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Seasonal Snowpack Classification Based on Physical Properties Using Near-Infrared Proximal Hyperspectral Data.


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El Oufir, Mohamed Karim, Chokmani, Karem ORCID: https://orcid.org/0000-0003-0018-0761, El Alem, Anas, Agili, Hachem et Bernier, Monique (2021). Seasonal Snowpack Classification Based on Physical Properties Using Near-Infrared Proximal Hyperspectral Data. Sensors , vol. 21 , nº 16. p. 5259. DOI: 10.3390/s21165259.

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This paper proposes an innovative method for classifying the physical properties of the seasonal snowpack using near-infrared (NIR) hyperspectral imagery to discriminate the optical classes of snow at different degrees of metamorphosis. This imaging system leads to fast and non-invasive assessment of snow properties. Indeed, the spectral similarity of two samples indicates the similarity of their chemical composition and physical characteristics. This can be used to distinguish, without a priori recognition, between different classes of snow solely based on spectral information. A multivariate data analysis approach was used to validate this hypothesis. A principal component analysis (PCA) was first applied to the NIR spectral data to analyze field data distribution and to select the spectral range to be exploited in the classification. Next, an unsupervised classification was performed on the NIR spectral data to select the number of classes. Finally, a confusion matrix was calculated to evaluate the accuracy of the classification. The results allowed us to distinguish three snow classes of typical shape and size (weakly, moderately, and strongly metamorphosed snow). The evaluation of the proposed approach showed that it is possible to classify snow with a success rate of 85% and a kappa index of 0.75. This illustrates the potential of NIR hyperspectral imagery to distinguish between three snow classes with satisfactory success rates. This work will open new perspectives for the modelling of physical parameters of snow using spectral data.

Type de document: Article
Mots-clés libres: seasonal snowpack; metamorphosis; classification; hyperspectral imaging; near-infrared
Centre: Centre Eau Terre Environnement
Date de dépôt: 28 mars 2022 17:32
Dernière modification: 28 mars 2022 17:32
URI: https://espace.inrs.ca/id/eprint/12533

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