机载激光雷达数据估测森林资源蓄积量研究

doi:10.13466/j.cnki.lyzygl.2023.01.018

摘要/Abstract

摘要：

针对传统森林资源二类调查方法周期长且费时费力,难以满足新形势下森林资源动态监测需求的问题,以南京市六合区内3个林场为研究区,利用平均点密度1点/m²的激光雷达数据提取特征变量,结合二类调查数据,使用SMLR与Boruta两种算法进行因子筛选,对比SMLR,SVM与RF这3种建模方法,估测森林蓄积量。结果表明:1)高度因子是影响森林蓄积量的主要特征参数;2)SVM和RF这算法在模型拟合与验证精度方面均表现较优,SVM算法在混交林方面表现略逊色于RF这算法,SMLR方法表现不佳。结果表明,利用激光雷达提取因子与森林蓄积量进行建模有较好的结果,稀疏性机载激光雷达对森林资源调查有较好的适用性,为今后森林资源调查提供了新的思路。

关键词: 激光雷达, 稀疏数据, 森林蓄积量估测, 森林资源调查

Abstract:

Aiming at the problem that the traditional second-class survey method of forest resources is time-consuming and laborious,it is difficult to meet the needs of forest resource dynamic monitoring under the new situation.Three forest farms in Liuhe District,Nanjing city were selected as the study area in this paper.Sparse airborne laser radar was used to extract feature parameters,combined with the data of Forest Management Inventory.Two algorithms,stepwise regression and Boruta,were used for factor screening.Three modeling methods including Stepwise Multiple Linear Regression (SMLR),Support Vector Machine (SVM) and Random Forest (RF) were compared to estimate the forest stock.The results showed that:1) Height factor was the main characteristic parameter affecting forest stock volume;2) SVM and RF algorithms performed better in model fitting and verification accuracy,SVM algorithms performed slightly inferior to RF algorithms in mixed forests,and stepwise regression methods performed poorly.In general,the modeling results of lidar extraction factor and forest stock were good,and Sparse airborne laser radar had good applicability to forest resource survey,which provided a new idea for forest resource survey in the future.

Key words: LiDAR, sparse data, forest stock estimates, forest resource survey

中图分类号:

S758

邱洁, 李倩楠, 虞瑶. 机载激光雷达数据估测森林资源蓄积量研究[J]. 林业资源管理, 2023,(1): 153-160.

QIU Jie, LI Qiannan, YU Yao. Research on Estimating Forest Stock Volume via Airborne LiDAR Data[J]. FOREST RESOURCES WANAGEMENT, 2023,(1): 153-160.

图/表 3

表1

模型拟合结果

树种	方法	$R C V 2$	$M A E C V$	$R M S E C V$	$r R M S E C V / %$	样本量
混交	RF	0.896	12.6	14.9	29.7	300
	SVM	0.776	10.9	20.8	41.1
	SMLR	0.400	25.0	67.6	129.7
阔叶	RF	0.911	17.0	35.9	39.3	173
	SVM	0.894	9.4	21.2	23.0
	SMLR	0.409	29.3	109.6	119.5
针叶	RF	0.900	15.0	41.5	47.1	112
	SVM	0.884	7.8	22.7	25.9
	SMLR	0.617	21.9	75.2	85.1

表1

表2

模型整体验证精度

树种	方法	$R C V 2$	$M A E C V$	$R M S E C V$	$r R M S E C V / %$	样本量
混交	RF	0.575	23.2	84.1	169.8	75
	SVM	0.506	25.7	107.7	207.7
	SMLR	0.439	25.9	115.0	221.0
阔叶	RF	0.406	31.7	217.3	237.2	43
	SVM	0.425	33.1	257.3	281.0
	SMLR	0.334	34.5	293.5	318.9
针叶	RF	0.541	23.8	188.1	211.9	27
	SVM	0.491	28.1	262.5	299.0
	SMLR	0.402	31.0	300.2	343.7

表2

图1

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