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Earth Surface Dynamics An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/esurf-2020-73
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esurf-2020-73
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  15 Sep 2020

15 Sep 2020

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This preprint is currently under review for the journal ESurf.

Beyond 2D inventories : synoptic 3D landslide volume calculation from repeat LiDAR data

Thomas G. Bernard, Dimitri Lague, and Philippe Steer Thomas G. Bernard et al.
  • Univ Rennes, CNRS, Géosciences Rennes - UMR 6118, 35000, Rennes, France

Abstract. Efficient and robust landslide mapping and volume estimation is essential to rapidly infer landslide spatial distribution, to quantify the role of triggering events on landscape changes and to assess direct and secondary landslide-related geomorphic hazards. Many efforts have been made during the last decades to develop landslide areal mapping methods, based on 2D satellite or aerial images, and to constrain empirical volume-area (V-A) allowing in turn to offer indirect estimates of landslide volume. Despite these efforts, some major issues remain including the uncertainty of the V-A scaling, landslide amalgamation and the under-detection of reactivated landslides. To address these issues, we propose a new semi-automatic 3D point cloud differencing method to detect geomorphic changes, obtain robust landslide inventories and directly measure the volume and geometric properties of landslides. This method is based on the M3C2 algorithm and was applied to a multi-temporal airborne LiDAR dataset of the Kaikoura region, New Zealand, following the Mw 7.8 earthquake of 14 November 2016. We demonstrate that 3D point cloud differencing offers a greater sensitivity to detect small changes than a classical difference of DEMs (digital elevation models). In a small 5 km2 area, prone to landslide reactivation and amalgamation, where a previous study identified 27 landslides, our method is able to detect 1431 landslide sources and 853 deposits with a total volume of 908,055 ± 215,640 m3 and 1,008,626 ± 172,745 m3, respectively. This high number of landslides is set by the ability of our method to detect subtle changes and therefore small landslides with a carefully constrained lower limit of 20 m2 (90 % with A < 300 m2). Moreover, the analysis of landslide geometric properties reveals the absence of a rollover in the landslide area distribution, which is a feature classically described in the literature. This result suggests that the rollover behaviour previously observed is due to an under detection of small landslides. Reactivated landslides represent 27.2 % of the total landslide source area and 29.9 ± 12.8 % of the total volume. Reactivated landslides are located in areas where landslide mapping methods based on 2D images are assumed to perform poorly due to the weak contrast in texture and colour between the two epochs. Our result therefore suggests that the number, area and volume of landslides can be significantly under-estimated by these methods. To our knowledge, this is the first approach to create a regional landslide inventory map from 3D point cloud differencing. Our results call for a more systematic use of high-resolution 3D topographic data to assess the impact of extreme events on topographic changes in regions prone to landsliding and to infer the geometric scaling properties of landslides.

Thomas G. Bernard et al.

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Thomas G. Bernard et al.

Data sets

LiDAR dataset T.Bernard https://doi.org/10.5281/zenodo.4011629

Final landslide source and deposit information T.Bernard https://doi.org/10.5281/zenodo.4010806

Model code and software

3D landslide detection workflow T.Bernard; D.Lague; P. Steer https://doi.org/10.5281/zenodo.4010806

Thomas G. Bernard et al.

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Latest update: 23 Sep 2020
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Short summary
Both landslide mapping and volume estimation accuracy are crucial to quantify landscape evolution and to manage such natural hazard. We developed a consistent method to robustly detect Landslides and measure volume directly from repeat 3D point cloud LiDAR data. This method allows to detect more landslides than classical 2D inventories and resolve known issues of indirect volume measurement. Our results also suggest that the number of small landslides classically detected is underestimated.
Both landslide mapping and volume estimation accuracy are crucial to quantify landscape...
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