{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T07:58:15Z","timestamp":1790755095753,"version":"4.1.0"},"reference-count":74,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2020,4,20]],"date-time":"2020-04-20T00:00:00Z","timestamp":1587340800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Continuous geodetic measurements in landslide prone regions are necessary to avoid disasters and better understand the spatiotemporal and kinematic evolution of landslides. The detection and characterization of landslides in high alpine environments remains a challenge associated with difficult accessibility, extensive coverage, limitations of available techniques, and the complex nature of landslide process. Recent studies using space-based observations and especially Persistent Scatterer Interferometry (PSI) techniques with the integration of in-situ monitoring instrumentation are providing vital information for an actual landslide monitoring. In the present study, the Stanford Method for Persistent Scatterers InSAR package (StaMPS) is employed to process the series of Sentinel 1-A and 1-B Synthetic Aperture Radar (SAR) images acquired between 2015 and 2019 along ascending and descending orbits for the selected area in the French Alps. We applied the proposed approach, based on extraction of Active Deformation Areas (ADA), to automatically detect and assess the state of activity and the intensity of the suspected slow-moving landslides in the study area. We illustrated the potential of Sentinel-1 data with the aim of detecting regions of relatively low motion rates that be can attributed to activate landslide and updated pre-existing national landslide inventory maps on a regional scale in terms of slow moving landslides. Our results are compared to pre-existing landslide inventories. More than 100 unknown slow-moving landslides, their spatial pattern, deformation rate, state of activity, as well as orientation are successfully identified over an area of 4000 km2 located in the French Alps. We also address the current limitations due the nature of PSI and geometric characteristic of InSAR data for measuring slope movements in mountainous environments like Alps.<\/jats:p>","DOI":"10.3390\/rs12081305","type":"journal-article","created":{"date-parts":[[2020,4,21]],"date-time":"2020-04-21T04:49:38Z","timestamp":1587444578000},"page":"1305","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":137,"title":["Landslide Mapping and Monitoring Using Persistent Scatterer Interferometry (PSI) Technique in the French Alps"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9482-4382","authenticated-orcid":false,"given":"Gokhan","family":"Aslan","sequence":"first","affiliation":[{"name":"Natural Risk Department, French Geological Survey (BRGM), 45000 Orl\u00e9ans, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Foumelis","sequence":"additional","affiliation":[{"name":"Natural Risk Department, French Geological Survey (BRGM), 45000 Orl\u00e9ans, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1715-3743","authenticated-orcid":false,"given":"Daniel","family":"Raucoules","sequence":"additional","affiliation":[{"name":"Natural Risk Department, French Geological Survey (BRGM), 45000 Orl\u00e9ans, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcello","family":"De Michele","sequence":"additional","affiliation":[{"name":"Natural Risk Department, French Geological Survey (BRGM), 45000 Orl\u00e9ans, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Severine","family":"Bernardie","sequence":"additional","affiliation":[{"name":"Natural Risk Department, French Geological Survey (BRGM), 45000 Orl\u00e9ans, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3050-5619","authenticated-orcid":false,"given":"Ziyadin","family":"Cakir","sequence":"additional","affiliation":[{"name":"Department of Geological Engineering, Istanbul Technical University (ITU), Istanbul 34467, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,20]]},"reference":[{"key":"ref_1","unstructured":"Cruden, D.M., and Varnes, D.J. (1996). Landslides: Investigation and Mitigation. Chapter 3-Landslide Types and Processes, The National Academies of Sciences, Engineering, and Medicine. Transportation Research Board Special Report."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"89","DOI":"10.2113\/gssgfbull.178.2.89","article-title":"Remote-sensing techniques for analysing landslide kinematics: A review","volume":"178","author":"Delacourt","year":"2007","journal-title":"Bull. Soci\u00e9t\u00e9 G\u00e9ologique Fr."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1007\/s10064-005-0023-0","article-title":"Landslide hazard and risk zonation\u2014Why is it still so difficult?","volume":"65","year":"2006","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Van Den Eeckhaut, M., and Herv\u00e1s, J. (2012). Landslide inventories in Europe and policy recommendations for their interoperability and harmonization. JRC Sci. Policy Rep.","DOI":"10.1007\/978-3-642-31325-7_4"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1007\/s10346-012-0335-7","article-title":"How to assess landslide activity and intensity with Persistent Scatterer Interferometry (PSI): The PSI-based matrix approach","volume":"10","author":"Cigna","year":"2013","journal-title":"Landslides"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s100640050066","article-title":"Landslide hazard assessment: Summary review and new perspectives","volume":"58","author":"Aleotti","year":"1999","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/S0013-7952(01)00093-X","article-title":"Landslide risk assessment and management: An overview","volume":"64","author":"Dai","year":"2002","journal-title":"Eng. Geol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.earscirev.2012.02.001","article-title":"Landslide inventory maps: New tools for an old problem","volume":"112","author":"Guzzetti","year":"2012","journal-title":"Earth Sci. Rev."},{"key":"ref_9","unstructured":"Burns, W.J., and Madin, I. (2009). Protocol for Inventory Mapping of Landslide Deposits from Light Detection and Ranging (LiDAR) Imagery Portland, Department of Geology and Mineral Industries."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1007\/s002679910020","article-title":"Comparing Landslide Maps: A Case Study in the Upper Tiber River Basin, Central Italy","volume":"25","author":"Guzzetti","year":"2000","journal-title":"Environ. Manag."},{"key":"ref_11","unstructured":"Frangioni, S., Bianchini, S., and Moretti, S. (2014). Geomatics, Natural Hazards and Risk. Landslide inventory updating by means of Persistent Scatterer Interferometry (PSI): The Setta basin (Italy) case study. Geomat. Nat. Hazards Risk."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1002\/esp.1064","article-title":"Landslide inventories and their statistical properties","volume":"29","author":"Malamud","year":"2004","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/S0169-555X(02)00355-0","article-title":"Landslide inventory in a rugged forested watershed: A comparison between air-photo and field survey data","volume":"54","author":"Brardinoni","year":"2003","journal-title":"Geomorphology"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/j.geomorph.2004.11.001","article-title":"The effectiveness of hillshade maps and expert knowledge in mapping old deep-seated landslides","volume":"67","author":"Poesen","year":"2005","journal-title":"Geomorphology"},{"key":"ref_15","unstructured":"Vietmeier, J., Wagner, W., and Dikau, R. (1999, January 10\u201312). Monitoring moderate slope movements (landslides) in the southern French Alps using differential SAR interferometry. Proceedings of the Fringe 1999 Workshop: Advancing ERS SAR Interferometry from Applications Towards Operations, Li\u00e8ge, Belgium."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.geomorph.2004.08.012","article-title":"On the application of SAR interferometry to geomorphological studies: Estimation of landform attributes and mass movements","volume":"66","author":"Catani","year":"2005","journal-title":"Geomorphology"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/s10346-005-0003-2","article-title":"Survey and monitoring of landslide displacements by means of L-band satellite SAR interferometry","volume":"2","author":"Strozzi","year":"2005","journal-title":"Landslides"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Calabro, M.D., Schmidt, D.A., and Roering, J.J. (2010). An examination of seasonal deformation at the Portuguese Bend landslide, southern California, using radar interferometry. J. Geophys. Res. Earth Surf., 115.","DOI":"10.1029\/2009JF001314"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.geomorph.2014.11.031","article-title":"Landslide deformation monitoring with ALOS\/PALSAR imagery: A D-InSAR geomorphological interpretation method","volume":"231","author":"Doubre","year":"2015","journal-title":"Geomorphology"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1080\/10106049.2018.1508311","article-title":"Landslide displacement mapping based on ALOS-2\/PALSAR-2 data using image correlation techniques and SAR interferometry: Application to the Hell-Bourg landslide (Salazie circle, La R\u00e9union Island)","volume":"35","author":"Raucoules","year":"2020","journal-title":"Geocarto Int."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2375","DOI":"10.1109\/TGRS.2002.803792","article-title":"A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms","volume":"40","author":"Berardino","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1377","DOI":"10.1109\/TGRS.2004.828196","article-title":"A small-baseline approach for investigating deformations on full-resolution differential SAR interferograms","volume":"42","author":"Lanari","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.rse.2006.01.023","article-title":"A quantitative assessment of the SBAS algorithm performance for surface deformation retrieval from DInSAR data","volume":"102","author":"Casu","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.enggeo.2006.09.007","article-title":"Permanent Scatterers for landslide investigations: Outcomes from the ESA-SLAM project","volume":"88","author":"Farina","year":"2006","journal-title":"Eng. Geol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.enggeo.2010.01.003","article-title":"Advanced low-and full-resolution DInSAR map generation for slow-moving landslide analysis at different scales","volume":"112","author":"Cascini","year":"2010","journal-title":"Eng. Geol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1007\/s10346-010-0239-3","article-title":"Analysis with C-and X-band satellite SAR data of the Portalet landslide area","volume":"8","author":"Herrera","year":"2011","journal-title":"Landslides"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2068","DOI":"10.1080\/01431161.2011.605087","article-title":"Updating landslide inventory maps using Persistent Scatterer Interferometry (PSI)","volume":"33","author":"Righini","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Bovenga, F., Wasowski, J., Nitti, D.O., Nutricato, R., and Chiaradia, M.T. (2012). Using COSMO\/SkyMed X-band and ENVISAT C-band SAR interferometry for landslides analysis. Remote Sens. Environ., 119.","DOI":"10.1016\/j.rse.2011.12.013"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.3390\/rs5031045","article-title":"Persistent Scatterer Interferometry (PSI) technique for landslide characterization and monitoring","volume":"5","author":"Tofani","year":"2013","journal-title":"Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1109\/TGRS.2011.2160644","article-title":"Repeat-pass SAR interferometry with partial coherent targets","volume":"50","author":"Perissin","year":"2011","journal-title":"IEEE Trans. Geoscience Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Foumelis, M., Raucoules, D., Colas, B., and de Michele, M. (August, January 28). On the effect of interferometric pairs selection for measuring fast moving landslides. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS 2019), Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8900566"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1016\/j.catena.2015.10.002","article-title":"Landslide detection integrated system (LaDIS) based on in-situ and satellite SAR interferometry measurements","volume":"137","author":"Tessitore","year":"2016","journal-title":"Catena"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1007\/s10346-018-01124-0","article-title":"Integration of persistent scatterer interferometry and ground data for landslide monitoring: The Pianello landslide (Bovino, Southern Italy)","volume":"16","author":"Refice","year":"2019","journal-title":"Landslides"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1016\/j.rse.2012.05.025","article-title":"Large-area landslide detection and monitoring with ALOS\/PALSAR imagery data over Northern California and Southern Oregon, USA","volume":"124","author":"Zhao","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhao, C., Kang, Y., Zhang, Q., Lu, Z., and Li, B. (2018). Landslide identification and monitoring along the Jinsha River catchment (Wudongde reservoir area), China, using the InSAR method. Remote Sens., 10.","DOI":"10.3390\/rs10070993"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Liu, X., Zhao, C., Zhang, Q., Peng, J., Zhu, W., and Lu, Z. (2018). Multi-temporal loess landslide inventory mapping with C-, X-and L-band SAR datasets\u2014A case study of Heifangtai Loess Landslides, China. Remote Sens., 10.","DOI":"10.3390\/rs10111756"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"7469","DOI":"10.3390\/s8117469","article-title":"Geological interpretation of PS InSAR data at regional scale","volume":"8","author":"Meisina","year":"2008","journal-title":"Sensors"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Barra, A., Solari, L., B\u00e9jar-Pizarro, M., Monserrat, O., Bianchini, S., Herrera, G., and Lig\u00fcerzana, S. (2017). A methodology to detect and update active deformation areas based on sentinel-1 SAR images. Remote Sens., 9.","DOI":"10.3390\/rs9101002"},{"key":"ref_39","first-page":"102028","article-title":"Satellite interferometric data for landslide intensity evaluation in mountainous regions","volume":"87","author":"Solari","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1080\/19475705.2017.1413013","article-title":"Fast detection of ground motions on vulnerable elements using Sentinel-1 InSAR data","volume":"9","author":"Solari","year":"2018","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Tom\u00e1s, R., Pag\u00e1n, J.I., Navarro, J.A., Cano, M., Pastor, J.L., Riquelme, A., and Lopez-Sanchez, J.M. (2019). Semi-automatic identification and pre-screening of geological\u2013geotechnical deformational processes using persistent scatterer interferometry datasets. Remote Sens., 11.","DOI":"10.3390\/rs11141675"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Bianchini, S., Cigna, F., Righini, G., Proietti, C., and Casagli, N. (2012). Landslide hotspot mapping by means of persistent scatterer interferometry. Environ. Earth Sci.","DOI":"10.1007\/s12665-012-1559-5"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"6198","DOI":"10.3390\/rs5126198","article-title":"Landslide activity maps generation by means of persistent scatterer interferometry","volume":"5","author":"Bianchini","year":"2013","journal-title":"Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.rse.2012.09.020","article-title":"Multi-sensor advanced DInSAR monitoring of very slow landslides: The Tena Valley case study (Central Spanish Pyrenees)","volume":"128","author":"Herrera","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2186","DOI":"10.1080\/01431161.2014.889864","article-title":"A methodology for improving landslide psi data analysis","volume":"35","author":"Notti","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.1007\/s10346-015-0623-0","article-title":"Structural control on the kinematics of the deep-seated La Clapi\u00e8re landslide revealed by L-band InSAR observations","volume":"13","author":"Malet","year":"2016","journal-title":"Landslides"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/S0013-7952(02)00198-9","article-title":"Nine years of spatial and temporal evolution of the La Valette landslide observed by SAR interferometry","volume":"68","author":"Squarzoni","year":"2003","journal-title":"Eng. Geol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2008EO010001","article-title":"Monitoring earth surface dynamics with optical imagery","volume":"89","author":"Leprince","year":"2008","journal-title":"Eos Trans. Am. Geophys. Union"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.rse.2013.08.006","article-title":"Time-variable 3D ground displacements from high-resolution synthetic aperture radar (SAR). Application to La Valette landslide (South French Alps)","volume":"139","author":"Raucoules","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Desrues, M., Lacroix, P., and Brenguier, O. (2019). Satellite Pre-Failure Detection and In Situ Monitoring of the Landslide of the Tunnel du Chambon, French Alps. Geosciences, 9.","DOI":"10.3390\/geosciences9070313"},{"key":"ref_51","first-page":"1","article-title":"A new technique for the detection of large scale landslides in glacio-lacustrine deposits using image correlation based upon aerial imagery: A case study from the French Alps","volume":"52","author":"Fernandez","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1016\/j.rse.2018.03.042","article-title":"Use of Sentinel-2 images for the detection of precursory motions before landslide failures","volume":"215","author":"Lacroix","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Walter, M., Niethammer, U., Rothmund, S., and Joswig, M. (2009). Joint analysis of the Super-Sauze (French Alps) mudslide by nanoseismic monitoring and UAV-based remote sensing. First Break, 27.","DOI":"10.3997\/1365-2397.27.1302.32182"},{"key":"ref_54","first-page":"223","article-title":"Evaluating Three Insar Time-Series Methods to Assess Creep Motion, Case Study: Masouleh Landslide In North Iran. Isprs Annals of Photogrammetry","volume":"4","author":"Mirzaee","year":"2017","journal-title":"Remote Sens. Spat. Inf. Sci."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1029\/2011EO280002","article-title":"Open Radar Interferometry Software for Mapping Surface Deformation","volume":"92","author":"Sandwell","year":"2011","journal-title":"Eos Trans. AGU"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Farr, T., Rosen, P., Caro, E., Crippen, R., Duren, R., Hensley, S., and Alsdorf, D. (2007). The shuttle radar topography mission. Rev. Geophys., 45.","DOI":"10.1029\/2005RG000183"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Hooper, A. (2008). A Multi-temporal InSAR method incorporating both persistent scatterer and small baseline approaches. Geophys. Res. Lett., 35.","DOI":"10.1029\/2008GL034654"},{"key":"ref_58","first-page":"1","article-title":"Recent advances in SAR interferometry time series analysis for measuring crustal deformation","volume":"514","author":"Hooper","year":"2011","journal-title":"Tectonophysics"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/36.898661","article-title":"Permanent scatterers in SAR interferometry","volume":"39","author":"Ferretti","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.rse.2015.08.035","article-title":"Statistical comparison of InSAR tropospheric correction techniques","volume":"170","author":"Bekaert","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1002\/qj.828","article-title":"The ERA-Interim reanalysis: Configuration and performance of the data assimilation system","volume":"137","author":"Dee","year":"2011","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Li, Y., and Mo, P. (2019). A unified landslide classification system for loess slopes: A critical review. Geomorphology.","DOI":"10.1016\/j.geomorph.2019.04.020"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Hanssen, R.F. (2001). Radar Interferometry: Data Interpretation and Error Analysis, Kluwer Academic Publishers.","DOI":"10.1007\/0-306-47633-9"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Alatza, S., Papoutsis, I., Paradissis, D., Kontoes, C., and Papadopoulos, G.A. (2020). Multi-Temporal InSAR Analysis for Monitoring Ground Deformation in Amorgos Island, Greece. Sensors, 20.","DOI":"10.3390\/s20020338"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.enggeo.2006.09.013","article-title":"Investigating landslides with space-borne Synthetic Aperture Radar (SAR) Interferometry","volume":"88","author":"Colesanti","year":"2006","journal-title":"Eng. Geol."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Del Soldato, M., Solari, L., Poggi, F., Raspini, F., Tom\u00e1s, R., Fanti, R., and & Casagli, N. (2019). Landslide-Induced Damage Probability Estimation Coupling InSAR and Field Survey Data by Fragility Curves. Remote Sens., 11.","DOI":"10.3390\/rs11121486"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Kalia, A.C. (2018). Classification of landslide activity on a regional scale using persistent scatterer interferometry at the moselle valley (Germany). Remote Sens., 10.","DOI":"10.3390\/rs10121880"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"B\u00e9jar-Pizarro, M., Notti, D., Mateos, R.M., Ezquerro, P., Centolanza, G., Herrera, G., and Fern\u00e1ndez, J. (2017). Mapping vulnerable urban areas affected by slow-moving landslides using Sentinel-1 InSAR data. Remote Sens., 9.","DOI":"10.3390\/rs9090876"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Bardi, F., Raspini, F., Ciampalini, A., Kristensen, L., Rouyet, L., Lauknes, T.R., and Casagli, N. (2016). Space-borne and ground-based InSAR data integration: The \u00c5knes test site. Remote Sens., 8.","DOI":"10.3390\/rs8030237"},{"key":"ref_70","unstructured":"Ester, M., Kriegel, H.P., Sander, J., and Xu, X. (1996, January 2\u20134). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, Portland, OR, USA."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1145\/3068335","article-title":"DBSCAN revisited, revisited: Why and how you should (still) use DBSCAN","volume":"42","author":"Schubert","year":"2017","journal-title":"ACM Trans. Database Syst."},{"key":"ref_72","unstructured":"Zhou, Q., Lees, B., and Tang, G. (2008). Accuracy Assessment of DEM Slope Algorithms Related to Spatial Autocorrelation of DEM Errors. Advances in Digital Terrain Analysis. Lecture Notes in Geoinformation and Cartography, Springer."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"14137","DOI":"10.1038\/s41598-019-50792-y","article-title":"Perspectives on the prediction of catastrophic slope failures from satellite InSAR","volume":"9","author":"Intrieri","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Fuhrmann, T., and Garthwaite, M.C. (2019). Resolving three-dimensional surface motion with InSAR: Constraints from multi-geometry data fusion. Remote Sens., 11.","DOI":"10.3390\/rs11030241"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/8\/1305\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:09:07Z","timestamp":1760364547000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/8\/1305"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,20]]},"references-count":74,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["rs12081305"],"URL":"https:\/\/doi.org\/10.3390\/rs12081305","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,20]]}}}