Machine-Learning Algorithms for Mapping Debris-Covered Glaciers: The Hunza Basin Case Study

Global warming is one of the main challenges of recent times. The glaciers are melting faster than expected which has resulted in global mean sea level rise and increased the risk of floods. The development of modern remote sensing technology has made it possible to obtain images more frequently than ever before. On the other side, the availability of high-performance computing hardware and processing techniques have made it possible to provide a cost-effective solution to monitor the temporal changes of glaciers at a large scale. In this study, supervised machine learning methods are investigated for automatic classification of glacier covers from multi-temporal Sentinel-2 imagery using texture, topographic, and spectral data. Three most commonly used supervised machine learning techniques were investigated: support vector machine (SVM), artificial neural network (ANN) and random forest (RF). The proposed method was employed on the data obtained from Passu watershed in Hunza Basin located along the Hunza river in Pakistan. Three main classes were considered: glaciers, debris-covered glaciers and non-glaciated areas. The data was split into training (70%) and testing datasets (30%). Finally, an area-based accuracy assessment was performed by comparing the results obtained for each classifier with the reference data. Experiments showed that the results produced for all classifiers were highly accurate and visually more consistent with the depiction of glacier cover types. For all experiments, random forest performed the best (Kappa = 0.95, f-measure = 95.06%) on all three classes compared to ANN (Kappa = 0.92, f-measure = 92.05) and SVM (Kappa = 0.89, f-measure = 91.86% on average). The high classification accuracy obtained to distinguished debris-covered glaciers using our approach will be useful to determine the actual available water resources which can be further helpful for hazard and water resource management.