{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T03:35:17Z","timestamp":1774928117406,"version":"3.50.1"},"reference-count":72,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2024,7,24]],"date-time":"2024-07-24T00:00:00Z","timestamp":1721779200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"MCIN\/AEI","award":["MCIN\/AEI\/10.13039\/501100011033"],"award-info":[{"award-number":["MCIN\/AEI\/10.13039\/501100011033"]}]},{"name":"GEAPAGE research group","award":["MCIN\/AEI\/10.13039\/501100011033"],"award-info":[{"award-number":["MCIN\/AEI\/10.13039\/501100011033"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Le\u2019an Town, located in the southwest of Qingchuan County, Guangyuan City, Sichuan Province, boasts a unique geographical position. The town\u2019s terrain is complex, and its geological environment is fragile. Multiple phases of tectonic movements have resulted in numerous cracks and faults, making the area prone to landslides, debris flows, and other disasters. Additionally, heavy rainfall and fluctuating groundwater levels further exacerbate the instability of the mountains. Human activities, such as overdevelopment and deforestation, have significantly increased the risk of geological disasters. Currently, the methods for landslide prediction in Le\u2019an Town are limited; traditional techniques cannot provide precise forecasts, and the study area is largely covered by tall vegetation. Therefore, this paper proposes a method that combines SBAS-InSAR technology with dynamic changes in land use and hydrological conditions. SBAS-InSAR technology is used to obtain surface deformation information, while land-use changes and hydrological condition data are incorporated to analyze the dynamic characteristics and potential influencing factors of landslide areas. The innovation of this method lies in its high-precision surface deformation monitoring capability and the integration of multi-source data, which can more comprehensively reveal the geological environmental characteristics of the study area, thereby achieving accurate predictions of landslide development. The study results indicate that the annual subsidence rate in most deformation areas of Le\u2019an Town ranges from \u221210 to 0 mm, indicating slow subsidence. In some areas, the subsidence rate exceeds \u221250 mm per year, showing significant slope aspect differences, reflecting the combined effects of geological structures, climatic conditions, and human activities. It is evident that land-use changes and hydrological conditions have a significant impact on the occurrence and development of landslides. Therefore, by utilizing SBAS-InSAR technology and cross-verifying it with other techniques, the consistency of identified landslide deformation areas can be enhanced, thereby improving results. This method provides a scientific basis for the monitoring and early warning of landslide disasters and has important practical application value.<\/jats:p>","DOI":"10.3390\/rs16152715","type":"journal-article","created":{"date-parts":[[2024,7,24]],"date-time":"2024-07-24T14:55:47Z","timestamp":1721832947000},"page":"2715","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Landslide Hazard Prediction Based on Small Baseline Subset\u2013Interferometric Synthetic-Aperture Radar Technology Combined with Land-Use Dynamic Change and Hydrological Conditions (Sichuan, China)"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-0129-7856","authenticated-orcid":false,"given":"Hongyi","family":"Guo","sequence":"first","affiliation":[{"name":"Geology Department, Faculty of Sciences, University of Salamanca, Plaza de la Caidos s\/n, 37008 Salamanca, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2242-5192","authenticated-orcid":false,"given":"A. M.","family":"Mart\u00ednez-Gra\u00f1a","sequence":"additional","affiliation":[{"name":"Geology Department, Faculty of Sciences, University of Salamanca, Plaza de la Caidos s\/n, 37008 Salamanca, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1007\/s11629-023-8083-9","article-title":"Landslide hazard susceptibility evaluation based on SBAS-InSAR technology and SSA-BP neural network algorithm: A case study of Baihetan Reservoir Area","volume":"21","author":"Guo","year":"2024","journal-title":"J. Mt. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Yang, Z., Du, G., Zhang, Y., Xu, C., Yu, P., Shao, W., and Mai, X. (2024). Seismic landslide hazard assessment using improved seismic motion parameters of the 2017 Ms 7.0 Jiuzhaigou earthquake, Tibetan Plateau. Front. 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