Estimating Forest Volume, Mean Height and Basal Area Based on Airborne Laser Scanning Data

doi:10.13466/j.cnki.lyzygl.2020.02.013

Abstract

Abstract:

Based on the airborne laser scanning (ALS) data and field measurement data of 191 sample plots distributed across the Korean pine (Pinus koraiensis) forest stands in northeastern China,the base regression models for estimating forest volume,mean height and basal area were determined through multiple linear regression and nonlinear regression methods at first.Then,a model system including stand volume,mean height,and basal area based on the ALS data was developed,using the error-in-variable simultaneous equations approach.The results show that for the developed multiple linear,nonlinear and two-variable nonlinear stand volume regression models,the coefficients of determination (R²) are 0.858,0.846 and 0.821;mean prediction errors (MPEs) are 2.57%,2.66% and 2.85%;and mean percent standard errors (MPSEs) are 26.35%,16.35% and 17.88%,respectively.For the mean height,basal area and stand volume simultaneous models,the R ² are 0.597,0.750 and 0.822;the MPEs are 1.90%,2.52% and 2.84%;and the MPSEs are 10.85%,15.28% and 17.73%,respectively.For estimating the forest parameters such as stand volume based on ALS data,nonlinear model is better than linear model,and the two-variable nonlinear model based on height (the median) and intensity (the percentile 75) of point clouds performed well.The error-in-variable simultaneous equations approach is a feasible method for developing a model system for estimation of main forest parameters.The developed mean height,basal area and stand volume simultaneous models in this study meet the need of precision requirements to relevant regulations on forest resources inventory,indicating that the models can be applied in practice.

Key words: airborne laser scanning, forest volume, nonlinear model, error-in-variable, simultaneous equations, Pinus koraiensis

CLC Number:

ZENG Weisheng, SUN Xiangnan, WANG Liuru, WANG Wei, PU Ying. Estimating Forest Volume, Mean Height and Basal Area Based on Airborne Laser Scanning Data[J]. FOREST RESOURCES WANAGEMENT, 2020, (2): 79-86.

Figures/Tables 6

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变量	平均值	最小值	最大值	标准差	变动系数/%
蓄积量/(m³/hm²)	273.86	9.94	667.27	128.59	46.95
断面积/(m²/hm²)	31.36	3.28	65.43	11.04	35.21
平均高/m	12.70	4.21	19.75	2.66	20.91

模型类别	模型	参数a₀或b₀	解释变量	参数估计值	t值
线性模型	(1)式	-66.583	X38 X41 X60 X68 X82 X84 X88	19.688 2.4588 0.018971 0.025752 -0.009416 -0.036448 0.006695	11.69 4.91 6.09 5.24 3.57 6.60 2.33
非线性模型	(2)式	20149	X38 X60 X82 X88	1.4420 0.6492 -2.4826 1.0301	21.52 3.28 7.64 2.56
非线性模型	(3)式	150960	X38 X82	1.5570 -1.0063	18.19 2.37

模型类别	模型	R²	SEE/m³	TRE/%	ASE/%	MPE/%	MPSE/%
线性模型	(1)式	0.858	49.42	-0.01	-5.82	2.57	26.35
非线性模型	(2)式	0.846	51.04	0.07	0.64	2.66	16.52
非线性模型	(3)式	0.821	54.70	0.00	0.22	2.85	17.88

目标变量	参数估计值			评价指标
目标变量	a₀/b₀/c₀	a₁/b₁/c₁	a₂/b₂/c₂	R²	SEE	TRE/%	ASE/%	MPE/%	MPSE/%
平均高	3.0730	0.55364	0	0.597	1.69	-0.37	-0.28	1.90	10.85
断面积	140000	0.94565	-1.0516	0.750	5.55	-0.13	-0.73	2.52	15.28
蓄积量	0.49025	1.0652	1.0264	0.822	54.55	-1.24	-0.53	2.84	17.73