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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/81999

    Title: 結合多光譜衛星影像與機器學習方法建立颱風事件崩塌地自動化辨識系統與崩塌因子分析( I );Combining Multi-Spectral Satellite Imagery and Machine Learning Methods to Establish Automatic Identification System of Landslides and Factor Analysis of Typhoon Events( I )
    Authors: 林遠見
    Contributors: 國立中央大學土木工程學系
    Keywords: 颱風事件;崩塌因子;衛星影像辨識;機器學習;Typhoon events;landslide factors;satellite image recognition;machine learning
    Date: 2020-01-13
    Issue Date: 2020-01-13 14:03:12 (UTC+8)
    Publisher: 科技部
    Abstract: 台灣因地理位置特殊,其地質、地形與氣候等條件,每年颱風、梅雨挾帶充沛降雨量常造成山崩、土石流等坡地災害。近年來,極端降雨事件發生的頻率逐漸增加,而極端降雨所引發的災害型態,已由過去單純局部區域的洪水、土砂災害,轉變為大規模區域的洪水與土砂複合型災害同步發生。例如2009年的莫拉克與2015年的蘇迪勒颱風。而隨著空間資訊技術的進步,衛星遙測影像因為拍攝範圍廣、拍攝週期短,常被用作坡地災害監測。尤其近年來,台灣自製福爾摩沙衛星二號及福爾摩沙衛星五號提供了更高時空解析度的台灣地區衛星影像。因此,本研究嘗試結合多光譜衛星影像與各種機器學習方法建立颱風事件崩塌地自動化辨識系統,與國家實驗研究院國家太空中心(NSPO)合作,應用及推廣福爾摩沙衛星二號及福爾摩沙衛星五號資料,進行坡地災害發生前後快速辨識之方法開發。同時,在崩塌辨識系統建立完成後,透過衛星影像分析歷史坡地災害型態。其次,透過本研究室原創開發之颱風特徵指標(Typhoon Type Index)及颱風特徵與降雨型態(Rainfall pattern)分類,探討不同類型或條件下對於崩塌地空間分布的影響。並進一步建立颱風特性與降雨特徵等致災因子對於坡地災害的空間與規模分佈的預測模式,期望可作為坡地災害辨識、災害情資分析、防災應變以及災害風險預警應用。 ;Due to its special geographical location, its geology, topography and climate, Taiwan's typhoon and plum rains often cause landslides and mudflows. In recent years, the frequency of extreme rainfall events has gradually increased, and the types of disasters caused by extreme rainfall have changed from only floods in the past to large-scale floods and landslide compound and complex disasters. For example, Typhoon Morakot in 2009 and Typhoon Soudelor in 2015. With the advancement of spatial information technology, satellite remote sensing images are often used as slope disaster monitoring because of their wide range of sensing and short sensing periods. In particular, in recent years, FORMOSAT-2 and FORMOSAT-5 made in Taiwan have provided satellite imagery of Taiwan in higher spatial and temporal resolution. Therefore, this study attempts to establish a typhoon event landslide automatic recognition system based on multi-spectral satellite imagery and various machine learning methods, and cooperate with the National Space Organization (NSPO) of the National Applied Research Laboratories to apply and promote FORMOSAT-2 and FORMOSAT-5 data, and develop the method of rapid identification before and after the occurrence of landslide disasters. At the same time, after the establishment of the landslide identification system, the historical landslide type is analyzed through satellite imagery. Secondly, through the Typhoon Type Index developed by our lab, typhoon characteristics and Rainfall pattern classification, the effects of different types or conditions on the spatial distribution of landslides were discussed. Furthermore, the prediction model of the spatial and scale distribution of landslide disasters caused by typhoon characteristics and rainfall characteristics is further established. It is expected to be used for landslide disaster identification, disaster information analysis, disaster prevention and response, and disaster risk warning application.
    Relation: 財團法人國家實驗研究院科技政策研究與資訊中心
    Appears in Collections:[土木工程學系 ] 研究計畫

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