When image correlation is needed: combining very dense radar-amplitude and optical times series for unravelling the complex dynamics of a not so slow slow-moving landslide in the tropics
- 1Royal Museum For Central Africa, Earth Sciences, Tervuren, Belgium
- 2Department of Geography, Earth System Science, Vrije Universiteit Brussel, Brussels, Belgium
- 3Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles, CA, USA
- 4Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
- 5European Centre for Geodynamics and Seismology, Walferdange, Luxembourg
- 6National Museum of Natural History, Luxembourg, Luxembourg
- 7Instituto de Investigación en Paleobiología y Geología, Universidad Nacional de Rio Negro - CONICET, Río Negro, Argentina
- 8Centre Spatial de Liège, Université de Liège, Angleur, Belgium
- 9Département de Géologie, Université Officielle de Bukavu, Bukavu, DR Congo
- 10Department of Geology, Ghent University, Ghent, Belgium
- 11Canada Centre for Mapping and Earth Observation, Natural Resources Canada, Ottawa, Canada
- 12Institut Terre et Environnement de Strasbourg, ITES, CNRS/Université de Strasbourg, Strasbourg, France
Slow-moving landslides exhibit persistent but non-uniform motion at low rates which makes them exceptional natural laboratories to study the mechanisms that control the dynamics of unstable hillslopes. Here we leverage 4.5+ years of satellite-based radar and optical remote sensing data to quantify the kinematics of a slow-moving landslide in the tropical rural environment of the Kivu Rift, with unprecedented high spatial and temporal resolution. We measure landslide motion using sub-pixel image correlation methods and invert these data into dense time series that capture weekly to multi-year changes in landslide kinematics. We cross-validate and compare our satellite-based results with very-high-resolution Unoccupied Aircraft System topographic datasets, and explore how rainfall, simulated pore-water pressure, and nearby earthquakes control the overall landslide behaviour. The landslide exhibited seasonal and multi-year velocity variations that varied across the landslide kinematic units. While rainfall-induced changes in pore-water pressure exerts a primary control on the landslide motion, these alone cannot explain the observed variability in landslide behaviour. We suggest instead that the observed landslide kinematics result from internal landslide dynamics, such as extension, compression, material redistribution, and interactions within and between kinematic units. Our study provides, a rare, detailed overview of the deformation pattern of a landslide located in a tropical environment. In addition, our work highlights the viability of sub-pixel image correlation with long time series of radar-amplitude satellite data to quantify surface deformation in tropical environments where optical data is limited by persistent cloud cover and emphasize the importance of exploiting synergies between multiple types of data to capture the complex kinematic pattern of landslides.
How to cite: Dille, A., Kervyn, F., Handwerger, A., d’Oreye, N., Derauw, D., Mugaruka Bibentyo, T., Samsonov, S., Malet, J.-P., Kervyn, M., and Dewitte, O.: When image correlation is needed: combining very dense radar-amplitude and optical times series for unravelling the complex dynamics of a not so slow slow-moving landslide in the tropics, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10627, https://doi.org/10.5194/egusphere-egu21-10627, 2021.
