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

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 M_w 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.