Applicability of alpine snow depth estimation based on multitemporal UAV-LiDAR data: A case study in the Maxian Mountains, Northwest China

Abstract

Unmanned aerial vehicle (UAV) LiDAR has emerged as a viable approach to generate high-resolution snow depth (SD) maps. However, implementing this technology in high-elevation mountains is difficult, and few studies have examined the SD on mountains in Asia until now. Here, we used UAV-LiDAR to estimate the SD on the Maxian Mountains along the eastern edge of High Mountain Asia to assess the reliability of this technology for obtaining SD distributions on a typical alpine periglacial geomorphology. Using LiDAR data from different periods (i.e., 2020/10/19, 2020/11/25 and 2021/04/08) and evenly-distributed in situ measurements, we obtained and validated SDs for both shallow (mean SD less than 15 cm) and deep (mean SD ≥ 15 cm) snow conditions. Our results indicated that the quality of the LiDAR point cloud data met the requirements of the relevant specifications, and the SD distribution map captured the spatial heterogeneity caused by the hummocks. The multitemporal SDs derived from LiDAR were all overestimated, but the accuracy for shallow snow was better than that for deep snow. The root mean square error (RMSE) and mean absolute error (MAE) are 2.23 cm and 1.57 cm, respectively, for deep snow, which were almost three times those of shallow snow. The seasonal deformation of the hummock and manual sampling errors caused by the ice layer over the soil were the main factors affecting accuracy. In contrast, snow surface conditions played a minor role in the error estimators. This study helps refine the use of UAV-LiDAR for SD estimation in similar environments.