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以机载LiDAR离散点云数据为数据源,基于植被冠层孔隙率与叶面积指数的关系,提出一种反演大田玉米叶面积指数的方法。对反演LAI和实测LAI进行对比分析,结果表明:基于Axelsson改进的不规则三角格网加密方法可以将地面点和非地面点分开,结合高分辨率影像能够提取出玉米冠层点云;基于孔隙率反演LAI,尼尔逊参数的选择对结果影响很大,利用扫描天顶角模拟尼尔逊参数,LAI反演结果接近于真实情况。利用机载LiDAR点云数据能精确地反演大田玉米LAI,该研究方法适用于中等高度的农作物,可以扩展到甜菜、甘蔗等其他中等高度农作物。
Based on the data of on-board LiDAR discrete point cloud data, based on the relationship between the vegetation canopy porosity and leaf area index, a method of field leaf area index inversion was proposed. The contrastive analysis of the LAI and the measured LAI shows that the improved triangle triangulation based on Axelsson can separate the ground point from the non-ground point, and can extract the corn canopy point cloud based on the high-resolution image. LAI porosity inversion and Nielsen parameter selection have a great influence on the results. Using the zenith angle to simulate the Nielsen parameters, the LAI inversion results are close to the real ones. The LAI of field corn can be inversed accurately by using on-board LiDAR point cloud data. This method is suitable for middle-altitude crops and can be extended to other medium-high-level crops such as sugar beet and sugarcane.