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

• Published in: Landslides 2018, Vol.15 (1), p.123-133
• Main Authors: Intrieri, Emanuele, Raspini, Federico, Fumagalli, Alfio, Lu, Ping, Del Conte, Sara, Farina, Paolo, Allievi, Jacopo, Ferretti, Alessandro, Casagli, Nicola
• Format: Article
• Language: English
• Subjects: Acceleration; Agriculture; C band; Civil Engineering; Data; Data acquisition; Deformation; Displacement; Earth and Environmental Science; Earth Sciences; Failure analysis; Geography; Gullies; Image acquisition; Interferometric synthetic aperture radar; Interferometry; Landslides; Landslides & mudslides; Measurement; Natural Hazards; Precursors; Radar; Radar data; Radar imaging; Recent Landslides; Remote sensing; Rivers; SAR (radar); Satellite imagery; Satellite radar; Satellites; Spaceborne remote sensing; Synthetic aperture radar; Synthetic aperture radar interferometry; Time series
• ISSN: 1612-510X; 1612-5118
• DOI: 10.1007/s10346-017-0915-7

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 km 2 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.