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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Aug 2021
Research article |
| 26 Aug 2021
| 26 Aug 2021
Beyond 2D landslide inventories and their rollover: synoptic 3D inventories and volume from repeat lidar data
Thomas G. Bernard
CORRESPONDING AUTHOR
Univ. Rennes, CNRS, Géosciences Rennes – UMR 6118, 35000 Rennes, France
Dimitri Lague
Univ. Rennes, CNRS, Géosciences Rennes – UMR 6118, 35000 Rennes, France
Philippe Steer
Univ. Rennes, CNRS, Géosciences Rennes – UMR 6118, 35000 Rennes, France
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23 citations as recorded by crossref.
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- Distribution-agnostic landslide hazard modelling via Graph Transformers G. Belvederesi et al. 10.1016/j.envsoft.2024.106231
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- Uncertainties in landslide susceptibility prediction modeling: A review on the incompleteness of landslide inventory and its influence rules F. Huang et al. 10.1016/j.gsf.2024.101886
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- LiDAR-Based Morphometry of Dolines in Aggtelek Karst (Hungary) and Slovak Karst (Slovakia) T. Telbisz et al. 10.3390/rs16050737
- Landslide type inference based on statistical analysis of a high-resolution digital elevation model in Gorce National Park, Poland R. Szczepanek et al. 10.1038/s41598-024-65026-z
- Automated determination of landslide locations after large trigger events: advantages and disadvantages compared to manual mapping D. Milledge et al. 10.5194/nhess-22-481-2022
- Regional‐Scale Landscape Response to an Extreme Precipitation Event From Repeat Lidar and Object‐Based Image Analysis S. DeLong et al. 10.1029/2022EA002420
- Research on Uncertainty of Landslide Susceptibility Prediction—Bibliometrics and Knowledge Graph Analysis Z. Yang et al. 10.3390/rs14163879
- Finite-hillslope analysis of landslides triggered by excess pore water pressure: the roles of atmospheric pressure and rainfall infiltration during typhoons L. Pelascini et al. 10.5194/nhess-22-3125-2022
- Size, shape and orientation matter: fast and semi-automatic measurement of grain geometries from 3D point clouds P. Steer et al. 10.5194/esurf-10-1211-2022
- Redistribution of debris‐flow sediment following severe wildfire and floods in the Jemez Mountains, New Mexico, USA J. Friedman et al. 10.1002/esp.5964
- Using Sentinel-1 radar amplitude time series to constrain the timings of individual landslides: a step towards understanding the controls on monsoon-triggered landsliding K. Burrows et al. 10.5194/nhess-22-2637-2022
- Power laws for accurate determination of landslide volume based on high-resolution LiDAR data L. Ju et al. 10.1016/j.enggeo.2022.106935
- Reconciling short- and long-term measurements of coastal cliff erosion rates L. Acharya-Chowdhury et al. 10.1016/j.enggeo.2024.107703
- Optimising sub-metre resolution 3D geomorphic change detection over large areas using multitemporal airborne laser scanning with Sentinel-1 InSAR and Sentinel-2 optical observations S. Walker et al. 10.1016/j.rse.2024.114522
1 citations as recorded by crossref.
Latest update: 13 Dec 2024
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.
Both landslide mapping and volume estimation accuracies are crucial to quantify landscape...
