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

Abstract

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.

Estimating Snowpack Density from Near-Infrared Spectral Reflectance Using a Hybrid Model

“…Recently, it has been demonstrated that three optical classes of snow with different degrees of metamorphosis (weakly to moderately metamorphosed (WMM), moderately to highly metamorphosed (MHM), and highly to very highly metamorphosed (HVM)) can be identified and discriminated against without prior recognition, based only on NIR hyperspectral data [39]. This study showed that the spectra of snow density are similar within the same optical class and significantly different from one optical class to another.…”

“…This ensures the recovery of both surface snow and snow from deeper layers (Figure 4a). Once the snow is extracted (Figure 4b), it is scanned with the NIR hyperspectral camera (Figure 5a), and the generated image is analyzed by the Spectronon Pro software (Figure 5b) [39]. The latter identifies the homogeneous layers previously measured in the field and analyzes the spectral responses of each one.…”

“…To do so, the size and type of snow grains are identified using a millimeter grid map and observed with a magnifying glass (10×). Finally, the isolated layers are weighed, and the density of each one is calculated [39]. All observations and measurements were performed by the same person to ensure consistency.…”

“…This study showed that the spectra of snow density are similar within the same optical class and significantly different from one optical class to another. In other words, densities expressed in terms of grain size, shape, and spectral response were discriminated and grouped into three different homogeneous subclasses [39]. With this finding, it is possible to train estimators specific to the identified homogeneous classes, which are governed by a bijective (In mathematics, a bijection, bijective function, one-to-one correspondence, or invertible function, is a function between the elements of two sets, where each element of one set is paired with exactly one element of the other set, and each element of the other set is paired with exactly one element of the first set.…”