| 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 |
| |
| 引用本文： | 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. |
| |
| 作者姓名： | 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 |
| |
| 作者单位： | 中国科学院南京土壤研究所, 南京 210008;中国科学院大学, 北京 100049;河海大学水文水资源学院, 南京 210024 |
| |
| 基金项目： | 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 |
| |
| 摘 要： | 为了进一步挖掘无人机载激光雷达(Light 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,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...
|
| 关 键 词： | 无人机 冬小麦 LiDAR点云结构参数 植被指数 叶面积指数 反演模型 |
| 收稿时间： | 2020/7/13 0:00:00 |
| 修稿时间： | 2020/11/2 0:00:00 |
Inversion of Leaf Area Index in Winter Wheat by Merging UAV LiDAR with Multispectral Remote Sensing Data |
| |
| 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. |
| |
| Authors: | NIU Yujie LI Xiaopeng ZHANG Jiabao MA Donghao JI Jingchun XUAN Kefan JIANG Yifei WANG Chunfen DENG Haodong LIU Jianli |
| |
| Institution: | Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China;University of Chinese Academy of Sciences, Beijing 100049, China;College of Hydrology and Water Resources, Hohai University, Nanjing 210024, China |
| |
| 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. |
| |
| Keywords: | Unmanned aerial vehicle (UAV) Winter wheat LiDAR point cloud structure parameters Vegetation index Leaf area index (LAI) Inversion model |
| 本文献已被 维普 等数据库收录！ |
| 点击此处可从《土壤学报》浏览原始摘要信息 |
| 点击此处可从《土壤学报》下载免费的PDF全文 |
|
