Research on LiDAR Stand Average High Inversion Method Based on Auto-adaptive Threshold and Peak Value

doi:10.13466/j.cnki.lyzygl.2021.01.009

Abstract

Abstract:

With the increasing density of point cloud obtained by LiDAR,it is possible to extract the average height of stand at sample plot scale.However,the relation between the extraction accuracy of average height of stand at sample plot scale and tree species is still unclear,so a stand average height extraction method suitable for various tree species is urgently needed.This study tries to take the state-owned Gaofeng forest farm in Guangxi Zhuang Autonomous Region as an example and use the canopy height model(CHM) generated by airborne LiDAR point cloud data.Based on 201 sample plots data measured on the ground,the paper proposes a stand average height extraction algorithm combining auto-adaptive threshold and peak value and analyzes the influence of tree species on extraction accuracy.The results show that:1) the average high extraction accuracy of different tree species is different.The accuracy of Chinese fir is the highest,followed by Eucalyptus and other broad-leaved trees;2) The auto-adaptive threshold combined with peak value algorithm can extract the average stand height(R²=0.75,RMSE=3.11 m,rRMSE=22.07%),and has strong robustness for different tree species;3) The sensitivity of broad-leaved and coniferous species to different extraction methods is different.The average stand height extracted in this study can be used as the basis and reference for the inversion of forest volume and biomass.

Key words: LiDA, Radaptive threshold, canopy height model, average stand height

CLC Number:

S758

WU Simin, SUN Hua, LIN Hui. Research on LiDAR Stand Average High Inversion Method Based on Auto-adaptive Threshold and Peak Value[J]. FOREST RESOURCES WANAGEMENT, 2021, (1): 61-68.

Figures/Tables 6

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方法	R²				RMSE/m				rRMSE/%
方法	总样本	桉树	杉木	其它	总样本	桉树	杉木	其它	总样本	桉树	杉木	其它
中心点(a)	0.48	0.50	0.29	0.45	4.47	5.19	3.12	4.45	31.72	33.71	22.78	34.96
均值(b)	0.70	0.72	0.31	0.72	2.53	2.83	1.92	2.58	18.00	18.39	14.05	20.26
阈值(c)	0.75	0.78	0.70	0.74	2.44	3.14	1.50	2.44	17.32	20.37	10.98	19.15
峰值(d)	0.70	0.71	0.48	0.75	3.80	3.72	4.14	3.58	26.96	24.16	30.27	28.15
阈值_峰值(e)	0.75	0.78	0.71	0.75	3.28	4.15	2.04	3.04	23.28	26.92	14.92	23.91
峰值_阈值(f)	0.75	0.77	0.79	0.74	3.11	3.87	1.84	2.95	22.07	25.10	13.46	23.19

方法	中心点(a)	均值(b)	阈值(c)	峰值(d)	阈值_峰值(e)	峰值_阈值(f)
中心点(a)		0.00	0.00	0.73	0.06	0.00
均值(b)	0.00		0.81	0.00	0.00	0.12
阈值(c)	0.00	0.81		0.00	0.00	0.04
峰值(d)	0.73	0.00	0.00		0.01	0.00
阈值_峰值(e)	0.06	0.00	0.00	0.01		0.19
峰值_阈值(f)	0.00	0.12	0.04	0.00	0.19