Globally, the occurrence of large landslides are thought to be increasing. Recent literature often cite permafrost degradation, or, slope response to ice loss as key controls on thisincreasing frequency. We are now better at detecting large failures, through eyewitness reporting, seismic approaches, or time-series remote sensing analyses, but what precedes failure? Can we leverage high temporal repeat remote sensing data to identify slopes that might fail? If so, can we use rates of precursory deformation to infer likely timescale of final failure? Our satellite archives cover a number of known events, but, to extend back in time requires thought to how such large masses of debris move through the sediment cascade to long-term storage in some altered form. This helps us to answer a fundamental question: are increases in failure occurrence real, or, an artefact of our methods of detection and temporal bias?
The PhD project will use time-series remote sensing data (including, but not limited to InSAR, Planet Labs, Sentinel and Landsat) applied initially to a known population of large rock-slope failures in Alaska, European Alps, China, Tibet, Greenland and New Zealand. This will yield insight into timescales and rates of precursors to failure, which may not always be present, a key outstanding question in landslide prediction. This approach can then be applied regionally to detect areas that are moving, but have not failed yet. The PhD project offers excellent opportunities for research training in remote sensing and field data collection in high mountains. Fieldwork v’s pure remote-sensing is dependent upon the interests of the successful applicant.
This project is part of the ONE Planet DTP. Find out more here: View Website