{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:26:07Z","timestamp":1760145967406,"version":"build-2065373602"},"reference-count":61,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,22]],"date-time":"2024-09-22T00:00:00Z","timestamp":1726963200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["41931296","41521002","2017YFC1501002","202208510134"],"award-info":[{"award-number":["41931296","41521002","2017YFC1501002","202208510134"]}]},{"name":"National Key R&amp;D Program of China","award":["41931296","41521002","2017YFC1501002","202208510134"],"award-info":[{"award-number":["41931296","41521002","2017YFC1501002","202208510134"]}]},{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","award":["41931296","41521002","2017YFC1501002","202208510134"],"award-info":[{"award-number":["41931296","41521002","2017YFC1501002","202208510134"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Traditional Newmark models estimate earthquake-induced landslide hazards by calculating permanent displacements exceeding the critical acceleration, which is determined from static factors of safety and hillslope geometries. However, these studies typically predict the potential landslide mass only for the source area, rather than the entire landslide zone, which includes both the source and sliding\/depositional areas. In this study, we present a modified Newmark Runout model that incorporates sliding and depositional areas to improve the estimation of landslide chain risks. This model defines the landslide runout as the direction from the source area to the nearest river channel within the same slope unit, simulating natural landslide behavior under gravitational effects, which enables the prediction of the entire landslide zone. We applied the model to a subset of the Minjiang Catchment affected by the 1933 MW 7.3 Diexi Earthquake in China to assess long-term landslide chain risks. The results indicate that the predicted total landslide zone closely matches that of the Xinmo Landslide that occurred on 24 June 2017, despite some uncertainties in the sliding direction caused by the old landslide along the sliding path. Distance-weighted kernel density analysis was used to reduce the prediction uncertainties. The hazard levels of the buildings and roads were determined by the distance to the nearest entire landslide zone, thereby assessing the landslide risk. The landslide dam risks were estimated using the kernel density module for channels blocked by the predicted landslides, modeling intersections of the total landslide zone and the channels. High-risk landslide dam zones spatially correspond to the locations of the knickpoints primarily induced by landslide dams, validating the model\u2019s accuracy. These analyses demonstrate the effectiveness of the presented model for Newmark-based landslide risk estimations, with implications for geohazard chain risk assessments, risk mitigation, and land use planning and management.<\/jats:p>","DOI":"10.3390\/rs16183517","type":"journal-article","created":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T03:49:46Z","timestamp":1727149786000},"page":"3517","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Incorporating Effects of Slope Units and Sliding Areas into Seismically Induced Landslide Risk Modeling in Tectonically Active Mountainous Areas"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8593-3832","authenticated-orcid":false,"given":"Hao","family":"Wu","sequence":"first","affiliation":[{"name":"School of Geoscience and Technology, Southwest Petroleum University, Chengdu 610500, China"},{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China"},{"name":"Center for Spatial Information Science, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa 277-8568, Chiba, Japan"}]},{"given":"Chenzuo","family":"Ye","sequence":"additional","affiliation":[{"name":"Graduate School of Frontier Science, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa 277-8568, Chiba, Japan"}]},{"given":"Xiangjun","family":"Pei","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8340-6994","authenticated-orcid":false,"given":"Takashi","family":"Oguchi","sequence":"additional","affiliation":[{"name":"Center for Spatial Information Science, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa 277-8568, Chiba, Japan"}]},{"given":"Zhihao","family":"He","sequence":"additional","affiliation":[{"name":"School of Emergency Management, Xihua University, Chengdu 611930, China"}]},{"given":"Hailong","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Emergency Management, Xihua University, Chengdu 611930, China"}]},{"given":"Runqiu","family":"Huang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1029\/2018RG000626","article-title":"Earthquake-Induced Chains of Geologic Hazards: Patterns, Mechanisms, and Impacts","volume":"57","author":"Fan","year":"2019","journal-title":"Rev. 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