In this study, we assessed the effect of airborne laser scanning (ALS) scan angle on point cloud metrics and the estimation of forest attributes in balsam fir (Abies balsamea (L.) Mill.) dominated forests of western Newfoundland, Canada. We collected calibration data from ground plot locations representing varying scan angles from two flight lines: within 4° of nadir in one flight line, and either 11–20° from nadir (low scan angle plots: L), or 21–30° from nadir (high scan angle plots: H) in an adjacent flight line. We computed three sets of ALS point cloud metrics for each ground plot using ALS data from: individual flight lines (near-nadir and off-nadir) and data from all available flight lines (up to 4) combined (aggregated, as commonly used in an operational inventory context). We generated three sets of models for each of the L and H plots using the ALS metric sets, and applied the models to independent validation data. We analysed the effect of scan angle on both the ALS metrics and performance statistics for area-based models generated using the L and H datasets. Our results demonstrate that off-nadir scan angles significantly affected (P < 0.05) specific metrics from both L (i.e. coefficient of variation (COVAR)) and H (i.e. maximum height, 95th percentile of height, mean height) plots, although the effects were trivial (mean absolute differences were ≤ 0.01 for COVAR and < 0.3 m for the height metrics). Forest attribute predictions using these and other metrics were also significantly affected (P < 0.05), namely gross merchantable volume (GMV), total volume (TVOL) and aboveground tree biomass (AGB) from L; and Lorey’s mean height (HGT), mean diameter at breast height (DBH), and GMV from H. We further demonstrated that combining ALS data from all available flight lines significantly increased errors for the predictions of HGT, GMV, and TVOL using L, and significantly reduced errors of HGT using H when compared to errors resulting from models developed with near-nadir data. While the differences in prediction errors were significant, they were small, with differences in mean absolute prediction errors all <1.3 per cent. Based on our results, we concluded that the effects of large scan angles, up to 30° off-nadir, on area-based forest attribute predictions were minimal in this study, which used ALS metrics calculated from ALS returns with a height above ground >2 m for balsam fir-dominated forests. This result may provide for operational efficiencies in implementing enhanced forest inventories in this particular forest environment.

中文翻译：

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扫描角度对 ALS 指标的影响以及香脂冷杉主导林分森林属性的基于区域的预测

在这项研究中，我们评估了机载激光扫描 (ALS) 扫描角度对点云指标的影响，并评估了加拿大纽芬兰西部以香脂冷杉 (Abies balsamea (L.) Mill.) 为主的森林中的森林属性。我们从代表两条飞行路线的不同扫描角度的地面图位置收集了校准数据：一条飞行路线的最低点 4° 以内，或者距离最低点 11-20°（低扫描角度图：L），或者距离最低点 21-30°相邻飞行路线中的最低点（高扫描角度图：H）。我们使用以下 ALS 数据为每个地面图计算了三组 ALS 点云指标：单独的航线（接近最低点和离最低点）和来自所有可用航线（最多 4 条）的组合数据（聚合，如在运营库存环境）。我们使用 ALS 度量集为每个 L 和 H 图生成了三组模型，并将模型应用于独立的验证数据。我们分析了扫描角度对使用 L 和 H 数据集生成的基于区域的模型的 ALS 指标和性能统计数据的影响。我们的结果表明，偏离最低点的扫描角度显着影响（P < 0.05）来自 L（即变异系数 (COVAR)）和 H（即最大高度、高度的 95%、平均高度）图的特定指标，尽管影响是微不足道的（对于 COVAR，平均绝对差异 ≤ 0.01，对于高度指标，平均绝对差异 < 0.3 m）。使用这些和其他指标的森林属性预测也受到显着影响（P < 0.05），即商品总销量（GMV），L 的总体积 (TVOL) 和地上树木生物量 (AGB)；和 Lorey 的平均身高 (HGT)、平均胸高直径 (DBH) 和来自 H 的 GMV。我们进一步证明，结合来自所有可用航线的 ALS 数据显着增加了使用 L 预测 HGT、GMV 和 TVOL 的误差，与使用接近最低点数据开发的模型产生的误差相比，使用 H 显着降低了 HGT 的误差。虽然预测误差的差异很大，但它们很小，平均绝对预测误差的差异均小于 1.3%。根据我们的结果，我们得出的结论是，在本研究中，大扫描角度（偏离最低点高达 30°）对基于区域的森林属性预测的影响最小，该研究使用 ALS 指标从地面以上高度的 ALS 返回计算; 2 m 用于香脂冷杉为主的森林。这一结果可以提高在这一特定森林环境中实施增强森林清单的运营效率。