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 to develop landslide mapping methods, based on 2D satellite or aerial images, and to constrain the empirical volume–area (V–A) relationship which, in turn, would allow for the provision of indirect estimates of landslide volume. Despite these efforts, major issues remain, including the uncertainty in the V–A scaling, landslide amalgamation and the underdetection of landslides. To address these issues, we propose a new semiautomatic 3D point cloud differencing method to detect geomorphic changes, filter out false landslide detections due to lidar elevation errors, obtain robust landslide inventories with an uncertainty metric, and directly measure the volume and geometric properties of landslides. This method is based on the multiscale model-to-model cloud comparison (M3C2) algorithm and was applied to a multitemporal airborne lidar dataset of the Kaikōura region, New Zealand, following the Mw 7.8 earthquake of 14 November 2016. In a 5 km2 area, the 3D point cloud differencing method detects 1118 potential sources. Manual labeling of 739 potential sources shows the prevalence of false detections in forest-free areas (24.4 %), due to spatially correlated elevation errors, and in forested areas (80 %), related to ground classification errors in the pre-earthquake (pre-EQ) dataset. Combining the distance to the closest deposit and signal-to-noise ratio metrics, the filtering step of our workflow reduces the prevalence of false source detections to below 1 % in terms of total area and volume of the labeled inventory. The final predicted inventory contains 433 landslide sources and 399 deposits with a lower limit of detection size of 20 m2 and a total volume of 724 297 ± 141 087 m3 for sources and 954 029 ± 159 188 m3 for deposits. Geometric properties of the 3D source inventory, including the V–A relationship, are consistent with previous results, except for the lack of the classically observed rollover of the distribution of source area. A manually mapped 2D inventory from aerial image comparison has a better lower limit of detection (6 m2) but only identifies 258 landslide scars, exhibits a rollover in the distribution of source area of around 20 m2, and underestimates the total area and volume of 3D-detected sources by 72 % and 58 %, respectively. Detection and delimitation errors in the 2D inventory occur in areas with limited texture change (bare-rock surfaces, forests) and at the transition between sources and deposits that the 3D method accurately captures. Large rotational/translational landslides and retrogressive scars can be detected using the 3D method irrespective of area's vegetation cover, but they are missed in the 2D inventory owing to the dominant vertical topographic change. The 3D inventory misses shallow (< 0.4 m depth) landslides detected using the 2D method, corresponding to 10 % of the total area and 2 % of the total volume of the 3D inventory. Our data show a systematic size-dependent underdetection in the 2D inventory below 200 m2 that may explain all or part of the rollover observed in the 2D landslide source area distribution. While the 3D segmentation of complex clustered landslide sources remains challenging, we demonstrate that 3D point cloud differencing offers a greater detection sensitivity to small changes than a classical difference of digital elevation models (DEMs). Our results underline the vast potential of 3D-derived inventories to exhaustively and objectively quantify the impact of extreme events on topographic change in regions prone to landsliding, to detect a variety of hillslope mass movements that cannot be captured by 2D landslide mapping, and to explore the scaling properties of landslides in new ways.

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

Topographic metrics are designed to quantify scale‐relevant relationships between geometric properties of landscapes to reveal the processes shaping them. They have long been derived from topographic flow routing algorithms, initially developed for coarse Digital Elevation Models (DEMs), whose resolution (≥30 m) and poor precision did not resolve correctly flow patterns and channel flow width. Since high resolution and precision DEMs make the description of meter‐scale flow patterns possible, new methods are required to analyze high resolution landforms structures such as hillslope‐channel connections, channel width or floodplains. Here, we investigate the potential of 2D hydraulic simulations based on the shallow water equations to replace the classical slope versus drainage area analysis, to analyze river morphology and to identify floodplains. We apply the Floodos model to the 1 m resolution DEM of the Elder Creek catchment, California, from which we derive three hydro‐geomorphic metrics accounting for the river geometry: a specific drainage area extended to channels, an effective flow width and the hydraulic slope. We analyze the Elder Creek catchment through what we call the hydraulic slope‐area diagram allowing a better identification of hillslope‐channel connections than the slope‐area approach. The effective flow width is analyzed along the drainage network and is characterized by a power‐law relationship consistent with previous observations. We derive metrics based on a multi‐runoff approach to automatically identify floodplains and evaluate along‐stream variations in hydraulic geometry. The hydro‐geomorphic metrics offer a geomorphic analysis suitable for high resolution DEMs and opens up new perspectives in fluvial landscape analysis.

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