Abstract
AbstractCharacterizing snowmelt both spatially and temporally from in situ observation remains a challenge. Available sensors (i.e., sonic ranger, lidar, airborne photogrammetry) provide either time series of local point measurements or sporadic surveys covering larger areas. We propose a methodology to recover from a minimum of three synchronized time‐lapse cameras changes in snow depth and snow cover extent over area smaller or equivalent to 0.12 km2. Our method uses photogrammetry to compute point clouds from a set of three or more images and automatically repeat this task for the entire time series. The challenges were (1) finding an optimal experimental setup deployable in the field, (2) estimating the error associated with this technique, and (3) being able to minimize the input of manual work in the data processing pipeline. Developed and tested in the field in Finse, Norway, over 1 month during the 2018 melt season, we estimated a median melt of 2.12 ± 0.48 m derived from three cameras 1.2 km away from the region of interest. The closest weather station recorded 1.94 m of melt. Other parameters like snow cover extent and duration could be estimated over a 300 × 400m region. The software is open source and applicable to a broader range of geomorphologic processes like glacier dynamic, snow accumulation, or any other processes of surface deformation, with the conditions of (1) having fixed visible points within the area of interest and (2) resolving sufficient surface textures in the photographs.
Highlights
We propose a methodology to recover from a minimum of three synchronized time-lapse cameras changes in snow depth and snow cover extent over area smaller or equivalent to 0.12 km2
Snow depth time series refers to a point measurement repeated in time at a particular location realized with a probe, an ultrasonic ranger, or a laser
With the advent of technology such as GPS, laser scanner (Deems et al, 2013), or digital photogrammetry (Bühler et al, 2016; Marti et al, 2016; Nolan et al, 2015) it is possible to acquire detailed, but sporadic, spatial information about snow depth at horizontal scales ranging from centimeters with ground-based laser scanner to 100 m or more with sensors on satellite platforms
Summary
Snow depth time series refers to a point measurement repeated in time at a particular location realized with a probe, an ultrasonic ranger, or a laser. In mountainous and northern regions, snowmelt releases a large amount of the annual freshwater available to ecosystems, agriculture, and hydropower production. It is the time during which the landscape albedo drops abruptly. Having coupled temporal and spatial information of snow depth during the accumulation and ablation seasons could (1) improve our understanding of deposition and ablation processes and (2) improve estimates of snow water equivalent in a given region (Margulis et al, 2019). While Picard et al (2016) successfully built a custom-made laser scanner for this purpose, we propose an alternative method based on time-lapse photogrammetry, called videogrammetry (Gruen, 1997), to resolve the evolution of snow depth distribution with time
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