Unmanned aerial vehicles (UAVs) and structure-from-motion photogrammetry enable detailed quantification of geomorphic change. However, rigorous precision-based change detection can be compromised by survey accuracy problems producing systematic topographic error (e.g. 'doming'), with error magnitudes greatly exceeding precision estimates. Here, we assess survey sensitivity to systematic error, directly correcting topographic data so that error magnitudes align more closely with precision estimates. By simulating conventional grid-style photogrammetric aerial surveys, we quantify the underlying relationships between survey accuracy, camera model parameters, camera inclination, tie point matching precision and topographic relief, and demonstrate a relative insensitivity to image overlap. We show that a current doming-mitigation strategy of using a gently inclined ( 0 center dot 3 m, representing accuracy issues an order of magnitude greater than precision-based error estimates. For higher-relief topography, and for nadir-imaging surveys of the lower-relief topography, systematic error was <0 center dot 09 m. Modelling and subtracting the systematic error directly from the topographic data successfully reduced error magnitudes to values consistent with twice the estimated precision. Thus, topographic correction can provide a more robust approach to uncertainty-based detection of event-scale geomorphic change than designing surveys with small off-nadir camera inclinations and, furthermore, can substantially reduce ground control requirements. (c) 2020 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd

中文翻译：

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减轻用于地貌变化检测的地形模型中的系统误差：超出最低点图像的准确性、精度和考虑因素

无人驾驶飞行器 (UAV) 和基于运动的结构摄影测量能够详细量化地貌变化。然而，严格的基于精度的变化检测可能会受到测量精度问题的影响，产生系统地形误差（例如“拱顶”），误差幅度大大超过精度估计。在这里，我们评估调查对系统误差的敏感性，直接校正地形数据，使误差幅度与精度估计更接近。通过模拟传统的网格式摄影测量航测，我们量化了测量精度、相机模型参数、相机倾角、连接点匹配精度和地形起伏之间的潜在关系，并证明了对图像重叠的相对不敏感。