Beyond 2D landslide inventories and their rollover: Synoptic 3D inventories and volume 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 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 KaikÅ ura region, New Zealand, following the Mw 7.8 earthquake of 14 November 2016. In a 5gkm2 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 (24.4g%), due to spatially correlated elevation errors, and in forested areas (80g%), 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 1g% 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 20gm2 and a total volume of 724g297g±g141g087gm3 for sources and 954g029g±g159g188gm3 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 (6gm2) but only identifies 258 landslide scars, exhibits a rollover in the distribution of source area of around 20gm2, and underestimates the total area and volume of 3D-detected sources by 72g% and 58g%, 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 (