Beyond 2D landslide inventories and their rollover: synoptic 3D inventories and volume from repeat lidar data

Bernard, Thomas G.; Lague, Dimitri; Steer, Philippe

doi:https://doi.org/10.5194/esurf-9-1013-2021

Articles | Volume 9, issue 4

https://doi.org/10.5194/esurf-9-1013-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/esurf-9-1013-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 9, issue 4

Research article

|

26 Aug 2021

Research article |

| 26 Aug 2021

Beyond 2D landslide inventories and their rollover: synoptic 3D inventories and volume from repeat lidar data

Thomas G. Bernard, Dimitri Lague, and Philippe Steer

Supplement

https://doi.org/10.5194/esurf-9-1013-2021-supplement

Data sets

Landslide inventories from Bernard et al. (2021) T. G. Bernard, D. Lague, and P. Steer https://doi.org/10.5281/zenodo.5113770

Model code and software

3D landslide detection T. G. Bernard https://github.com/Thomas-Brd/3D_landslide_detection

3D landslide detection V2.0.0 T. G. Bernard and P. Steer https://doi.org/10.5281/zenodo.5220611

Short summary

Both landslide mapping and volume estimation accuracies are crucial to quantify landscape evolution and manage such a natural hazard. We developed a method to robustly detect landslides and measure their volume from repeat 3D point cloud lidar data. This method detects more landslides than classical 2D inventories and resolves known issues of indirect volume measurement. Our results also suggest that the number of small landslides classically detected from 2D imagery is underestimated.