94 citations as recorded by crossref.

1. New insights on the Salmon Falls Creek Canyon landslide complex based on geomorphological analysis and multitemporal satellite InSAR techniques M. Necsoiu et al. https://doi.org/10.1007/s10346-014-0523-8
2. Multisource data integration to investigate one century of evolution for the Agnone landslide (Molise, southern Italy) M. Del Soldato et al. https://doi.org/10.1007/s10346-018-1015-z
3. Improving Ground Deformation Classification by Integrating InSAR Time Series With Geospatial Information Y. Dong et al. https://doi.org/10.1109/TGRS.2025.3635271
4. Land subsidence, Ground Fissures and Buried Faults: InSAR Monitoring of Ciudad Guzmán (Jalisco, Mexico) C. Brunori et al. https://doi.org/10.3390/rs70708610
5. Slope Superficial Displacement Monitoring by Small Baseline SAR Interferometry Using Data from L-band ALOS PALSAR and X-band TerraSAR: A Case Study of Hong Kong, China F. Chen et al. https://doi.org/10.3390/rs6021564
6. The contribution of satellite SAR-derived displacement measurements in landslide risk management practices F. Raspini et al. https://doi.org/10.1007/s11069-016-2691-4
7. Multidisciplinary approach for fault detection: Integration of PS-InSAR, geomorphological, stratigraphic and structural data in the Venafro intermontane basin (Central-Southern Apennines, Italy) V. Amato et al. https://doi.org/10.1016/j.geomorph.2017.01.027
8. Integrated InSAR monitoring and structural assessment of tunnelling‐induced building deformations V. Macchiarulo et al. https://doi.org/10.1002/stc.2781
9. Satellite InSAR survey of structurally-controlled land subsidence due to groundwater exploitation in the Aguascalientes Valley, Mexico F. Cigna & D. Tapete https://doi.org/10.1016/j.rse.2020.112254
10. Supervised Machine Learning Algorithms for Ground Motion Time Series Classification from InSAR Data S. Mirmazloumi et al. https://doi.org/10.3390/rs14153821
11. Mapping dune dynamics by InSAR coherence S. Havivi et al. https://doi.org/10.1002/esp.4309
12. Rule-based semi-automated approach for the detection of landslides induced by 18 September 2011 Sikkim, Himalaya, earthquake using IRS LISS3 satellite images S. Siyahghalati et al. https://doi.org/10.1080/19475705.2014.898702
13. Landslide HotSpot Mapping by means of Persistent Scatterer Interferometry S. Bianchini et al. https://doi.org/10.1007/s12665-012-1559-5
14. Small slope tilts caused by meteorological effects and vital processes of trees on a wooded slope in Hidegvíz Valley, Hungary G. Mentes et al. https://doi.org/10.1016/j.geomorph.2013.09.027
15. 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
16. Classification of ground deformation using sentinel-1 persistent scatterer interferometry time series S. Mirmazloumi et al. https://doi.org/10.1080/15481603.2022.2030535
17. A simple method to help determine landslide susceptibility from spaceborne InSAR data: the Montescaglioso case study T. Carlà et al. https://doi.org/10.1007/s12665-016-6308-8
18. Instability mechanisms affecting cultural heritage sites in the Maltese Archipelago G. Gigli et al. https://doi.org/10.5194/nhess-12-1883-2012
19. Tunnel-induced land subsidence assessment in a densely populated residential area using Sentinel-1 PS-InSAR A. Karunathilake et al. https://doi.org/10.1007/s44288-024-00086-2
20. 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
21. Persistent Scatterer Interferometry (PSI) Technique for Landslide Characterization and Monitoring V. Tofani et al. https://doi.org/10.3390/rs5031045
22. Long Term Subsidence Analysis and Soil Fracturing Zonation Based on InSAR Time Series Modelling in Northern Zona Metropolitana del Valle de Mexico G. Siles et al. https://doi.org/10.3390/rs70606908
23. A hybrid automatic thresholding approach using panchromatic imagery for rapid mapping of landslides Y. Li et al. https://doi.org/10.1080/15481603.2014.972867
24. Geological and hydrogeological drivers of seismic deformation in L’Aquila, Italy: insights from InSAR analysis A. Sciortino et al. https://doi.org/10.1080/19475705.2024.2362395
25. Modeling of fast ground subsidence observed in southern Saskatchewan (Canada) during 2008–2011 S. Samsonov et al. https://doi.org/10.5194/nhess-14-247-2014
26. SARClust—A New Tool to Analyze InSAR Displacement Time Series for Structure Monitoring D. Roque et al. https://doi.org/10.3390/su15043728
27. Semi-automated extraction of Deviation Indexes (DI) from satellite Persistent Scatterers time series: tests on sedimentary volcanism and tectonically-induced motions F. Cigna et al. https://doi.org/10.5194/npg-19-643-2012
28. Automated classification of Persistent Scatterers Interferometry time series M. Berti et al. https://doi.org/10.5194/nhess-13-1945-2013
29. Spatiotemporal Evolution of Land Subsidence in the Beijing Plain 2003–2015 Using Persistent Scatterer Interferometry (PSI) with Multi-Source SAR Data C. Zhou et al. https://doi.org/10.3390/rs10040552
30. Post-Disaster High-Frequency Ground-Based InSAR Monitoring and 3D Deformation Reconstruction of Large Landslides Using MIMO Radar X. Shi et al. https://doi.org/10.3390/rs17183183
31. Satellite A-DInSAR pattern recognition for seismic vulnerability mapping at city scale: insights from the L’Aquila (Italy) case study A. Sciortino et al. https://doi.org/10.1080/15481603.2023.2293522
32. Monitoring Displacements and Damage Detection through Satellite MT-InSAR Techniques: A New Methodology and Application to a Case Study in Rome (Italy) G. Bonaldo et al. https://doi.org/10.3390/rs15051177
33. Millimeter ground deformation retrieving from high-resolution PAZ SAR images with a combined correction of unwrapping and long-wavelength errors: A case study in Alcoy, Spain X. Liu et al. https://doi.org/10.1016/j.rse.2025.114876
34. Long-Term Peatland Condition Assessment via Surface Motion Monitoring Using the ISBAS DInSAR Technique over the Flow Country, Scotland L. Alshammari et al. https://doi.org/10.3390/rs10071103
35. Landslide inventory maps: New tools for an old problem F. Guzzetti et al. https://doi.org/10.1016/j.earscirev.2012.02.001
36. Accuracy Assessment of Different Open-Source Digital Elevation Model Through Morphometric Analysis for a Semi-arid River Basin in the Western Part of India M. Shaikh et al. https://doi.org/10.1007/s41651-021-00089-4
37. Recent advancements in multi-temporal methods applied to new generation SAR systems and applications in South America P. Euillades et al. https://doi.org/10.1016/j.jsames.2021.103410
38. Satellite interferometry for regional assessment of landslide hazard to pipelines in northeastern British Columbia, Canada S. Samsonov & A. Blais-Stevens https://doi.org/10.1016/j.jag.2023.103273
39. Innovative methods to monitor rock and mountain slope deformation A. Hormes et al. https://doi.org/10.1002/geot.201900074
40. A-DInSAR Performance for Updating Landslide Inventory in Mountain Areas: An Example from Lombardy Region (Italy) B. Antonielli et al. https://doi.org/10.3390/geosciences9090364
41. PSInSAR Analysis in the Pisa Urban Area (Italy): A Case Study of Subsidence Related to Stratigraphical Factors and Urbanization L. Solari et al. https://doi.org/10.3390/rs8020120
42. Detection of reclamation-induced ground deformation with GACOS-corrected StaMPS PSI: Case study Hatay Airport M. Görken et al. https://doi.org/10.51489/tuzal.1800982
43. Ground subsidence phenomena in the Delta municipality region (Northern Greece): Geotechnical modeling and validation with Persistent Scatterer Interferometry F. Raspini et al. https://doi.org/10.1016/j.jag.2013.11.010
44. Mapping landslide phenomena in landlocked developing countries by means of satellite remote sensing data: the case of Dilijan (Armenia) area S. Bianchini et al. https://doi.org/10.1080/19475705.2016.1189459
45. Integration between ground based and satellite SAR data in landslide mapping: The San Fratello case study F. Bardi et al. https://doi.org/10.1016/j.geomorph.2014.06.025
46. Regression Analysis of Subsidence in the Como Basin (Northern Italy): New Insights on Natural and Anthropic Drivers from InSAR Data N. Nappo et al. https://doi.org/10.3390/rs12182931
47. Automated identification of ground kinematic patterns based on InSAR time series displacement and K-SC clustering Y. Liang et al. https://doi.org/10.1016/j.enggeo.2025.108367
48. Integrated geomorphological mapping in the north-western sector of Agrigento (Italy) A. Ciampalini et al. https://doi.org/10.1080/17445647.2012.680775
49. A-Differential Synthetic Aperture Radar Interferometry analysis of a Deep Seated Gravitational Slope Deformation occurring at Bisaccia (Italy) D. Di Martire et al. https://doi.org/10.1016/j.scitotenv.2016.01.102
50. A Semi-Automated Object-Based Approach for Landslide Detection Validated by Persistent Scatterer Interferometry Measures and Landslide Inventories D. Hölbling et al. https://doi.org/10.3390/rs4051310
51. Review of Satellite Interferometry for Landslide Detection in Italy L. Solari et al. https://doi.org/10.3390/rs12081351
52. The Role of Satellite InSAR for Landslide Forecasting: Limitations and Openings S. Moretto et al. https://doi.org/10.3390/rs13183735
53. Seismic hazard sensitivity assessment in the Ethiopian Rift, using an integrated approach of AHP and DInSAR methods T. Fentahun et al. https://doi.org/10.1016/j.ejrs.2021.05.001
54. Deformation Retrievals for North America and Eurasia from Sentinel-1 DInSAR: Big Data Approach, Processing Methodology and Challenges S. Samsonov & W. Feng https://doi.org/10.1080/07038992.2023.2247095
55. How to assess landslide activity and intensity with Persistent Scatterer Interferometry (PSI): the PSI-based matrix approach F. Cigna et al. https://doi.org/10.1007/s10346-012-0335-7
56. Landslide detection, monitoring and prediction with remote-sensing techniques N. Casagli et al. https://doi.org/10.1038/s43017-022-00373-x
57. Active tectonics of the onshore Hengchun Fault using UAS DSM combined with ALOS PS-InSAR time series (Southern Taiwan) B. Deffontaines et al. https://doi.org/10.5194/nhess-18-829-2018
58. Basin Scale Assessment of Landslides Geomorphological Setting by Advanced InSAR Analysis F. Bozzano et al. https://doi.org/10.3390/rs9030267
59. Subsidence activity maps derived from DInSAR data: Orihuela case study M. Sanabria et al. https://doi.org/10.5194/nhess-14-1341-2014
60. A GIS-Based Procedure for Landslide Intensity Evaluation and Specific risk Analysis Supported by Persistent Scatterers Interferometry (PSI) S. Bianchini et al. https://doi.org/10.3390/rs9111093
61. Unsupervised detection of InSAR time series patterns based on PCA and K-means clustering D. Festa et al. https://doi.org/10.1016/j.jag.2023.103276
62. 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
63. On the Use of Interferometric Synthetic Aperture Radar Data for Monitoring and Forecasting Natural Hazards M. Bernardi et al. https://doi.org/10.1007/s11004-021-09948-8
64. Continuous, semi-automatic monitoring of ground deformation using Sentinel-1 satellites F. Raspini et al. https://doi.org/10.1038/s41598-018-25369-w
65. Monitoring Landslide-Induced Displacements with TerraSAR-X Persistent Scatterer Interferometry (PSI): Gimigliano Case Study in Calabria Region (Italy) B. Silvia et al. https://doi.org/10.4236/ijg.2013.410144
66. Site-Specific Analysis of Deformation Patterns on Archaeological Heritage by Satellite Radar Interferometry D. Tapete & F. Cigna https://doi.org/10.1557/opl.2012.1397
67. Rapid Mapping and Deformation Analysis over Cultural Heritage and Rural Sites Based on Persistent Scatterer Interferometry D. Tapete & F. Cigna https://doi.org/10.1155/2012/618609
68. InterpolatiON of InSAR Time series for the dEtection of ground deforMatiOn eVEnts (ONtheMOVE): application to slow-moving landslides L. Pedretti et al. https://doi.org/10.1007/s10346-023-02073-z
69. CGHD: Dual-Temporal Dataset of Composite Geological Hazards via Multi-Source Optical Remote Sensing Images Y. Wang et al. https://doi.org/10.3390/rs18081198
70. A Robust Attention-Based Time-Gated LSTM for Change Point Detection in Challenging InSAR Time Series with Data Gaps S. Fakhri & M. Satari https://doi.org/10.1007/s41064-026-00401-1
71. Geomorphological assessment as basic complement of InSAR analysis for landslide processes understanding D. Torre et al. https://doi.org/10.1007/s10346-024-02216-w
72. A quasi-elastic aquifer deformational behavior: Madrid aquifer case study P. Ezquerro et al. https://doi.org/10.1016/j.jhydrol.2014.08.040
73. Persistent Scatterers continuous streaming for landslide monitoring and mapping: the case of the Tuscany region (Italy) F. Raspini et al. https://doi.org/10.1007/s10346-019-01249-w
74. Landslide mapping and monitoring by using radar and optical remote sensing: Examples from the EC-FP7 project SAFER N. Casagli et al. https://doi.org/10.1016/j.rsase.2016.07.001
75. Monitoring the land subsidence with persistent scatterer interferometry in Nansha District, Guangdong, China M. Ao et al. https://doi.org/10.1007/s11069-014-1471-2
76. Interpretation and sensitivity analysis of the InSAR line of sight displacements in landslide measurements K. Dai et al. https://doi.org/10.1080/15481603.2022.2100054
77. Hybrid deep learning for automated detection of spatiotemporal change points in InSAR time series: a case study on the Campi Flegrei volcano using a CNN–LSTM model S. Fakhri & M. Satari https://doi.org/10.1007/s00531-026-02572-z
78. 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
79. Long-term ground deformation patterns of Bucharest using multi-temporal InSAR and multivariate dynamic analyses: a possible transpressional system? I. Armaş et al. https://doi.org/10.1038/srep43762
80. Integrating radar and laser-based remote sensing techniques for monitoring structural deformation of archaeological monuments D. Tapete et al. https://doi.org/10.1016/j.jas.2012.07.024
81. Review on remote sensing methods for landslide detection using machine and deep learning A. Mohan et al. https://doi.org/10.1002/ett.3998
82. Trend Change Point Detection in InSAR Derived Displacement Time Series Using MALkCNN: A Deep Learning Approach S. Arya Fakhri & M. Satari https://doi.org/10.1007/s41064-025-00342-1
83. Spatiotemporal reconstruction and post-failure stability analysis of rainfall-induced landslides via multi-modal SAR-optical data fusion L. Zhang et al. https://doi.org/10.1016/j.catena.2025.109399
84. Ground Movement Classification Using Statistical Tests Over Persistent Scatterer Interferometry Time Series S. Mirmazloumi et al. https://doi.org/10.1016/j.procs.2021.11.068
85. Multi-Temporal Evaluation of Landslide Movements and Impacts on Buildings in San Fratello (Italy) By Means of C-Band and X-Band PSI Data S. Bianchini et al. https://doi.org/10.1007/s00024-014-0839-2
86. Neural Network Pattern Recognition Experiments Toward a Fully Automatic Detection of Anomalies in InSAR Time Series of Surface Deformation P. Milillo et al. https://doi.org/10.3389/feart.2021.728643
87. 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
88. Potential sliding zone recognition method for the slow-moving landslide based on the Hurst exponent H. Yang et al. https://doi.org/10.1016/j.jrmge.2023.08.007
89. The combination of DInSAR and facility damage data for the updating of slow-moving landslide inventory maps at medium scale L. Cascini et al. https://doi.org/10.5194/nhess-13-1527-2013
90. Automatic Mapping of Potential Landslides Using Satellite Multitemporal Interferometry Y. Zhang et al. https://doi.org/10.3390/rs15204951
91. Assessing climate impacts on slow-moving landslides in the western Alps of Piemonte: integration of monitoring techniques for detecting displacements R. Narcisi et al. https://doi.org/10.3389/feart.2024.1365469
92. Semiautomatic analysis of EGMS InSAR data for geohazards detection: a study on Lazio Region (Italy) through ADATools C. Medici et al. https://doi.org/10.1080/19475705.2025.2582755
93. Unsupervised Clustering of InSAR Time-Series Deformation in Mandalay Region from 2022 to 2025 Using Dynamic Time Warping and Longest Common Subsequence J. Qin et al. https://doi.org/10.3390/rs17233920
94. Remote Sensing Applications for Geological Mapping in the Mediterranean Region: A Review A. Tompolidi et al. https://doi.org/10.3390/geosciences15110425