基于UAV-LiDAR的人工林林分郁闭度估测

doi:10.11707/j.1001-7488.LYKX20210955

摘要/Abstract

摘要：

目的: 提取无人机激光雷达（UAV-LiDAR）点云数据特征变量，结合样地实测数据构建林分郁闭度估测模型，为快速准确估测人工林林分郁闭度提供基础数据和技术参考。方法: 以东北林业大学城市林业示范基地为研究区，基于多旋翼无人机激光雷达获取的点云数据进行人工针叶林和阔叶林林分郁闭度反演，根据点云的三维坐标和能量值计算高度、强度、冠层特征变量并采用主成分分析法降维，处理后的变量与利用植物冠层分析仪获取的郁闭度进行逐步回归分析，建立人工针叶林和阔叶林林分郁闭度估测模型，在ArcGIS平台上应用估测模型和反距离权重插值法进行林分郁闭度反演制图。结果: 冠层特征变量对针叶林郁闭度的估测精度影响最显著，强度特征变量对阔叶林郁闭度的估测精度影响最显著。人工阔叶林郁闭度的估测精度（Adj R²=0.725，RMSE=0.005）优于人工针叶林（Adj R²=0.722，RMSE=0.007）。应用估测模型和反距离权重插值法估测整个样地的郁闭度范围在0.81~0.87之间，筛选10个检验点的郁闭度与实测郁闭度显示出较高相关性（r=0.859）。结论: 结合多组LiDAR特征变量估测林分郁闭度能够充分挖掘LiDAR数据包含的冠层结构特性，提升估测精度。

关键词: 无人机激光雷达, 林分郁闭度, 特征变量, 主成分分析, 逐步回归

Abstract:

Objective: This study aims to construct stand canopy closure estimation models based on the extracted feature variables from the UAV-LiDAR point cloud data and the measured data of the sample plots, which would provide basic data and technical reference for rapid and accurate estimation of artificial forest canopy closure. Method: Taking the Urban Forestry Demonstration Base of Northeast Forestry University as the study area, the inversion of the forest closure for the artificial coniferous forests and broad-leaved forests was carried out based on the point cloud data obtained by multi-rotor UAV LiDAR. The height, intensity and canopy characteristic variables were calculated according to the three-dimensional coordinates and energy values of the point cloud, and the principal component analysis method was used to reduce the data dimension. Then, a stepwise regression procedure was conducted on the canopy closure obtained by the plant canopy analyzer and the processed variables, and the canopy closure estimation models for artificial coniferous forests and broad-leaved forests were established, respectively. Finally, the estimation models and inverse distance weight interpolation method were applied on the ArcGIS platform to perfom canopy closure inversion mapping. Result: Canopy characteristic variables had the most significant effect on the canopy closure estimation accuracy of coniferous forests, while intensity characteristic variables had the most significant effect on the estimation accuracy of broad-leaved forests. The canopy closure estimation accuracy of artificial broad-leaved forests (Adj R² = 0.725, RMSE = 0.005 ) was superior to that of artificial coniferous forests (Adj R² = 0.722, RMSE = 0.007 ). The canopy closure of the entire plot estimated by the estimation models and inverse distance weighted interpolation method ranged between 0.81 and 0.87, and the canopy closure of 10 testing points showed a high correlation with the measured canopy closure (r = 0.859). Conclusion: Combining multiple sets of LiDAR feature variables to estimate forest canopy closure can fully exploit the canopy structure characteristics contained in LiDAR data and improve the estimation accuracy.

Key words: UAV-LiDAR, canopy closure, characteristic variables, principal component analysis, stepwise regression

中图分类号:

S758.1

杨晓慧,吴金卓,刘浩然,钟浩,林文树. 基于UAV-LiDAR的人工林林分郁闭度估测[J]. 林业科学, 2023, 59(8): 12-21.

Xiaohui Yang,Jinzhuo Wu,Haoran Liu,Hao Zhong,Wenshu Lin. Estimation on Canopy Closure for Plantation Forests Based on UAV-LiDAR[J]. Scientia Silvae Sinicae, 2023, 59(8): 12-21.

图/表 11

图1

表1

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表2

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表3

LiDAR 特征变量及其描述①"

特征变量 Characteristic variable	变量描述 Variable description
elev_asd	归一化点云平均绝对偏差：总差异与给定点之间的总差值 Mean absolute deviation of normalized point cloud：the difference between the total difference and given points
elev_AIH_25th	归一化点云累计高程百分数的25分位数 The 25th quantile of normalized point cloud cumulative elevation percentage
elev_aihInterDis	归一化累计高程四分位数间距 Normalized cumulative elevation interquartile spacing
elev_cv	首次回波归一化点云变异系数 Coefficient of variation of first echo normalized point cloud
elev_interDis	归一化四分位数间距 Normalized interquartile spacing
elev_mad	归一化绝对偏差中值 Median value of normalized absolute deviation
elev_mean	归一化平均值 Normalized mean
elev_median	归一化中值 Normalized median
elev_std	归一化标准差 Normalized standard deviation
elev_crr	归一化冠层起伏比率 Normalized canopy fluctuation ratio
int_asd	首次回波平均绝对偏差 Mean absolute deviation of first echo
int_cv	首次回波归一化点云强度的变异系数 Variation coefficient of normalized point cloud intensity of first echo
int_interDis	首次回波四分位数间距 Interquartile interval of first echo
int_mad	首次回波绝对偏差中值 Median absolute deviation of first echo
int_mean	首次回波平均值 Mean of first echo
int_median	首次回波中值 Median of first echo
int_std	首次回波标准差 Standard deviation of first echo
int_percentile_25th	首次回波强度百分数 First echo intensity percentage
int_AII_25th	首次回波累计强度百分数 Cumulative intensity percentage of first echo
int_Skewness/kurtosis	首次回波所有激光雷达点强度分布的偏度/峭度 Skewness/kurtosis of intensity distribution of all LiDAR points in the first echo
Open/closed	在冠层容积模型中无体元区域的上层/下层 In the canopy volume model, the upper/lower layer of the region without voxel of free region
Euphotic/oligophotic	在冠层容积模型中有体元区域的上65%区域和下35%区域的比值 The ratio of upper 65% and lower 35% of voxel regions in the canopy volume model
elev_density_30th、elev_density_60th	冠层密度参数 Canopy closure parameters
Hskewness/Hkurtosis	冠层首次回波的偏度/峭度 Skewness/kurtosis of canopy first echo point
CC_2m	首次回波中高于2 m的激光返回点占所有返回点的比例 The first return points above 2 m accounts for the percentage of all return points

表3

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图7

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森林类型 Forest type	林龄 Age/a	平均树高 Average height/m	平均胸径 Average DBH/cm	林分密度 Stand density/（tree·hm^?2）	蓄积量 Volume/(m³·hm^?2)
白桦Betula platyphylla	61	19.1	18.1	986	82.9
蒙古栎Quercus mongolica	60	14.7	17.2	2 690	181.0
兴安落叶松Larix gmelinii	62	18.0	19.8	1 120	160.8
樟子松Pinus sylvestris var. mongolica	64	17.8	22.1	1 140	195.6
黑皮油松Pinus tabuliformis	69	17.6	20.6	954	146.2

样方编号 Sample plot No.	1	2	3	4	5	6	7	8	9	10
针叶林 Coniferous forest	0.86	0.86	0.84	0.84	0.86	0.86	0.86	0.86	0.84	0.86
阔叶林 Broad-leaved forest	0.84	0.84	0.86	0.84	0.84	0.85	0.85	0.85	0.84	0.84

样方编号 Sample plot No.	11	12	13	14	15	16	17	18	19	20
针叶林 Coniferous forest	0.84	0.84	0.86	0.84	0.84	0.85	0.85	0.85	0.84	0.84
阔叶林 Broad-leaved forest	0.83	0.83	0.83	0.84	0.84	0.82	0.81	0.83	0.84	0.84

植被类型 Vegetation types	精度指标 Precision index
植被类型 Vegetation types	R²	Adj R²	RMSE	rRMSE（%）	P
针叶林 Coniferous forest	0.782	0.722	0.007	0.83	0.000 6
阔叶林 Broad-leaved forest	0.784	0.725	0.005	0.59	0.000 6

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