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研究生:謝啟文
研究生(外文):Wen HsiehChi
論文名稱:利用Landsae ETM+熱波段影像於災害後復健狀況評估
論文名稱(外文):Evaluating the Restoring Conditions for Those Areas Visited with Natural Disasters with Employing Landsat ETM+Thermal Infrared Data
指導教授:黃怡碩黃怡碩引用關係
指導教授(外文):Yi-Shuo Huang
學位類別:碩士
校院名稱:朝陽科技大學
系所名稱:營建工程系碩士班
學門:工程學門
學類:土木工程學類
論文種類:學術論文
論文出版年:2011
畢業學年度:99
語文別:中文
論文頁數:66
中文關鍵詞:災害後重建多重水平集地表溫度
外文關鍵詞:Land Surface TemperatureLevel Set ApproachThermal Infrared
相關次數:
  • 被引用被引用:1
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如何有效地評估災害後重建情況,一直是防災上重要且容易被忽略的研究課題。隨著環境變遷與溫室效應的影響,自然災害侵襲台灣已成為定規,幾乎已成為每年必定上演的戲碼;防災單位多著重災情調查與事前防災策略的擬定與執行,鮮少作後續的災後重建評估,以致常無法有效地提供重建情形的量化指標,供執政者評估災後重建情況。為了解決此一問題,利用遙感探測,則提供一有效的評估方法。地表表面溫度(Land Surface Temperature, LST)可以用來表示地上人口聚集的多寡;當人口聚集越多時,該區域的地表溫度也會隨之增高,形成熱景緻型態(Thermal Landscape Pattern, TLP)。相較於鄰近具有植被或親水區域,地表溫度在都市或人口密集的區域會有較高的數值。Landsat ETM+所提供熱影像,藉著影像數值的轉換,可將影像數值轉換成地表溫度,藉以評估地表溫度變化的趨勢。本文利用Landsat ETM+ 所獲得的熱影像,將像素數值轉換成地表表面溫度,藉著多重水平集將地表面溫度根據事前決定的層級與予分析,使得區域地表溫度分佈特徵能夠被有效地分類表示;利用比較分析災前與災後地表溫度的空間分佈模式,決定災後重建的情況。本文採用四幅2009年2月24日、5月31日、9月4日及12月9日的Landsat 7 SLC-off衛星影像 ,針對台灣中部陳有蘭溪流域在莫拉克颱風侵襲前後,進行地表表面溫度分佈模式分析,建立地表表面的溫度分佈空間模式。
Evaluating the restoration conditions for those areas affected by natural disasters is an important research issue in disaster prevention and protection programs. The issue needs to simultaneously evaluate the hardware (the restored conditions for the physical environment) as well as the software (the restored conditions for psychical development). This thesis focuses on evaluating the hardware restoration conditions using the thermal infrared remote sensing. In this thesis, the study area lies between the longitudes of - E and latitudes of - N, and is an area prone to typhoon attacks. The studied area suffered serious damage from Typhoon Morakot on 2009/08/08. Landsat-7 ETM+ SLC-Off scenes collected on 2009/2/24,2009/05/31,2009/09/04 and 2009/12/09, were used to generate land surface temperature (LST) images. By employing the multilayer level set approach to segment the given LST images, landscape patterns for the study on different dates were identified and compared, in order to evaluate the time required for the landscape patterns to return to their original states. The multilayer level set approach employs two implicit functions with pre-selected level values. Based on the fact that the segmented regions are homogeneous and presented as regional constants, the energy defined by the segmented regions and their corresponding regional boundaries is minimized such that the relationships between the defined energy and the implicit functions can be transformed into the relationships between the implicit functions and time. By implementing the algorithm in terms of finite difference, this method offers an efficient and stable approach to a numerical solution. By increasing iterations and preselected level values, the implicit functions evolve close to the regional boundaries based on the energy minimization. This thesis demonstrates that the generated LST scenes can provide the opportunity for better understanding the land surface characteristics and landscape patterns, especially for those areas visited with natural disasters.
摘要 V
目錄 X
圖目錄 XII
第一章 緒論 1
1.1 研究動機 1
1.2 研究目的 2
1.3 研究流程圖 4
第二章 文獻回顧 5
2.1 遙感探測(Remote Sensing, RS) 5
2.2 影像修補 6
2.3 紅外線相關理論介紹 9
2.3.1 黑體輻射 10
2.3.2 普朗克定律(Planck`s Law) 11
2.3.3 史蒂芬-波茲曼定律(Stefan-Boltzmann Law) 12
2.3.4 韋恩位移定律(Wien''s displacement Law) 13
2.3.5 放射率(Emissivity) 13
2.3.6 熱傳遞基本概念 14
2.4 熱島效應 14
2.5 地表溫度計算方法 16
2.5.1 Mono-window Algorithm 18
2.5.2 Single-channel Algorithm 19
2.5.3 植被指數(NDVI)與地表溫度(LST)的關係 20
第三章 相關理論 22
3.1 層集多重分類法 22
3.2 影像相關匹配 28
第四章 實驗與分析 32
4.1 演算法 35

圖目錄
圖2.1 經過Spline間隔闡釋與未經間隔闡釋的像素數值比較(黃怡碩,2010) 9
圖2.2 經過Spline間隔闡釋與未經間隔闡釋的衛星影像及其所對應像素數值長條圖之比較(黃怡碩,2010) 9
圖2.3 模擬黑體(工業技術研究院,1994) 11
圖2.4 黑體輻射強度圖 12
圖3.1 分割子區域,事前定義的層集與 與 間之關係 22
圖3.2 Heaviside函數圖形 24
圖3.3 模板比對方法 29
圖3.4 (a) 2009年2月24日 30
圖3.4(b) 2009年5月31日 30
圖3.4(c) 共同區域 31
圖4.1 實驗流程圖 33
圖4.2 2009年12月9日陳有蘭溪流域衛星影像圖 34
圖4.3 Mono-window Algorithm顯示 36
圖4.4 Single-channel Algorithm顯示 36
圖4.5 配合NDVI顯示 37
圖4.6 (a) 以2D平面顯示 38
圖4.6(b) 以3D立體顯示 38
圖4.6(c) 將2D與3D套疊 39
圖4.7 (a) 疊代次數為500次 40
圖4.7(b) 疊代次數為1000次 41
圖4.7(c) 疊代次數為1500次 41
圖4.7(d) 疊代次數為2000次 42
圖4.7(e) 放射率 42
圖4.7(f) 地表溫度 43
圖4.8 (a) 疊代次數為500次 44
圖4.8(b) 疊代次數為1000次 45
圖4.8(c) 疊代次數為1500次 45
圖4.8(d) 疊代次數為2000次 46
圖4.8(e) 放射率 46
圖4.8(f) 地表溫度 47
圖4.9 (a) 疊代次數為500次 48
圖4.9(b) 疊代次數為1000次 49
圖4.9(c) 疊代次數為1500次 49
圖4.9(d) 疊代次數為2000次 50
圖4.9(e) 放射率 50
圖4.9(f) 地表溫度 51
圖4.10 (a) 疊代次數為500次 52
圖4.10(b) 疊代次數為1000次 53
圖4.10(c) 疊代次數為1500次 53
圖4.10(d) 疊代次數為2000次 54
圖4.10(e) 放射率 54
圖4.10(f) 地表溫度 55
圖4.11 疊代次數與能量間關係 56
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