Abstract
<jats:p>Lidar and photogrammetry techniques provide highly accurate methods for mapping snow depth distribution. However, postprocessing point clouds for snow depth estimation is more complex compared to other earth science applications. This paper presents ice-road-copters (IRC), an open-source Python toolkit that facilitates processing and georeferencing of lidar and photogrammetry point clouds. Case studies demonstrate the tool’s utility across different sensors and platforms over a complex mountainous study area. Results show that a well-configured digital elevation model (DEM) filter effectively removes most noise and outliers from point cloud data. The Simple Morphological Filter (SMRF) generally perform well for ground segmentation but optimal results across diverse terrains may require site-specific tuning, particularly of the elevation threshold and scalar parameters in more complex landscapes. Different methods, including manual depth measurements or snow-free features, can be used to coregister DEMs, reducing vertical errors, eliminating large bias and achieving comparable accuracy to using exposed control surfaces. Derived snow depth rasters showed strong agreement with in situ probe measurements—root-mean-square error (RMSE) of less than 16 cm. Overall, IRC simplifies the transformation of raw point clouds into high-resolution DEMs and value-added snow products, facilitating efficient multitemporal analysis to support military and hydrology applications.</jats:p>