Kinematics Characterization of Slow-Moving Landslide using InSAR Time Series Analysis in the South-Central Andes of NW Argentina

Abstract

<p>Slow-moving landslides pose significant natural hazards to humans and infrastructure.  Analysis of Interferometric Synthetic Aperture Radar (InSAR) time series provide the opportunity to monitor unstable hillslopes in difficult to access terrains at large spatial scales.</p><p>The geological conditions and land cover of the eastern Central Andes in northwestern Argentina ranges from densely vegetated areas in the low elevation foreland at around 1000 metres to arid, vegetation free conditions at high elevations at about 6000 metres. The land cover has a significant impact on the spatial and temporal InSAR signal decorrelation and deformation estimation. In our study, we extract InSAR time series from Sentinel-1 ascending and descending data acquired between 2014 and 2021 using both linear small baseline technique and non-linear phase inversion techniques to have a better understanding of deformation rate estimation techniques for landslide detection in complex areas. We identified several landslides including three main translational bodies with areas exceeding 1 km<sup>2</sup> and downslope deformation rates in excess of 5-10 cm/yr. </p><p>Our study is influenced by ionospheric total electron content variation for the C band Sentinel-1 ascending phase observations. We applied the split range-spectrum technique to minimize the ionospheric contribution on the phase measurements. The tropospheric signal was estimated using both statistical approaches based on topography and weather models to reduce the effects of atmospheric water vapor during South American Monsoon activity. We explore the impact of topographic relief on tropospheric phase delay. We compared our deformation-rate estimates with a double-differencing time series with local and regional spatial filters to mitigate tropospheric noise and unwrapping problems in the time series. We take advantage of connected component analysis and hierarchical clustering approaches on the mean velocity from the double-difference time series and vertical component derived from the 3D decomposition of InSAR time series to map landslides with similar characteristics. Our results highlight the importance of the several processing parameters during InSAR time-series analysis and their sensitivity toward slow-moving landslide detection.</p>