Earth Surface Dynamics
Earth Surface Dynamics
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Articles | Volume 11, issue 4
Articles | Volume 11, issue 4
https://doi.org/10.5194/esurf-11-593-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esurf-11-593-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 11, issue 4
Research article
 | 
18 Jul 2023
Research article |  | 18 Jul 2023

Full four-dimensional change analysis of topographic point cloud time series using Kalman filtering

Lukas Winiwarter, Katharina Anders, Daniel Czerwonka-Schröder, and Bernhard Höfle

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Abellán, A., Jaboyedoff, M., Oppikofer, T., and Vilaplana, J. M.: Detection of millimetric deformation using a terrestrial laser scanner: experiment and application to a rockfall event, Nat. Hazards Earth Syst. Sci., 9, 365–372, https://doi.org/10.5194/nhess-9-365-2009, 2009. a
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Anders, K., Winiwarter, L., Lindenbergh, R., Williams, J. G., Vos, S. E., and Höfle, B.: 4D objects-by-change: Spatiotemporal segmentation of geomorphic surface change from LiDAR time series, ISPRS J. Photogramm., 159, 352–363, https://doi.org/10.1016/j.isprsjprs.2019.11.025, 2020. a
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Short summary
We present a method to extract surface change information from 4D time series of topographic point clouds recorded with a terrestrial laser scanner. The method uses sensor information to spatially and temporally smooth the data, reducing uncertainties. The Kalman filter used for the temporal smoothing also allows us to interpolate over data gaps or extrapolate into the future. Clustering areas where change histories are similar allows us to identify processes that may have the same causes.
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