Beyond 2D landslide inventories and their rollover: synoptic 3D inventories and volume from repeat lidar data
Authors: Bernard Thomas G. , Lague Dimitri , Steer Philippe 
Published: August 2021

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.