PSInSAR ＆ Landslide

PSInSAR の活用のお話。
技術者が時間を割くのは PSInSAR 自体ではなく、その結果をどう問題に応用するかというところ。活用例として見本になるような文献でしたので要点を( ..)φメモメモ。

Kamila Pawluszek-Filipiak et al. (2021)
Multi-temporal landslide activity investigation by spaceborne SAR interferometry: The case study of the Polish Carpathians
https://www.sciencedirect.com/science/article/pii/S2352938521001658

However, due to the high temporal decorrelation resulting from vegetative cover and short wavelengths (X-band), it was only partially successful (Perski et al., 2009; Perski et al. (2011). This is mostly related to the low PS density due to the application of TerraSAR-X data. Therefore, the exploitation of C-band (Sentinel-1) and L-band (ALOS PALSAR) data can present more advantages, especially in rural areas.

The PSInSAR approach is based on the following steps:
(1)interferogram generation with respect to a common master image
(2) PS candidate selection based on amplitude dispersion index
(3) first estimation of atmospheric phase screen (atmospheric influence) and topographical and displacement components
(4) second estimation of interferometric components and final PS points selection
For a precise description of the algorithms applied within each step, see Ferretti et al. (2000; 2001).

The interferograms are then generated for each slave image always using the same master image.
Amplitude dispersion index of 0.25 was utilized as a threshold for PS candidate selection, and the final PSs were extracted with a coherence value higher than 0.75.

SARscape® software was used for SAR data processing (Sahraoui et al., 2006).

Persistent scattering (PS) postprocessing
R index: > 0.33
cosβ: >0.3
Moreover, we discarded PS points which showed Vslope>0 because positives values represent uphill movements and it is not representative for small landslide movements, even though positive values exist within landslides, especially in the toe area.

three categories based on PSI velocities :negligible, extremely slow, and very slow

It is worth emphasizing that the reality of the PSInSAR velocity estimation itself is not assessed due to the lack of external field measurements, but this measurement may be related to landslide activity. For this reason, field verification was performed.

In general, applying the PSI approach in rural areas is challenging. However, the results of this study prove that increasing the temporal sampling rate in view of launching the second Sentinel 1 satellite, provides a higher PS density and, therefore, this technique can deliver useful information for landslide activity assessment.
From another point of view, the application of two Sentinel datasets from ascending and descending orbits, using only one Sentinel satellite (C band), allows us to achieve a similar PS density in comparison to the one ascending dataset of ALOS PALSAR (L-band).