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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- Inversions of landslide strength as a proxy for subsurface weathering S. Alberti et al. https://doi.org/10.1038/s41467-022-33798-5
- Size scaling of large landslides from incomplete inventories O. Korup et al. https://doi.org/10.5194/nhess-24-3815-2024
- Multi‐decadal sediment dynamics of a tropical watershed – The relative roles of surface erosion and shallow landsliding C. Ramos Scharrón & E. Arima https://doi.org/10.1002/esp.70062
- Improving the accuracy and reliability of UAV LiDAR-derived digital subsidence model using the probability integral method in mining areas Z. junliang et al. https://doi.org/10.1016/j.measurement.2025.120043
- Depth of rainfall induced landslides revealed by DEM of difference analysis using airborne LiDAR data in igneous terrains Y. Kudo et al. https://doi.org/10.1038/s41598-026-46714-4
- Integrated machine learning segmentation and 3D change detection for a scalable coastal cliff monitoring workflow C. Mack et al. https://doi.org/10.1016/j.cageo.2026.106165
- Change Detection Needs Change Information: Improving Deep 3-D Point Cloud Change Detection I. de Gélis et al. https://doi.org/10.1109/TGRS.2024.3359484
- Detection of slow‐moving landslides through automated monitoring of surface deformation using Sentinel‐2 satellite imagery M. Van Wyk de Vries et al. https://doi.org/10.1002/esp.5775
- Where the Hills Slide Slowly: A LiDAR-Based Morphometric Framework for Landslide Instability Regimes in Soft-Rock Terrains S. Kósik & C. Rees https://doi.org/10.3390/rs18081135
- Updating Active Deformation Inventory Maps in Mining Areas by Integrating InSAR and LiDAR Datasets L. Hu et al. https://doi.org/10.3390/rs15040996
- Uncertainties in landslide susceptibility prediction modeling: A review on the incompleteness of landslide inventory and its influence rules F. Huang et al. https://doi.org/10.1016/j.gsf.2024.101886
- Failure process and three-dimensional motions of mining-induced Jianshanying landslide in China observed by optical, LiDAR and SAR datasets C. Zhao et al. https://doi.org/10.1080/15481603.2023.2268367
- Advancing Global Landslide Segmentation: A Coupled Multispectral Attention and Data Augmentation Approach Using the Novel MRGSLD Dataset G. Emani et al. https://doi.org/10.1007/s41748-025-00577-3
- Rockfall susceptibility analysis through 3D simulations in marine protected areas of the Portofino coastline: case studies of San Fruttuoso and Paraggi bays J. Clemente et al. https://doi.org/10.1007/s10064-023-03133-3
- Enabling 3D landslide event statistics using satellite and UAV-enabled topographic differencing M. Clark et al. https://doi.org/10.1007/s10346-024-02374-x
- LiDAR-Based Morphometry of Dolines in Aggtelek Karst (Hungary) and Slovak Karst (Slovakia) T. Telbisz et al. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/10.1029/2022EA002420
- Research on Uncertainty of Landslide Susceptibility Prediction—Bibliometrics and Knowledge Graph Analysis Z. Yang et al. https://doi.org/10.3390/rs14163879
- Evaluating map quality and model performance through iterative statistics-based landslide susceptibility in eastern KY M. Crawford et al. https://doi.org/10.1007/s11069-025-07255-7
- 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. https://doi.org/10.5194/nhess-22-3125-2022
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- Size, shape and orientation matter: fast and semi-automatic measurement of grain geometries from 3D point clouds P. Steer et al. https://doi.org/10.5194/esurf-10-1211-2022
- Assessing the paraglacial evolution magnitude affecting proglacial areas through multi-temporal digital models and historical archives G. Tronti et al. https://doi.org/10.1016/j.catena.2026.110227
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- Exploring short‐term rockfall inventories in deglaciating catchments: From evidencing glacial history to modelling rockfall runout R. Stewart et al. https://doi.org/10.1002/esp.70217
- 3D Slope Reconstruction and Point Cloud Analysis Using UAV Multi-Source Data D. Zai & J. Liu https://doi.org/10.1007/s10706-025-03228-6
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- 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. https://doi.org/10.1016/j.rse.2024.114522
Saved (final revised paper)
Latest update: 01 Jun 2026
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...
