Abstract The quantification of leaf area index (LAI) is essential for modeling the interaction between atmosphere and biosphere. The airborne LiDAR has emerged as an effective tool for mapping plant area index (PAI) in a landscape consisting of both woody and leaf materials. However, the discrimination between woody and leaf materials and the estimation of effective LAI (eLAI) have, to date, rarely been studied at landscape scale. We applied a voxel matching algorithm to estimate eLAI of deciduous forests using simulated and field LiDAR data under leaf-on and leaf-off conditions. We classified LiDAR points as either a leaf or a woody hit on leaf-on LiDAR data by matching the point with leaf-off data. We compared the eLAI result of our voxel matching algorithm against the subtraction method, where the leaf-off effective woody area index (eWAI) is subtracted from the effective leaf-on PAI (ePAI). Our results, which were validated against terrestrial LiDAR derived eLAI, showed that the voxel matching method, with an optimal voxel size of 0.1 m, produced an unbiased estimation of terrestrial LiDAR derived eLAI with an R2 of 0.70 and an RMSE of 0.41 (RRMSE: 20.1%). The subtraction method, however, yielded an R2 of 0.62 and an RMSE of 1.02 (RRMSE: 50.1%) with a significant underestimation of 0.94. Reassuringly, the same outcome was observed using a simulated dataset. In addition, we evaluated the performance of 96 LiDAR metrics under leaf-on conditions for eLAI prediction using a statistical model. Based on the importance scores derived from the random forest regression, nine of the 96 leaf-on LiDAR metrics were selected. Cross-validation showed that eLAI could be predicted using these metrics under leaf-on conditions with an R2 of 0.73 and an RMSE of 0.27 (RRMSE: 17.4%). The voxel matching method yielded a slightly lower accuracy (R2: 0.70, RMSE:0.41, RRMSE: 20.1%) than the statistical model. We, therefore, suggest that the voxel matching method offers a new opportunity for the estimating eLAI and other ecological applications that require the classification between leaf and woody materials using airborne LiDAR data. It potentially allows transferability to different sites and flight campaigns.

中文翻译：

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基于叶上和叶下机载激光雷达数据的温带落叶林叶面积指数有效估计的体素匹配方法

摘要 叶面积指数 (LAI) 的量化对于模拟大气和生物圈之间的相互作用至关重要。机载 LiDAR 已成为在由木质和树叶材料组成的景观中绘制植物面积指数 (PAI) 的有效工具。然而，迄今为止，很少在景观尺度上研究木质和树叶材料之间的区分以及有效 LAI (eLAI) 的估计。我们应用体素匹配算法来估计落叶林的 eLAI，使用模拟和现场 LiDAR 数据在叶子上和叶子下条件下。我们将 LiDAR 点与叶子上的 LiDAR 数据匹配，将其与叶子上的数据进行匹配，将其分类为叶子上的或木质的命中点。我们将体素匹配算法的 eLAI 结果与减法方法进行了比较，其中从有效叶上 PAI (ePAI) 中减去叶下有效木本面积指数 (eWAI)。我们的结果针对地面 LiDAR 派生的 eLAI 进行了验证，结果表明，体素匹配方法的最佳体素大小为 0.1 m，可以对地面 LiDAR 派生的 eLAI 进行无偏估计，R2 为 0.70，RMSE 为 0.41（RRMSE： 20.1%)。然而，减法方法的 R2 为 0.62，RMSE 为 1.02（RRMSE：50.1%），显着低估了 0.94。令人欣慰的是，使用模拟数据集观察到了相同的结果。此外，我们使用统计模型评估了 96 个 LiDAR 指标在 eLAI 预测的叶子条件下的性能。根据从随机森林回归得出的重要性得分，选择了 96 个叶上 LiDAR 指标中的 9 个。交叉验证表明，在叶上条件下使用这些指标可以预测 eLAI，R2 为 0.73，RMSE 为 0.27（RRMSE：17.4%）。体素匹配方法的准确度（R2：0.70，RMSE：0.41，RRMSE：20.1%）比统计模型略低。因此，我们建议体素匹配方法为估计 eLAI 和其他需要使用机载 LiDAR 数据对叶子和木质材料进行分类的生态应用提供了新的机会。它可能允许转移到不同的网站和航班活动。表明体素匹配方法为估计 eLAI 和其他需要使用机载 LiDAR 数据对叶子和木质材料进行分类的生态应用提供了新的机会。它可能允许转移到不同的网站和航班活动。表明体素匹配方法为估计 eLAI 和其他需要使用机载 LiDAR 数据对叶子和木质材料进行分类的生态应用提供了新的机会。它可能允许转移到不同的网站和航班活动。