利用机载激光雷达点云数据，计算了9种度量指标，并将其分为冠层的高度指标、结构复杂度指标和覆盖度指标。利用高度指标和结构复杂度指标，结合大量实测单木结构与年龄估测数据，从样点和区域尺度分别分析了青海云杉林冠层垂直结构分布，分析得知实验区内主要以中龄林和成熟林为主，冠层垂直分布复杂程度偏低，高度分化程度一般。通过回归分析发现首次回波覆盖度指标FCI与实测的有效植被面积指数PAI_e有良好的相关性（R²=0.66），在此基础上基于辐射传输模型反演了实验区内PAI_e的水平分布，且用实测数据验证发现反演的PAI_e略高于实测值（R²=0.67），绝对平均误差为0.65。分析结果很好地反映了激光雷达在森林空间结构信息提取方面的应用潜力。

Nine Light Detection And Ranging (LiDAR) metrics were calculated and classified into three classes: canopy height, structural complexity, and cover metrics. Combined with a huge amount of field-measured structural data and estimated age data for individual trees, canopy height and structural complexity metrics were used to analyze the vertical distribution of forest canopy at both plot and regional levels. Picea crassifolia in the study area was primarily half-mature and mature stands; the complexity of the canopy vertical distribution and the differentiation grade of canopy height were low. The canopy cover metrics had a good correlation with the field-measured effective Plant Area Index (PAI_e) according to the regression analysis (R²=0.66). The PAI_e of the entire study area was retrieved based on the radiative transfer model and validated by field data. The retrieved PAI_e was slightly higher than the field data (R²=0.67), and the average absolute error was 0.65. These results show the potential of LiDAR in retrieving forest structure information.