Combining Landform Thematic Layer and Object-Based Image Analysis to Map the Surface Features of Mountainous Flood Plain and Surrounding Areas

• Main Authors: Hsin-Kai Chuang, 莊心凱
• Other Authors: Ming-Lang Lin
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/29428367125616072948

碩士 === 國立臺灣大學 === 土木工程學研究所 === 100 === The Typhoon Morakot on August 2009 brought more than 2,000 mm of cumulative rainfall in southern Taiwan, the extreme rainfall event caused serious damage to the Kaoping River basin. The losses were mostly blamed on the landslides along sides of the river, and shifting of the watercourse even led to the failure of roads and bridges, as well as flooding and levees damage happened around the villages on alluvial plain and river terraces. Alluvial fans resulted from debris flow of stream feeders blocked the main watercourse and debris dam was even formed and collapsed. These disasters have highlighted the importance of mapping the watercourse alteration, surface features of flood plain area and artificial structures soon after the catastrophic typhoon event for natural hazard mitigation. Interpretation of remote sensing images is an efficient approach to acquire spatial information for vast areas, therefore making it suitable for the interpretation of terrain and surface features near the vast flood plain areas in a short term. The object-oriented image analysis program (Definiens Developer 7.0) and multi-band high resolution satellite images (QuickBird) were utilized to interpret the flood plain features from Liouguei to Baolai of the Kaoping River basin after Typhoon Morakot. Object-based image analysis split an image into meaningful homogenized segments for obtaining information such as shapes, textures, area and the mutual relationships of segments. After segmentation, we develop classification criteria to classify the image segments semi-automatically. Specific “landform thematic layers” produced by digital terrain models (DTM) are also employed along with the above process. These layers are especially helpful in differentiating some confusing categories in the spectrum analysis with improved accuracy, such as landslide and riverbed. The river terraces and alluvial plain, which are with significant meaning in terrain, can also be defined qualitatively. Finally, a fast, quantitative, large-scale and semi-automated image interpretation process is proposed. The result including river channel, vegetation, sandbar, alluvial plain, river terrace, alluvial fan, landslide, and the nearby artificial structures of mountainous flood plain and surrounding areas. It can be used as references for safety evaluation of riverside engineering structures, disaster prevention and mitigation, and even future land-use planning.