Assessment of the Sentinel-1 based ground motion data feasibility for large scale landslide monitoring

• Published in: Landslides Vol. 17; no. 10; pp. 2287 - 2299
• Main Authors: Bonì, Roberta, Bordoni, Massimiliano, Vivaldi, Valerio, Troisi, Carlo, Tararbra, Mauro, Lanteri, Luca, Zucca, Francesco, Meisina, Claudia
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2020; Springer Nature B.V
• Subjects: Agriculture; Algorithms; Civil Engineering; Data; Earth and Environmental Science; Earth Sciences; Feasibility studies; Geography; Ground motion; Image acquisition; Information systems; Interferometric synthetic aperture radar; Interferometry; Landslides; Landslides & mudslides; Methodology; Monitoring; Natural Hazards; Original Paper; Post-production processing; Regional development; SAR (radar); Synthetic aperture radar; Landslides; A-DInSAR; Ground motion monitoring; Alpine context; Sentinel-1
• ISSN: 1612-510X; 1612-5118
• DOI: 10.1007/s10346-020-01433-3

In this paper, a systematic procedure to assess the feasibility of Advanced Differential Interferometric SAR (A-DInSAR) technique for landslide monitoring using SAR images acquired by Sentinel-1 sensors is presented. The methodology is named “Assessment of the advanced differentiaL interferometric synthetic aperture radar technique Feasibility for large scale lAndslide monitoring – ALFA” and it is structured in two main phases, which includes pre-processing and post-processing elaborations. The methodology was developed and tested in the Alpine sector of the Piedmont region in Italy, which represents a landslide prone area. In particular, ALFA represents a methodology based on previous algorithms available in the literature to assess the a-prior feasibility assessment and post-processing analysis of A-DInSAR data for landslide, which introduces three novel aspects such as (1) a systematic scheme suitable within regional practices; (2) the use of Sentinel-1 data and the development of (3) an index to take into account of the kind of distribution of the measuring points along the landslide. The approach was applied over an area extended about 5300 km 2 affected by 5703 landslides mapped in the database of the Piedmont Region (Landslides information system in Piedmont—SIFRAP). Sentinel-1 images covering the period 2014–2018 were analysed. The results show the potential of the Sentinel-1 data for large-scale landslide monitoring. The developed methodology presents reliable tools that could be useful as feasibility for the use of Sentinel-1 data for landslide monitoring at regional and national scale.