The Maoxian landslide as seen from space: detecting precursors of failure with Sentinel-1 data

TLDR

The Maoxian landslide struck Xinmo village on 24 June 2017, killing over 100 residents. The study used a multi‑interferogram InSAR analysis of 45 Sentinel‑1 C‑band SAR images from 2014‑2017, generating dense deformation maps and displacement time series over 460 km², and applied the Fukuzono method to forecast failure timing. InSAR revealed precursory deformation with a peak LOS displacement rate of 27 mm yr⁻¹, acceleration beginning in April 2017, and distinct deformation patterns, demonstrating that Sentinel‑1 data can map and monitor landslide activity.

Abstract

Post-event Interferometric Synthetic Aperture Radar (InSAR) analysis on a stack of 45 C-band SAR images acquired by the ESA Sentinel-1 satellites from 9 October 2014 to 19 June 2017 allowed the identification of a clear precursory deformation signal for the Maoxian landslide (Mao County, Sichuan Province, China). The landslide occurred in the early morning of 24 June 2017 and killed more than 100 people in the village of Xinmo. Sentinel-1 images have been processed through an advanced multi-interferogram analysis capable of maximising the density of measurement points, generating ground deformation maps and displacement time series for an area of 460 km2 straddling the Minjiang River and the Songping Gully. InSAR data clearly show the precursors of the slope failure in the source area of the Maoxian landslide, with a maximum displacement rate detected of 27 mm/year along the line of sight of the satellite. Deformation time series of measurement points identified within the main scarp of the landslide exhibit an acceleration starting from April 2017. A detailed time series analysis leads to the classification of different deformation behaviours. The Fukuzono method for forecasting the time of failure appear to be applicable to the displacement data exhibiting progressive acceleration. Results suggest that satellite radar data, systematically acquired over large areas with short revisiting time, could be used not only as a tool for mapping unstable areas, but also for landslide monitoring, at least for some typologies of sliding phenomena.