臺灣博碩士論文加值系統

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

中文部份
1.王鑫，「遙測學」，大中國出版社，1977。
2.禹威樺，「建築物層狀元件之紅外線檢測與影像處理」，碩士論文，朝陽科技大學營建工程所，台中，2007。
3.工業技術研究院，「紅外線技術之應用」，計量產業透析，1992.04。
4.黃怡碩、林商譽、吳協政，「利用Landsat ETM+熱波段影像於災後復健狀況評估」，第二十九屆測量及空間資訊研討會，台北，第167頁，2010。
5.Incropera、DeWitt著，侯順雄、王松浩、張仲卿譯，「熱傳遞」，高立圖書有限公司，台北，2006。
6.Gonzalez.Woods著，繆紹綱譯，「數位影像處理」，培生教育出版集團，台北，200
英文部分
1.Avery, T. E. and G. L. Berlin., “Fundamentals of Remote Sensing and Air photo interpretation,” (fifth edition). Prentice-hall, Upper Saddle River, New Jersey 07458,1992.
2.B.K.P.Horn and R.J.Woodham., “Destriping Landsat MSS images by histogram modification,” Comput. Graph. Image Process., vol. 10, no. 1,pp. 69–83,1979.
3.B. Datt, T. R. McVicar, T. G. Van Niel, D. L. B. Jupp, and J. S. Pearlman, “Preprocessing EO-1 Hyperion hyperspectral data to support the application of agricultural indexes,” IEEE Trans. Geosci. Remote Sens., vol.41, no6 pp. 1246–1259, 2003.
4.Betts, A., Ball, J., Beljaars, A., Miller, M., & Viterbo, P., “The land surface– atmosphere interaction: A review based on observational and global modeling perspectives,” Journal of Geophysical Research, 101, 7209– 7225,1996.
5.Byrne, G. F., “Remotely sensed land cover temperature and soil water status—a brief review. Remote Sensing of Environment,” 8, 291–305,1979.
6.Carnahan, W. H., & Larson, R. C., “An analysis of an urban heat sink. Remote Sensing of Environment,” 33, 65–71,1990.
7.Carson, T. N., Gillies, R. R., & Perry, E. M., “A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover,” Remote Sensing Reviews, 9, 161– 173,1994.
8.Caselles, V., Coll, C., Valor, E., & Rubio, E., “Mapping land surface emissivity using AVHRR data: Application to La Mancha,” Spain. Remote Sensing Reviews, 12, 311– 3330,1995.
9.Chung G and Vese LA., “Image segementation using a multilayer level-set approach,” Computing and Visualization in Science, Springer,2008.
10.Cooper, D. I. and Asrar, G., “Evaluating atmospheric correction models for retrieving surface temperature from AVHRR over a tall-grass prairie,” Remote Sensing of Environment, Vol 27, pp.93-102,1989.
11.Coll, C., Caselles, V. and Galve, J.M., “Ground measurements for the validation of the land surface temperatures from AASTER and MODIS data,” Remote Sensing of Environment, Vol. 97, pp. 288-300,2005.
12.Criminisi, A., Perez, P., and Toyama, K., “Region Filling and Object Removal by Exemplar-Based Image Inpainting,” IEEE Transactions On Image Processing, Vol. 13, No. 9,2001.
13.Dash, P., Gottsche, F. -M., Olesen, F. -S., & Fischer, H., “Land surface temperature and emissivity estimation from passive sensor data: Theory and practice-current trends. International Journal of Remote Sensing,” 23(13), 2563–2594,2002.
14.E.Choi and M.G.Kang., “Striping noise removal of satellite images by nonlinear mapping,” in Image Analysis and Recognition, vol. 4142.Berlin, Germany: Springer-Verlag, pp. 722–729,2006.
15.F.Kruse., “Comparison of AVIRIS and Hyperion for hyperspectral mineral mapping,” in Proc. 11th JPL Airborne Geosci. Workshop, Pasadena, CA,2002.
16.F.L.Gadallah ,F.Csillag, and E.J.M.Smith., “Destriping multisensory imagery with moment matching,” Int. J. Remote Sens., vol. 21, no. 12,pp. 2505–2511,2000.
17.Franca, G. B., & Cracknell, A. P., “Retrieval of land and sea surface temperature using NOAA-11 AVHRR data in north-eastern Brazil,” International Journal of Remote Sensing, 15, 1695–1712,1994.
18.Friedl, M. A., & Davis, F. W., ”Sources of variation in radiometric surface temperature over a tallgrass prairie,” Remote Sensing of Environment,48, 1 – 17,1994.
19.G.Corsini, M.Diani, and T.Walzel., “Striping removal in MOS-B data, IEEE Trans.Geosci,” Remote Sens., vol. 38, no. 3, pp. 1439–1446,2000.
20.G. Chander, D. L. Helder, and W. C. Boncyk, “Landsat-4/5 BAND 6 relative radiometry,” IEEE Trans. Geosci. Remote Sens., vol. 40, no.1 ,pp. 206–210,2002.
21.Gillies, R. R., & Carlson, T. N., “Thermal remote sensing of surface soil water content with partial vegetation cover for incorporation into climate models. Journal of Applied Meteorology,” 34, 745– 756,1995.
22.Gillies, R. R., Carlson, T. N., Cui, J., Kustas, W. P., & Humes, K. S., “A verification of the ‘triangle’ method for obtaining surface soil water content and energy fluxes from remote measurements of the Normalized Difference Vegetation index (NDVI) and surface radiant temperature.International Journal of Remote Sensing,” 18, 3145– 3166,1997.
23.Heeger, D., and J. Bergen., “Pyramd based texture analysis/synthesis,” Computer GraPhics SIGGRAPH ,pp.229-238,1995.
24.I.N.Aizenberg and C.Butakoff., “Frequency domain medianlike filter for periodic and quasi-periodic noise removal,” in Proc. SPIE—Image Processing: Algorithms Systems,vol. 4667, pp. 181–191,2002.
25.International Conference on Computer Graphics and Interactive Techniques Proceedings of the 27th annual conference on Computer graphics and interactive techniques, Pages 417-424.
26.J.Chen, Y.Shao, H.Guo, W.Wang, and B.Zhu., “Destriping CMODIS data by power filtering,” IEEE Trans. Geosci. Remote Sens., vol. 41, no. 9, pp. 2119–2124,2003.
27.J.D.Dykstra and D.B.Segal., “Analysis of AIS data of the recluse oil field, Recluse Wyoming,” in Proc. AIS workshop,Pasadena,CA,Apr., pp. 86–91. ser. JPL Publication 85-41,1985.
28.J.G.Liu and G.L.K.Morgan., “FFT selective and adaptive filtering for removal of systematic noise in ETM+ imageodesy images,” IEEE Trans. Geosci. Remote Sens., vol. 44, no. 12, pp. 3716–3724,2006.
29.Jime’nez-Munˇ oz, J.C., Sobrino, J.A., “A generalized singlechannel method for retrieving land surface temperature from remote sensing data,” Journal of Geophysical Research 108, 1,2003.
30.J.J.Simpson, J.R.Stitt, and D.M.Leath., “Improved finite impulseresponse filters for enhanced destriping of geostationary satellite data,” Remote Sens. Environ., vol. 66, no. 3, pp. 235–249,1998.
31.J. Torres and S. O. Infante., “Wavelet analysis for the elimination of striping noise in satellite images,” Opt. Eng., vol. 40, no. 7, pp. 1309–1314,2001.
32.Kim, H. H., “Urban heat island. International Journal of Remote Sensing,” 13, 2319– 2336,1992.
33.Landsat Project Science Office., “Landsat 7 Science Data User’s Handbook,” Goddard Space Flight Center, Greenbelt, MD,2002.
34.Liang, L., C. Liu, Y. Q. Xu, B. Guo, and H.Y. Shum., “Real-time texture synthesis by patch-based sampling,” in ACM Trans. on Graphics, vol. 20, pp. 127-150,2001.
35.M.Wegener., “Destriping multiple sensor imagery by improved histogram matching, Int. J. Remote Sens.,” vol. 11, no. 5, pp. 859–875,1990.
36.Mumford, D. and Shah, J., “Optimal approximation by piecewise smooth functions and associated variational problems,” Communications on Pure and Applied Mathematics, 42:577-685,1989.
37.Nichol, J. E., “A GIS-based approach to microclimate monitoring in Singapore’s high-rise housing estates,” Photogrammetric Engineering and Remote Sensing, 60, 1225– 1232,1994.
38.Norman, J.M., Chen, J.L. and Goel, N.S., “Thermal emissivity and infrared temperature dependence of plant canopy architecture and view angle,” 10th Annual international Geoscience and Remote Sensing Symposium, pp. 1747-1750,1990.
39.Oke, T.R., “Technical Note No. 169: Review of Urban Climatology, World Meteorological Organization,” Geneva, Switzerland, 43 pp,1979.
40.Osher,S. and Sethian,J., “Fronts propagationing with curvature-dependent speed - algorithms based on Hamilton-Jacobi formulations, Journal of Computational Physics,” 79, pp. 12-49,1988.
41.Owen, T. W., Carlson, T. N., & Gillies, R. R., “An assessment of satellite remotely-sensed land cover parameters in quantitatively describing the climatic effect of urbanization,” International Journal of Remote Sensing, 19, 1663–1681,1998.
42.Prata, A. J., “Land surface temperatures derived from the advanced very high resolution radiometer and the along-track scanning radiometer,” 1. Theory. Journal of Geophysical Research, 98, 16689– 16702,1993.
43.Portkka,J.,and E.P. Simoncelli., “A parametic texture model based on joint statistics of complex wavelet coefficients,” Int’1J. Computer Vision, vol.40,No.1,pp.49-71,2000.
44.Qin, Z., Karnieli, A., Berliner, P., “A mono-algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel–Egypt border region,” International Journal of Remote Sensing 22 (18), 583–594,2001.
45.Qin, Z., Karnieli, A. and Berliner, P., “A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region,” International Journal of Remote Sesning, 28, p. 3719-3746,2001.
46.R.Nishii, T.Yanagimoto, and K. Kusanobu., “The use of univariate Bayes regression models for spatial smoothing,” Comput. Stat. Data Anal., vol. 24, no. 3, pp. 321–336,1997.
47.R. Srinivasan and M. Cannon, “Landsat data destriping using power spectral filtering,” Opt. Eng., vol. 27, no. 11, pp. 939–943,1988.
48.Schmugge, T., Hook, S. J., & Coll, C., “Recovering surface temperature and emissivity from thermal infrared multispectral data,” Remote Sensing of Environment, 65, 121– 131,1998.
49.Schott, J.R., Barsi, J.A. and Nordgren, B.L., “Calibration of Landsat thermal data and application to water resource studies,” Remote Sesning of Environment, 78, pp. 108-117,2001.
50.Snyder, W.C., Wan, Z., Zhang, Y., Feng, Y.Z., “Classification-based emissivity for land surface temperature measurement from space.” International Journal of Remote Sensing 19 (14), 2753–2774,1998.
51.Sobrino, J.A, Raissouni, N. and Li, Z.,” A comparative study of land surface emissivity retrieval from NOAA data,” Remote Sensing of Environment, 75, pp. 256-266,2001.
52.Srivastava, P.K., Majumdar, T.J. and Bhattacharya, A.K., “Surface temperature estimation in Singhbhum Shear Zone of India using Landsat-7 ETM+ thermal infrared data,” Advances in SpaceResearch, 43, pp. 1563-1574,2009.
53.Streutker, D. R., “A remote sensing study of the urban heat island of Houston,” Texas. International Journal of Remote Sensing,23, 2595– 2608,2002.
54.Voogt, J.A. and Oke, T.R., ”Effects of urban surface geometry on remote sensed surface temperature,” International Journal of Remote Sensing, 19(5), pp. 895-920,1998.
55.Voogt, J.A., Oke, T.R., ”Thermal remote sensing of urban climates,” Remote Sensing of Environment 86, 370–384,2003.
56.Wei, L.Y., and M. Levoy., ”Fast texture synthesis using tree-structured vector quantization,” in Proc. ACM Conf. Computer Graphics, pp. 479-488,2000.
57.Weng, Q., Lu, D. and Schubringa, J., ”Estimation of landsurface temperature vegetation abundance relationship for urban heat island studies”, Remote Sensing of Environment, 89, pp. 467-483,2004.
58.Wukelic, G.E., Gibbons, D.E., Martucci, L.M. and Foote, H. P., “Radiometric calibration of Landsat Thematic Mapper thermal band”, Remote Sensing of Environment, Vol. 28, pp. 339-347,1989.
59.Z.-Y.Liu, K.-C.Chiu, and L.Xu., “Strip line detection and thining by RPCL-based local PCA, Pattern Recognit”, Lett., vol. 24, no. 14,pp. 2335–2344,2003.