Abstract. In this paper, we propose a method to monitor surface dynamics in mountain cryosphere environments, based on a device with two fixed cameras at two different locations and with a convergent angle. Computer vision methods are used to derive pixel displacements between images taken at different times and to reconstruct the 3D geometry of the scene to convert pixel displacements into meter displacements. The proposed methods overcome the drawbacks of traditional methods such as lack of time repeatability or lack of spatial resolution. We present the results on two study sites instrumented with the device located in the French Alps: a temperate glacier and a rock glacier. We propose different ways to assess the accuracy of each processing steps solely based on stereo images. The method is validated using traditional measurements (GPS and LiDAR) and shows results comparable or even more robust than these traditional methods.

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In this paper, we present a method for combining image-based displacements to build time series. This method takes advantage of the redundancy of these displacements, which comes from multiple possible combinations between images. The proposed method combines common master series with a different master date to construct a single time series of relative displacements. We were able to test this method on displacements computed using images from a stereo time-lapse device recording images of the Argentière glacier during the summer and fall of 2019. Our method was compared to two other displacement aggregation strategies: a simple common master approach and the classical inversion method. In order to perform this comparison, displacement data from 4 permanent GPS are used. The results show that our method provides a more accurate time series of relative displacements than those obtained with the other methods. The higher accuracy of the proposed method makes the detection of seasonal variations in glacier velocity possible.