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.
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.
the Creative Commons Attribution 4.0 License.
Full four-dimensional change analysis of topographic point cloud time series using Kalman filtering
3DGeo Research Group, Institute of Geography, Heidelberg University, Heidelberg, Germany
Integrated Remote Sensing Studio (IRSS), Faculty of Forestry, University of British Columbia, Vancouver, Canada
Research Unit Photogrammetry, Department of Geodesy and Geoinformation, TU Wien, Vienna, Austria
Katharina Anders
3DGeo Research Group, Institute of Geography, Heidelberg University, Heidelberg, Germany
Daniel Czerwonka-Schröder
Department of Civil and Mining Engineering, DMT GmbH & Co. KG, Essen, Germany
Faculty of Geoscience, Geotechnology and Mining, University of Mining and Technology Freiberg, Freiberg, Germany
Bernhard Höfle
3DGeo Research Group, Institute of Geography, Heidelberg University, Heidelberg, Germany
Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany
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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.
We present a method to extract surface change information from 4D time series of topographic...