Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data journals
Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data journals
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DOI：
10.1080/01431160412331331012
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发表时间：
2005-04-01
影响因子：
3.4
通讯作者：
Lee, S
中科院分区：
文献类型：
--
作者：
Lee, S
本研究的目的是评估在马来西亚槟城的滑坡灾害，使用地理信息系统（GIS）和遥感。滑坡的位置确定在研究领域的航空照片和实地调查的解释。收集、处理了地形和地质数据以及卫星图像，并利用地理信息系统和图像处理技术将其建成一个空间数据库。选定的影响滑坡发生的因素有：地形坡度、地形纵横比、地形曲率和离排水系统的距离，均来自地形数据库;岩性和离线性构造的距离，来自地质数据库;土地使用情况，来自专题成像仪卫星图像;植被指数值，来自地球观测试验系统卫星图像。利用滑坡发生因子，采用Logistic回归模型，对滑坡危险区进行了分析和制图。利用滑坡定位数据对分析结果进行了验证，并与概率模型进行了比较。验证结果表明，Logistic回归模型的预测效果优于概率模型。
The aim of this study is to evaluate the hazard of landslides at Penang, Malaysia, using a Geographical Information System (GIS) and remote sensing. Landslide locations were identified in the study area from interpretation of aerial photographs and from field surveys. Topographical and geological data and satellite images were collected, processed and constructed into a spatial database using GIS and image processing. The factors chosen that influence landslide occurrence were: topographic slope, topographic aspect, topographic curvature and distance from drainage, all from the topographic database; lithology and distance from lineament, taken from the geologic database; land use from Thematic Mapper (TM) satellite images; and the vegetation index value from Systeme Probatoire de l'Observation de la Terre ( SPOT) satellite images. Landslide hazardous areas were analysed and mapped using the landslide-occurrence factors by logistic regression model. The results of the analysis were verified using the landslide location data and compared with probabilistic model. The validation results showed that the logistic regression model is better in prediction than probabilistic model.