Research has demonstrated the utility of digital aerial photogrammetry (DAP) for area-based predictions of forest inventory attributes. To date, studies have used DAP data acquired with a range of spatial resolutions and image overlaps. The systematic benchmarking of DAP acquisition parameters remains an outstanding research and operational gap for forest applications. While the impact of along-track overlap on point cloud metrics and area-based attribute estimates can be readily simulated, the impact of image resolution or across-track overlap requires purpose-acquired data. Moreover, although increases in along-track overlap are enabled by digital camera systems, costs for increasing across-track overlap can be substantial and may negate the cost-effectiveness of DAP for forest inventory. Hence, determining the impacts of varying acquisition parameters is of practical value for inventory programs. Researchers and practitioners have often assumed that more overlap will result in better DAP data, and that minimal overlaps often associated with historic airborne image campaigns are inadequate to support DAP processing. In our study, we found no marked difference among 15 and 20 cm spatial resolutions and overlap scenarios unless across-track overlap was reduced to 40%. Mean differences between DAP metrics and the ALS reference generally increased with decreasing overlap, and mean differences were larger for lower height percentiles (p10). Estimates of canopy height using the p90 metric varied by a root mean squared difference (RMSD) of approximately 5% between 15 cm and 20 cm datasets when along-track overlap was greater than 40%. Lower height percentiles were more strongly impacted by overlaps and resolution. Cover metrics varied by 2% RMSD across all overlap scenarios and resolutions. Comparisons between forest types (conifer, deciduous, mixed), terrain slope and aspect, and ALS-derived canopy cover were conducted to determine whether significant mean differences existed between DAP and the ALS reference. Although some significant differences were found by forest type and terrain variables, significant differences were most commonly associated with canopy cover. Based on the results reported herein, along and across-track overlaps ≥ 60% result in DAP metrics that were more similar to ALS. Increasing across-track overlap from 60% to 80% did not consistently improve the level of agreement between DAP metrics and ALS reference metrics. Conversely, DAP metrics generated using across-track overlaps <60% resulted in metrics with greater differences from the ALS reference, and a greater range of variability in DAP metric values. Image acquisitions for forest inventory must consider a broad range of factors and herein we have quantified that increasing along- or across-track overlap beyond 60% does not improve agreement with ALS area-based point cloud metrics commonly used to model forest inventory attributes. Likewise, overlap that is <60% does result in greater differences with ALS reference. Other applications beyond forest inventory may have different overlap requirements.

研究表明，数字航空摄影测量 (DAP) 可用于基于区域的森林清单属性预测。迄今为止，研究已使用以一系列空间分辨率和图像重叠获取的 DAP 数据。DAP 采集参数的系统基准测试仍然是森林应用的一个突出研究和操作差距。虽然可以很容易地模拟沿轨道重叠对点云度量和基于区域的属性估计的影响，但图像分辨率或跨轨道重叠的影响需要专门获取的数据。此外，尽管数码相机系统可以增加沿航迹重叠，但增加跨航迹重叠的成本可能很大，并且可能会否定 DAP 对森林清查的成本效益。因此，确定不同购置参数的影响对于清查计划具有实用价值。研究人员和从业者经常假设更多的重叠会产生更好的 DAP 数据，并且通常与历史机载图像活动相关的最小重叠不足以支持 DAP 处理。在我们的研究中，我们发现 15 和 20 厘米空间分辨率和重叠场景之间没有显着差异，除非跨轨道重叠减少到 40%。DAP 指标和 ALS 参考之间的平均差异通常随着重叠的减少而增加，并且对于较低的高度百分位数 (p10)，平均差异更大。当沿航迹重叠大于 40% 时，使用 p90 度量的冠层高度估计值在 15 cm 和 20 cm 数据集之间的均方根差 (RMSD) 变化约为 5%。较低的高度百分位数受重叠和分辨率的影响更大。在所有重叠场景和分辨率中，覆盖指标的变化幅度为 2% RMSD。进行了森林类型（针叶林、落叶林、混合林）、地形坡度和坡向以及 ALS 衍生的冠层覆盖率之间的比较，以确定 DAP 和 ALS 参考之间是否存在显着的平均差异。尽管森林类型和地形变量存在一些显着差异，但显着差异最常与冠层覆盖有关。根据本文报告的结果，沿和跨轨道重叠≥ 60% 导致 DAP 指标更类似于 ALS。将跨轨道重叠从 60% 增加到 80% 并没有持续提高 DAP 指标和 ALS 参考指标之间的一致性水平。反过来，使用跨轨道重叠 <60% 生成的 DAP 指标导致指标与 ALS 参考的差异更大，并且 DAP 指标值的可变性范围更大。森林清单的图像采集必须考虑广泛的因素，在此我们量化了增加超过 60% 的沿或跨轨道重叠并不会提高与常用于模拟森林清单属性的基于 ALS 区域的点云指标的一致性。同样，<60% 的重叠确实会导致与 ALS 参考的更大差异。森林清查以外的其他应用程序可能有不同的重叠要求。森林清单的图像采集必须考虑广泛的因素，在此我们量化了增加超过 60% 的沿或跨轨道重叠并不会提高与常用于模拟森林清单属性的基于 ALS 区域的点云指标的一致性。同样，<60% 的重叠确实会导致与 ALS 参考的更大差异。森林清查以外的其他应用程序可能有不同的重叠要求。森林清单的图像采集必须考虑广泛的因素，在此我们量化了增加超过 60% 的沿或跨轨道重叠并不会提高与常用于模拟森林清单属性的基于 ALS 区域的点云指标的一致性。同样，<60% 的重叠确实会导致与 ALS 参考的更大差异。森林清查以外的其他应用程序可能有不同的重叠要求。