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遥感技术与应用  2022, Vol. 37 Issue (5): 1097-1108    DOI: 10.11873/j.issn.1004-0323.2022.5.1097
LiDAR专栏     
联合无人机激光雷达和高光谱数据反演玉米叶面积指数
张亚倩1(),骆社周1(),王成2,习晓环2,聂胜2,黎东2,李光辉3
1.福建农林大学 资源与环境学院,福建 福州 350002
2.中国科学院空天信息创新研究院,北京 100094
3.河南省航空物探遥感中心,河南 郑州 450053
Combining UAV LiDAR and Hyperspectral Data for Retrieving Maize Leaf Area Index
Yaqian Zhang1(),Shezhou Luo1(),Cheng Wang2,Xiaohuan Xi2,Sheng Nie2,Dong Li2,Guanghui Li3
1.College of Resources and Environment,Fujian Agriculture and Forestry University,Fuzhou 350002,China
2.Key Laboratory of Digital Earth Science,Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China
3.Aerogeophysical and Remote Sensing Center of Henan Province,Zhengzhou 450053,China
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摘要:

叶面积指数(Leaf Area Index, LAI)是作物长势监测及产量估算的重要指标,准确高效的LAI反演对农田经济的宏观管理具有重要作用。研究探索了联合无人机激光雷达(Light Detec-tion and Ranging, LiDAR) 和高光谱数据反演玉米叶面积指数的潜力,并分析了LiDAR数据不同采样尺寸、高度阈值、点密度对LAI反演精度的影响同时确定三者的最优值。该研究分别从重采样的LiDAR数据和高光谱影像中提取了LiDAR变量和植被指数,然后基于偏最小二乘回归(Partial Least Square Regression,PLSR)和随机森林(Random Forest, RF) 回归两种算法分别利用LiDAR变量、植被指数、联合LiDAR变量和植被指数构建预测模型,并确定反演玉米LAI的最优预测模型。结果表明:反演玉米LAI的最优采样尺寸、高度阈值、点密度分别为5.5 m、0.55 m、18 points/m2,研究发现最高的点密度(420 points/m2)并没有产生最优的玉米LAI反演精度,因此单独依靠增加点密度的方法提高LAI的反演精度并不可靠。基于LiDAR变量获得的LAI反演精度(PLSR:R2=0.874,RMSE=0.317;RF:R2=0.942,RMSE=0.222)高于基于植被指数获得的LAI反演精度(PLSR: R2=0.741,RMSE=0.454;RF:R2=0.861,RMSE=0.338),而使用组合变量构建预测模型的反演精度(PLSR:R2=0.885, RMSE=0.304;RF:R2=0.950,RMSE=0.203)优于使用单一变量建立的LAI预测模型,其中利用联合LiDAR变量和植被指数建立的随机森林回归模型为最优预测模型。因此,将两种数据源融合在提高植被LAI反演精度方面具有一定的潜力。

关键词: 无人机激光雷达高光谱叶面积指数玉米点云密度    
Abstract:

Leaf Area Index (LAI) is an important index for crop growth monitoring and yield estimation. Accurate and efficient LAI retrieval plays an important role in the macroscopic management of farmland economy. This study explored the potential of combining UAV LiDAR and hyperspectral data to retrieve maize leaf area index, studied the effects of different sampling size, height threshold and point density of LiDAR data on LAI inversion accuracy, and determined the optimal values of the three parameters. In this study, LiDAR variables and vegetation indices were extracted from resampled LiDAR data and hyperspectral imagery respectively. Then, based on Partial Least Squares Regression (PLSR) and Random Forest (RF) regression, LiDAR variables, vegetation indices, combined LiDAR variables and vegetation indices were used to construct prediction models, and the optimal prediction model for LAI inversion of maize was determined. The results show that the optimal sampling size, height threshold and point density of maize LAI inversion are 5.5 m, 0.55 m and 18 points/m2 respectively. We found that the highest point density (420 points/m2) did not obtain the optimal LAI inversion accuracy of maize. Therefore, it is not reliable to improve the inversion accuracy of LAI by increasing point density alone. The LAI inversion accuracies based on LiDAR variables (PLSR: R2 = 0.874, RMSE = 0.317; RF: R2 = 0.942, RMSE = 0.222) were higher than those based on vegetation indices (PLSR: R2 = 0.741, RMSE = 0.454; RF: R2 = 0.861, RMSE = 0.338), and the inversion accuracies of the prediction model constructed using combination variable (PLSR: R2=0.885, RMSE=0.304; RF: R2=0.950, RMSE=0.203) were better than using single variable, in which the random forest regression model established by using combined LiDAR variables and vegetation indices is the best prediction model. Therefore, the fusion of the two data sources has a certain potential in improving the accuracy of vegetation LAI retrieval.

Key words: UAV-LiDAR    Hyperspectral    Leaf Area Index(LAI)    Maize    LiDAR point density
收稿日期: 2021-10-08 出版日期: 2022-12-13
ZTFLH:  TP958.98  
基金资助: 国家自然科学基金项目(41871264)
通讯作者: 骆社周     E-mail: xzyq771@163.com;luoshezhou@163.com
作者简介: 张亚倩(1996-),女,江西新余人,硕士研究生,主要从事激光雷达植被遥感研究。E?mail: xzyq771@163.com
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引用本文:

张亚倩,骆社周,王成,习晓环,聂胜,黎东,李光辉. 联合无人机激光雷达和高光谱数据反演玉米叶面积指数[J]. 遥感技术与应用, 2022, 37(5): 1097-1108.

Yaqian Zhang,Shezhou Luo,Cheng Wang,Xiaohuan Xi,Sheng Nie,Dong Li,Guanghui Li. Combining UAV LiDAR and Hyperspectral Data for Retrieving Maize Leaf Area Index. Remote Sensing Technology and Application, 2022, 37(5): 1097-1108.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2022.5.1097        http://www.rsta.ac.cn/CN/Y2022/V37/I5/1097

图1  研究区及样地位置审图号:GS(2019)3266号
变量描述
Hmean平均高度
Hmax最大高度
Hsd高度标准差
Hvar高度方差
Hcv变异系数
HMADmode高度总体模态的绝对偏差的中位数
Hskew高度偏度
H(40th,50th,60th,70th,75th,80th,90th,95th,99th)百分位高度
Canopy relief ratio (CRR)冠层起伏比
fcover覆盖度
HSQRT mean SQ (Hss)高度平方根平均值的平方
HCURT mean CUBE (Hcc)高度立方根平均值的立方
表1  LiDAR特征变量
植被指数计算公式来源
红边归一化植被指数(Red Edge Normalized Difference Vegetation Index,RNDVI)RNDVI=(R760.6-R701.4)/(R760.6+R701.4)[10]
红边模型植被指数(CIrededge)CIrededge=R749.5/R719.9-1[12]
改进红边比值植被指数(Modified Red Edge Simple Ratio Index MSR)MSR=(R749.5-R444.3)/(R705.1+R444.3)[14]
红边指数2(Vogelmann Red Edge Index VOG2)VOG2=(R734.7-R747.7)/(R714.4+R725.5)[23]
红边位置(Red Edge Position Index REP)REP=705+35*(0.5*(R782.8+R664.4)-R705.1)/(R740.3-R705.1)[24]
新型植被指数(New Vegetation Index NVI)NVI=(R777.3-R747.7)/R673.7[14]
增强型植被指数2(Enhanced Vegetation Index EVI2)EVI2=2.5*(R867.9-R636.7)/(R867.9+2.4*R636.7+1)[10]
简单比值植被指数(Simple Ratio Index SRI)SRI=R799.5/R670.0[12]
归一化植被指数(Normalized Difference Vegetation Index,NDVI)NDVI=(R799.5-R670.0)/(R799.5+R670)[12]
表2  植被指数及计算公式
高度阈值/m采样尺寸/m
2.02.53.03.54.04.55.05.56.06.57.0
R2R2R2R2R2R2R2R2R2R2R2
0.000.7070.7540.7630.7860.8000.8150.8200.8140.8090.7920.774
0.050.7100.7370.7640.7870.8010.8170.8260.8250.8220.8090.795
0.010.7240.7470.7730.7960.8110.8280.8380.8410.8400.8300.817
0.150.7310.7520.7790.8040.8180.8350.8450.8500.8500.8410.830
0.200.7390.7590.7840.8090.8230.8390.8500.8550.8550.8470.836
0.250.7420.7610.7880.8120.8270.8430.8530.8580.8580.8500.840
0.300.7440.7630.7900.8150.8290.8450.8540.8600.8600.8520.842
0.350.7460.7650.7910.8160.8300.8460.8560.8610.8620.8540.844
0.400.7480.7670.7930.8180.8320.8480.8570.8630.8630.8560.846
0.450.7490.7670.7930.8180.8330.8490.8580.8640.8640.8580.848
0.500.7490.7680.7940.8190.8340.8500.8590.8650.8660.8600.850
0.550.7490.7680.7940.8200.8350.8510.8600.8660.8660.8610.852
0.600.7500.7680.7950.8200.8340.8510.8600.8660.8670.8620.854
0.650.7490.7670.7930.8190.8340.8500.8590.8660.8660.8630.855
表3  不同采样尺寸和高度阈值下LAI预测值与实测值的回归分析
图2  基于最优采样尺寸的高度阈值对玉米LAI预测精度的影响
图3  基于最优采样尺寸和高度阈值的点密度对玉米LAI预测精度的影响
图4  利用偏最小二乘回归模型估算LAI的变量重要性排序
变量偏最小二乘回归随机森林回归
R2RMSER2RMSE
LiDAR变量0.8740.3170.9420.222
植被指数0.7410.4540.8610.338
LiDAR变量+植被指数0.8850.3040.9500.203
表4  利用不同预测模型和变量的LAI预测精度分析
图5  基于偏最小二乘回归模型的LAI预测值与实测值散点图
图6  利用随机森林回归模型估算LAI的变量重要性排序
图7  基于随机森林回归模型的LAI预测值和实测值的散点图
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