Factors influencing the accuracy of UAV-photogrammetry-based snow depth distribution maps were investigated. First, UAV-based surveys were performed on the 0.04 km² snow-covered study site in South Korea for 37 times over the period of 13 days under 16 prescribed conditions composed of various photographing times, flight altitudes, and photograph overlap ratios. Then, multi-temporal Digital Surface Models (DSMs) of the study area covered with shallow snow were obtained using digital photogrammetric techniques. Next, the multi-temporal snow depth distribution maps were created by subtracting the snow-free DSM from the multi-temporal DSMs of the study area. Then, snow depth in these UAV-Photogrammetry-based snow maps were compared to the in situ measurements at 21 locations. The accuracy of each of the multi-temporal snow maps were quantified in terms of bias (median of residuals, QΔD) and precision (the Normalized Median Absolute Deviation, NMAD). Lastly, various factors influencing these performance metrics were investigated. The results are as follows: (1) the QΔD and NMAD of the eight surveys performed at the optimal condition (50 m flight altitude and 80% overlap ratio) ranged from −2.30 cm to 5.90 cm and from 1.78 cm to 4.89 cm, respectively. The best survey case had −2.30 cm of QΔD and 1.78 cm of NMAD; (2) Lower UAV flight altitude and greater photograph overlap lower the NMAD and QΔD; (3) Greater number of Ground Control Points (GCPs) lowers the NMAD and QΔD; (4) Spatial configuration and accuracy of GCP coordinates influenced the accuracy of the snow depth distribution map; (5) Greater number of tie-points leads to higher accuracy; (6) Smooth fresh snow cover did not provide many tie-points, either resulting in a significant error or making the entire photogrammetry process impossible.

中文翻译：

---

基于无人机的摄影测量法测量浅雪深度精度的因素

研究了基于无人机摄影测量的雪深分布图精度的影响因素。首先，在0.04 km ²上进行了基于无人机的调查在16个规定的条件下，由13天组成的韩国冰雪覆盖的学习场所在13天内进行了37次拍摄，拍摄条件包括拍摄时间，飞行高度和照片重叠率。然后，使用数字摄影测量技术获得了被浅雪覆盖的研究区域的多时相数字表面模型（DSM）。接下来，通过从研究区域的多时间DSM中减去无雪DSM，创建多时间雪深分布图。然后，将这些基于无人机摄影测量法的积雪图中的积雪深度与21个位置的原位测量结果进行比较。根据偏差（残差中位数，QΔD）和精度（归一化中位数绝对偏差，NMAD）对每个多时空雪图的准确性进行量化。最后，研究了影响这些性能指标的各种因素。结果如下：（1）在最佳条件下（50 m飞行高度和80％重叠率）进行的八次测量的QΔD和NMAD分别为-2.30 cm至5.90 cm和1.78 cm至4.89 cm。 。最佳调查案例的QΔD为-2.30 cm，NMAD为1.78 cm。（2）较低的无人机飞行高度和较大的照片重叠会降低NMAD和QΔD；（3）更多的地面控制点（GCP）降低了NMAD和QΔD；（4）GCP坐标的空间配置和准确性影响着雪深分布图的准确性；（5）更多的联系点导致更高的准确性；（6）光滑的新鲜积雪没有提供很多联系点，这可能导致重大错误或使整个摄影测量过程无法进行。