Intercomparison of remotely sensed methods for delineation of high mountain debris-covered glaciers using machine learning

• Main Authors: Racoviteanu, A., Miles, E., Davies, B., Bolch, T., Watson, S., Buri, P., Liu, Q., Mateo, E., Wilson, R., Robson, B., Schellenberger, T., Maslov, K., Persello, C., King, O.
• Format: Conference Object
• Language: English
• Published: 2023
• Subjects: Wrangell Range; glacier; glaciers; Alaska
• Online Access: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5021016

Glaciers are important contributors to water resources in many parts of the world, as well as constituting potential hazards. Monitoring of glaciers with remote sensing data is complicated by the presence of supraglacial debris which limits the effectiveness of semi-automated glacier delineation techniques. Furthermore, debris-covered glaciers are notoriously hard to survey in the field due to their complex, chaotic topography, and the presence of ephemeral ice cliffs and supraglacial lakes. As such, debris-covered glaciers are poorly represented in global inventories such as GLIMS and RGI, which consist of an assemblage of outlines from various dates. These inventories are subject to uncertainties due to mapping by multi-analysts using different methods. Despite recent efforts to map supraglacial debris cover at regional level, there remains an urgent need to develop a global, robust automated mapping approach based on open access data. Novel remote sensing data including high-resolution optical and radar data combined with emerging machine learning offer unique opportunities to advance current mapping methods. Here we evaluate current indices used in mapping supraglacial debris-cover and derive a best-practice workflow recommendation from particular combinations of these indices to derive glacier outlines for six representative subregions chosen globally: Khumbu and Manaslu regions (Nepal), Cordillera Blanca (Peru), Northern Patagonian Ice fields, Alaska Wrangell range, Hunza valley (Karakoram) and the Tien Shan. We present preliminary results of machine learning algorithms that combine the various remote sensing indices. The aim is to develop a robust, systematic method to map debris cover in a consistent manner at multi-temporal scales.