{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:36:52Z","timestamp":1760233012307,"version":"build-2065373602"},"reference-count":63,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,14]],"date-time":"2022-12-14T00:00:00Z","timestamp":1670976000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China Major Program","doi-asserted-by":"publisher","award":["41941019","2020M673322","2021YFB3901403","20220006","SKLGP2020Z012"],"award-info":[{"award-number":["41941019","2020M673322","2021YFB3901403","20220006","SKLGP2020Z012"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"China Postdoctoral Science Foundation","award":["41941019","2020M673322","2021YFB3901403","20220006","SKLGP2020Z012"],"award-info":[{"award-number":["41941019","2020M673322","2021YFB3901403","20220006","SKLGP2020Z012"]}]},{"name":"National Key Research and Development Program of China","award":["41941019","2020M673322","2021YFB3901403","20220006","SKLGP2020Z012"],"award-info":[{"award-number":["41941019","2020M673322","2021YFB3901403","20220006","SKLGP2020Z012"]}]},{"name":"Open Research Fund Program of MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area","award":["41941019","2020M673322","2021YFB3901403","20220006","SKLGP2020Z012"],"award-info":[{"award-number":["41941019","2020M673322","2021YFB3901403","20220006","SKLGP2020Z012"]}]},{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project","award":["41941019","2020M673322","2021YFB3901403","20220006","SKLGP2020Z012"],"award-info":[{"award-number":["41941019","2020M673322","2021YFB3901403","20220006","SKLGP2020Z012"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The Niexia slope, located in Danba County, Sichuan Province, China, with steep slope terrain and dense vegetation coverage, has a height difference of about 3002 m. A traditional manual survey cannot be performed here, and single remote sensing technology is not comprehensive enough to identify potential landslides on such high and steep slopes. In this paper, an integrated approach with multi-remote sensing techniques was proposed to identify potential landslides of the Niexia slope, which combined Interferometry Synthetic Aperture Radar (InSAR), airborne Light Detection and Ranging (LiDAR), and optical remote sensing technologies. InSAR technology was used to monitor the small displacements of the whole slope, and three potential landslides on Niexia slope were identified. The maximum cumulative displacement reached up to 11.9 cm over 1 year. Subsequently, high-resolution optical remote sensing images acquired by remote sensing satellites and a Digital Elevation Model (DEM) without vegetation influence obtained by LiDAR were used to finely interpret the sign of landslide micro-geomorphology and to determine the potential landslide geometry boundaries. As a result, four and nine potential landslides with landslide micro-geomorphic features were identified, respectively. Finally, the identification results of the three techniques were fused and analyzed to assess the potential landslides on the Niexia slope. We compared the results from multi-remote sensing technologies, showing that the three techniques have advantages and disadvantages in terms of monitoring objects, monitoring range, and monitoring accuracy. The integrated use of these three technologies can identify and monitor potential landslides more comprehensively, which could play an important role in the future.<\/jats:p>","DOI":"10.3390\/rs14246328","type":"journal-article","created":{"date-parts":[[2022,12,14]],"date-time":"2022-12-14T05:59:40Z","timestamp":1670997580000},"page":"6328","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Identifying Potential Landslides on Giant Niexia Slope (China) Based on Integrated Multi-Remote Sensing Technologies"],"prefix":"10.3390","volume":"14","author":[{"given":"Xiujun","family":"Dong","sequence":"first","affiliation":[{"name":"State Key Laboratory of Geological Disaster Prevention and Geological Environmental Protection, 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"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8989-3113","authenticated-orcid":false,"given":"Keren","family":"Dai","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geological Disaster Prevention and Geological Environmental Protection, Chengdu University of Technology, Chengdu 610059, China"},{"name":"College of Earth Science, Chengdu University of Technology, Chengdu 610059, China"},{"name":"Key Laboratory of Earth Exploration and Information Techniques, Chengdu University of Technology, Ministry of Education, Chengdu 610059, China"},{"name":"The College of Geological Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710064, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3177-037X","authenticated-orcid":false,"given":"Saied","family":"Pirasteh","sequence":"additional","affiliation":[{"name":"GeoAI, Smarter Map and LiDAR Lab, Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610097, China"},{"name":"Department of Geotechnics and Geomatics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 602105, India"}]},{"given":"Guanchen","family":"Zhuo","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geological Disaster Prevention and Geological Environmental Protection, Chengdu University of Technology, Chengdu 610059, China"},{"name":"College of Earth Science, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Zhiyu","family":"Li","sequence":"additional","affiliation":[{"name":"College of Earth Science, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Bing","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu 610500, China"}]},{"given":"Qiang","family":"Xu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geological Disaster Prevention and Geological Environmental Protection, Chengdu University of Technology, Chengdu 610059, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1130\/G33217.1","article-title":"Global patterns of loss of life from landslides","volume":"40","author":"Petley","year":"2012","journal-title":"Geology"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2161","DOI":"10.5194\/nhess-18-2161-2018","article-title":"Global fatal landslide occurrence from 2004 to 2016","volume":"18","author":"Froude","year":"2018","journal-title":"Nat. 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