{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T22:17:29Z","timestamp":1782944249911,"version":"3.54.5"},"reference-count":47,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2021,9,14]],"date-time":"2021-09-14T00:00:00Z","timestamp":1631577600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China Major Program","award":["41941019"],"award-info":[{"award-number":["41941019"]}]},{"name":"National Natural Science Foundation of China","award":["41801391"],"award-info":[{"award-number":["41801391"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Landslide disasters occur frequently in the mountainous areas in southwest China, which pose serious threats to the local residents. Interferometry Synthetic Aperture Radar (InSAR) provides us the ability to identify active slopes as potential landslides in vast mountainous areas, to help prevent and mitigate the disasters. Quickly and accurately identifying potential landslides based on massive SAR data is of great significance. Taking the national highway near Wenchuan County, China, as study area, this paper used a Stacking-InSAR method to quickly and qualitatively identify potential landslides based on a total of 40 Sentinel SAR images acquired from November 2017 to March 2019. As a result, 72 active slopes were successfully detected as potential landslides. By comparing the results from Stacking-InSAR with the results from the traditional SBAS-InSAR (Small Baselines Subset) time series method, it was found that the two methods had a high consistency, with 81.7% potential landslides identified by both of the two methods. A detailed comparison on the detection differences was performed, revealing that Stacking-InSAR, compared to SBAS-InSAR may miss a few active slopes with small spatial scales, small displacement levels and the ones affected by the atmosphere, while it has good performance on poor-coherence regions, with the advantages of low technical requirements and low computation labor. The Stacking-InSAR method would be a fast and powerful method to qualitatively and effectively identify potential landslides in vast mountainous areas, with a comprehensive understanding of its specialty and limitations.<\/jats:p>","DOI":"10.3390\/rs13183662","type":"journal-article","created":{"date-parts":[[2021,9,14]],"date-time":"2021-09-14T03:46:14Z","timestamp":1631591174000},"page":"3662","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":155,"title":["Identifying Potential Landslides by Stacking-InSAR in Southwestern China and Its Performance Comparison with SBAS-InSAR"],"prefix":"10.3390","volume":"13","author":[{"given":"Lele","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Earth Science, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8989-3113","authenticated-orcid":false,"given":"Keren","family":"Dai","sequence":"additional","affiliation":[{"name":"College of Earth Science, Chengdu University of Technology, Chengdu 610059, China"},{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin","family":"Deng","sequence":"additional","affiliation":[{"name":"College of Earth Science, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daqing","family":"Ge","sequence":"additional","affiliation":[{"name":"China Aero Geophysical Survey & Remote Sensing, Center for Land and Resources (AGRS), Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rubing","family":"Liang","sequence":"additional","affiliation":[{"name":"College of Earth Science, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3741-8801","authenticated-orcid":false,"given":"Weile","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiang","family":"Xu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,14]]},"reference":[{"key":"ref_1","first-page":"1043","article-title":"Tracking the Deformation History of Large-Scale Rocky Landslides and Its Enlightenment","volume":"44","author":"Li","year":"2019","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"351","DOI":"10.5194\/nhess-12-351-2012","article-title":"Earthquake-triggered landslides in southwest China","volume":"12","author":"Chen","year":"2012","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Meng, W., Xu, Y., Cheng, W., and Arulrajah, A. (2018). Landslide Event on 24 June in Sichuan Province, China: Preliminary Investigation and Analysis. Geosciences, 8.","DOI":"10.3390\/geosciences8020039"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1007\/s10346-009-0148-5","article-title":"Landslide hazards triggered by the 2008 Wenchuan earthquake, Sichuan, China","volume":"6","author":"Yin","year":"2009","journal-title":"Landslides"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1359","DOI":"10.1007\/s10346-018-0953-9","article-title":"Some considerations on the use of numerical methods to simulate past landslides and possible new failures: The case of the recent Xinmo landslide (Sichuan, China)","volume":"15","author":"Scaringi","year":"2018","journal-title":"Landslides"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"907","DOI":"10.1007\/s00254-003-0952-5","article-title":"Landslide activity as a geoindicator in Italy: Significance and new perspectives from remote sensing","volume":"45","author":"Canuti","year":"2004","journal-title":"Environ. Geol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1016\/j.rse.2005.08.004","article-title":"Remote sensing of landslides: An analysis of the potential contribution to geo-spatial systems for hazard assessment in mountainous environments","volume":"98","author":"Metternicht","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.enggeo.2008.03.010","article-title":"Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview","volume":"102","author":"Castellanos","year":"2008","journal-title":"Eng. Geol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1007\/s10346-010-0215-y","article-title":"Monitoring, prediction, and early warning using ground-based radar interferometry","volume":"7","author":"Casagli","year":"2010","journal-title":"Landslides"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1167","DOI":"10.14358\/PERS.70.10.1167","article-title":"Landslide Monitoring in the Three Gorges Area Using D-INSAR and Corner Reflectors","volume":"70","author":"Ye","year":"2004","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_11","first-page":"967","article-title":"Application of Satellite Radar Remote Sensing to Landslide Detection and Monitoring: Challenges and Solutions","volume":"44","author":"Li","year":"2019","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1007\/s10346-017-0914-8","article-title":"Measuring precursory movements of the recent Xinmo landslide in Mao County, China with Sentinel-1 and ALOS-2 PALSAR-2 datasets","volume":"15","author":"Dong","year":"2017","journal-title":"Landslides"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.rse.2014.03.003","article-title":"Evaluating sub-pixel offset techniques as an alternative to D-InSAR for monitoring episodic landslide movements in vegetated terrain","volume":"147","author":"Singleton","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"2202","DOI":"10.1109\/36.868878","article-title":"Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry","volume":"38","author":"Ferretti","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","first-page":"1305","DOI":"10.1016\/j.procs.2016.09.246","article-title":"Sentinel-1 Support in the GAMMA Software","volume":"100","author":"Werner","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2739","DOI":"10.1007\/s10346-021-01678-6","article-title":"Deformation monitoring and failure mode research of mining-induced Jianshanying landslide in karst mountain area, China with ALOS\/PALSAR-2 images","volume":"18","author":"Chen","year":"2021","journal-title":"Landslides"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1109\/MGRS.2019.2954395","article-title":"Entering the Era of Earth Observation-Based Landslide Warning Systems: A Novel and Exciting Framework","volume":"8","author":"Dai","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1016\/j.rse.2016.09.009","article-title":"Monitoring activity at the Daguangbao mega-landslide (China) using Sentinel-1 TOPS time series interferometry","volume":"186","author":"Dai","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1007\/s11430-011-4259-1","article-title":"Landslide monitoring with high-resolution SAR data in the Three Gorges region","volume":"55","author":"Liao","year":"2011","journal-title":"Sci. China Earth Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"14576","DOI":"10.3390\/rs71114576","article-title":"Exploitation of Amplitude and Phase of Satellite SAR Images for Landslide Mapping: The Case of Montescaglioso (South Italy)","volume":"7","author":"Raspini","year":"2015","journal-title":"Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.rse.2014.09.029","article-title":"Slope deformation prior to Zhouqu, China landslide from InSAR time series analysis","volume":"156","author":"Sun","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"026030","DOI":"10.1117\/1.JRS.10.026030","article-title":"Small-scale loess landslide monitoring with small baseline subsets interferometric synthetic aperture radar technique\u2014case study of Xingyuan landslide, Shaanxi, China","volume":"10","author":"Zhao","year":"2016","journal-title":"J. Appl. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1007\/s10346-014-0522-9","article-title":"The contribution of PSInSAR interferometry to landslide hazard in weak rock-dominated areas","volume":"12","author":"Oliveira","year":"2014","journal-title":"Landslides"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2704","DOI":"10.3390\/rs5062704","article-title":"Characterization of Landslide Deformations in Three Gorges Area Using Multiple InSAR Data Stacks","volume":"5","author":"Tantianuparp","year":"2013","journal-title":"Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1007\/s10346-018-0954-8","article-title":"Investigating slow-moving landslides in the Zhouqu region of China using InSAR time series","volume":"15","author":"Zhang","year":"2018","journal-title":"Landslides"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"675","DOI":"10.5194\/nhess-14-675-2014","article-title":"Landslide observation and volume estimation in central Georgia based on L-band InSAR","volume":"14","author":"Nikolaeva","year":"2014","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Hu, B., Chen, J., and Zhang, X. (2019). Monitoring the Land Subsidence Area in a Coastal Urban Area with InSAR and GNSS. Sensors, 19.","DOI":"10.3390\/s19143181"},{"key":"ref_30","first-page":"253","article-title":"Using advanced InSAR time series techniques to monitor landslide movements in Badong of the Three Gorges region, China","volume":"21","author":"Liu","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_31","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_32","doi-asserted-by":"crossref","first-page":"44528","DOI":"10.1117\/1.JRS.13.044528","article-title":"Locating and monitoring of landslides based on small baseline subset interferometric synthetic aperture radar","volume":"13","author":"Wang","year":"2019","journal-title":"J. Appl. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1560\/IJES.57.2.71","article-title":"Current surface displacement along the carmel Fault system in Israel from InSAR stacking and PSInSAR","volume":"57","author":"Novali","year":"2008","journal-title":"Isr. J. Earth Sci."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Dai, K., Liu, G., Li, Z., Ma, D., Wang, X., Zhang, B., Tang, J., and Li, G. (2018). Monitoring Highway Stability in Permafrost Regions with X-band Temporary Scatterers Stacking InSAR. Sensors, 18.","DOI":"10.3390\/s18061876"},{"key":"ref_35","first-page":"1756","article-title":"Combining Application of TOPS and ScanSAR InSAR in Large-Scale Geohazards Identification","volume":"45","author":"Liu","year":"2020","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_36","unstructured":"Zhang, C., Li, Z., Yu, C., Song, C., Xiao, R., and Peng, J. (2020). Landslide Detection: GACOS-assisted InSAR Stacking and Its Application to the Jinsha River Region. Geomat. Inf. Sci. Wuhan Univ., 1\u201316."},{"key":"ref_37","first-page":"2612","article-title":"The Xinmocun landslide on June 24, 2017 in Maoxian, Sichuan: Characteristics and failure mechanism","volume":"36","author":"Xu","year":"2017","journal-title":"Chin. J. Rock Mech. Eng."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2465","DOI":"10.1007\/s10346-018-1073-2","article-title":"New understandings of the June 24th 2017 Xinmo Landslide, Maoxian, Sichuan, China","volume":"15","author":"Hu","year":"2018","journal-title":"Landslides"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1007\/s10346-019-01152-4","article-title":"Post-disaster assessment of 2017 catastrophic Xinmo landslide (China) by spaceborne SAR interferometry","volume":"16","author":"Dai","year":"2019","journal-title":"Landslides"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/s10346-017-0915-7","article-title":"The Maoxian landslide as seen from space: Detecting precursors of failure with Sentinel-1 data","volume":"15","author":"Intrieri","year":"2017","journal-title":"Landslides"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2129","DOI":"10.1007\/s10346-017-0907-7","article-title":"Failure mechanism and kinematics of the deadly June 24th 2017 Xinmo landslide, Maoxian, Sichuan, China","volume":"14","author":"Fan","year":"2017","journal-title":"Landslides"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1701","DOI":"10.1007\/s11629-017-4613-7","article-title":"Numerical modeling and dynamic analysis of the 2017 Xinmo landslide in Maoxian County, China","volume":"14","author":"Ouyang","year":"2017","journal-title":"J. Mt. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Farr, T., Rosen, P., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., and Roth, L. (2007). The Shuttle Radar Topography Mission. Rev. Geophys., 45.","DOI":"10.1029\/2005RG000183"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"30183","DOI":"10.1029\/1998JB900008","article-title":"Phase gradient approach to stacking interferograms","volume":"103","author":"Sandwell","year":"1998","journal-title":"J. Geophys. Res. Solid Earth"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1016\/j.jastp.2006.03.002","article-title":"Modeling of atmospheric effects on InSAR measurements by incorporating terrain elevation information","volume":"68","author":"Li","year":"2006","journal-title":"J. Atmos. Sol.-Terr. Phys."},{"key":"ref_46","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_47","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.rse.2006.11.015","article-title":"Surface deformation of Long Valley caldera and Mono Basin, California, investigated with the SBAS-InSAR approach","volume":"108","author":"Tizzani","year":"2007","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/18\/3662\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:02:12Z","timestamp":1760166132000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/18\/3662"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,14]]},"references-count":47,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["rs13183662"],"URL":"https:\/\/doi.org\/10.3390\/rs13183662","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,14]]}}}