雷蕾, 李振洪, 杨浩, 杨贵军. 利用无人机激光雷达提取玉米叶面积密度[J]. 武汉大学学报 ( 信息科学版), 2021, 46(11): 1737-1745. DOI: 10.13203/j.whugis20200674
引用本文: 雷蕾, 李振洪, 杨浩, 杨贵军. 利用无人机激光雷达提取玉米叶面积密度[J]. 武汉大学学报 ( 信息科学版), 2021, 46(11): 1737-1745. DOI: 10.13203/j.whugis20200674
LEI Lei, LI Zhenhong, YANG Hao, YANG Guijun. Extraction of the Leaf Area Density of Maize Using UAV-LiDAR Data[J]. Geomatics and Information Science of Wuhan University, 2021, 46(11): 1737-1745. DOI: 10.13203/j.whugis20200674
Citation: LEI Lei, LI Zhenhong, YANG Hao, YANG Guijun. Extraction of the Leaf Area Density of Maize Using UAV-LiDAR Data[J]. Geomatics and Information Science of Wuhan University, 2021, 46(11): 1737-1745. DOI: 10.13203/j.whugis20200674

利用无人机激光雷达提取玉米叶面积密度

Extraction of the Leaf Area Density of Maize Using UAV-LiDAR Data

  • 摘要: 叶面积密度可以表征冠层内部叶面积的垂直分布,是作物生长发育、营养诊断和育种研究的重要结构参数。激光雷达通过发射多脉冲和接收多回波信号可以探测到作物冠层内部信息。首先基于无人机激光雷达获取60个小区多航线的玉米点云数据,采用基于接触频率的体素法对叶面积密度进行估算,再对多个体素大小进行分析得到最优体素大小(0.2 m);其次对各航线以及航线叠加效果进行对比,得到无人机激光雷达获取点云数据的最优激光脉冲入射角(-30°~52°);然后结合玉米叶倾角和激光脉冲入射角对叶面积密度估算模型进行校正,从而提高叶面积密度估算精度;最后通过对不同种植密度和不同品种的玉米叶面积密度分布进行分析,得到不同品种玉米的发育快慢、株型特点以及最合理的种植密度。以上结果可为基于无人机激光雷达数据估算叶面积密度提供指导,并为玉米育种和科学管理提供参考。

     

    Abstract:
      Objectives  Leaf area density (LAD) represents the vertical distribution of canopy leaf area, and it is an important structural parameter for crop growth, crop nutritional diagnosis and breeding. Light detection and ranging (LiDAR) can detect the structural information of crop canopy by transmitting multiple pulses and receiving multiple echo signals.
      Methods  Firstly, unmanned aerial vehicle-LiDAR (UAV-LiDAR) is utilized to collect Maize point cloud data with multiple trajectories in 60 plots, which in turn is employed to estimate the leaf area density of Maize with the contact-frequency-based voxel method. Then, combined with the width of the Maize leaf, the optimal voxel size is determined by analyzing multiple voxel sizes (0.2 m), and the optimal pulse incidence angle for UAV-LiDAR to get point cloud data is obtained through the comparison between different trajectories (-30°-52°). By superimposing two non-orthogonal trajectorys, and comparing the two non-orthophoto graphic trajectorys, it is proved that the superposition of the two non-orthogonal trajectorys can reach or exceed the effect of orthographic data. In order to improve the accuracy of the estimated LAD, Maize leaf inclination and pulse incidence angle are considered, and the estimation accuracy of leaf area density is improved compared with that before correction. With the optimal trajectory and the optimal voxel size, the leaf area density distribution of different planting densities and varieties is analyzed, the growth rate, plant type characteristics and the most reasonable planting density of different varieties of Maize plant can be obtained through the analysis of the leaf area density distribution of different plant densities and different varieties.
      Conclusions  It is believed that our findings in this paper can provide guidance for estimating leaf area density using UAV-LiDAR data and provide reference for Maize breeding and scientific management.

     

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