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Mapping snow conditions in alpine areas is crucial for monitoring local hydrology to support water resource management decisions. Recently, the use of structure‐from‐motion multiview stereo 3‐D reconstruction (or SFM photogrammetry) to derive high‐resolution digital elevation models (DEMs) has become popular for mapping snow depth in alpine areas. In this study, methods for communicating spatial uncertainties in snow depth calculated from SFM‐derived DEMs are presented using a case study in the French Alps. A spatially varying snow depth precision estimate was determined using an error propagation model based on the precision of the acquired SFM DEMs, which was obtained from repeated unmanned aerial vehicle flights. Spatially varying snow depth detection limits were determined using Student's t distribution. It was found that snow depths as shallow as 1 to 5 cm could be detected with high confidence for most of the study area. Areas of high uncertainties were generally related to where the extent of the ground control coverage did not match in the snow‐on and snow‐off surveys and in areas with higher surface roughness. A map of the snow depth detection threshold was found to be useful for identifying areas with high uncertainties and potential biases in the SFM snow depths, such as errors due to changes in topography between DEM acquisition dates and poor SFM reconstruction. Key Points: Spatially varying structure‐from‐motion precision estimates can be used to communicate snow depth measurement uncertaintiesStructure‐from‐motion derived snow depths from unmanned‐aerial‐vehicle imagery can detect shallow depths (1–5 cm) with high confidenceRepeated UAV surveys can be used as a robust approach for estimating SFM DEM precision for various snow‐covered terrain conditions [ABSTRACT FROM AUTHOR]