- 1University of Potsdam, Institute of Environmental Science and Geography, Potsdam, Germany (ugur.oeztuerk@uni-potsdam.de)
- 2GFZ Helmholtz Centre for Geosciences, Potsdam, Germany (ugur.oeztuerk@gfz-potsdam.de)
- 3State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, China (kushanav.bhuyan@phd.unipd.it)
- 4Machine Intelligence and Slope Stability Laboratory, Department of Geosciences, University of Padova, Padova, Italy (kushanav.bhuyan@phd.unipd.it)
- 5Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, USA (kr7843@rit.edu)
Landslide planforms are commonly used as a chunk in various applications, from volume estimates to hazard/susceptibility modelling. These oversimplifications may decrease the accuracy of predictive models. We developed two complementary models that leverage a landslide’s topology and morphology to improve information in existing landslide databases by distinguishing movement types such as slides, flows, and falls and delineating the kinematic zones, source versus runout.
The first model identifies underlying movements by examining the 3D shapes of landslides (Bhuyan et al., 2024). Tested on inventories across Italy, the United States Pacific Northwest, and Türkiye, the method achieves >80% accuracy in distinguishing various and even complex coupled movement types. Further application to undocumented landslides in the 2008 Wenchuan earthquake-affected region illustrates the method’s potential to inform hazard evaluations.
The second model classifies source and runout zones of landslides with margins of error below 15–20% (Bhuyan et al., 2025). The initial model is developed and validated in geomorphologically diverse regions such as Dominica, Türkiye, Italy, Nepal, and Japan. Subsequent deployments in Chile, Japan (Hokkaido), Colombia, Papua New Guinea, and China reveal source areas commonly occupy less than 30% of a landslide’s total footprint.
These complementary steps hence provide robust and scalable solutions for missing landslide data, which are essential for improving predictive models. They lead to better hazard assessments and a deeper understanding of landslide initiation and propagation. To ease reusability, we will soon integrate these modelling steps into the existing classifier library (Rana et al., 2022).
References
Bhuyan, K., Rana, K., Ferrer, J. V., Cotton, F., Ozturk, U., Catani, F., and Malik, N.: Landslide topology uncovers failure movements, Nat Commun, 15, 2633, https://doi.org/10.1038/s41467-024-46741-7, 2024.
Bhuyan, K., Rana, K., Ozturk, U., Nava, L., Rosi, A., Meena, S. R., Fan, X., Floris, M., Van Westen, C., and Catani, F.: Towards automatic delineation of landslide source and runout, Engineering Geology, 345, 107866, https://doi.org/10.1016/j.enggeo.2024.107866, 2025.
Rana, K., Malik, N., and Ozturk, U.: Landsifier v1.0: a Python library to estimate likely triggers of mapped landslides, Nat. Hazards Earth Syst. Sci., 22, 3751–3764, https://doi.org/10.5194/nhess-22-3751-2022, 2022.
How to cite: Ozturk, U., Bhuyan, K., and Rana, K.: Decoding Landslide Movements and Kinematic Zones from Landslide Planforms, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3649, https://doi.org/10.5194/egusphere-egu25-3649, 2025.
