Effect of Leaf Occlusion on Leaf Area Index Inversion of Maize Using UAV-LiDAR Data

文献类型： 外文期刊

第一作者： Lei, Lei

作者： Lei, Lei;Li, Zhenhai;Han, Dong;Xu, Bo;Feng, Haikuan;Yang, Hao;Yang, Guijun;Lei, Lei;Qiu, Chunxia;Han, Dong;Han, Liang;Zhu, Yaohui;Wu, Jintao

作者机构：

关键词： UAV-LiDAR; LAI; occlusion effect; different layers; different planting densities; ridge direction; optimal voxel size

期刊名称：REMOTE SENSING （ 影响因子：4.848； 五年影响因子：5.353 ）

ISSN： 2072-4292

年卷期： 2019 年 11 卷 9 期

页码：

收录情况： SCI

摘要： The leaf area index (LAI) is a key parameter for describing crop canopy structure, and is of great importance for early nutrition diagnosis and breeding research. Light detection and ranging (LiDAR) is an active remote sensing technology that can detect the vertical distribution of a crop canopy. To quantitatively analyze the influence of the occlusion effect, three flights of multi-route high-density LiDAR dataset were acquired at two time points, using an Unmanned Aerial Vehicle (UAV)-mounted RIEGL VUX-1 laser scanner at an altitude of 15 m, to evaluate the validity of LAI estimation, in different layers, under different planting densities. The result revealed that normalized root-mean-square error (NRMSE) for the upper, middle, and lower layers were 10.8%, 12.4%, 42.8%, for 27,495 plants/ha, respectively. The relationship between the route direction and ridge direction was compared, and found that the direction of flight perpendicular to the maize planting ridge was better than that parallel to the maize planting ridge. The voxel-based method was used to invert the LAI, and we concluded that the optimal voxel size were concentrated on 0.040 m to 0.055 m, which was approximately 1.7 to 2.3 times of the average ground point distance. The detection of the occlusion effect in different layers under different planting densities, the relationship between the route and ridge directions, and the optimal voxel size could provide a guideline for UAV-LiDAR application in the crop canopy structure analysis.

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