{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T04:36:17Z","timestamp":1774499777686,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,16]],"date-time":"2023-03-16T00:00:00Z","timestamp":1678924800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["41941016"],"award-info":[{"award-number":["41941016"]}]},{"name":"National Natural Science Foundation of China","award":["U1839204"],"award-info":[{"award-number":["U1839204"]}]},{"name":"National Natural Science Foundation of China","award":["U2139201"],"award-info":[{"award-number":["U2139201"]}]},{"name":"National Natural Science Foundation of China","award":["41572193"],"award-info":[{"award-number":["41572193"]}]},{"name":"National Natural Science Foundation of China","award":["42104008"],"award-info":[{"award-number":["42104008"]}]},{"name":"National Natural Science Foundation of China","award":["ZDJ2018-22"],"award-info":[{"award-number":["ZDJ2018-22"]}]},{"name":"National Institute of Natural Hazards, Ministry of Emergency Management of China Research Fund","award":["41941016"],"award-info":[{"award-number":["41941016"]}]},{"name":"National Institute of Natural Hazards, Ministry of Emergency Management of China Research Fund","award":["U1839204"],"award-info":[{"award-number":["U1839204"]}]},{"name":"National Institute of Natural Hazards, Ministry of Emergency Management of China Research Fund","award":["U2139201"],"award-info":[{"award-number":["U2139201"]}]},{"name":"National Institute of Natural Hazards, Ministry of Emergency Management of China Research Fund","award":["41572193"],"award-info":[{"award-number":["41572193"]}]},{"name":"National Institute of Natural Hazards, Ministry of Emergency Management of China Research Fund","award":["42104008"],"award-info":[{"award-number":["42104008"]}]},{"name":"National Institute of Natural Hazards, Ministry of Emergency Management of China Research Fund","award":["ZDJ2018-22"],"award-info":[{"award-number":["ZDJ2018-22"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The increasing number of landslide hazards worldwide has placed greater demands on the production and updating of landslide inventory maps. As an important data source for landslide detection, interferometric synthetic aperture radar (InSAR) data processing is time-consuming and also requires specialized knowledge, which severely hinders its widespread application. At present, a new cloud-based online platform, i.e., Alaska Satellite Facility\u2019s Hybrid Pluggable Processing Pipeline (ASF HyP3) was developed for massive SAR data processing. In this study, combining the HyP3 online platform and Stacking-InSAR method, we constructed a new easy-to-use processing chain for rapidly identifying slow-moving landslides over large areas. With this processing chain, a total of 923 interferometric pairs covering an area of over 1800 km2 were processed within a few hours (about 4 to 5 h). A total of 81 slow-moving landslides were immediately detected and mapped using Stacking-InSAR method, of which 65 landslides were confirmed by previous studies and 16 landslides were newly detected. Results show that the new processing chain can greatly improve the efficiency of wide-area landslide mapping and is expected to serve as an effective tool for rapid updating the existing landslide inventories and contribute to the prevention and management of geological hazards.<\/jats:p>","DOI":"10.3390\/rs15061611","type":"journal-article","created":{"date-parts":[[2023,3,16]],"date-time":"2023-03-16T02:40:11Z","timestamp":1678934411000},"page":"1611","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Rapid Mapping of Slow-Moving Landslides Using an Automated SAR Processing Platform (HyP3) and Stacking-InSAR Method"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2653-8920","authenticated-orcid":false,"given":"Yaning","family":"Yi","sequence":"first","affiliation":[{"name":"National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China"}]},{"given":"Xiwei","family":"Xu","sequence":"additional","affiliation":[{"name":"National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China"},{"name":"School of Earth Sciences and Resources, China University of Geosciences (Beijing), Beijing 100083, China"}]},{"given":"Guangyu","family":"Xu","sequence":"additional","affiliation":[{"name":"Faculty of Geomatics, East China University of Technology, Nanchang 330013, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6437-7868","authenticated-orcid":false,"given":"Huiran","family":"Gao","sequence":"additional","affiliation":[{"name":"National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1016\/j.scitotenv.2019.03.415","article-title":"The human cost of global warming: Deadly landslides and their triggers (1995\u20132014)","volume":"682","author":"Haque","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2179","DOI":"10.5194\/nhess-10-2179-2010","article-title":"Post-disaster assessment of landslides in southern Taiwan after 2009 Typhoon Morakot using remote sensing and spatial analysis","volume":"10","author":"Tsai","year":"2010","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_3","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_4","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_5","first-page":"1651","article-title":"Understanding and Consideration of Related Issues in Early Identification of Potential Geohazards","volume":"45","author":"Xu","year":"2020","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2262","DOI":"10.1038\/s41467-021-22398-4","article-title":"Global connections between El Nino and landslide impacts","volume":"12","author":"Emberson","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.geomorph.2010.04.009","article-title":"Deciphering the effect of climate change on landslide activity: A review","volume":"124","author":"Crozier","year":"2010","journal-title":"Geomorphology"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1038\/s43017-020-0072-8","article-title":"Life and death of slow-moving landslides","volume":"1","author":"Lacroix","year":"2020","journal-title":"Nat. Rev. Earth Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1007\/s10346-013-0436-y","article-title":"The Varnes classification of landslide types, an update","volume":"11","author":"Hungr","year":"2014","journal-title":"Landslides"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"9183","DOI":"10.1029\/JB094iB07p09183","article-title":"Mapping small elevation changes over large areas: Differential radar interferometry","volume":"94","author":"Gabriel","year":"1989","journal-title":"J. Geophys. Res."},{"key":"ref_11","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_12","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_13","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_14","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.geog.2021.09.007","article-title":"Review of the SBAS InSAR Time-series algorithms, applications, and challenges","volume":"13","author":"Li","year":"2022","journal-title":"Geod. Geodyn."},{"key":"ref_15","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_16","doi-asserted-by":"crossref","first-page":"113342","DOI":"10.1016\/j.rse.2022.113342","article-title":"Mapping and characterizing land deformation during 2007\u20132011 over the Gulf Coast by L-band InSAR","volume":"284","author":"Qu","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1134","DOI":"10.1080\/19475705.2022.2065939","article-title":"Monitoring of Maskun landslide and determining its quantitative relationship to different climatic conditions using D-InSAR and PSI techniques","volume":"13","author":"Pourkhosravani","year":"2022","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"103574","DOI":"10.1016\/j.earscirev.2021.103574","article-title":"Landslide failures detection and mapping using Synthetic Aperture Radar: Past, present and future","volume":"216","author":"Mondini","year":"2021","journal-title":"Earth-Sci. Rev."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Tsironi, V., Ganas, A., Karamitros, I., Efstathiou, E., Koukouvelas, I., and Sokos, E. (2022). Kinematics of Active Landslides in Achaia (Peloponnese, Greece) through InSAR Time Series Analysis and Relation to Rainfall Patterns. Remote Sens., 14.","DOI":"10.5194\/egusphere-egu22-5958"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"112400","DOI":"10.1016\/j.rse.2021.112400","article-title":"InSAR monitoring of creeping landslides in mountainous regions: A case study in Eldorado National Forest, California","volume":"258","author":"Kang","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"111983","DOI":"10.1016\/j.rse.2020.111983","article-title":"InSAR-based detection method for mapping and monitoring slow-moving landslides in remote regions with steep and mountainous terrain: An application to Nepal","volume":"249","author":"Bekaert","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_23","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_24","first-page":"16","article-title":"Primary Recognition of Active Landslides and Development Rule Analysis for Pan Three-river-parallel Territory of Tibet Plateau","volume":"52","author":"Xin","year":"2020","journal-title":"Adv. Eng. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1007\/s10346-022-01982-9","article-title":"Investigating slow-moving shallow soil landslides using Sentinel-1 InSAR data in Gisborne, New Zealand","volume":"20","author":"Cook","year":"2022","journal-title":"Landslides"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2293","DOI":"10.1080\/10106049.2020.1818854","article-title":"Investigating persistent scatterer InSAR (PSInSAR) technique efficiency for landslides mapping: A case study in Artvin dam area, in Turkey","volume":"37","author":"Yazici","year":"2020","journal-title":"Geocarto Int."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2022.04.022","article-title":"Nation-wide mapping and classification of ground deformation phenomena through the spatial clustering of P-SBAS InSAR measurements: Italy case study","volume":"189","author":"Festa","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1007\/s11069-021-05079-9","article-title":"A time series processing chain for geological disasters based on a GPU-assisted sentinel-1 InSAR processor","volume":"111","author":"Li","year":"2021","journal-title":"Nat. Hazards"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Morishita, Y., Lazecky, M., Wright, T., Weiss, J., Elliott, J., 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.","DOI":"10.3390\/rs12030424"},{"key":"ref_30","unstructured":"Hogenson, K., Kristenson, H., Kennedy, J., Johnston, A., Rine, J., Logan, T.A., Zhu, J., Williams, F., Herrmann, J., and Smale, J. (2016, January 12\u201316). Hybrid Pluggable Processing Pipeline (HyP3): A Cloud-Native Infrastructure for Generic Processing of SAR Data. Proceedings of the 2016 AGU Fall Meeting, San Francisco, CA, USA."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"e2020GL087376","DOI":"10.1029\/2020GL087376","article-title":"High-Resolution Surface Velocities and Strain for Anatolia From Sentinel-1 InSAR and GNSS Data","volume":"47","author":"Weiss","year":"2020","journal-title":"Geophys. Res. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"6115","DOI":"10.1109\/JSTARS.2020.3028272","article-title":"Detecting Displacements Within Archaeological Sites in Cyprus After a 5.6 Magnitude Scale Earthquake Event Through the Hybrid Pluggable Processing Pipeline (HyP3) Cloud-Based System and Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) Analysis","volume":"13","author":"Agapiou","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_33","first-page":"102214","article-title":"Assessing SAR C-band data to effectively distinguish modified land uses in a heavily disturbed Amazon forest","volume":"94","author":"Nicolau","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"104331","DOI":"10.1016\/j.cageo.2019.104331","article-title":"Small baseline InSAR time series analysis: Unwrapping error correction and noise reduction","volume":"133","author":"Yunjun","year":"2019","journal-title":"Comput. Geosci."},{"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","unstructured":"Kang, Y., Zhao, C., Zhang, Q., Lu, Z., and Li, B. (2017). Application of InSAR Techniques to an Analysis of the Guanling Landslide. Remote Sens., 9.","DOI":"10.3390\/rs9101046"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Ren, T., Gong, W., Bowa, V.M., Tang, H., Chen, J., and Zhao, F. (2021). An Improved R-Index Model for Terrain Visibility Analysis for Landslide Monitoring with InSAR. Remote Sens., 13.","DOI":"10.3390\/rs13101938"},{"key":"ref_38","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_39","first-page":"693","article-title":"How to avoid false interpretations of Sentinel-1A TOPSAR interferometric data in landslide mapping? A case study: Recent landslides in Transdanubia, Hungary","volume":"96","author":"Bugya","year":"2018","journal-title":"Nat. Hazards"},{"key":"ref_40","unstructured":"Xin, Y. (2022, June 10). Active Landslides by InSAR Recognition in Three-River-Parallel Territory of Qinghai-Tibet Plateau (2007\u20132019). Available online: https:\/\/poles.tpdc.ac.cn\/en\/data\/a0c9b7cb-3184-4214-990d-76dc27aa2722\/?q=."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"e2022GL099835","DOI":"10.1029\/2022GL099835","article-title":"The Impact of Plate Motions on Long-Wavelength InSAR-Derived Velocity Fields","volume":"49","author":"Stephenson","year":"2022","journal-title":"Geophys. Res. Lett."},{"key":"ref_42","first-page":"103082","article-title":"InSAR stacking with atmospheric correction for rapid geohazard detection: Applications to ground subsidence and landslides in China","volume":"115","author":"Xiao","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/6\/1611\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:56:18Z","timestamp":1760122578000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/6\/1611"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,16]]},"references-count":42,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["rs15061611"],"URL":"https:\/\/doi.org\/10.3390\/rs15061611","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,16]]}}}