Abstract
It is a crucial issue to better understand the usability of Sentinel-1 satellites in geomorphologic applications, since Sentinel-1 and the Copernicus Program are considered to be the workhorse of Earth observation by the European Space Agency during the next decades. Yet, a very limited experience is available on the applicability of Sentinel-1 images in the detection and identification of surface deformations and especially landslide mapping and monitoring in densely vegetated (low-coherence) areas. Few Synthetic Aperture Radar images (not more than 20) are sufficient for a successful run of interferometric stacking algorithms. This number is really low compared to the tremendous data flow of Sentinel-1 images that are available for interferometric analysis nowadays. Despite the availability of acquisitions, only a few papers exist on the accuracy of Sentinel-1 data, signal-to-noise ratio and the value of the acquired imagery for geomorphologic interpretation. Two test sites and a control site—affected by active surface deformations—have been investigated using 40 Sentinel-1A images and conventional persistent scatterers (PSI) method. PSI results have been combined with the geomorphologic information of the studied sites. We verified that the given number of Sentinel-1A acquisitions provide a unique base for surface deformation recognition and mapping in low-coherence areas. We found that scatterers were corrupted by a strong noise if their line of sight (LOS) velocity was below ± 6–7 mm/year all over the three test sites, although noise can easily be reduced. Noise reduction was achieved by a significant increase of the length of time series, i.e., time range between the first and last image to reduce the effect of atmospheric phase screen (APS).
Highlights
Sensed data such as orthophotographs, satellite images and LiDAR data are essential materials for the analysis of surface deformation and, in particular, landslide mapping and monitoring (Chung and Fabbri 2003; Glade et al 2005)
Among them velocity precision (Vp) and height precision (Hp) have a direct link to the phase changes, but Hp is estimated from the phase component which is linearly proportional to the normal baseline
During data processing, we focused on the line of sight (LOS) velocity and Vp of persistent scatterers (PSI) points
Summary
Sensed data such as orthophotographs, satellite images and LiDAR data are essential materials for the analysis of surface deformation and, in particular, landslide mapping and monitoring (Chung and Fabbri 2003; Glade et al 2005). Since Synthetic Aperture Radar (SAR) satellites were introduced in the early 1990s by launching ERS and Envisat, they have produced one of the most precise raw materials for the recognition of surface displacement. Conventional processing technologies of SAR data, such as interferometric SAR processing (InSAR), operate with coherence calculated from SAR acquisition pairs (Béjar-Pizarro et al 2017). This technique has several limitations, which may affect the usability of InSAR deformation measurements (Casagli et al 2016). Advanced differential interferometry (DInSAR) stacking techniques, such as persistent scatterers (PSI) (Ferretti et al 2001; Tofani et al 2013; Piacentini et al 2015), overcome the limitation of InSAR (Pasquali et al 2014) and make it suitable for landslide monitoring. Due to the popularity of ERS and Envisat acquisitions in landslide research, abundant information is available on their applicability and limitations (Herrera et al 2010, 2013; Chen et al 2014; Singleton et al 2014; Tomás et al 2014)
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