{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T10:35:12Z","timestamp":1763202912346,"version":"build-2065373602"},"reference-count":48,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2024,7,12]],"date-time":"2024-07-12T00:00:00Z","timestamp":1720742400000},"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>The InSAR technique is known to be a powerful tool for precise monitoring of wide areas in terms of displacements. It is conceivable to also use this technique to monitor landslide areas, but geometrical distortions due to ground morphology and land cover could make InSAR processing ineffective for such applications. Because of the computational burden of InSAR processing, it is important to have preliminary knowledge about the possible suitability of the technique for the inspected area before acquiring and processing the data. This paper aims to perform a preliminary analysis of the InSAR sensitivity for the specific case of landslide monitoring. A new approach is proposed considering aspects specific to landslide displacements, which are basically tangent to the slope direction. Pre-processed coherence maps were used to account for the impact of land cover. The whole analysis can be carried out without acquiring cumbersome SAR datasets and can be used as a preliminary step. The Italian Emilia-Romagna region has been considered as the study area, with landslide areas accounting for more than 12% of its territory. The outcomes show that the inspected area has favourable morphological conditions, mainly thanks to its mild slopes and the limited number of landslides facing north, but the land cover has a strong negative impact on the InSAR sensitivity. Nevertheless, 7.5% of the landslide areas have promising conditions for monitoring using radar interferometry.<\/jats:p>","DOI":"10.3390\/rs16142562","type":"journal-article","created":{"date-parts":[[2024,7,12]],"date-time":"2024-07-12T11:28:03Z","timestamp":1720783683000},"page":"2562","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Priori Estimation of Radar Satellite Interferometry\u2019s Sensitivity for Landslide Monitoring in the Italian Emilia-Romagna Region"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6524-9216","authenticated-orcid":false,"given":"Enrica","family":"Vecchi","sequence":"first","affiliation":[{"name":"Department of Civil, Environmental Engineering and Architecture (DICAAR), University of Cagliari, 09123 Cagliari, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0395-7185","authenticated-orcid":false,"given":"Luca","family":"Tavasci","sequence":"additional","affiliation":[{"name":"Department of Civil, Chemical, Environmental and Materials Engineering (DICAM), University of Bologna, 40136 Bologna, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3249-3976","authenticated-orcid":false,"given":"Eugenia","family":"Giorgini","sequence":"additional","affiliation":[{"name":"Department of Civil, Chemical, Environmental and Materials Engineering (DICAM), University of Bologna, 40136 Bologna, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2096-5670","authenticated-orcid":false,"given":"Stefano","family":"Gandolfi","sequence":"additional","affiliation":[{"name":"Department of Civil, Chemical, Environmental and Materials Engineering (DICAM), University of Bologna, 40136 Bologna, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/S0169-555X(99)00078-1","article-title":"Landslide hazard evaluation: A review of current techniques and their application in a multi-scale study, Central Italy","volume":"31","author":"Guzzetti","year":"1997","journal-title":"Geomorphology"},{"unstructured":"(2023, September 01). EuroGeoSurveys. Available online: http:\/\/m.eurogeosurveys.org.","key":"ref_2"},{"unstructured":"Gozza, G., and Pizziolo, M. (2023, December 01). 13. Analisi del Dissesto da Frana in Emilia-Romagna, Available online: http:\/\/isprambiente.gov.it.","key":"ref_3"},{"doi-asserted-by":"crossref","unstructured":"Aslan, G., Foumelis, M., Raucoules, D., De Michele, M., Bernardie, S., and Cakir, Z. (2020). Landslide mapping and monitoring using persistent scatterer interferometry (PSI) technique in the French Alps. Remote Sens., 12.","key":"ref_4","DOI":"10.3390\/rs12081305"},{"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.","key":"ref_5","DOI":"10.3390\/rs10121880"},{"unstructured":"(2023, September 01). Ambiente Regione Emilia-Romagna. Available online: https:\/\/ambiente.regione.emilia-romagna.it.","key":"ref_6"},{"unstructured":"Servizio Geologico Sismico e dei Suoli (SGSS) (2016, May 31). Le Frane. Available online: https:\/\/ambiente.regione.emilia-romagna.it\/it\/geologia\/pubblicazioni\/opuscoli\/le-frane-2016.","key":"ref_7"},{"doi-asserted-by":"crossref","unstructured":"Kuang, J., Ng, A.H.-M., and Ge, L. (2021). Displacement characterization and spatial-temporal evolution of the 2020 Aniangzhai landslide in Danba county using time-series InSAR and multi-temporal optical dataset. Remote Sens., 14.","key":"ref_8","DOI":"10.3390\/rs14010068"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"7485","DOI":"10.1038\/s41598-023-34030-0","article-title":"Landslide detection and inventory updating using the time-series InSAR approach along the Karakoram Highway, Northern Pakistan","volume":"13","author":"Hussain","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_10","first-page":"23","article-title":"Frane, il GIS a supporto della conoscenza","volume":"3","author":"Gussoni","year":"2015","journal-title":"Ecoscienza"},{"key":"ref_11","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."},{"doi-asserted-by":"crossref","unstructured":"Sun, Q., Hu, J., Zhang, L., and Ding, X. (2016). Towards slow-moving landslide monitoring by integrating multi-sensor InSAR time series datasets: The Zhouqu case study, China. Remote Sens., 8.","key":"ref_12","DOI":"10.3390\/rs8110908"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1016\/j.protcy.2014.10.106","article-title":"PS-InSAR monitoring of landslide activity in the Black Sea coast of the Caucasus","volume":"16","author":"Kiseleva","year":"2014","journal-title":"Procedia Technol."},{"doi-asserted-by":"crossref","unstructured":"B\u00e9jar-Pizarro, M., Notti, D., Mateos, R.M., Ezquerro, P., Centolanza, G., Herrera, G., Bru, G., Sanabria, M., Solari, L., and Duro, J. (2017). Mapping vulnerable urban areas affected by slow-moving landslides using Sentinel-1 InSAR data. Remote Sens., 9.","key":"ref_14","DOI":"10.3390\/rs9090876"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.rse.2017.11.022","article-title":"Mapping landslide surface displacements with time series SAR interferometry by combining persistent and distributed scatterers: A case study of Jiaju landslide in Danba, China","volume":"205","author":"Dong","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_16","first-page":"101949","article-title":"Detecting the slope movement after the 2018 baige landslides based on ground-based and space-borne radar observations","volume":"84","author":"Li","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2013.2248301","article-title":"A tutorial on synthetic aperture radar","volume":"1","author":"Moreira","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"112441","DOI":"10.1016\/j.rse.2021.112441","article-title":"Joint exploitation of space-borne and ground-based multitemporal InSAR measurements for volcano monitoring: The Stromboli volcano case study","volume":"260","author":"Manzo","year":"2021","journal-title":"Remote Sens. Environ."},{"doi-asserted-by":"crossref","unstructured":"Chen, X., Sun, Q., and Hu, J. (2018). Generation of complete SAR geometric distortion maps based on DEM and neighbor gradient algorithm. Appl. Sci., 8.","key":"ref_20","DOI":"10.3390\/app8112206"},{"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.","key":"ref_21","DOI":"10.3390\/rs11030241"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1226","DOI":"10.1080\/15481603.2022.2100054","article-title":"Interpretation and sensitivity analysis of the InSAR line of sight displacements in landslide measurements","volume":"59","author":"Dai","year":"2022","journal-title":"GISci. Remote Sens."},{"doi-asserted-by":"crossref","unstructured":"He, L., Pei, P., Zhang, X., Qi, J., Cai, J., Cao, W., Ding, R., and Mao, Y. (2023). Sensitivity Evaluation of Time Series InSAR Monitoring Results for Landslide Detection. Remote Sens., 15.","key":"ref_23","DOI":"10.3390\/rs15153906"},{"doi-asserted-by":"crossref","unstructured":"Deng, J., Dai, K., Liang, R., Chen, L., Wen, N., Zheng, G., and Xu, H. (2023). Interferometric Synthetic Aperture Radar Applicability Analysis for Potential Landslide Identification in Steep Mountainous Areas with C\/L Band Data. Remote Sens., 15.","key":"ref_24","DOI":"10.3390\/rs15184538"},{"doi-asserted-by":"crossref","unstructured":"Zhang, R., Zhao, X., Dong, X., Dai, K., Deng, J., Zhuo, G., Yu, B., Wu, T., and Xiang, J. (2024). Potential Landslide Identification in Baihetan Reservoir Area Based on C-\/L-Band Synthetic Aperture Radar Data and Applicability Analysis. Remote Sens., 16.","key":"ref_25","DOI":"10.3390\/rs16091591"},{"key":"ref_26","first-page":"102829","article-title":"World-wide InSAR sensitivity index for landslide deformation tracking","volume":"111","author":"Bogaard","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1593","DOI":"10.1109\/JSTARS.2018.2803074","article-title":"Monitoring Line-Infrastructure with Multisensor SAR Interferometry: Products and Performance Assessment Metrics","volume":"11","author":"Chang","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"598","DOI":"10.1016\/j.isprsjprs.2009.05.003","article-title":"Analysis at medium scale of low-resolution DInSAR data in slow-moving landslide-affected areas","volume":"64","author":"Cascini","year":"2009","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_29","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_30","doi-asserted-by":"crossref","first-page":"1865","DOI":"10.5194\/nhess-10-1865-2010","article-title":"Assessment of the performance of X-band satellite radar data for landslide mapping and monitoring: Upper Tena Valley case study","volume":"10","author":"Notti","year":"2010","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_31","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."},{"unstructured":"Notti, D., Meisina, C., Zucca, F., and Colombo, A. (2011, January 19\u201323). Models to predict Persistent Scatterers data distribution and their capacity to register movement along the slope. Proceedings of the Fringe 2011 Workshop, Frascati, Italy.","key":"ref_32"},{"unstructured":"Plank, S., Singer, J., Thuro, K., and Minet, C. (2010, January 5\u201310). The suitability of the differential radar interferometry method for deformation monitoring of landslides\u2014A new GIS based evaluation tool. Proceedings of the 11th IAEG Congress Geologically Active, Auckland, New Zealand.","key":"ref_33"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.rse.2014.06.025","article-title":"Simulating SAR geometric distortions and predicting Persistent Scatterer densities for ERS-1\/2 and ENVISAT C-band SAR and InSAR applications: Nationwide feasibility assessment to monitor the landmass of Great Britain with SAR imagery","volume":"152","author":"Cigna","year":"2014","journal-title":"Remote Sens. Environ."},{"unstructured":"Colombo, A., Mallen, L., Pispico, R., Giannico, C., Bianchi, M., and Savio, G. (2006, January 14\u201317). Mappatura regionale delle aree monitorabili mediante l\u2019uso della tecnica PS. Proceedings of the 10 National Conference ASITA, Bolzano, Italy.","key":"ref_35"},{"doi-asserted-by":"crossref","unstructured":"Lazeck\u00fd, M., Spaans, K., Gonz\u00e1lez, P.J., Maghsoudi, Y., Morishita, Y., Albino, F., Elliott, J., Greenall, N., Hatton, E., and Hooper, A. (2020). LiCSAR: An Automatic InSAR Tool for Measuring and Monitoring Tectonic and Volcanic Activity. Remote Sens., 12.","key":"ref_36","DOI":"10.3390\/rs12152430"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1186\/s40645-020-00402-7","article-title":"Nationwide urban ground deformation monitoring in Japan using Sentinel-1 LiCSAR products and LiCSBAS","volume":"8","author":"Morishita","year":"2021","journal-title":"Prog. Earth Planet. Sci."},{"unstructured":"(2023, September 01). Sentinel Online. Available online: https:\/\/sentinels.copernicus.eu\/.","key":"ref_38"},{"unstructured":"(2023, December 01). Geoportale Emilia-Romagna. Available online: https:\/\/geoportale.regione.emilia-romagna.it.","key":"ref_39"},{"unstructured":"APAT (2023, December 01). Rapporto Sulle Frane in Italia. Il Progetto IFFI\u2013Metodologia, Risultati e Rapporti Regionali; APAT Report; APAT: Roma. 78\/2007, Available online: http:\/\/www.isprambiente.gov.it\/it\/pubblicazioni\/rapporti\/Rapporto-sulle-frane-in-Italia.","key":"ref_40"},{"unstructured":"(2023, September 01). Qgis Documentation. Available online: https:\/\/docs.qgis.org\/2.8\/en\/.","key":"ref_41"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"L23611","DOI":"10.1029\/2004GL021737","article-title":"A new method for measuring deformation on volcanoes and other natural terrains using InSAR persistent scatterers","volume":"31","author":"Hooper","year":"2004","journal-title":"Geophys. Res. Lett."},{"key":"ref_43","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_44","first-page":"637","article-title":"An overview of the small baseline subset algorithm: A DInSAR technique for surface deformation analysis","volume":"164","author":"Lanari","year":"2007","journal-title":"Deform. Gravity Chang Indic. Isostasy Tecton. Volcanism Clim. Change"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"3460","DOI":"10.1109\/TGRS.2011.2124465","article-title":"A new algorithm for processing interferometric data-stacks: SqueeSAR","volume":"49","author":"Ferretti","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"doi-asserted-by":"crossref","unstructured":"Morishita, Y., Lazecky, M., Wright, T.J., Weiss, J.R., Elliott, J.R., and Hooper, A. (2020). LiCSBAS: An Open-Source InSAR Time Series Analysis Package Integrated with the LiCSAR Automated Sentinel-1 InSAR Processor. Remote Sens., 12.","key":"ref_46","DOI":"10.3390\/rs12030424"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/36.45752","article-title":"The generation of SAR layover and shadow maps from digital elevation models","volume":"28","author":"Kropatsch","year":"1990","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","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."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/14\/2562\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:15:54Z","timestamp":1760109354000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/14\/2562"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,12]]},"references-count":48,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["rs16142562"],"URL":"https:\/\/doi.org\/10.3390\/rs16142562","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2024,7,12]]}}}