Preprints
https://doi.org/10.5194/nhess-2022-21
https://doi.org/10.5194/nhess-2022-21
 
27 Jan 2022
27 Jan 2022
Status: this preprint is currently under review for the journal NHESS.

Establishing the timings of individual rainfall-triggered landslides using Sentinel-1 satellite radar data

Katy Burrows, Odin Marc, and Dominique Remy Katy Burrows et al.
  • Géoscience Environnement Toulouse, Toulouse, France

Abstract. Heavy rainfall events in mountainous areas can trigger thousands of destructive landslides, which pose a risk to people and infrastructure and significantly affect the landscape. Landslide locations are typically mapped using optical satellite imagery, but in some regions their timings are often poorly constrained due to persistent cloud cover. Physical and empirical models that provide insights on the processes behind the triggered landsliding require information on both the spatial extent and timing of landslides. Here we demonstrate that Sentinel-1 SAR amplitude time series can be used to constrain landslide timing to within a few days and present three methods to accomplish this based on time series of: (i) the difference in amplitude between the landslide and its surroundings, (ii) the spatial variability of amplitude between pixels within the landslide, and (iii) geometric shadows cast within the landslide. We test these methods on three inventories of landslides of known timing, covering various settings and triggers, and demonstrate that, when used in combination, our methods allow 20 % of landslides to be timed with an accuracy of 80 %. This will allow multi-temporal landslide inventories to be generated for long rainfall events such as the Indian summer monsoon, which triggers large numbers of landslides every year and has until now been limited to annual-scale analysis.

Katy Burrows et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on nhess-2022-21', Tapas Martha, 17 Feb 2022
  • RC1: 'Comment on nhess-2022-21', Magdalena Stefanova Vassileva, 05 Mar 2022
    • AC2: 'Reply on RC1', Katy Burrows, 27 Mar 2022
    • AC4: 'Complete response to RC1', Katy Burrows, 26 Apr 2022
  • RC2: 'Comment on nhess-2022-21', Anonymous Referee #2, 10 Mar 2022
    • AC1: 'Preliminary response to Reviewer 2', Katy Burrows, 18 Mar 2022
    • AC5: 'Complete response to RC2', Katy Burrows, 26 Apr 2022
  • RC3: 'Comment on nhess-2022-21', Anonymous Referee #3, 15 Mar 2022
    • AC3: 'Preliminary response to RC3', Katy Burrows, 27 Mar 2022
    • AC6: 'Complete response to RC3', Katy Burrows, 26 Apr 2022

Katy Burrows et al.

Katy Burrows et al.

Viewed

Total article views: 807 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
548 228 31 807 7 7
  • HTML: 548
  • PDF: 228
  • XML: 31
  • Total: 807
  • BibTeX: 7
  • EndNote: 7
Views and downloads (calculated since 27 Jan 2022)
Cumulative views and downloads (calculated since 27 Jan 2022)

Viewed (geographical distribution)

Total article views: 685 (including HTML, PDF, and XML) Thereof 685 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 24 May 2022
Download
Short summary
The locations of triggered landslides following a rainfall event can be identified in optical satellite images. However cloud cover associated with the rainfall means that these images cannot be used to identify landslide timing. Timings of landslides triggered during long rainfall events are often unknown. Here we present methods of using Sentinel-1 satellite radar data, acquired every 12 days globally in all weather conditions, to better constrain the timings of rainfall-triggered landslides.
Altmetrics