Automated classification of debris-covered glaciers combining optical, SAR and topographic data in an object-based environment

Abstract

Abstract Satellite imagery is increasingly used to monitor glacier area changes and create glacier inventories. Robust and efficient pixel-based band ratios have proven to be accurate for automatically delineating clean glacier ice, however such classifications are restricted on debris-covered ice due to its spectral similarity with surrounding terrain. Object-Based Image Analysis (OBIA) has emerged as a new analysis technique within remote sensing. It offers many advantages over pixel-based classification techniques due to the ability to work with multiple data sources and handle data contextually and hierarchically. By making use of OBIA capabilities we automatically classify clean ice and debris-covered ice in the challenging area surrounding Mount Manaslu in Nepal using optical (Landsat 8), topographic (void-filled SRTM) and SAR coherence (ALOS PALSAR) data. Clean ice was classified with a mean accuracy of 93% whilst debris-covered ice was classified with an accuracy of 83% when compared to manually corrected outlines, providing a total glacier accuracy of 91%. With further developments in the classification, steep tributary sections of ice could be contextually included, raising the accuracy to over 94%. One prominent advantage of OBIA is that it allows some post-processing and correction of the glacier outlines automatically, reducing the amount of manual correction needed. OBIA incorporating SAR coherence data is recommended for future mapping of debris-covered ice.