{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T12:32:48Z","timestamp":1745843568179,"version":"3.37.3"},"reference-count":66,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,6]],"date-time":"2022-09-06T00:00:00Z","timestamp":1662422400000},"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":"crossref","award":["41807285"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Graduate innovation foundation of Nanchang University","award":["YC2021-S138"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"Landslides are affected not only by their own environmental factors, but also by the neighborhood environmental factors and the landslide clustering effect, which are represented as the neighborhood characteristics of modelling spatial datasets in landslide susceptibility prediction (LSP). This study aims to innovatively explore the neighborhood characteristics of landslide spatial datasets for reducing the LSP uncertainty. Neighborhood environmental factors were acquired and managed by remote sensing (RS) and the geographic information system (GIS), then used to represent the influence of landslide neighborhood environmental factors. The landslide aggregation index (LAI) was proposed to represent the landslide clustering effect in GIS. Taking Chongyi County, China, as example, and using the hydrological slope unit as the mapping unit, 12 environmental factors including elevation, slope, aspect, profile curvature, plan curvature, topographic relief, lithology, gully density, annual average rainfall, NDVI, NDBI, and road density were selected. Next, the support vector machine (SVM) and random forest (RF) were selected to perform LSP considering the neighborhood characteristics of landslide spatial datasets based on hydrologic slope units. Meanwhile, a grid-based model was also established for comparison. Finally, the LSP uncertainties were analyzed from the prediction accuracy and the distribution patterns of landslide susceptibility indexes (LSIs). Results showed that the improved frequency ratio method using LAI and neighborhood environmental factors can effectively ensure the LSP accuracy, and it was significantly higher than the LSP results without considering the neighborhood conditions. Furthermore, the Wilcoxon rank test in nonparametric test indicates that the neighborhood characteristics of spatial datasets had a great positive influence on the LSP performance.<\/jats:p>","DOI":"10.3390\/rs14184436","type":"journal-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T08:18:32Z","timestamp":1662625112000},"page":"4436","source":"Crossref","is-referenced-by-count":49,"title":["Landslide Susceptibility Prediction Considering Neighborhood Characteristics of Landslide Spatial Datasets and Hydrological Slope Units Using Remote Sensing and GIS Technologies"],"prefix":"10.3390","volume":"14","author":[{"given":"Faming","family":"Huang","sequence":"first","affiliation":[{"name":"School of Infrastructure Engineering, Nanchang University, Nanchang 330031, China"}]},{"given":"Siyu","family":"Tao","sequence":"additional","affiliation":[{"name":"School of Infrastructure Engineering, Nanchang University, Nanchang 330031, China"}]},{"given":"Deying","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Engineering Geology and Geotechnical Engineering, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Zhipeng","family":"Lian","sequence":"additional","affiliation":[{"name":"Wuhan Center, China Geological Survey, Wuhan 430223, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5185-4725","authenticated-orcid":false,"given":"Filippo","family":"Catani","sequence":"additional","affiliation":[{"name":"Department of Geosciences, University of Padova, Via Gradenigo, 35131 Padova, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5159-1635","authenticated-orcid":false,"given":"Jinsong","family":"Huang","sequence":"additional","affiliation":[{"name":"Discipline of Civil, Surveying and Environmental Engineering, Priority Research Centre for Geotechnical Science and Engineering, University of Newcastle, Callaghan, NSW 2308, Australia"}]},{"given":"Kailong","family":"Li","sequence":"additional","affiliation":[{"name":"School of Infrastructure Engineering, Nanchang University, Nanchang 330031, China"}]},{"given":"Chuhong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Infrastructure Engineering, Nanchang University, Nanchang 330031, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1007\/s12583-020-1398-3","article-title":"Using physical model experiments for hazards assessment of rainfall-induced debris landslides","volume":"32","author":"Li","year":"2021","journal-title":"J. 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