Estimating Standing Volume in Southern Collective Forest Region Based on the Unmanned Aerial Vehicle LiDAR Characteristic Variables

doi:10.13466/j.cnki.lyzygl.2021.04.020

Abstract

Abstract:

Based on the annual monitoring and evaluation data of forest resources in Guangxi Zhuang Autonomous Region,the characteristic variables of airborne LiDAR were selected with the stepwise regression,and multiple linear regression,logistic regression and random forest models were established to predict the volume of sample plots of Eucalyptus robusta,Cunninghamia lanceolate and natural broad-leaved forest in southern collective forest region.The results showed that:1) For the plots of Eucalyptus robusta and Cunninghamia lanceolate,the stepwise regression characteristic variables were mainly height and intensity variables,while for the natural broad-leaved forest plots,they were comprehensive variables,such as clearance ratio,coverage,and leaf area index;2) For Eucalyptus robusta and natural broad-leaved forest plots,the estimation accuracy( Eucalyptus robusta:R ²=0.97,RMSE=12.60 m³/hm²;natural broad-leaved:R²=0.90,RMSE=18.45 m³/hm²) of the volume of the random forest model was higher than that of the multiple linear regression and logistic regression models,for the Cunninghamia lanceolate plot,the multiple linear regression model obtained the best estimation result(R ²=0.91,RMSE=24.30 m³/hm²);3) Among the estimation accuracy of the three models,the artificial Eucalyptus robusta and Cunninghamia lanceolate plots were higher than of the natural broad-leaved forest plots.It can be seen that the high-density LiDAR point cloud can obtain better characteristic variables.In view of the complex sample plot conditions,we need to flexibly select the estimation model to realize the volume survey,which can provide a scientific basis for the forestry and grassland authorities to carry out the forest resources survey,monitoring and management.

Key words: southern collective forest region, UAV LiDAR, regression model, random forest, stock volume

CLC Number:

DU Zhi, CHEN Zhenxiong, MA Kaisen, LIU Ziwei, GU Xinggui. Estimating Standing Volume in Southern Collective Forest Region Based on the Unmanned Aerial Vehicle LiDAR Characteristic Variables[J]. FOREST RESOURCES WANAGEMENT, 2021, (4): 157-165.

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References 23

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树种	数量/个	树高/m	胸径/cm	蓄积量/(m³/hm²)
树种	数量/个	树高/m	胸径/cm	均值	最大值	最小值
桉树(Eucalyptus robusta)	26	12.7±2.8	10.0±1.8	76.35	277.65	2.1
杉木(Cunninghamia lanceolata)	26	9.6±2.5	12.1±3.5	122.25	340.05	3.9
天然阔叶林	38	8.7±2.2	11.6±2.6	87.6	231.3	3.75

项目	描述	文献出处
高度变量	46个与高度相关的统计变量,10个点云密度相关的统计变量	[7]
强度变量	42个与点云强度信息相关的统计变量	[13]
冠层覆盖度	林分冠层的垂直投影占林地面积的百分比	[17]
叶面积指数	单位地表面积上所有叶片表面积的一半	[12]
间隙率	地面点数量与点云总数的比值	[16]

树种	变量	标准误差	T检验	VIF
桉树 (Eucalyptus robusta)	常数Constant	1.115	-4.728	-
	EP_30th	0.088	11.009	1.002
	IK	0.090	-2.670	1.002
杉木 (Cunninghamia lanceolata)	常数Constant	1.210	-5.753	-
	EP_5th	0.182	5.823	1.282
	CC	1.605	6.234	1.125
	EK	0.001	6.360	1.245
	DM_5	4.718	4.015	1.347
天然阔叶	常数Constant	1.566	1.913	-
	EP_5th	0.287	4.664	5.390
	GR	1.888	-2.428	1.777
	LAI	0.147	-2.095	4.300

树种	变量数	模型
桉树	2	$14.6 × EP 30 th - 3.60 × IK - 79.05$
杉木	4	$15.9 × EP_5 th + 150 × CC + 0.075 × EK + 284 × D M 5 - 104$
天然阔叶林	3	$20.0 × EP_5 th - 68.7 × GR - 4.62 × LAI + 44.85$

树种	变量数	模型
桉树(Eucalyptus robusta)	2	$15 e 0.319 × EP_30 th - 0.0640 × IK - 4.71 1 + e 0.319 × EP_30 th - 0.0640 × IK - 4.71$
杉木(Cunninghamia lanceolata)	4	$15 e 2.66 × EP_5 th + 0.00200 × CC + 0.233 × EK + 4.45 × DM_5 - 4.41 1 + e 2.66 × EP_5 th + 0.00200 × CC + 0.233 × EK + 4.45 × DM_5 - 4.41$
天然阔叶林	3	$15 e - 2.30 × EP_5 th - 0.0860 × GR + 0.405 × EA_80 th - 1.38 1 + e - 2.30 × EP_5 th - 0.0860 × GR + 0.405 × EA_80 th - 1.38$