methods in different forest stands.

Key words: terrestrial laser scanning, single tree detection, point cloud slicing, clustering algorithm, extraction of forest parameters, plantation

CLC Number:

S758
TP391

YI Jing, MA Kaisen, XIANG Jianping, TANG Jie, JIANG Fugen, CHEN Song, SUN Hua. Research on TLS single tree detection method based on point cloud slicing combined with clustering algorithm[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2024, 48(4): 113-122.

Figures/Tables 9

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样地号 sample plot No.	优势树种 dominant tree species	林木株树 number of trees	密度/ (株·hm^-2) density	林木分布 distribution of forest trees	胸径/cm DBH
样地号 sample plot No.	优势树种 dominant tree species	林木株树 number of trees	密度/ (株·hm^-2) density	林木分布 distribution of forest trees	平均值 mean	最小值 min	最大值 max	平均值 mean	最小值 min	最大值 max
1	杉木	28	420	离散	20.2	9.4	28.5	12.5	6.3	17.6
2	桉树	52	780	聚集	9.0	5.5	11.5	11.4	8.2	13.5
3	桉树	75	1 125	均匀	8.2	5.0	11.0	11.1	6.5	13.8
4	杉木	94	1 410	均匀	10.1	5.3	16.5	7.6	5.2	11.5
5	桉树	103	1 545	局部聚集	11.2	5.6	20.9	15.8	4.1	19.5
6	杉木	114	1 710	局部聚集	11.4	5.1	40.0	8.8	3.7	18.5

样地号 sample plot number	方法 method	探测株数 number of detected	正确探测株数 number of correctively detected	召回率/% recall	准确率/% precision	总体精度得分F F-score
1	LM	28	27	96.43	96.43	0.96
	PCS	27	27	96.43	100.00	0.98
	DBSCAN	28	28	100.00	100.00	1.00
	MS	28	28	100.00	100.00	1.00
2	LM	46	37	71.15	80.43	0.76
	PCS	47	38	73.08	80.85	0.77
	DBSCAN	52	47	90.38	90.38	0.90
	MS	56	48	92.31	85.71	0.89
3	LM	75	61	81.33	81.33	0.81
	PCS	71	60	80.00	84.51	0.82
	DBSCAN	75	68	90.67	90.67	0.91
	MS	75	68	90.67	90.67	0.91
4	LM	101	78	82.98	77.23	0.80
	PCS	110	84	89.36	76.36	0.82
	DBSCAN	111	92	97.87	82.88	0.90
	MS	96	86	91.49	89.58	0.91
5	LM	84	73	70.87	86.90	0.78
	PCS	79	71	68.93	89.87	0.78
	DBSCAN	104	90	87.38	86.54	0.87
	MS	107	92	89.32	85.98	0.88
6	LM	112	89	78.07	79.46	0.79
	PCS	114	91	79.82	79.82	0.80
	DBSCAN	108	94	82.46	87.04	0.85
	MS	116	99	86.84	85.34	0.86

Research on TLS single tree detection method based on point cloud slicing combined with clustering algorithm

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