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研究生:林正杰
研究生(外文):Cheng-JieLin
論文名稱:運用多期福衛二號高時空分辨率影像建立崩塌災害預警模式
論文名稱(外文):Identification and validation of inventory-based susceptibility model for landslide pontential assessment and hazard warning
指導教授:張智華張智華引用關係
指導教授(外文):Chih-Hua Chang
學位類別:碩士
校院名稱:國立成功大學
系所名稱:環境工程學系碩博士班
學門:工程學門
學類:環境工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:188
中文關鍵詞:崩塌颱風高屏溪流域衛星遙測
外文關鍵詞:LandslideTyphoonLSIRemote sensing
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台灣因地形陡峭、地質年輕且位於環太平洋地震帶上,屬複合型坡地災害熱點,再加上鄰近西太平洋颱風形成的區域,夏、秋兩季常有颱風侵襲及極端降雨事件誘發崩塌災害的發生,造成降雨事件所帶來的財產損失及人員傷亡。國人自主之福衛二號衛星影像完整保存過去七年來(2005~2012年)台灣地區歷經各種天然災害事件後的地表記錄,是分析致災因子及建立其預警模式的影像資料庫。因此本研究運用福衛二號多期影像之優勢,選取2005年海棠、2007年聖帕及柯羅莎、2008年卡玫基、2010年凡那比以及2011年南瑪都等六颱風事件研究區域之前後期影像,利用福衛二號影像自動處理系統(Formosat-2 Automatic Image Processing System, F-2 AIPS)(Liu 2006)製成2m 解析度的正射影像(Liu and Chen 2009)、進行崩塌及陰影區的專家判釋分析及利用GIS分析方法建立各事件之新增崩塌圖資。
 Identification and validation of inventory-based susceptibility model for landslide pontential assessment
本研究以高屏溪流域內ㄧ處為研究區域,首先運用各種地理圖資及地理資訊系統(Geographic Information System, GIS)空間分析方法,將地理潛勢因子依特性區分為地質因子、地形因子、水文因子以及區位因子等四大類,並根據文獻使用次數,以及因子與崩塌之相關性統計,選出最常見的五個因子(坡度、岩性強度、距水系的距離、地形曲率以及土壤),隨後,探討及確認這五個地理潛勢因子計算崩塌潛勢指標(Landslide Susceptibility Index, LSI)之最佳組合,並以不同計算方法製作研究區域的崩塌潛勢圖層。驗證及確立各因子間之關係,統計發現坡度與岩性強度因子以及水系與地形曲率因子之間的相互影響,以此關係建立之LSI驗證結果最佳。
 Identification and validation of inventory-based susceptibility model for landslide hazard warning
藉由前五期颱風事件的最新雷達雨量系統(Quantitative Precipitation Estimation-Segregation Using Multiple Sensor, QPESUMS)的最大連續3小時級最大連續6小時的降雨記錄,探討降雨與地形效應之相互關係,建立新的致災因子,且以不同迴歸方法分析新增崩塌地LSI與致災因子之統計關係進而建立最佳的崩塌預警模式,之後將2011年的南瑪都颱風事件進行崩塌預警模式的驗證。新致災因子在不同迴歸分析下驗證結果均優於原致災因子,顯示降雨與地形效應關係的重要性,並以LSI、新致災因子以及簡線型迴歸分析為最佳。

研究結果顯示,六期颱風事件前後期皆有明顯的新增崩塌(0.2~0.8%) ,又以海棠颱風的新崩塌地比例(0.74%)為最高;大部分新增崩塌地均發生於LSI較高的區位。簡線型迴歸所構成之崩塌預警模式較為精確,可提供未來坡地災害預警之參考,讓應變模式由被動等待災害情報轉為主動預警疏散。

The high mountains, steep slope, broken terrain and frequent earthquakes, together with the heavy rainfall during the rainy and typhoon seasons, cause more and more geohazards of landslides and debris, as well as a considerable loss of lives and properties in mountainous areas of Taiwan. To enhance the capability of disaster response and mitigation, remotely sensed imagery and geospatial information have been collected national-wide in the past two decades using a variety of multi-stage platforms and sensors. Among those spatial-information collected, the archive of Formosat-2 imagery provides high temporal-spatial resolution surface data of Taiwan during the past 7.5 years, and promising to be a key data for analyzing disaster-causing factors and establishing landslide hazard prediction models.
Three study areas within the Gaoping River Basin were chosen in this work. By integrating various GIS data and spatial analysis approaches, a Landslide Susceptibility Index (LSI) evaluation method was developed base on the slope value, geological data and drainage orders. The pre- and post- event images of Typhoon Haitang (2005), Typhoon Sepat (2007), Typhoon Krosa (2007), Typhoon Kalmaegi (2007), Typhoon Fanapi (2010) and Nanmadol typhoon were obtained from the archive of Formosat-2 and processed to 2m resolution orthorectified images for landslide interpretation by the application of Formosat-2 Automatic Imager Processing System (F-2 AIPS). A new system combine expert and statistics that integrates all useful spatial information to assist the interpreters to determine the landslide areas quickly and accurately was employed to inventory the landslide data from the orthorectified images.
The result indicates that the newly developed landslides account for 0.2~0.8% of the study areas while the landslide caused by Typhoon Haitang reaches the highest proportion (0.74%) among the six events. The LSI maps are built for three study areas and found to have good agreement with the landslide inventory results. Additionally, three regression model that determines the rainfall-duration threshold for triggering landslide was developed by analyzing the relationships between the event precipitation, LSI and the landslide inventory result, and hence was employed to build three regression models estimating the rainfall-duration threshold for different for different levels of LSI. Both of the cross-event and cross-region.
This study encourages the use of our LSI evaluation method and the rainfall-duration threshold to generate a landslide hazard map for future slope disaster preventions.

誌謝 I
摘要 II
Abstract IV
圖目錄 VIII
表目錄 XII
第一章 緒論 1
1.1研究動機與目的 1
1.2論文架構與研究流程 2
第二章 文獻回顧 5
2.1崩塌災害類型 5
2.2崩塌清單 9
2.3崩塌影響因子 11
2.3.1評估崩塌影響因子 11
2.3.2崩塌影響因子之選取 18
2.3.3單項因子潛勢評估: 24
2.3.4複項因子潛勢指標計算 29
2.4 致災因子 34
2.5崩塌預警模式 37
第三章 研究區域與空間資訊 39
3.1研究區域之地理圖資 44
3.1.1地形因子 44
3.1.2地質因子 46
3.1.3水文因子 49
3.1.4區位因子 50
3.1.5雨量因子 52
3.2研究區域崩塌清單 55
3.2.1颱風事件前後影像之選取 56
3.2.2福衛二號影像處理流程(F2-AIPS) 57
3.2.3崩塌及陰影區專家判釋系統 62
3.2.4崩塌目錄及變異分析 65
第四章 崩塌潛勢指標之建立與驗證 74
4.1單項崩塌因子之初步篩選及潛勢評估 74
4.1.1地形因子之潛勢評估-坡度 77
4.1.2地形因子之潛勢評估-地形曲率 78
4.1.3 地質因子之潛勢評估-岩性強度 80
4.1.4 地質因子之潛勢評估-土壤 81
4.1.5 水文因子之潛勢評估-水系 82
4.1.6區位因子之潛勢評估-距斷層的距離 84
4.1.7區位因子-距道路的距離 87
4.1.8區位因子-土地使用圖 90
4.1.9崩塌因子之選定 91
4.2崩塌潛勢指標之建立 94
4.2.1 崩塌潛勢因子間的關係 94
4.2.2 複項因子之崩塌潛勢指標計算 100
4.3 崩塌潛勢指標之驗證 101
4.4 崩塌潛勢指標之比較 115
第五章 崩塌預警模式之建立及驗證 118
5.1 致災因子 119
5.1.1 最大連續3及6小時降雨量(R1): 119
5.1.2 最大連續3及6小時降雨與地形效應值(R2) 123
5.2崩塌預警模式之建立 129
5.2.1 簡線型迴歸分析 (Simple Linear Regression, SLR) 129
5.2.2 Logistic迴歸分析 (Multivariate Logistic Regression, MLR) 134
5.2.3 因子相乘 (Factor Multiplication, FM) 140
5.3 崩塌預警模式之驗證與比較 143
5.3.1 崩塌預測成功率(MSR) 151
5.3.2 崩塌預測比(MSR1) 154
5.3.3 MSR與MSR1模式驗證之比較 157
5.3.4以MSR驗證崩塌預警模式與前人研究之比較 158
第六章 結論與建議 159
6.1 結論 159
6.2 建議 161
參考文獻 163
附錄A 多期福衛二號影像處理成果 167
附錄B 多期福衛二號影像崩塌判釋成果 178

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