林业科学 ›› 2020, Vol. 56 ›› Issue (1): 74-86.doi: 10.11707/j.1001-7488.20200108
吴项乾,曹林*,申鑫,汪贵斌,曹福亮
收稿日期:
2018-01-10
出版日期:
2020-01-25
发布日期:
2020-02-24
通讯作者:
曹林
基金资助:
Xiangqian Wu,Lin Cao*,Xin Shen,Guibin Wang,Fuliang Cao
Received:
2018-01-10
Online:
2020-01-25
Published:
2020-02-24
Contact:
Lin Cao
Supported by:
摘要:
目的: 精确估测银杏人工林有效叶面积指数(eLAI),以更好了解银杏人工林的生长和竞争、理解人工林生态系统的功能和生产力。方法: 基于多旋翼无人机激光雷达(LiDAR)系统获取的点云数据,结合45块地面实测样地数据,使用孔隙度模型法(通过计算点云的冠层穿透率,根据Beer-Lambert定律计算有效叶面积指数)和统计模型法(首先通过地面实测的有效叶面积指数和所提取的LiDAR特征变量建模,然后借助拟合的模型估测有效叶面积指数)对我国典型银杏人工林进行样地尺度的有效叶面积指数估测。结果: 1)使用统计模型法估测eLAI时,仅利用LiDAR高度特征变量估测精度为R2=0.38(rRMSE=54%),引入其他特征变量(冠层密度特征、冠层容积比以及强度特征变量)后精度分别达到R2=0.64(rRMSE=26%)、R2=0.61(rRMSE=28%)、R2=0.74(rRMSE=23%);2)根据Cover将样地分组建模后发现,分组建模的精度优于不分组建模的精度;3)孔隙度模型法估测有效叶面积指数的精度为R2=0.71(rRMSE=32.0%)。结论: 结合多组LiDAR特征变量估测有效叶面积指数能够充分挖掘LiDAR数据包含的冠层结构特性,从而提升估测精度;同时,使用孔隙度模型法可以有效估测银杏人工林有效叶面积指数。无人机LiDAR点云在估测银杏人工林有效叶面积指数上具有较好的潜力。
中图分类号:
吴项乾,曹林,申鑫,汪贵斌,曹福亮. 基于无人机激光雷达的银杏人工林有效叶面积指数估测[J]. 林业科学, 2020, 56(1): 74-86.
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.
表1
研究区样地林分参数调查信息汇总"
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) | |||||||||
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 |
表2
LiDAR特征变量及其描述"
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(25th, 50th, 75th, and 95th) |
hmean | 归一化点云高度的平均值The mean height of all points after normalized |
hcv | 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) |
hskewness/hkurtosis | 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(10th, 30th, 50th, 70th, and 90th)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(25th, 50th, 75th, and 95th) |
Imean | 首次回波激光雷达强度的平均值The mean intensity of all points by first echo |
Icv | 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) |
Iskewness/Ikurtosis | 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 |
表3
不同特征变量组合作为预测变量的有效叶面积指数估测模型比较及精度评价"
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×hcv-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 |
表4
不同Cover分组的有效叶面积指数估测模型比较及交叉验证精度评价"
组Group | 模型Model | R2 | RMSE/m2·m-2) | rRMSE(%) | AIC |
第1组Group 1 | 22.4×hcv+3.18×hskewness+11.06×d5-9.69 | 0.86 | 0.21 | 15.6 | 132.4 |
第2组Group 2 | 0.72×Iskewness+3.75×d5-6.85×d9-0.76 | 0.88 | 0.14 | 12.6 | 127.5 |
第3组Group 3 | -9.42×hcv+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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