太阳成8722(中国)有限公司-GREEN NO.1

标题：Landslide susceptibility assessment using object mapping units, decision tree, and support vector machine models in the Three Gorges of China作者：Wu Xueling, Ren Fu, Niu Ruiqing

来源出版物：Environmental Earth Sciences DOI：10.1007/s12665-013-2863-4 出版年：Octorber 2013

摘要：Due to the particular geographical location and complex geological conditions, the Three Gorges of China suffer from many landslide hazards that often result in tragic loss of life and economic devastation. To reduce the casualty and damages, an effective and accurate method of assessing landslide susceptibility is necessary. Object-based data mining methods were applied to a case study of landslide susceptibility assessment on the Guojiaba Town of the Three Gorges. The study area was partitioned into object mapping units derived from 30 m resolution Landsat TM images using multi-resolution segmentation algorithm based on the landslide factors of engineering rock group, homogeneity, and reservoir water level. Landslide locations were determined by interpretation of Landsat TM images and extensive field surveys. Eleven primary landslide-related factors were extracted from the topographic and geologic maps, and satellite images. Those factors were selected as independent variables using significance testing and correlation coefficient analysis, including slope, profile curvature, engineering rock group, slope structure, distance from faults, land cover, tasseled cap transformation wetness index, reservoir water level, homogeneity, and first and second principal components of the images. Decision tree and support vector machine (SVM) models with the optimal parameters were trained and then used to map landslide susceptibility, respectively. The analytical results were validated by comparing them with known landslides using the success rate and prediction rate curves and classification accuracy. The object-based SVM model has the highest correct rate of 89.36 % and a kappa coefficient of 0.8286 and outperforms the pixel-based SVM, object-based C5.0, and pixel-based SVM models.

文献类型：Article

语种：English

作者关键词：Landslides,Susceptibility,Multi-resolution segmentation,Data mining,Three Gorges

通讯作者地址：(任福) Ren Fu; School of Resource and Environmental Sciences, Wuhan University, Wuhan, 430079, China

电子邮件地址：renfu@whu.edu.cn

地址：

[Wu Xueling; Niu Ruiqing]Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan, 430074, China

[Ren Fu]School of Resource and Environmental Sciences, Wuhan University, Wuhan, 430079, China

研究方向：Geology

ISSN：1866-6280

eISSN：1866-6299

全文链接：http://link.springer.com/article/10.1007/s12665-013-2863-4