Towards a webcam-based snow cover monitoring network: methodology and evaluation
| Autor: | Stefan Wunderle, Céline Portenier, Stefan Härer, Fabia Hüsler |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
lcsh:GE1-350
Mean squared error Computer science Photography lcsh:QE1-996.5 ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Field of view Snow Grid GeneralLiterature_MISCELLANEOUS law.invention Lens (optics) lcsh:Geology law 550 Earth sciences & geology Digital elevation model Projection (set theory) lcsh:Environmental sciences Earth-Surface Processes Water Science and Technology Remote sensing ComputingMethodologies_COMPUTERGRAPHICS |
| Zdroj: | Portenier, Céline; Hüsler, Fabia; Härer, Stefan; Wunderle, Stefan (2020). Towards a webcam-based snow cover monitoring network: methodology and evaluation. The Cryosphere, 14(4), pp. 1409-1423. Copernicus Publications 10.5194/tc-14-1409-2020 The Cryosphere, Vol 14, Pp 1409-1423 (2020) |
| DOI: | 10.5194/tc-2019-142 |
| Popis: | 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∘) 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. |
| Databáze: | OpenAIRE |
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