EGU24-15272, updated on 09 Mar 2024
https://doi.org/10.5194/egusphere-egu24-15272
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Using Google Earth Engine to map landslide hazard and exposure across Nepal

Erin Harvey1, Nick Rosser1, Mark Kincey2, Alexander Densmore1, Ram Shrestha3, Dammar Singh Pujara3, Alexandre Dunant1, Max Van Wyk de Vries4, and Katherine Arrell5
Erin Harvey et al.
  • 1Department of Geography, Durham University, Durham, United Kingdom of Great Britain – England, Scotland, Wales (erin.l.harvey@durham.ac.uk)
  • 2School of Geography, Politics and Sociology, Newcastle University, Newcastle, UK
  • 3NSET, Lalitpur, Nepal
  • 4Department of Geography, University of Cambridge, Cambridge UK
  • 5Department of Geography and Environmental Sciences, Northumbria University, Newcastle, UK

Nepal is one of the most susceptible countries to landsliding, with much of the country characterised by steep topography, annual monsoon rainfall and active tectonics. Current understanding of landslides in Nepal is predominantly based on static, catchment-scale landslide inventories or centred around data from specific events, such as the 2015 Gorkha earthquakes. Whilst static inventories provide a useful snapshot of past landslide characteristics, we cannot use these to infer how long landslides persist or how the hazard posed by landslides may evolve through time. In addition, the large number of small-scale inventories that currently exist cannot be readily compared, making it difficult to assess whether trends observed in specific catchments can be applied on a national scale. In this study, we aim to utilise advances in openly accessible remote sensing of large geospatial datasets, namely Google Earth Engine, to record the spatial and temporal evolution of landslides across the full extent of Nepal.

 

We build on an existing automated landslide detection algorithm in Google Earth Engine to compile a national scale landslide probability map, which is re-mapped annually. This allows us to capture changes in landslide hazard both spatially and temporally across the country. The algorithm uses NDVI differencing to identify possible new landslides. Our work seeks to refine this output by using landslide-specific information obtained from a series of existing manually mapped landslide inventories. This step includes applying spectral and object-based filters as well as using susceptibility metrics, such as topography, trained using manually mapped landslide inventories. By adding a landslide-specific filtering step, we aim to build on existing NDVI differencing approaches and improve per pixel landslide probability values. We present preliminary findings using this record of landslide hazard through time to better understand controls on slope failure evolution and persistence, to tackle questions such as whether new landslides evolve and runout from existing landslides, to consider how landslide mechanism changes through time, and how hazard translates into physical exposure through the use of metrics such as landslide proximity to roads and buildings.

How to cite: Harvey, E., Rosser, N., Kincey, M., Densmore, A., Shrestha, R., Singh Pujara, D., Dunant, A., Van Wyk de Vries, M., and Arrell, K.: Using Google Earth Engine to map landslide hazard and exposure across Nepal, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15272, https://doi.org/10.5194/egusphere-egu24-15272, 2024.