Study on the Inversion of Basal Area from Airborne LiDAR Data

doi:10.13466/j.cnki.lyzygl.2021.05.009

Abstract

Abstract:

Airborne LiDAR data has been widely studied in the estimation of forest biomass,tree height and canopy density,but the research on the estimation of basal area is rare.Taking the Gaofeng Forest Farm as the research area,the forest basal area was inversed by using airborne LiDAR data and combining with 105 sample plots measured on the ground.Firstly,in order to find the optimal filtering method to complete the LiDAR data filtering,four algorithms,namely,progressive triangulated irregular network(PTIN),the progressive morphological filter (PMF),the cloth simulation filter (CSF)and the Interpolation-based filtering (IBF)were used in the LiDAR data for 4 sample plots with different slopes and different forest canopy densities respectively,and then,random forest (RF)and iterative decision tree (GBRT)algorithms were used to estimate the basal area of the forest respectively.Lastly,the model with good precision was selected to complete the inversion and mapping of stand basal area.The results showed that cloth simulation filter (CSF)had good filtering effect on the LiDAR data of the sample plots with slope of 25~33° and canopy density of 0.5~0.7,which was basically consistent with the forest condition of the study area,therefore,CSF algorithm was selected to filter the LiDAR data in this study;in the stand basal area inversion model,the generalization ability of RF model was superior to that of GBRT model,with the R² of 0.77,RMSE of 3.99m²/hm² and rRMSE of 17.76%,respectively;while for the independent sample test for the RF model,the correspondent R ²,RMSE and rRMSE was 0.66,3.27m²/hm²,and 14.73% respectively.So RF model was used in the inversion of basal area of forest stand in the study area.

Key words: basal area, airborne LiDAR data, filtering, feature selection, machine learning

CLC Number:

S771.8

LONG Fei, YUE Cairong, JIN Jing, LI Chungan, LUO Hongbin, XU Wanting. Study on the Inversion of Basal Area from Airborne LiDAR Data[J]. FOREST RESOURCES WANAGEMENT, 2021, 0(5): 62-69.

Figures/Tables 9

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森林参数	针叶林			阔叶林
森林参数	范围	平均值	标准差	范围	平均值	标准差
林分平均高/m	6.2~ 18.22	13.35	2.97	6.52~ 23.56	13.74	4.30
蓄积量/(m³/hm²)	57.93~387.2	17.59	63.76	21.28~317.13	139.23	76.61
胸高断面积/(m²/hm²)	15.20~ 50.21	25.60	6.70	3.92~ 36.69	19.25	7.46

		滤波结果		误差计算公式
		地面点	非地面点	TⅠ=b/(a+b)	Po=(a+d)/e
参考	地面点	a	b	TⅡ=c/(c+d)	Pe=((a+b)(a+c)+(c+d)(b+d))/e²
参考	非地面点	c	d	TE=(b+c)/e	Kp=(Po-Pe)/(1-pe)

点云变量符号	变量描述
Hmin/Hmax/ Hmean/ Hmad	点云最低高度/最高高度/平均高度/中位数
Hccr	冠层起伏率;Hccr=(mean-min)/(max-min)。mean,min,max分别为每一统计单元内所有点的平均高度、最小高度、最大高度
Hvar/Hstdv/Hskew/Hkurt/Hcv	点云高度方差/标准差/偏态/峭度/变动系数
H10,H20,H25, H30,H40,H50, H60,H70,H75, H80,H95,H99	点云高度百分位数,样地内所有归一化的激光雷达点云按高度进行排序,然后计算每一统计单元内X%的点所在的高度
Hiq	高度百分位数四分位数间距:Hiq=H75-H25
D[0-9]	密度变量(10个):将点云数据从低到高分成10个相同高度的切片,每层回波数的比例就是相应的密度变量
CC	郁闭度^[24]

样地号	坡度/ (°)	郁闭度	点云个数/ point	点云密度/ (point/m²)
1	35.69	0.49	2312	0.93
2	26.11	0.58	4109	2.65
3	32.56	0.52	4590	2.24
4	25.69	0.65	2828	1.18

样地	滤波方法	TⅠ	TⅡ	TE	Kp/%
1	PMF	0.20	0.04	0.19	74.70
	CSF	0.09	0.08	0.09	72.40
	PTIN	0.11	0.08	0.01	69.00
	IBF	0.13	0.04	0.05	78.70
2	PMF	0.08	0.23	0.20	51.49
	CSF	0.04	0.19	0.16	59.12
	PTIN	0.02	0.21	0.17	57.78
	IBF	0.12	0.16	0.15	58.90
3	PMF	0.20	0.17	0.18	53.73
	CSF	0.20	0.16	0.17	54.90
	PTIN	0.17	0.11	0.12	64.90
	IBF	0.22	0.10	0.12	63.76
4	PMF	0.09	0.21	0.13	71.17
	CSF	0.13	0.13	0.13	71.60
	PTIN	0.05	0.26	0.13	71.00
	IBF	0.11	0.17	0.13	70.66