基于无人机激光雷达的银杏人工林有效叶面积指数估测

doi:10.11707/j.1001-7488.20200108

Abstract

Abstract:

Objective: Ginkgo biloba is one of the most important tree species in China. Real-time, quantitative and accurate estimation of its eLAI(effective leaf area index) plays a key role in analyzing its growth and competition as well as understanding the functions and productivity of the plantation ecosystems. Method: Combined with the point cloud data acquired from the multi-rotor UAV-based LiDAR system and 45 sample plots data, this paper used the following two approaches:gap-fraction modeling(calculating the canopy gap fraction of the point cloud, then computing eLAI according to the Beer-Lambert law)and statistical modeling(modeling based on the eLAI measured from ground and the metrics derived from LiDAR first return, then the fitted models were applied to calculate the eLAI)method, to estimate the eLAI in a typical ginkgo plantation of China. Result: 1) When using the statistical model to estimate the eLAI, the accuracy of estimation was R²=0.38(rRMSE=54%) by modeling of LiDAR height metrics only. By gradually introducing multiple sets of metrics (density metrics, canopy volume metrics and intensity metrics), the accuracy of the estimation was increased to R²=0.64(rRMSE=26%), R²=0.61(rRMSE=28%)and R²=0.74(rRMSE=23%), respectively. 2) The sample plots were grouped according to Cover, the accuracy of group modeling was better than that of non-group modeling. 3) When using the gap-fraction model to estimate the eLAI, the accuracy was R²=0.71(rRMSE=32.0%). Conclusion: Combining multiple sets of LiDAR metrics to estimate the eLAI could fully excavate the canopy structure characteristics contained in LiDAR data, so as to improve the estimation accuracy. Meanwhile, the gap-fraction model approach could effectively estimate the eLAI of ginkgo plantations. This study illustrated that the metrics obtained from the UAV LiDAR might have certain potentials in estimating the eLAI of the ginkgo plantations.

Key words: UAV, LiDAR, ginkgo, plantation, effective, leaf area index

CLC Number:

S757

Xiangqian Wu,Lin Cao,Xin Shen,Guibin Wang,Fuliang Cao. Estimation of Effective Leaf Area Index Using UAV-Based LiDAR in Ginkgo Plantations[J]. Scientia Silvae Sinicae, 2020, 56(1): 74-86.

Figures/Tables 12

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The description of LiDAR metrics"

特征变量Metrics	变量描述Description
	高度百分位数Height percentiles
h₂₅，h₅₀，h₇₅，h₉₅	森林冠层首次回波高度垂直分布的分位数The percentiles of the canopy height distributions by first echo(25^th, 50^th, 75^th, and 95^th)
h_mean	归一化点云高度的平均值The mean height of all points after normalized
h_cv	归一化点云高度的变异系数(标准差与平均数的比值)The coefficient of variation of height of all points after normalized(the ratio of the standard deviation to the mean)
h_skewness/h_kurtosis	首次回波所有激光雷达高度点分布的偏度(分布曲线偏斜方向和程度)/峭度(分布曲线顶端的高耸程度) The skewness and kurtosis of the heights of all points by first echo
	冠层密度Ganopy density
d₁, d₃, d₅, d₇, d₉	大于10%、30%、50%、70%、90%高度的植被点占所有激光点的比例The proportion of points above the quantiles(10^th, 30^th, 50^th, 70^th, and 90^th)to total number of points
	覆盖度Goverage
CC₁	首次回波中高于1 m的激光返回点占所有返回点的比例The first return points above 1 m accounts for the percentage of all return points
	Weibull特征变量Weibull metrics
α/β	枝叶剖面中Weibull模型分布的尺度(宽度)/形状(高度)参数The α and β parameter of the Weibull distribution fitted to foliage density profile
	冠层容积特征变量Canopy volume metrics
Open/Closed	在冠层容积模型中无体元区域的上层/下层The empty voxels located above and below the canopy respectively
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The voxels located within an uppermost percentile(65%)of all filled grid cells of that column, and voxels located below the point in the profile
	强度特征变量Intensily metrics
I₂₅, I₅₀, I₇₅, I₉₅	首次回波返回点的能量强度分布百分位数The percentiles of the intensity distributions by first echo(25^th, 50^th, 75^th, and 95^th)
I_mean	首次回波激光雷达强度的平均值The mean intensity of all points by first echo
I_cv	首次回波激光雷达强度的变异系数(标准差与平均数的比值)The coefficient of variation of intensity of LiDARby first echo(the ratio of the standard deviation to the mean)
I_skewness/I_kurtosis	首次回波所有激光雷达强度分布的偏度(分布曲线偏斜方向和程度)/峭度(分布曲线顶端的高耸程度) The skewness and kurtosis of the intensity of LiDAR by first echo

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组 Group	胸径 DBH/cm		Lorey’s平均树高 Lorey’s mean height/m		株数 Number of stems		密度 Density/hm^-2		有效叶面积指数 eLAI/(m²·m^-2)
组 Group	范围 Range	平均值 Mean	范围 Range	平均值 Mean	范围 Range	平均值 Mean	范围 Range	平均值 Mean	范围 Range	平均值 Mean
第1组Group 1	10.2~22.9	15.2	6.6~13.4	9.5	23~94	41.5	312~1 332	602.7	1.03~2.89	2.15
第2组Group 2	16.0~23.4	19.5	10.1~14.5	12.2	22~54	37.6	328~952	533.6	0.93~2.42	1.67
第3组Group 3	15.6~21.0	18.9	10.6~14.6	12.2	30~79	45.4	440~1 120	631.5	0.27~1.62	0.95
全部样地All	10.2~23.4	17.9	6.6~14.6	11.3	23~94	41.2	312~1 332	598.2	0.27~2.89	1.59

特征变量组合 Metrics contained	模型Model	R²	RMSE/(m²·m^-2)	rRMSE(%)	AIC
Model 1 (基于高度特征变量Height percentile)	-7.93×h_cv-0.36×h₇₅+0.49×h₉₅ +1.58	0.44	0.74	48.3	202.5
Model 2 (基于高度+密度特征变量 Height percentile+ density metrics)	-0.98×h₇₅+0.78×h₉₅+4.68×d₅-0.45	0.67	0.70	43.2	172.1
Model 3 (基于高度+冠层容积特征变量 Height percentile+canopy volume metrics)	0.28×β-6.96×Open-5.03×Euphotic+3.98	0.65	0.71	43.8	175.2
Model 4 (基于所有特征变量All LiDAR metrics)	-7.10×d₃+0.10×CC₁-0.03×I₉₅+0.47	0.78	0.39	22.1	158.3

组Group	模型Model	R²	RMSE/m²·m^-2)	rRMSE(%)	AIC
第1组Group 1	22.4×h_cv+3.18×h_skewness+11.06×d₅-9.69	0.86	0.21	15.6	132.4
第2组Group 2	0.72×I_skewness+3.75×d₅-6.85×d₉-0.76	0.88	0.14	12.6	127.5
第3组Group 3	-9.42×h_cv+0.06×I₂₅-0.01×CC₁+2.04	0.79	0.37	21.9	150.2
总体样地All	-7.10×d₃+0.10×CC₁-0.03×I₉₅+0.47	0.74	0.39	23.0	158.3

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Estimation of Effective Leaf Area Index Using UAV-Based LiDAR in Ginkgo Plantations

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