Prediction of landslides using ASTER imagery and data mining models
作者: Kyo-Young SongHyun-Joo OhJaewon ChoiInhye ParkChangwook LeeSaro Lee
作者单位: 1Geological Mapping Group, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124, Gwahang-no, Yuseong-Gu, Daejeon 305-350, Republic of Korea
2Dept. of Overseas Mineral Resource, KIGAM, 92, Gwahang-no, Yuseong-gu, Daejeon 305-350, Republic of Korea
3Geospatial Analysis & Evaluation Center, National Disaster Management Institute, 253-42, Gongdeok 2-Dong, Mapo-Gu, Seoul 121-719, Republic of Korea
4Dept. of Geoinformatics, University of Seoul, Siripdae-gil 13, Dongdaemun-gu, Seoul 130-743, Republic of Korea
5Geoscience Information Center, KIGAM, 124, Gwahang-no, Yuseong-gu, Daejeon 305-350, Republic of Korea
刊名: Advances in Space Research, 2012, Vol.49 (5), pp.978-993
来源数据库: Elsevier Journal
DOI: 10.1016/j.asr.2011.11.035
关键词: Landslide susceptibilityASTERANNANFISGIS
英文摘要: Abstract(#br)The aim of this study was to identify landslide-related factors using only remotely sensed data and to present landslide susceptibility maps using a geographic information system, data-mining models, an artificial neural network (ANN), and an adaptive neuro-fuzzy interface system (ANFIS). Landslide-related factors were identified in Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery. The slope, aspect, and curvature of topographic features were calculated from a digital elevation model that was made using the ASTER imagery. Lineaments, land-cover, and normalized difference vegetative index layers were also extracted from the imagery. Landslide-susceptible areas were analyzed and mapped based on occurrence factors using the ANN and ANFIS....
全文获取路径: Elsevier  (合作)
影响因子:1.183 (2012)