My PhD project aims to provide insight into and continue to develop hypotheses regarding the evolution of glacier-rock glacier complexes over large spatial and temporal scales using remote sensing and field techniques. These techniques include the integrated use of deep learning and object-based image analysis for image classification, photogrammetry, structure from motion, time-series analysis, topographic analysis, drone surveys, ground penetrating radar surveys, clast sampling and DGNSS measurements.
My project has three sub-aims; (1) to explore state-of-the-art methodologies to create standardised glacier-rock glacier inventories over large spatial scales; (2) to investigate the spatial variability of glacier-rock glacier transition; and (3) to explore the geomorphological and dynamic processes governing ongoing transitions.
The outcomes of the project will be: (1) a large-scale, integrated clean ice-, debris-covered-, and rock glacier inventory with distinctions between the landforms; (2) a local-scale, pluri-decadal change assessment of glacier-rock glacier complexes from the mid-20th century to the present day; and (3) a high-resolution assessment of a single glacier-rock glacier complex to identify geomorphological and kinematic properties indicative of transition.
My research focuses on the glacier-rock glacier complexes in the semi-arid Andes, where the rock glacier-to-glacier annual streamflow contribution ratios are some of the highest and glacier-rock glacier complexes are at some of the most advanced stages in the world.
- (2023). An integrated deep learning and object-based image analysis approach for mapping debris-covered glaciers. Frontiers in Remote Sensing.