Dynamic analysis of point cloud characteristic parameters and volume structure based on multi-station scan

doi:10.3969/j.issn.1000-2006.201901020

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2019, Vol. 43 ›› Issue (6): 83-90.doi: 10.3969/j.issn.1000-2006.201901020

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Dynamic analysis of point cloud characteristic parameters and volume structure based on multi-station scan

JIANG Jiawen¹(), WEN Xiaorong¹^,², GU Haibo¹, ZHANG Zhengnan¹, LIU Fangzhou³, ZHANG Yanli⁴, SUN Yuan¹^,²^,^*()

1. College of Forestry, Nanjing Forestry University, Nanjing 210037,China
2. Co-Innovation Center for the Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037,China
3. College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037,China

Received:2019-01-17 Revised:2019-06-05 Online:2019-11-30 Published:2019-11-30
Contact: SUN Yuan E-mail:1054191055@qq.com;sunyuan1123@126.com

Abstract

Abstract:

【Objective】 The use of terrestrial laser scanner (TLS) in multistations to obtain the point cloud information of a standing tree, extracting the characteristic parameters of the point cloud distribution that expand the tree measurement factor, and establishing a volume model based on these characteristic parameters. 【Method】Liriodendron chinense plantation was studied, and the structural changes of the two stages (2014, 2017)were analyzed using the upper diameter (d) and tree height (H) of the standing tree provided by the point cloud data. The characteristic parameters of TLS point cloud named high cumulative percentage was designed and extracted, and other height-related feature parameters were extracted as a set of variables; the extracted DBH and feature parameters were considered as another set of variables; finally, the characteristic parameters were analyzed. The correlation between DBH and volume was established by stepwise regression method to build a volume model based on two sets of variables, and analyze the dynamic changes of the two phases. 【Result】The characteristic parametersH₂₅ and H_{t, var} (point cloud height variance) were used to fit the two-stage volume model, and their R² were 0.771 1 and 0.742 6, respectively. The accuracy of the model using the characteristic parameter H₂₅ and the DBH improved. The two sets of volume models mentioned earlier were used to predict the volume change in each step; the model value and the measured value was not significantly different, and R² is higher than 0.9. 【Conclusion】 The high cumulative percentage extracted in this study is closely related to the tree measurement factor, which can invert the dynamic structure of forest trees. The volume model developed by the research achieves high precision and can be used for the monitoring of the dynamic change in forest wood product, which provides a new reference for TLS point cloud to participate in the dynamic monitoring of forest resources.

Key words: stand structure, dynamic change, terrestrial laser scanner, volume model, Liriodendron chinense

CLC Number:

S758
TP79

JIANG Jiawen, WEN Xiaorong, GU Haibo, ZHANG Zhengnan, LIU Fangzhou, ZHANG Yanli, SUN Yuan. Dynamic analysis of point cloud characteristic parameters and volume structure based on multi-station scan[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2019, 43(6): 83-90.

Figures/Tables 9

Fig.1

Fig.2

Table 1

Fig.3

Table 2

Table 3

Volume prediction models"

年分 year	模型 model	参数与系数 parameter and coefficient	R²	F	均方根误差 RMSE
2014	2014-1	-0.609 8+0.077 3H_{t, mean}+0.022 6H_{t, IQ}-0.056 7H_{t, MAD}+0.042 3H₂₅	0.770 6	47.880 1	0.178 5
	2014-2	-0.339 2+0.005 1H_{t, var}+0.059 7H₂₅	0.771 1	99.408 9	0.175 2
	2014-3	-0.520 2+0.232 6H_{t, CV}+0.016 4H_{t, IQ}+0.072 6H₂₅	0.753 4	59.070 2	0.175 3
	2014-4	-0.795 3+0.021 0H₂₅+4.847 4D_BH	0.950 0	570.604 1	0.082 8
	2014-5	0.105 5 $D BH 2.0352$ H^{1.352 0}	0.974 0	122.418 5	0.059 3
2017	2017-1	-0.750 0-0.081 8H_{t, IQ}+0.404 6H_AAD+0.052 6H₂₅-0.029 9H₇₀	0.740 9	40.752 2	0.211 4
	2017-2	-0.183 3+0.011 5H_{t, var}+0.022 8H₂₅	0.742 6	85.097 8	0.213 0
	2017-3	-0.800 0+0.162 8H_{t, stddev}+0.031 5H₂₅	0.736 7	82.576 9	0.209 5
	2017-4	-0.928 0+5.655 1H₂₅ +0.016 1D_BH	0.956 6	661.537 6	0.086 1
	2017-5	0.246 7 $D BH 2.0180$ H^{1.075 1}	0.974 4	140.053 3	0.065 6

Table 3

Fig.4

Fig.5

Fig.6

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参数 parameter	2014年			2017年
参数 parameter	范围 range	平均值 mean	标准差 standard deviation	范围 range	平均值 mean	标准差 standard deviation
胸径/cm DBH	14.21~42.58	26.43	5.95	15.21~45.04	27.97	6.38
树高/m tree height	17.11~32.61	28.66	3.45	17.53~34.60	30.18	3.82
材积/m³ volume	0.13~1.75	0.73	0.36	0.14~1.96	0.81	0.40

2014年		2017年
参数 parameter	相关性R² correlation	参数 parameter	相关性R² correlation
D_BH	0.967 9	D_BH	0.967 9
H₂₅	0.866 2	H_{t, var}	0.767 6
H₅₀	0.842 4	H_{t, stddev}	0.765 4
H_{t, IQ}	0.838 5	H_AAD	0.7561 3
H_AAD	0.836 3	H₇₅	0.744 9
H_{t, mean}	0.830 0	H₈₀	0.740 9
H_{t, var}	0.827 6	H₅₀	0.724 1
H_{t, stddev}	0.811 8	H_{t, IQ}	0.658 7
H₇₅	0.767 4	H	0.648 7
H_{MAD, median}	0.756 1	H₂₅	0.636 0
H₈₀	0.752 7	H_{t, mean}	0.591 5
H	0.689 4	H_{MAD, median}	0.494 9

Dynamic analysis of point cloud characteristic parameters and volume structure based on multi-station scan

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