Landslide Identification from Remote Sensing and GIS with Data Ming-A case study in the Shihmen Reservoir Watershed

• Main Authors: Tesn-Yu Lin, 林岑彧
• Other Authors: Fuan Tsai
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/44779874400517736620

碩士 === 國立中央大學 === 土木工程研究所 === 98 === The fractural geological conditions in Taiwan have caused serious landslides in mountainous regions after typhoon or earthquake every year. Remote sensing and other spatial data have been used successfully to evaluate and monitor landslide hazards. Satellite remote sensing and GIS-based data are effective sources to obtain information about environmental conditions covering large areas with high spatial details. For landslide related issues, the effect of environmental characteristics on the probability of landslide is an important factor and commonly used to predict landslide risks. In addition, other spatial data, such as digital terrainn model (DTM), land-cover types, vegetation, soil, and other natural and man-made factors may all contribute to the prediction of landslide susceptibility. This study utilizes data mining techniques to analyze complicated datasets in order to understand landslide risks in the Shihmen Reservoir watershed located in northern Taiwan. An inventory of collected known landslides caused by typhoons from 2004 to 2007 in the study site is used as training data. Decision rules for detecting landslide from selected attributes have been established. The rules are applied to predict landslides induced by typhoons. The rules constructed from decision tree algorithms are refined to improve the classification accuracy. The identification accuracy is about 79% for the test data with 2004 Aere typhoon. With the developed algorithms and data mining techniques, landslides induced by heavy rainfall can be mapped efficiently from remotely sensed images and geo-spatial analysis.