An Optical-Topographic Fusion Approach for Landslide Detection

• Main Authors: Justine Douglas, 賈絲汀
• Other Authors: Shou-Hao Chiang
• Format: Others
• Language: en_US
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/23de5c

碩士 === 國立中央大學 === 國際永續發展碩士在職專班 === 105 === Landslide hazards are common in Taiwan due to its mountainous topography and high number of earthquakes and typhoons experienced yearly. It is essential to develop a method of landslide detection that is capable of providing results with a reasonable level of accuracy, and may also be integrated into an early warning or monitoring system. Object Based Image Analysis (OBIA) is a new method of geographical image investigation which uses segmentation to analyze and process images. Unlike traditional methods, segmentation allows for easy integration of ancillary data during the research process, allowing for the creation of more accurate results. This research attempts to (1) detect and map landslides which occurred following Typhoon Morakot in 2009, (2) incorporate topographic attributes with known effects on the landsliding process (slope, aspect, elevation, curvature, and convexity) directly into the classification process using OBIA and (3) determine the benefits of using segmentation in the landslide mapping process by. A very high resolution 8m FORMOSAT-2 image of the Huaguoshan Basin was used in combination with topographic attributes derived from a 10m digital elevation model for the study area. In order to prove the effectiveness of the topographic attributes in the classification process, the segmentation and classification were first performed with topographic attributes before repeating the process after their removal. The results were subsequently classified based on 6 land use types present within the study area – bare soil, channel, forest, landslide runout and landslide source. Once the classification the accuracy of the process assessed using a random point sampling method. Landslide areas were detected with an overall accuracy of 81.8% and kappa value of 0.64 when using topographic attributes and with an overall accuracy of 77.5% and kappa value of 0.55 without them. The addition of topographic attributes assisted in reducing the amount of misclassifications that occurred in shadowed forest areas and helped separate small urban areas from the surrounding landslide and bare soil areas. This indicates that the integration of topographic attributes is a good means of improving classification accuracy in mountainous areas such as the Huaguoshan basin.