融合无人机载激光雷达与多光谱遥感数据的冬小麦叶面积指数反演
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国家重点研发计划项目(2016YFD0300601)和国家自然科学基金项目(41877021,41771265)资助


Inversion of Leaf Area Index in Winter Wheat by Merging UAV LiDAR with Multispectral Remote Sensing Data
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National Key R&D Program of China (No. 2016YFD0300601), National Natural Science Foundation of China (Nos. 41877021, 41771265)

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    摘要:

    为了进一步挖掘无人机载激光雷达(Light Laser Detection and Ranging,LiDAR)在农作物长势监测方面的潜力,探究机载LiDAR与多光谱遥感数据融合反演冬小麦叶面积指数(Leaf Area Index,LAI)的效果,本研究以无人机载LiDAR和可见光-近红外多光谱为研究手段,获取试验区冬小麦孕穗期的无人机载LiDAR点云和多光谱数据,从中提取并筛选合适的LiDAR点云结构参数和植被指数,借助多元线性回归法(Multivariable Linear Regression,MLR)和偏最小二乘回归法(Partial Least Squares Regression,PLSR),通过融合LiDAR点云结构参数与植被指数以及单独使用植被指数作为模型输入参数,分别与实测LAI构建了LAI反演模型。用决定系数(Coefficient of Determination,R2)和均方根误差(Root Mean Square Error,RMSE)来评价模型时,结果显示融合LiDAR点云与多光谱数据能够较好地反演冬小麦LAI。而且,无论是利用MLR还是PLSR法,融合LiDAR点云结构参数与植被指数的模型(MLR:R2=0.901,RMSE=0.480;PLSR:R2=0.909,RMSE=0.445(n=16))均优于仅使用植被指数的模型(MLR:R2=0.897,RMSE=0.492;PLSR:R2=0.892,RMSE=0.486(n=16))。因此,加入无人机载LiDAR数据可以一定程度上弥补光谱数据在作物垂直方向上信息提取不足的缺陷,提高冬小麦LAI的反演精度,为冬小麦LAI反演提供了更优的手段。

    Abstract:

    [Objective] In order to further tap the potential of unmanned aerial vehicle (UAV) carried LiDAR to monitor crop growth and to explore effect of merging UAV LiDAR with multispectral data in inversing leaf area index (LAI) in winter wheat, this study was carried out.[Method] In this study, with the aid of UAV LiDAR scanners and visible-near infrared multispectral cameras, UAV LiDAR point cloud and multispectral data of the winter wheat at the booting stage in experiment zone were collected. From the data, four LiDAR point cloud structure parameters, i.e., three-dimensional volumetric parameters (BIOVP), mean plant height (Hmean), 75 percentile plant height (H75) and laser penetration index (LPI), and six vegetation indices, i.e., NDVI, SAVI, MCARI, TVI, NDRE and RVI were extracted. Then correlation analysis was performed of these parameters for screening suitable modeling parameters. With the aid of the multiple linear regression (MLR) and the partial least squares regression (PLSR), a LAI inversion model was constructed through merging the LiDAR point cloud structure parameters with vegetation indices as input parameters of the model. In applying the MLR method, the two vegetation indices, NDVI and SAVI, that are the most closely correlated with the field-observed LAI and the two point cloud structure parameters, H75 and BIOVP, that are the most closely correlated with the field-observed LAI, were used as input parameters of the model. While in adopting the PLSR method, the number of principal components in modeling was determined in the light of the result of the cross-validation. Before modeling, the experimental dataset had been randomly divided into a modeling set (n=32) and a validation set (n=16) at a ratio of 7:3 in all treatments. A LAI inversion model was built up based on the modeling dataset and then the validation dataset was used to evaluate effect of the model. Meanwhile, in order to determine whether the inversion with the LiDAR point cloud data merged with the multispectral data was better than that based on multispectral data alone, LAI inversion models were constructed using the same modeling method with vegetation indices as input parameters of the model only.[Result] The evaluation of the model using the coefficient of determination (R2) and the root mean square error (RMSE) shows that the inversion model using the LiDAR point cloud data merged with the multispectral data well reflect the LAI in winter wheat, with R2 of the modeling set being all > 0.900 and RMSE being < 0.400 and R2 of the validation set being all > 0.800, and RMSE being < 0.500. In addition, no matter whether using MLR or PLSR, the models with the LiDAR point cloud data and vegetation indices (MLR:R2=0.901, RMSE=0.480; PLSR:R2=0.909, RMSE=0.445 (n=16)) are all superior to the models using vegetation indices only (MLR:R2=0.897, RMSE=0.492; PLSR:R2=0.892, RMSE=0.486 (n=16)).[Conclusion] In conclusion, although the LAI data in this research were too scattered, leading to insignificant difference between models in comparison, it could still be seen that the addition of UAV LiDAR data could make up for the defect of using the multispectral data alone that insufficient information could be extracted along the vertical direction of the crop, and improve accuracy of the inversion of LAI in winter wheat. Therefore, the model with UAV LiDAR data merged with multispectral data is a superior means for inversion of LAI in winter wheat and even other crops smaller in plant type.

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牛玉洁,李晓鹏,张佳宝,马东豪,纪景纯,宣可凡,蒋一飞,汪春芬,邓皓东,刘建立.融合无人机载激光雷达与多光谱遥感数据的冬小麦叶面积指数反演[J].土壤学报,2022,59(1):161-171. DOI:10.11766/trxb202007130239 NIU Yujie, LI Xiaopeng, ZHANG Jiabao, MA Donghao, JI Jingchun, XUAN Kefan, JIANG Yifei, WANG Chunfen, DENG Haodong, LIU Jianli. Inversion of Leaf Area Index in Winter Wheat by Merging UAV LiDAR with Multispectral Remote Sensing Data[J]. Acta Pedologica Sinica,2022,59(1):161-171.

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  • 收稿日期:2020-07-13
  • 最后修改日期:2020-11-02
  • 录用日期:2020-11-12
  • 在线发布日期: 2020-12-10
  • 出版日期: 2022-01-11