Sensitivity of advanced InSAR strategies for landslide monitoring

<p>Landslides are an important hazard worldwide in particular in mountainous environment. Monitoring the evolution of the slope motion is hence crucial to detect zones at risk and further understand and control their evolution. Monitoring landslides may be done via the installation of in-situ sensors requiring efforts to maintain the instruments in difficult field conditions. Remote sensing offers the advantage to monitor the Earth at a regular frequency by remote satellite. Among the many processing strategies to monitor landslides using satellite data, InSAR has drastically evolved in the past 30 years and became a widely used technique to monitor ground deformation. Numerous processing chains are now available and there are many examples of its interest for landslide application. However, landslides remain in most cases challenging to monitor with this technique and it is not always easy to understand pros and limitations of the different processing chains available.<span class="Apple-converted-space">&#160;</span></p> <p>In this work we propose to analyze and compare the output products of four different advanced InSAR processing chains: a) SNAPPING based on the Permanent Scatterer Interferometry (PSI) approach (Foumelis et al, 2022), b) P-SBAS based on Small-Subset Baseline Analysis (SBAS) approach (Casu et al, 2014), c) SqueeSAR based on PS and DS interferometry (Ferretti et al, 2011) and d) the product of the Copernicus European Ground Motion Service (EGMS, Level 2B). We selected three test areas with known landslides in different environnments: Villerville (France), Canton de Vaud (Switzerland) and Tavernola (Italy). The SNAPPING and P-SBAS processing chains are accessible through the Geohazard Exploitation Platform (GEP) and the results were obtained with default parameterization of these services. The SqueeSAR and the EGMS products were processed independently.<span class="Apple-converted-space">&#160;</span></p> <p>We use different metrics to estimate the similarity of the ground motion time series in space and in time as well as the coverage and the information density of each products. We also analyze the georeferencing of the results by comparing the location of measurement points with man-made structures and known reference points. We also determine the sensitivity of each technique to monitor landslides by inter-comparing the coverage of measurement points in specific landslide targets. The results of this inter-comparison shows that the different products are in general in agreement over large region although their coverage and density may differ significantly. However, significant discrepancies exist in the estimation of the velocity and displacement time series in the studied landslides and this will be discussed.</p> <p>&#160;</p> <p>References:</p> <p>Foumelis, M., Delgado Blasco, J. M., Brito, F., Pacini, F., Papageorgiou, E., Pishehvar, P., & Bally, P. (2022). SNAPPING Services on the Geohazards Exploitation Platform for Copernicus Sentinel-1 Surface Motion Mapping. <em>Remote Sensing</em>, <em>14</em>(23), 6075.</p> <p><em>Casu, F., Elefante, S., Imperatore, P., Zinno, I., Manunta, M., De Luca, C., & Lanari, R. (2014). SBAS-DInSAR parallel processing for deformation time-series computation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(8), 3285-3296.</em></p> <p>Ferretti, A., Fumagalli, A., Novali, F., Prati, C., Rocca, F., & Rucci, A. (2011). A new algorithm for processing interferometric data-stacks: SqueeSAR. <em>IEEE transactions on geoscience and remote sensing</em>, <em>49</em>(9), 3460-3470.</p>