Towards a webcam-based snow cover monitoring network: methodology and evaluation

Abstract

Abstract. Snow cover variability has a significant impact on climate and the environment and is of great socioeconomic importance for the European Alps. Terrestrial photography offers a high potential to monitor snow cover variability, but its application is often limited to small catchment scales. Here, we present a semiautomatic procedure to derive snow cover maps from publicly available webcam images in the Swiss Alps and propose a procedure for the georectification and snow classification of such images. In order to avoid the effort of manually setting ground control points (GCPs) for each webcam, we implement a novel registration approach that automatically resolves camera parameters (camera orientation; principal point; field of view, FOV) by using an estimate of the webcams' positions and a high-resolution digital elevation model (DEM). Furthermore, we propose an automatic image-to-image alignment to correct small changes in camera orientation and compare and analyze two recent snow classification methods. The resulting snow cover maps indicate whether a DEM grid is snow-covered, snow-free, or not visible from webcams' positions. GCPs are used to evaluate our novel automatic image registration approach. The evaluation reveals a root mean square error (RMSE) of 14.1 m for standard lens webcams (FOV<48∘) and a RMSE of 36.3 m for wide-angle lens webcams (FOV≥48∘). In addition, we discuss projection uncertainties caused by the mapping of low-resolution webcam images onto the high-resolution DEM. Overall, our results highlight the potential of our method to build up a webcam-based snow cover monitoring network.

Remote sensing of the mountain cryosphere: Current capabilities and future opportunities for research

“…Autonomous structure-from-motion photogrammetry has been proven in studies of soil displacement (Eltner et al, 2017), landslides (Kromer et al, 2017) and rockfalls (Blanch et al, 2019), which indicates it could also be a lucrative line of investigation for glaciology. In a similar vein, webcam images from ski resorts have been used to create snow cover classification maps in the Alps (Portenier et al, 2020), removing the need for physical visits by researchers altogether.…”

A seasonal algorithm of the snow-covered area fraction for mountainous terrain

“…Figure 1 in Helbig et al, 2015a). To obtain f SCA values from the camera images, we followed the workflow described by Portenier et al (2020). We used the algorithm of Salvatori et al (2011) to classify pixels in the images as snow or snow free.…”

“…Furthermore, when deriving f SCA from camera images, clouds/fog and uneven illumination due to for instance shading or partial cloud cover may compromise the possibility of detecting snow by the snow classification algorithm of Salvatori et al (2011) and can deteriorate the accuracy (e.g. Farinotti et al, 2010;Fedorov et al, 2016;Härer et al, 2016;Portenier et al, 2020). The choice of the threshold method when automatically deriving f SCA from the images also introduces uncertainty.…”

Sentinel-1 time series for mapping snow cover and timing of snowmelt in Arctic periglacial environments: Case study from the Zackenberg Valley, Greenland

“…While ground-based methods lack in spatial coverage, established SC mapping methods based on optical spaceborne earth observation suffer from reduced temporal resolution due to cloud coverage and sun illumination, e.g. during polar night (Dong, 2018;Portenier et al, 2020;Tsai et al, 2019b). SAR remote sensing can overcome these limitations, as it operates independent of sun illumination and atmospheric conditions (Marin et al, 2020;Tsai et al, 2019b;Ulaby et al, 2014).…”

“…likeWestergaard-Nielsen et al, 2017). (iii) The master image is then orthorectified using the python package georef_webcam(Buchelt, 2020) based on the approaches ofCorripio (2004),Härer et al (2016) andPortenier et al (2020). The ArcticDEM(Porter et al, 2018) is used for the projection and ground control points (GCPs) of remarkable landscape features are derived from same-day high-resolution PlanetScope imagery to optimize the orthorectification procedure resulting in a projection with high geospatial accuracy.…”