Beyond 2D landslide inventories and their rollover: synoptic 3D inventories and volume from repeat lidar data

Abstract

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.

Using Sentinel-1 radar amplitude time series to constrain the timings of individual landslides: a step towards understanding the controls on monsoon-triggered landsliding

“…A small number of landslides (5 %-10 %) can be timed with an accuracy of 90 %. Here we applied our method to optically derived landslide inventories, but it could also be applied to datasets from other sources, for example those based on lidar scans or high-resolution optical images that allow landslide volumes to be estimated (Bernard et al, 2021). The precision of our method, which in most cases is 12 d, should be sufficient in the case of multiple successive storms or earthquakes to attribute landslides to a given event (Ferrario, 2019;Janapati et al, 2019;Tanyaş et al, 2022).…”

The impact of terrain model source and resolution on snow avalanche modeling

“…While aerial lidar is often considered the gold standard for topographic mapping and hazard modeling, offering both a high-resolution DTM and DSM over large geographic areas, ALS is expensive compared with other platforms (Bühler et al, 2012). Repeat ALS is useful for topographic change detection (Bernard et al, 2021;Booth et al, 2020), but rapidresponse acquisitions after major events in remote alpine regions (e.g., Shugar et al, 2021) are often easier with SPM given acquisition logistics and data processing. An advantage for hazard modelers with access to terrestrial lidar is deployment immediately after an event to document landscape change (Bossi et al, 2015;Maggioni et al, 2013;Bartelt et al, 2012;Sovilla et al, 2010).…”

Finite-hillslope analysis of landslides triggered by excess pore water pressure: the roles of atmospheric pressure and rainfall infiltration during typhoons

“…Most datasets on landslides occurring during a triggering event are based on comparisons between pre-and post-event satellite images (Cheng et al, 2004;Nichol and Wong, 2005;Martha et al, 2015) or even Lidar data (Bernard et al, 2021), often acquired days or weeks apart. The timing of landslide occurrence during the event itself remains poorly constrained.…”