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

Abstract

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.

Timing landslide and flash flood events from SAR satellite: a new method illustrated in African cloud-covered tropical environments

“…SAR time series at the GH location are constructed using the Copernicus S1 Level-1 Single Look Complex polarizations. Different polarizations result in different backscattering values (Shibayama et al, 2015, Psomiadis, 2016, Park & Lee, 2019, Burrows et al, 2022. Mondini et al, 2019 noted a better definition of landslide-induced changes in vegetated areas using the VH channel.…”

“…Mondini et al, 2019 noted a better definition of landslide-induced changes in vegetated areas using the VH channel. In contrast, Burrows et al (2022) https://doi.org/10. found VV to perform better than VH for landslide event timing estimation.…”

“…Amplitude values, on the other hand, show to have a quite complex reaction to terrain change. Due to the influence of soil moisture and roughness change on the amplitude values, the occurrence of a GH event could both increase and decrease the amplitude values at the location of the GH event (Mondini et al, 2021, Burrows et al, 2022. Both precipitation (in changing leaf-and soil wetness) and vegetation patterns, can dynamically influence SAR amplitude and coherence values, causing a cumulative effect on the time series (Srivastava et al, 2006, Brancato et al, 2017.…”

“…Moreover, SAC is created to already consider seasonal vegetation dynamics so no additional detrending was needed. We decide not to detrend individual GH feature time series (timing workflow 2), which would include the use of a detrending buffer such as in Burrows et al (2022). Since we deal with complex heterogenous land cover, proximate landscape does not necessarily represent the landscape at the individual GH feature.…”

“…The complex reaction of the SAR signal to soil moisture and roughness change can causes both an inand decrease of the amplitude at the same GH event location (Mondini et al, 2021, Burrows et al, 2022. Next to the seasonal influence (fig.…”