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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17 citations as recorded by crossref.
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- Regional‐Scale Landscape Response to an Extreme Precipitation Event From Repeat Lidar and Object‐Based Image Analysis S. DeLong et al. 10.1029/2022EA002420
- Analysis of regional large-gradient land subsidence in the Alto Guadalentín Basin (Spain) using open-access aerial LiDAR datasets L. Hu et al. 10.1016/j.rse.2022.113218
- Inversions of landslide strength as a proxy for subsurface weathering S. Alberti et al. 10.1038/s41467-022-33798-5
- Research on Uncertainty of Landslide Susceptibility Prediction—Bibliometrics and Knowledge Graph Analysis Z. Yang et al. 10.3390/rs14163879
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- Change Detection Needs Change Information: Improving Deep 3-D Point Cloud Change Detection I. de Gélis et al. 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. 10.1002/esp.5775
- 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
- Updating Active Deformation Inventory Maps in Mining Areas by Integrating InSAR and LiDAR Datasets L. Hu et al. 10.3390/rs15040996
- Failure process and three-dimensional motions of mining-induced Jianshanying landslide in China observed by optical, LiDAR and SAR datasets C. Zhao et al. 10.1080/15481603.2023.2268367
- Power laws for accurate determination of landslide volume based on high-resolution LiDAR data L. Ju et al. 10.1016/j.enggeo.2022.106935
- 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. 10.1007/s10064-023-03133-3
- LiDAR-Based Morphometry of Dolines in Aggtelek Karst (Hungary) and Slovak Karst (Slovakia) T. Telbisz et al. 10.3390/rs16050737
- Sedimentary Coastal Cliff Erosion in Greenland G. Luetzenburg et al. 10.1029/2022JF007026
16 citations as recorded by crossref.
- 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
- The impact of terrain model source and resolution on snow avalanche modeling A. Miller et al. 10.5194/nhess-22-2673-2022
- Regional‐Scale Landscape Response to an Extreme Precipitation Event From Repeat Lidar and Object‐Based Image Analysis S. DeLong et al. 10.1029/2022EA002420
- Analysis of regional large-gradient land subsidence in the Alto Guadalentín Basin (Spain) using open-access aerial LiDAR datasets L. Hu et al. 10.1016/j.rse.2022.113218
- Inversions of landslide strength as a proxy for subsurface weathering S. Alberti et al. 10.1038/s41467-022-33798-5
- 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
- Change Detection Needs Change Information: Improving Deep 3-D Point Cloud Change Detection I. de Gélis et al. 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. 10.1002/esp.5775
- 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
- Updating Active Deformation Inventory Maps in Mining Areas by Integrating InSAR and LiDAR Datasets L. Hu et al. 10.3390/rs15040996
- Failure process and three-dimensional motions of mining-induced Jianshanying landslide in China observed by optical, LiDAR and SAR datasets C. Zhao et al. 10.1080/15481603.2023.2268367
- Power laws for accurate determination of landslide volume based on high-resolution LiDAR data L. Ju et al. 10.1016/j.enggeo.2022.106935
- 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. 10.1007/s10064-023-03133-3
- LiDAR-Based Morphometry of Dolines in Aggtelek Karst (Hungary) and Slovak Karst (Slovakia) T. Telbisz et al. 10.3390/rs16050737
1 citations as recorded by crossref.
Latest update: 25 Apr 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...
