{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T18:46:53Z","timestamp":1770230813787,"version":"3.49.0"},"reference-count":41,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,11,24]],"date-time":"2024-11-24T00:00:00Z","timestamp":1732406400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["42307193"],"award-info":[{"award-number":["42307193"]}]},{"name":"National Natural Science Foundation of China","award":["BNORSG202409"],"award-info":[{"award-number":["BNORSG202409"]}]},{"name":"National Natural Science Foundation of China","award":["2024JJ8346"],"award-info":[{"award-number":["2024JJ8346"]}]},{"name":"National Natural Science Foundation of China","award":["23CGL069"],"award-info":[{"award-number":["23CGL069"]}]},{"name":"Badong National Observation and Research Station of Geohazards, China University of Geosciences","award":["42307193"],"award-info":[{"award-number":["42307193"]}]},{"name":"Badong National Observation and Research Station of Geohazards, China University of Geosciences","award":["BNORSG202409"],"award-info":[{"award-number":["BNORSG202409"]}]},{"name":"Badong National Observation and Research Station of Geohazards, China University of Geosciences","award":["2024JJ8346"],"award-info":[{"award-number":["2024JJ8346"]}]},{"name":"Badong National Observation and Research Station of Geohazards, China University of Geosciences","award":["23CGL069"],"award-info":[{"award-number":["23CGL069"]}]},{"name":"Natural Science Foundation of Hunan Province","award":["42307193"],"award-info":[{"award-number":["42307193"]}]},{"name":"Natural Science Foundation of Hunan Province","award":["BNORSG202409"],"award-info":[{"award-number":["BNORSG202409"]}]},{"name":"Natural Science Foundation of Hunan Province","award":["2024JJ8346"],"award-info":[{"award-number":["2024JJ8346"]}]},{"name":"Natural Science Foundation of Hunan Province","award":["23CGL069"],"award-info":[{"award-number":["23CGL069"]}]},{"name":"National Foundation for Social Science of China","award":["42307193"],"award-info":[{"award-number":["42307193"]}]},{"name":"National Foundation for Social Science of China","award":["BNORSG202409"],"award-info":[{"award-number":["BNORSG202409"]}]},{"name":"National Foundation for Social Science of China","award":["2024JJ8346"],"award-info":[{"award-number":["2024JJ8346"]}]},{"name":"National Foundation for Social Science of China","award":["23CGL069"],"award-info":[{"award-number":["23CGL069"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The phenomenon of stepwise landslides, characterized by displacement exhibiting a step-like pattern, is often influenced by reservoir operations and seasonal rainfall. Traditional early warning models face challenges in accurately predicting the sudden initiation and cessation of displacement, primarily because conventional indicators such as rate or acceleration are ineffective in these scenarios. This underscores the urgent need for innovative early warning models and indicators. Viewing step-like displacement through the lens of three phases\u2014stop, start, and acceleration\u2014aligns with the green-yellow-red warning paradigm of the Traffic Light System (TLS). This study introduces a novel early warning model based on the TLS, incorporating jerk, the derivative of displacement acceleration, as a critical indicator. Empirical data and theoretical analysis validate jerk\u2019s significance, demonstrating its clear pattern before and after step-like deformations and its temporal alignment with the deformation\u2019s conclusion. A comprehensive threshold network encompassing rate, acceleration, and jerk is established for the TLS. The model\u2019s application to the Shuiwenzhan landslide case illustrates its capability to signal in a timely manner the onset and acceleration of step-like deformations with yellow and red lights, respectively. It also uniquely determines the deformation\u2019s end through jerk differential analysis, which is a feat seldom achieved by previous models. Furthermore, leveraging the C5.0 machine learning algorithm, a comparison between the predictive capabilities of the TLS model and a pure rate threshold model reveals that the TLS model achieves a 93% accuracy rate, outperforming the latter by 7 percentage points. Additionally, in response to the shortcomings of existing warning and emergency response strategies for this landslide, a closed-loop management framework is proposed, grounded in the TLS. This framework encompasses four critical stages: hazard monitoring, warning issuance, emergency response, and post-event analysis. We also suggest support measures to ensure implementation of the framework.<\/jats:p>","DOI":"10.3390\/rs16234391","type":"journal-article","created":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T08:38:24Z","timestamp":1732523904000},"page":"4391","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Early Warning for Stepwise Landslides Based on Traffic Light System: A Case Study in China"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2659-2767","authenticated-orcid":false,"given":"Shuangshuang","family":"Wu","sequence":"first","affiliation":[{"name":"School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China"},{"name":"Geological Survey Institute of Hunan Province, Changsha 410014, China"}]},{"given":"Zhigang","family":"Tao","sequence":"additional","affiliation":[{"name":"School of Public Administration, Hohai University, Nanjing 211100, China"}]},{"given":"Li","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China"}]},{"given":"Song","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"105267","DOI":"10.1016\/j.enggeo.2019.105267","article-title":"Geohazards in the three Gorges Reservoir Area, China\u2014Lessons learned from decades of research","volume":"261","author":"Tang","year":"2019","journal-title":"Eng. 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