English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

. Efficient and robust landslide mapping and volume estimation is essential torapidly infer landslide spatial distribution, to quantify the role oftriggering events on landscape changes, and to assess direct and secondarylandslide-related geomorphic hazards. Many efforts have been made to developlandslide mapping methods, based on 2D satellite or aerial images, and toconstrain the empirical volume–area (V–A) relationship which, in turn, would allow for the provision of indirect estimates of landslide volume. Despite these efforts, majorissues remain, including the uncertainty in the V–A scaling, landslideamalgamation and the underdetection of landslides. To address these issues,we propose a new semiautomatic 3D point cloud differencing method to detectgeomorphic changes, filter out false landslide detections due to lidarelevation errors, obtain robust landslide inventories with an uncertaintymetric, and directly measure the volume and geometric properties oflandslides. This method is based on the multiscale model-to-model cloud comparison (M3C2) algorithm and was applied to amultitemporal 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 methoddetects 1118 potential sources. Manual labeling of 739 potential sourcesshows the prevalence of false detections in forest-free areas (24.4 %), dueto spatially correlated elevation errors, and in forested areas (80 %),related to ground classification errors in the pre-earthquake (pre-EQ) dataset. Combining thedistance to the closest deposit and signal-to-noise ratio metrics, thefiltering step of our workflow reduces the prevalence of false sourcedetections to below 1 % in terms of total area and volume of the labeledinventory. The final predicted inventory contains 433 landslide sources and399 deposits with a lower limit of detection size of 20 m2and a total volume of 724 297 ± 141 087 m3 for sources and 954 029 ± 159 188 m3 for deposits. Geometric properties of the 3Dsource inventory, including the V–A relationship, are consistent withprevious results, except for the lack of the classically observed rolloverof the distribution of source area. A manually mapped 2D inventory fromaerial image comparison has a better lower limit of detection (6 m2) but only identifies 258 landslide scars, exhibits arollover in the distribution of source area of around 20 m2, andunderestimates the total area and volume of 3D-detected sources by 72 %and 58 %, respectively. Detection and delimitation errors in the 2D inventory occur inareas with limited texture change (bare-rock surfaces, forests) and at thetransition between sources and deposits that the 3D method accuratelycaptures. Large rotational/translational landslides and retrogressive scarscan be detected using the 3D method irrespective of area's vegetation cover, but they are missed in the 2Dinventory owing to the dominant vertical topographic change. The 3Dinventory misses shallow (&lt; 0.4 m depth) landslides detected using the 2D method,corresponding to 10 % of the total area and 2 % of the total volume ofthe 3D inventory. Our data show a systematic size-dependent underdetectionin the 2D inventory below 200 m2 that may explain all or partof the rollover observed in the 2D landslide source area distribution. While the3D segmentation of complex clustered landslide sources remains challenging,we demonstrate that 3D point cloud differencing offers a greater detection sensitivityto small changes than a classical difference of digital elevationmodels (DEMs). Our results underline the vast potential of 3D-derivedinventories to exhaustively and objectively quantify the impact ofextreme events on topographic change in regions prone to landsliding, to detecta variety of hillslope mass movements that cannot be captured by 2Dlandslide mapping, and to explore the scaling properties oflandslides in new ways.

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