Airborne laser scanning (ALS) has been widely used to map gap probability and leaf area index (LAI) distribution at plot and landscape scales. As an indirect measurement, most ALS methods to estimate LAI combine waveform or point density information with supporting field measurements such as the leaf angle distribution, gap probability, or direct LAI measures. The development of a more independent estimation approach would facilitate more widespread use of existing ALS data to investigate patterns of forest structure and build realistic 3-D vegetation scenes to simulate remote sensing imagery and energy balance. Here, we develop a data processing workflow (named PVlad) using ALS point cloud apparent reflectance to estimate LAI and voxel-based leaf area density (LAD), aiming to reduce the need for associated field measurements such as the gap probability. The adaptation of the path volume (PV) concept derived from apparent reflectance integrates information from multi-directional ALS pulses, and quantifies the percentage exploration of each voxel for classification and occlusion correction, such that rigorous volumetric sampling approaches can be developed to derive LAI and LAD. The PVlad workflow was applied to discrete-return lidar data (Riegl VQ480i) acquired by NASA Goddard's LiDAR, Hyperspectral and Thermal Imager (G-LiHT) Airborne Imager during leaf-on (summer) and leaf-off (spring) conditions at the Smithsonian Environmental Research Center (SERC). The estimates of LAI and LAD captured structural differences between mature, logged, and intermediate-aged stands over eight deciduous forest plots. The derived LAI values were compared to field litter collection measurements, and the derived LAD vertical distribution was compared to the output of the VoxLAD model using terrestrial laser scan (TLS) field survey data. Using voxel sizes ranging from 0.5 m to 5 m, overall LAI estimation showed linear fitting coefficient bias $<0.035$ and $\text{RMSE}<0.5m^2/m^2$ for 1 and 2 m voxel sizes, and vertical LAD distribution showed strong correlation with $R\approx 0.9$ and $RMSE\approx 0.028m^2/m^3$ for 0.5 and 1m voxel sizes. For every forest stand, upper-canopy LAD had a low variance for voxel sizes of ≤ $2m$. Application of PVlad to the G-LiHT and other similar ALS data archives enables the development of fine-resolution LAI map products, including voxelization of LAD for ecosystem science and radiative transfer simulations of remote sensing imagery or surface energy balance.

机载激光扫描 (ALS) 已广泛用于绘制地块和景观尺度的间隙概率和叶面积指数 (LAI) 分布图。作为间接测量，大多数用于估计 LAI 的 ALS 方法将波形或点密度信息与支持的现场测量（例如叶角分布、间隙概率或直接 LAI 测量）相结合。开发更独立的估计方法将有助于更广泛地使用现有的 ALS 数据来研究森林结构模式并构建逼真的 3-D 植被场景以模拟遥感图像和能量平衡。在这里，我们开发了一个数据处理工作流（名为 PVlad），使用 ALS 点云表观反射率来估计 LAI 和基于体素的叶面积密度（LAD），旨在减少对相关现场测量（例如间隙概率）的需求。从表观反射导出的路径体积 (PV) 概念的适应性整合了来自多方向 ALS 脉冲的信息，并量化了每个体素的百分比探索以进行分类和遮挡校正，从而可以开发严格的体积采样方法来导出 LAI 和小伙子。PVlad 工作流程应用于在史密森尼 (Smithsonian) 叶片开启（夏季）和叶片关闭（春季）条件下由 NASA Goddard 的 LiDAR、高光谱和热成像仪 (G-LiHT) 机载成像仪获取的离散返回激光雷达数据 (Riegl VQ480i)环境研究中心（SERC）。LAI 和 LAD 的估计值反映了成熟、伐木和中年林分超过 8 个落叶林地块之间的结构差异。将导出的 LAI 值与现场垃圾收集测量值进行比较，并将导出的 LAD 垂直分布与使用地面激光扫描 (TLS) 现场调查数据的 VoxLAD 模型的输出进行比较。使用范围从 0.5 m 到 5 m 的体素大小，整体 LAI 估计显示线性拟合系数偏差$<0.035$ 和 $\text{均方根误差}<0.5{米}^2/{米}^2$ 对于 1 m 和 2 m 体素大小，垂直 LAD 分布与 $\mathrm{电阻}\approx 0.9$ 和 $\mathrm{电阻}米秒乙\approx 0.028{米}^2/{米}^3$适用于 0.5 和 1m 体素尺寸。对于每个林分，当体素大小≤$2米$. PVlad 在 G-LiHT 和其他类似 ALS 数据档案中的应用能够开发高分辨率 LAI 地图产品，包括用于生态系统科学的 LAD 体素化和遥感图像或表面能量平衡的辐射传输模拟。