{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T19:50:08Z","timestamp":1769457008420,"version":"3.49.0"},"reference-count":41,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,6,26]],"date-time":"2023-06-26T00:00:00Z","timestamp":1687737600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the fellowship of China Postdoctoral Science Foundation","award":["2020M673322"],"award-info":[{"award-number":["2020M673322"]}]},{"name":"the fellowship of China Postdoctoral Science Foundation","award":["2023NSFSC1909"],"award-info":[{"award-number":["2023NSFSC1909"]}]},{"name":"the fellowship of China Postdoctoral Science Foundation","award":["41801391"],"award-info":[{"award-number":["41801391"]}]},{"name":"the fellowship of China Postdoctoral Science Foundation","award":["SKLGP2020Z012"],"award-info":[{"award-number":["SKLGP2020Z012"]}]},{"name":"the fellowship of China Postdoctoral Science Foundation","award":["20220006"],"award-info":[{"award-number":["20220006"]}]},{"name":"Sichuan Science Foundation for Outstanding Youth","award":["2020M673322"],"award-info":[{"award-number":["2020M673322"]}]},{"name":"Sichuan Science Foundation for Outstanding Youth","award":["2023NSFSC1909"],"award-info":[{"award-number":["2023NSFSC1909"]}]},{"name":"Sichuan Science Foundation for Outstanding Youth","award":["41801391"],"award-info":[{"award-number":["41801391"]}]},{"name":"Sichuan Science Foundation for Outstanding Youth","award":["SKLGP2020Z012"],"award-info":[{"award-number":["SKLGP2020Z012"]}]},{"name":"Sichuan Science Foundation for Outstanding Youth","award":["20220006"],"award-info":[{"award-number":["20220006"]}]},{"name":"National Natural Science Foundation of China","award":["2020M673322"],"award-info":[{"award-number":["2020M673322"]}]},{"name":"National Natural Science Foundation of China","award":["2023NSFSC1909"],"award-info":[{"award-number":["2023NSFSC1909"]}]},{"name":"National Natural Science Foundation of China","award":["41801391"],"award-info":[{"award-number":["41801391"]}]},{"name":"National Natural Science Foundation of China","award":["SKLGP2020Z012"],"award-info":[{"award-number":["SKLGP2020Z012"]}]},{"name":"National Natural Science Foundation of China","award":["20220006"],"award-info":[{"award-number":["20220006"]}]},{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project","award":["2020M673322"],"award-info":[{"award-number":["2020M673322"]}]},{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project","award":["2023NSFSC1909"],"award-info":[{"award-number":["2023NSFSC1909"]}]},{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project","award":["41801391"],"award-info":[{"award-number":["41801391"]}]},{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project","award":["SKLGP2020Z012"],"award-info":[{"award-number":["SKLGP2020Z012"]}]},{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project","award":["20220006"],"award-info":[{"award-number":["20220006"]}]},{"name":"Open Research Fund Program of MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area","award":["2020M673322"],"award-info":[{"award-number":["2020M673322"]}]},{"name":"Open Research Fund Program of MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area","award":["2023NSFSC1909"],"award-info":[{"award-number":["2023NSFSC1909"]}]},{"name":"Open Research Fund Program of MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area","award":["41801391"],"award-info":[{"award-number":["41801391"]}]},{"name":"Open Research Fund Program of MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area","award":["SKLGP2020Z012"],"award-info":[{"award-number":["SKLGP2020Z012"]}]},{"name":"Open Research Fund Program of MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area","award":["20220006"],"award-info":[{"award-number":["20220006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Landslides are a major concern in the mountainous regions of southwest China, leading to significant loss of life and property damage. Therefore, it is crucial to identify potential landslides for early warning and mitigation. stacking-InSAR, a technique used for landslide identification in a wide area, has been found to be faster than conventional time-series InSAR. However, the dense vegetation in southwest China mountains has an adverse impact on the coherence of stacking-InSAR, resulting in more noise and inaccuracies in landslide identification. To address this problem, this paper proposes an improved seasonal interferometry stacking-InSAR method. It uses Sentinel-1 satellite data from 2017 to 2022. The study area is the river valley section of the G213 road from Wenchuan County to Mao County. The study reveals the characteristics of seasonal decoherence in the steep mountainous region, and identifies a total of 21 potential landslides using the improved method. Additionally, optical satellite imagery and LiDAR data were used to assist in the identification of potential landslides. The results of the conventional stacking-InSAR method and the improved seasonal interferometry stacking-InSAR method are compared, showing that the latter is more effective in noise suppression caused by low coherence. Their standard deviations were reduced by 46%, 22%, 10%, and 14%, respectively, using the quantitative statistics for the four tested areas. The proposed method provides an efficient and effective approach for detecting potential landslides in the mountainous regions of southwest China. It can serve as a valuable technical reference for future landslide identification studies in this area.<\/jats:p>","DOI":"10.3390\/rs15133278","type":"journal-article","created":{"date-parts":[[2023,6,26]],"date-time":"2023-06-26T03:14:56Z","timestamp":1687749296000},"page":"3278","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Identifying Potential Landslides in Steep Mountainous Areas Based on Improved Seasonal Interferometry Stacking-InSAR"],"prefix":"10.3390","volume":"15","author":[{"given":"Zhiyu","family":"Li","sequence":"first","affiliation":[{"name":"College of Earth Science, Chengdu University of Technology, Chengdu 610059, China"}]},{"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"},{"name":"College of Geological Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710064, China"},{"name":"Kay Laboratory of Earth Exploration and Information Techniques (Chengdu University of Technology), Ministry of Education, Chengdu 610059, China"}]},{"given":"Jin","family":"Deng","sequence":"additional","affiliation":[{"name":"College of Earth Science, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Chen","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Earth Science, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Xianlin","family":"Shi","sequence":"additional","affiliation":[{"name":"College of Earth Science, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Guangmin","family":"Tang","sequence":"additional","affiliation":[{"name":"College of Earth Science, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Tao","family":"Yin","sequence":"additional","affiliation":[{"name":"College of Earth Science, Chengdu University of Technology, Chengdu 610059, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,26]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"97","DOI":"10.3724\/SP.J.1235.2011.00097","article-title":"Formation, Distribution and Risk Control of Landslides in China","volume":"3","author":"Huang","year":"2011","journal-title":"J. Rock Mech. Geotech. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2357","DOI":"10.1007\/s10346-018-1037-6","article-title":"Spatial and Temporal Analysis of a Fatal Landslide Inventory in China from 1950 to 2016","volume":"15","author":"Lin","year":"2018","journal-title":"Landslides"},{"key":"ref_4","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_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":"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_7","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_8","doi-asserted-by":"crossref","first-page":"1003","DOI":"10.1007\/s10346-019-01159-x","article-title":"Successive Landsliding and Damming of the Jinsha River in Eastern Tibet, China: Prime Investigation, Early Warning, and Emergency Response","volume":"16","author":"Fan","year":"2019","journal-title":"Landslides"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1965","DOI":"10.1007\/s10346-019-01247-y","article-title":"Erosion-Based Analysis of Breaching of Baige Landslide Dams on the Jinsha River, China, in 2018","volume":"16","author":"Zhang","year":"2019","journal-title":"Landslides"},{"key":"ref_10","first-page":"415","article-title":"A Preliminary Analysis of the Formation Mechanism and Development Tendency of the Huge Baige Landslide in Jinsha River on October 11, 2018","volume":"27","author":"Feng","year":"2019","journal-title":"J. Eng. Geol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1115","DOI":"10.1007\/s10346-020-01612-2","article-title":"Emergency Response to the Reactivated Aniangzhai Landslide Resulting from a Rainstorm-Triggered Debris Flow, Sichuan Province, China","volume":"18","author":"Zhao","year":"2021","journal-title":"Landslides"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"106280","DOI":"10.1016\/j.enggeo.2021.106280","article-title":"Analyzing the Multi-Hazard Chain Induced by a Debris Flow in Xiaojinchuan River, Sichuan, China","volume":"293","author":"Zhu","year":"2021","journal-title":"Eng. Geol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1405","DOI":"10.1007\/s10346-023-02044-4","article-title":"Identification and Evaluation of the High Mountain Upper Slope Potential Landslide Based on Multi-Source Remote Sensing: The Aniangzhai Landslide Case Study","volume":"20","author":"Dai","year":"2023","journal-title":"Landslides"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.tecto.2011.10.013","article-title":"Recent Advances in SAR Interferometry Time Series Analysis for Measuring Crustal Deformation","volume":"514\u2013517","author":"Hooper","year":"2012","journal-title":"Tectonophysics"},{"key":"ref_15","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_16","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_17","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1007\/s10346-010-0225-9","article-title":"Integration of GPS with InSAR to Monitoring of the Jiaju Landslide in Sichuan, China","volume":"7","author":"Yin","year":"2010","journal-title":"Landslides"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"106033","DOI":"10.1016\/j.enggeo.2021.106033","article-title":"Integration of Sentinel-1 and ALOS\/PALSAR-2 SAR Datasets for Mapping Active Landslides along the Jinsha River Corridor, China","volume":"284","author":"Liu","year":"2021","journal-title":"Eng. Geol."},{"key":"ref_19","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_20","doi-asserted-by":"crossref","first-page":"2110","DOI":"10.1109\/JSTARS.2022.3228948","article-title":"Applicability Analysis of Potential Landslide Identification by InSAR in Alpine-Canyon Terrain\u2014Case Study on Yalong River","volume":"15","author":"Dai","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s10346-017-0861-4","article-title":"The New Landslide Inventory of Tuscany (Italy) Updated with PS-InSAR: Geomorphological Features and Landslide Distribution","volume":"15","author":"Rosi","year":"2018","journal-title":"Landslides"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Xu, Q., Guo, C., Dong, X., Li, W., Lu, H., Fu, H., and Liu, X. (2021). Mapping and Characterizing Displacements of Landslides with InSAR and Airborne LiDAR Technologies: A Case Study of Danba County, Southwest China. Remote Sens., 13.","DOI":"10.3390\/rs13214234"},{"key":"ref_23","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_24","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_25","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_26","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.isprsjprs.2015.10.003","article-title":"Time series analysis of InSAR data: Methods and trends","volume":"115","author":"Sunar","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Xu, Y., Li, T., Tang, X., Fan, H., and Wang, Y. (2022). Research on the Applicability of DInSAR, Stacking-InSAR and SBAS-InSAR for Mining Region Subsidence Detection in the Datong Coalfield. Remote Sens., 14.","DOI":"10.3390\/rs14143314"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhang, L., Dai, K., Deng, J., Ge, D., Liang, R., Li, W., and Xu, Q. (2021). Identifying Potential Landslides by Stacking-InSAR in Southwestern China and Its Performance Comparison with SBAS-InSAR. Remote Sens., 13.","DOI":"10.3390\/rs13183662"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Liang, J., Dong, J., Zhang, S., Zhao, C., Liu, B., Yang, L., Yan, S., and Ma, X. (2022). Discussion on InSAR Identification Effectivity of Potential Landslides and Factors That Influence the Effectivity. Remote Sens., 14.","DOI":"10.3390\/rs14081952"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.rse.2011.05.028","article-title":"GMES Sentinel-1 Mission","volume":"120","author":"Torres","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Cao, C., Zhu, K., Song, T., Bai, J., Zhang, W., Chen, J., and Song, S. (2022). Comparative study on potential landslide identification with ALOS-2 and sentinel-1A data in heavy forest reach, upstream of the Jinsha River. Remote Sens., 14.","DOI":"10.3390\/rs14091962"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"950","DOI":"10.1109\/36.175330","article-title":"Decorrelation in Interferometric Radar Echoes","volume":"30","author":"Zebker","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","first-page":"391","article-title":"Interferometric Synthetic Aperture Radar Geodesy","volume":"3","author":"Simons","year":"2007","journal-title":"Geodesy"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1109\/36.739146","article-title":"Coherence estimation for SAR imagery","volume":"37","author":"Touzi","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","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_36","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1016\/j.geomorph.2006.10.003","article-title":"Comparison of satellite and air photo based landslide susceptibility maps","volume":"87","author":"Weirich","year":"2007","journal-title":"Geomorphology"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.geomorph.2011.01.013","article-title":"Seasonal landslide mapping and estimation of landslide mobilization rates using aerial and satellite images","volume":"129","author":"Fiorucci","year":"2011","journal-title":"Geomorphology"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"754","DOI":"10.1002\/esp.1417","article-title":"Use of LIDAR-derived images for mapping old landslides under forest","volume":"32","author":"Eeckhaut","year":"2007","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_39","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_40","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s11069-010-9634-2","article-title":"Use of LIDAR in landslide investigations: A review","volume":"61","author":"Jaboyedoff","year":"2012","journal-title":"Nat. Hazards"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1214\/aoms\/1177730491","article-title":"On a test of whether one of two random variables is stochastically larger than the other","volume":"18","author":"Mann","year":"1947","journal-title":"Ann. Math. Stat."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/13\/3278\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:00:50Z","timestamp":1760126450000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/13\/3278"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,26]]},"references-count":41,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["rs15133278"],"URL":"https:\/\/doi.org\/10.3390\/rs15133278","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,26]]}}}