Estimation of Snow Line Altitude Utilising Satellite Imagery of Alpine Glaciers

Mountain glaciers form a critical component of the cryosphere and are sensitive to climate change. Snowline altitude (SLA) at the end of the ablation season is an indicator of climate change and a proxy forequilibrium line altitude (ELA). Here, we compute SLA by incorporating satellite imagery of 38 years(1984-2022) through mapping snow and ice over the elevation. A digital elevation model is being utilized toderive SLA over a period of time. This proxy database is quite useful in various glacier dynamics estimatedthrough numerical modelling such as mass balance reconstruction, thickness gradient, or glacier lengthestimation. The study explores various techniques to estimate the snow and glacier cover area (Otsu, k-means) apart from manual thresholding. Furthermore, the study also includes glacier surface shape changesto reduce the occurrence of misclassification. We further evaluate the performance of these techniques,however, each one has its own redundancies. In the case of Otsu image segmentation, the errors are quite eminentas the technique does not take into account the variation in glacier dimensions. The results are better interms of classification in the case of manual thresholding but the whole process is quite cumbersome. Inthe case of K-means, the clustering algorithm takes into account the glacier dynamics which improves theclassification but the technique does not work well in the case of large datasets. Furthermore, for validation,Careser glacier is being considered as it has the longest monitored observational dataset in the Italian Alps.The results are mostly in alignment with the observed dataset, particularly for years where the Sentineldataset is available. The SLA seems to depict a descending trend in the case of Careser in recent years.However, this recent behavior further needs to be evaluated. The algorithm is then further applied to theglaciers of Aosta Valley.