Extraction and construction of a QSM-based model of first-order branches of Larix gmelinii plantations

doi:10.12302/j.issn.1000-2006.202306010

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2025, Vol. 49 ›› Issue (2): 185-193.doi: 10.12302/j.issn.1000-2006.202306010

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Extraction and construction of a QSM-based model of first-order branches of Larix gmelinii plantations

PENG Wenyue¹^,²(), JIA Weiwei¹^,³^,^*(), WANG Fan¹, LI Xin¹^,², LI Dandan¹

1. School of Forestry, Northeast Forestry University, Harbin 150040, China
2. Forestry Investigation and Planning Institute of Jilin Province, Changchun 130607, China
3. Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, Northeast Forestry University, Harbin 150040, China

Received:2023-06-13 Accepted:2023-09-21 Online:2025-03-30 Published:2025-03-28
Contact: JIA Weiwei E-mail:1246584738@qq.com;jiaww2002@163.com

Abstract

Abstract:

【Objective】The quantitative structural model (QSM) algorithm was used to extract the number of first-order branches at different relative branch depths using a fully automatic strategy based on terrestrial laser scanning (TLS) point cloud data. A linear mixed prediction model of first-order branch density was constructed to provide a theoretical basis for research studies on Larix gmelinii plantation canopies. 【Method】 The TLS data of 30 L. gmelinii plantations and the QSM algorithm were used to obtain parameters pertaining to tree structure, and the extracted and measured values were subjected to regression analysis for exploring the accuracy of modeling based on point cloud data. The extraction accuracy of the method used for determining the number of first-order branches at different relative branch depths was assessed using the point cloud layering method. The optimal mixing model of the first-order branch density of the sample tree effect was constructed using the Poisson regression model, and the model was evaluated. 【Result】 The average extraction accuracy of the model constructed based on 30 L. gmelinii plantation branches was 80.71%, and the RMSE was 6.959 4. There were differences among the number of first-order branches extracted from different canopies, and the best results were obtained when the relative branch depth ranged from 0.7 to 0.8. The average accuracy was 87.78%, and a mixed model based on the sample tree effect was found to be optimal for determining the first-order branch density. The optimal model of branch density was a linear mixed model based on three random effect parameters, namely natural logarithm of the relative branching depth [ln(RDINC)], square of the relative branching depth (RDINC²), and ratio of tree height to breast diameter [HT/DBH], and the values of R² and RMSE were 0.745 4 and 0.229, respectively. 【Conclusion】 The parameters pertaining to the branch structure of individual trees were obtained based on the ground-based laser radar scanning data, using the quantitative structure model that was applicable or reliable. Based on the effects observed in the sample wood, the density mixed model of the first-order branches of L. gmelinii plantations not only reflects the changes in the distribution density of the primary branches in the canopy, but can also predict the overall growth trend of the crown.

Key words: Larix gmelinii, point cloud data, number of first-order branches, quantitative structural model (QSM), linear mixed model

CLC Number:

S791.43

PENG Wenyue, JIA Weiwei, WANG Fan, LI Xin, LI Dandan. Extraction and construction of a QSM-based model of first-order branches of Larix gmelinii plantations[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2025, 49(2): 185-193.

Figures/Tables 12

Table 1

Table 2

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Table 3

Table 4

Fitting results of the best basic model"

参数 parameter	样本容量 sample size	估计值 estimation	标准误差 SE	t	P
$β 0$	118 6	2.787	0.244	11.42	<0.000 1
$β 1$	118 6	0.236	0.023	10.50	<0.000 1
$β 2$	118 6	-1.155	0.063	-18.26	<0.000 1
$β 3$	118 6	0.051	0.004	14.06	<0.000 1
$β 4$	118 6	-0.105	0.008	-12.35	<0.000 1
$β 5$	118 6	-0.299	0.169	-1.77	<0.000 1

Table 4

Table 5

Comparison of fitting results and accuracy of different random effect parameters"

随机效应 random effect	模型 model	随机效应参数 random effect parameter	R²	AIC	BIC	-2LL	LRT	P
	6	—	0.392 8	920.24	955.78	906.24
样地效应 sample plot effect	6.1	$β 2$	0.464 5	792.11	832.74	776.12	130.12	<0.001
	6.2	$β 1 、 β 4$	0.493 3	737.29	788.40	717.62	63.83	<0.001
	6.3	$β 1 、 β 3 、 β 4$	0.500 8	738.62	804.31	712.29	5.34	0.149
样木效应 sample tree effect	6.4	$β 2$	0.602 4	511.96	552.59	495.96
	6.5	$β 1 、 β 2$	0.678 3	325.61	376.39	305.61	190.36	<0.001
	6.6	$β 1 、 β 2 、 β 5$	0.745 4	148.21	214.27	122.21	183.40	<0.001

Table 5

Table 6

Fixed parameters, variance composition and fitting statistics of different models"

项目 item	参数 parameter	模型6 model 6	模型6.6 model 6.6
固定效应参数 fixed effect parameter	$β 0$	2.787 0	3.761 0
	$β 1$	0.236 0	0.376 0
	$β 2$	-1.155 0	-1.638 0
	$β 3$	0.051 0	0.040 0
	$β 4$	-0.105 0	-0.108 0
	$β 5$	-0.299 0	-0.824 0
随机效应方差-协方差结构 covariance-structure	$σ β 1 2$		0.352 4
	$σ β 2 2$		0.910 9
	$σ β 5 2$		0.504 9
	$σ β 1 β 2 2$		-0.577 0
	$σ β 1 β 5 2$		0.939 0
	$σ β 2 β 5 2$		-0.745 0
拟合统计量 fitting statistics	R²	0.392 8	0.745 4
	RMSE	0.355 3	0.229 0

Table 6

Fig. 5

Fig. 6

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样地号 plot No.	胸径/cm DBH			树高/m tree height			海拔/m elevation	林龄/a age	龄组 age group	林分密度/ (株·hm^-2) stand density
样地号 plot No.	平均值 mean	最小值 min	最大值 max	平均值 mean	最小值 min	最大值 max	海拔/m elevation	林龄/a age	龄组 age group	林分密度/ (株·hm^-2) stand density
720	24.8	16.2	36.6	26.7	20.4	30.5	218	51	成熟林	667
3	19.8	12.1	29.0	23.9	17.3	27.6	234	37	近熟林	1 167
4	18.6	10.8	30.6	22.4	14.4	26.6	234	36	近熟林	1 217
13	13.3	6.4	24.8	15.6	7.4	20.4	276	23	中龄林	1 450
14	13.9	5.9	33.2	16.0	10.2	21.5	274	23	中龄林	1 517
15	14.4	7.8	29.6	16.0	8.9	22.2	289	23	中龄林	1 733

样木号 tree No.	胸径/cm DBH		树高/m HT		冠幅/m CW		冠长/m CL		一级枝条数 NB	样木号 tree No.	胸径/cm DBH	树高/m HT	冠幅/m CW	冠长/m CL	一级枝条数 NB
3-1	27.0		22.1		4.41		12.5		75	14-1	20.0	16.5	4.15	10.2	37
3-2	21.8		23.6		3.45		10.5		57	14-2	17.0	16.4	4.25	7.9	56
3-3	22.2		21.4		3.25		7.7		45	14-3	14.4	17.6	2.90	6.2	74
3-4	16.9		17.9		3.65		11.2		72	14-4	12.3	14.1	2.85	6.4	87
3-5	15.2		16.3		4.40		3.8		36	14-5	9.0	13.2	2.35	3.8	56
4-1	27.1		23.4		3.40		9.5		68	15-1	20.4	19.4	4.25	8.4	36
4-2	22.4		23.2		3.25		5.7		45	15-2	16.4	17.2	3.90	8.3	55
4-3	20.1		22.7		5.10		5.8		51	15-3	15.0	17.2	3.65	8.6	28
4-4		17.0		21.7		2.00		4.8	38	15-4	12.4	14.3	3.20	4.6	45
4-5		14.1		18.9		2.10		3.2	45	15-5	10.6	13.6	2.95	5.3	28
13-1		19.0		16.0		7.60		7.3	55	720-1	31.0	25.0	4.35	8.4	70
13-2		14.8		18.8		2.50		6.6	56	720-2	27.6	26.0	2.75	7.5	48
13-3		14.1		13.9		3.75		9.2	25	720-3	26.1	21.5	4.25	7.2	55
13-4		10.7		14.6		2.70		4.3	38	720-4	23.1	23.2	3.00	6.9	41
13-5		10.0		14.6		2.75		5.1	50	720-5	18.2	19.8	3.80	10.1	66

相对着枝深度 relative distance from apex	对比组数 number of compare groups	提取精度/% extraction accuracy
(0,0.1]	26	65.20
(0.1,0.2]	30	73.12
(0.2,0.3]	30	67.80
(0.3,0.4]	30	71.10
(0.4,0.5]	30	77.76
(0.5,0.6]	30	70.76
(0.6,0.7]	29	80.28
(0.7,0.8]	30	87.78
(0.8,0.9]	30	87.47
(0.9,1.0]	30	85.69
总体total	295	76.70

Extraction and construction of a QSM-based model of first-order branches of Larix gmelinii plantations

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