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Automated Classification of Terrestrial Images: The Contribution to the Remote Sensing of Snow Cover

1
National Research Council of Italy, Institute of Atmospheric Pollution Research, via Madonna del Piano 10, 50019 Sesto Fiorentino (FI), Italy
2
National Research Council of Italy, Institute of Atmospheric Pollution Research, via Salaria km 29,300, 00015 Monterotondo (RM), Italy
3
Veneto Regional Agency for Environmental Protection and Prevention, Arabba Avalanche Center, via Pradat 5, 32020 Arabba (BL), Italy
4
EnviroSPACE Lab, Institute for Environmental Sciences, University of Geneva, Bd Carl-Vogt 66, CH-1211 Geneva, Switzerland
5
Institute for Environmental Sciences, University of Geneva, GRID-Geneva, Bd Carl-Vogt 66, CH-1211 Geneva, Switzerland
6
Department of Science, University of Roma TRE, l.go San Leonardo Murialdo 1, 00146 Roma, Italy
*
Author to whom correspondence should be addressed.
Geosciences 2019, 9(2), 97; https://doi.org/10.3390/geosciences9020097
Received: 11 December 2018 / Revised: 11 February 2019 / Accepted: 13 February 2019 / Published: 19 February 2019
(This article belongs to the Special Issue Remote Sensing of Snow and Its Applications)
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Abstract

The relation between the fraction of snow cover and the spectral behavior of the surface is a critical issue that must be approached in order to retrieve the snow cover extent from remotely sensed data. Ground-based cameras are an important source of datasets for the preparation of long time series concerning the snow cover. This study investigates the support provided by terrestrial photography for the estimation of a site-specific threshold to discriminate the snow cover. The case study is located in the Italian Alps (Falcade, Italy). The images taken over a ten-year period were analyzed using an automated snow-not-snow detection algorithm based on Spectral Similarity. The performance of the Spectral Similarity approach was initially investigated comparing the results with different supervised methods on a training dataset, and subsequently through automated procedures on the entire dataset. Finally, the integration with satellite snow products explored the opportunity offered by terrestrial photography for calibrating and validating satellite-based data over a decade. View Full-Text
Keywords: fractional snow cover; remote sensing; terrestrial photography; cold regions fractional snow cover; remote sensing; terrestrial photography; cold regions
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Salzano, R.; Salvatori, R.; Valt, M.; Giuliani, G.; Chatenoux, B.; Ioppi, L. Automated Classification of Terrestrial Images: The Contribution to the Remote Sensing of Snow Cover. Geosciences 2019, 9, 97.

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