34 citations as recorded by crossref.

1. Exploitation of Large Archives of ERS and ENVISAT C-Band SAR Data to Characterize Ground Deformations C. Del Ventisette et al. https://doi.org/10.3390/rs5083896
2. Giant landslide displacement analysis using a point cloud set conflict technique: a case in Xishancun landslide, Sichuan, China C. Liu et al. https://doi.org/10.1080/01431161.2018.1541331
3. Persistent Scatterer Interferometry: A review M. Crosetto et al. https://doi.org/10.1016/j.isprsjprs.2015.10.011
4. Ground instability in the old town of Agrigento (Italy) depicted by on-site investigations and Persistent Scatterers data F. Cigna et al. https://doi.org/10.5194/nhess-12-3589-2012
5. Comparing the results of PSInSAR and GNSS on slow motion landslides, Koyulhisar, Turkey K. Hastaoglu https://doi.org/10.1080/19475705.2014.978822
6. The effectiveness of high-resolution LiDAR data combined with PSInSAR data in landslide study A. Ciampalini et al. https://doi.org/10.1007/s10346-015-0663-5
7. Sentinel-1 DInSAR for Monitoring Active Landslides in Critical Infrastructures: The Case of the Rules Reservoir (Southern Spain) C. Reyes-Carmona et al. https://doi.org/10.3390/rs12050809
8. Monitoring of large-scale landslides in Zongling, Guizhou, China, with improved distributed scatterer interferometric SAR time series methods J. Wang et al. https://doi.org/10.1007/s10346-020-01407-5
9. Monitoring of post-failure landslide deformation by the PS-InSAR technique at Lubietova in Central Slovakia V. Greif & J. Vlcko https://doi.org/10.1007/s12665-011-0951-x
10. Modeling Accumulated Volume of Landslides Using Remote Sensing and DTM Data Z. Chen et al. https://doi.org/10.3390/rs6021514
11. On the potential of time series InSAR for subsidence and ground rupture evaluation: application to Texcoco and Cuautitlan–Pachuca subbasins, northern Valley of Mexico G. Siles et al. https://doi.org/10.1007/s11069-015-1894-4
12. Landslides detection through optimized hot spot analysis on persistent scatterers and distributed scatterers P. Lu et al. https://doi.org/10.1016/j.isprsjprs.2019.08.004
13. InSAR analysis of landslide evolution in the Baihetan Reservoir: Impoundment effects and response to combined reservoir–rainfall forcing L. Zhang et al. https://doi.org/10.1016/j.enggeo.2026.108814
14. The Role of Satellite InSAR for Landslide Forecasting: Limitations and Openings S. Moretto et al. https://doi.org/10.3390/rs13183735
15. 1:5000 Landslide map of the upper Gállego Valley (central Spanish Pyrenees) J. Guerrero et al. https://doi.org/10.1080/17445647.2012.751345
16. Analysis of building deformation in landslide area using multisensor PSInSAR™ technique A. Ciampalini et al. https://doi.org/10.1016/j.jag.2014.05.011
17. The contribution of PSInSAR interferometry to landslide hazard in weak rock-dominated areas S. Oliveira et al. https://doi.org/10.1007/s10346-014-0522-9
18. First insights on the potential of Sentinel-1 for landslides detection A. Barra et al. https://doi.org/10.1080/19475705.2016.1171258
19. Monitoring Creeping Landslides with InSAR in a Loess-covered Mountainous Area in the Ili Valley, Central Asia B. Fan et al. https://doi.org/10.1007/s41064-024-00292-0
20. Ground Deformation in The Ciloto Landslides Area Revealed by Multi-Temporal InSAR N. Hayati et al. https://doi.org/10.3390/geosciences10050156
21. Landslide inventory updating by means of Persistent Scatterer Interferometry (PSI): the Setta basin (Italy) case study F. Sara et al. https://doi.org/10.1080/19475705.2013.866985
22. Interpretation of Aerial Photographs and Satellite SAR Interferometry for the Inventory of Landslides T. Strozzi et al. https://doi.org/10.3390/rs5052554
23. Spaceborne, UAV and ground-based remote sensing techniques for landslide mapping, monitoring and early warning N. Casagli et al. https://doi.org/10.1186/s40677-017-0073-1
24. Tracking and evolution of complex active landslides by multi-temporal airborne LiDAR data: The Montaguto landslide (Southern Italy) G. Ventura et al. https://doi.org/10.1016/j.rse.2011.07.007
25. Satellite SAR interferometry for the improved assessment of the state of activity of landslides: A case study from the Cordilleras of Peru T. Strozzi et al. https://doi.org/10.1016/j.rse.2018.08.014
26. Copernicus Sentinel-1 MT-InSAR, GNSS and Seismic Monitoring of Deformation Patterns and Trends at the Methana Volcano, Greece T. Gatsios et al. https://doi.org/10.3390/app10186445
27. Multitemporal landslides inventory map updating using spaceborne SAR analysis C. Del Ventisette et al. https://doi.org/10.1016/j.jag.2014.02.008
28. Site scale modeling of slow-moving landslides, a 3D viscoplastic finite element modeling approach G. Bru et al. https://doi.org/10.1007/s10346-017-0867-y
29. DInSAR analysis of ALOS PALSAR images for the assessment of very slow landslides: the Tena Valley case study J. García-Davalillo et al. https://doi.org/10.1007/s10346-012-0379-8
30. Geohazards Monitoring in Roma from InSAR and In Situ Data: Outcomes of the PanGeo Project V. Comerci et al. https://doi.org/10.1007/s00024-015-1066-1
31. Updating landslide inventory maps using Persistent Scatterer Interferometry (PSI) G. Righini et al. https://doi.org/10.1080/01431161.2011.605087
32. Landslide Activity Maps Generation by Means of Persistent Scatterer Interferometry S. Bianchini et al. https://doi.org/10.3390/rs5126198
33. Landslide HotSpot Mapping by means of Persistent Scatterer Interferometry S. Bianchini et al. https://doi.org/10.1007/s12665-012-1559-5
34. Can satellite InSAR innovate the way of large landslide early warning? P. Zeng et al. https://doi.org/10.1016/j.enggeo.2024.107771