{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T12:10:45Z","timestamp":1770984645300,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T00:00:00Z","timestamp":1724284800000},"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","doi-asserted-by":"publisher","award":["42201020"],"award-info":[{"award-number":["42201020"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42371409"],"award-info":[{"award-number":["42371409"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["22KJB170002"],"award-info":[{"award-number":["22KJB170002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42201020"],"award-info":[{"award-number":["42201020"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42371409"],"award-info":[{"award-number":["42371409"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["22KJB170002"],"award-info":[{"award-number":["22KJB170002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010023","name":"Natural Science Research of Jiangsu Higher Education Institutions of China","doi-asserted-by":"publisher","award":["42201020"],"award-info":[{"award-number":["42201020"]}],"id":[{"id":"10.13039\/501100010023","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010023","name":"Natural Science Research of Jiangsu Higher Education Institutions of China","doi-asserted-by":"publisher","award":["42371409"],"award-info":[{"award-number":["42371409"]}],"id":[{"id":"10.13039\/501100010023","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010023","name":"Natural Science Research of Jiangsu Higher Education Institutions of China","doi-asserted-by":"publisher","award":["22KJB170002"],"award-info":[{"award-number":["22KJB170002"]}],"id":[{"id":"10.13039\/501100010023","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>With global climate change and increased human activities, landslides increasingly threaten human safety and property. Precisely extracting large-scale spatiotemporal information on landslides is crucial for risk management. However, existing methods are either locally based or have coarse temporal resolution, which is insufficient for regional analysis. In this study, spatiotemporal information on landslides was extracted using multiple remote sensing data from Emilia, Italy. An automated algorithm for extracting spatial information of landslides was developed with NDVI datasets. Then, we established a landslide prediction model based on a hydrometeorological threshold of three-day soil moisture and three-day accumulated rainfall. Based on this model, the locations and dates of rainfall-induced landslides were identified. Then, we further matched these identified locations with the extracted landslides from remote sensing data and finally determined the occurrence time. This approach was validated with recorded landslides events in Emilia. Despite some temporal clustering, the overall trend matched historical records, accurately reflecting the dynamic impacts of rainfall and soil moisture on landslides. The temporal bias for 87.3% of identified landslides was within seven days. Furthermore, higher rainfall magnitude was associated with better temporal accuracy, validating the effectiveness of the model and the reliability of rainfall as a landslide predictor.<\/jats:p>","DOI":"10.3390\/rs16163089","type":"journal-article","created":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T04:26:57Z","timestamp":1724300817000},"page":"3089","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Extraction of Spatiotemporal Information of Rainfall-Induced Landslides from Remote Sensing"],"prefix":"10.3390","volume":"16","author":[{"given":"Tongxiao","family":"Zeng","sequence":"first","affiliation":[{"name":"Key Laboratory of VGE of Ministry of Education, Nanjing Normal University, Nanjing 210023, China"}]},{"given":"Jun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of VGE of Ministry of Education, Nanjing Normal University, Nanjing 210023, China"}]},{"given":"Yulin","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of VGE of Ministry of Education, Nanjing Normal University, Nanjing 210023, China"}]},{"given":"Shaonan","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2101","DOI":"10.1007\/s10346-024-02267-z","article-title":"Mobility Characteristics of Rainfall-Triggered Shallow Landslides in a Forest Area in Mengdong, China","volume":"21","author":"Bingli","year":"2024","journal-title":"Landslides"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1007\/s12517-009-0089-2","article-title":"Manifestation of Remote Sensing Data and GIS on Landslide Hazard Analysis Using Spatial-Based Statistical Models","volume":"3","author":"Pradhan","year":"2010","journal-title":"Arab. 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