探討颱風特性於農損及坡地災害遙測影像辨識之研究

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王玟心(Wen-Hsin Wang) 查詢紙本館藏

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土木工程學系

論文名稱

探討颱風特性於農損及坡地災害遙測影像辨識之研究
(The study of Typhoon characteristics on agricultural damage, landslide disasters, and the remote sensing image recognition on landslides.)

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摘要(中)

颱風帶來最直接的破壞就是狂風及豪雨，而每場颱風事件的特性差異性甚大，例如：影響時間、侵襲路徑、生成位置以及所帶來不同強度的豪雨及強風，不同強風豪雨致災因子的特性使得災損情形也不相同，臺灣無法避免颱風的侵襲，因此必須探討颱風特性及所造成的災害現象，才能於颱風來臨時，做好相關防災準備，降低颱風所造成的損害。
　　傳統上，描述颱風強度分類的依據為風速，然而在探討颱風災害時卻多以降雨量作為致災因子之參數，使得在災害評估中可能忽略了強風的影響性，因此本研究從颱風的風、雨兩特性之角度將颱風分為「風型颱風」以及「雨型颱風」，發展了創新之颱風分類指標(Typhoon Type Index)以將颱風於不同地區之風雨特徵偏向量化，並探討颱風的風雨特性於農作物損失與坡地災害觸發之影響機制。例如：2015年蘇迪勒颱風為典型的風型颱風(全台平均TTI =2.66 )，其所造成之作物損失主要以香蕉、柚子、芭樂等果樹類作物，而造成之崩塌點位之氣象資料中風速特徵整體來說高於降雨(崩塌點位TTI =2.74 )，說明該崩塌事件可能由強風所觸發。而2008年卡玫基颱風為一雨型颱風(全台平均TTI =-0.62)，所造成之農損以香瓜、蔬菜、西瓜等作物浸泡為主，而卡玫基引致之崩塌附近亦為雨型颱風分布(崩塌點位TTI = -0.76)，說明該崩塌事件由降雨誘發之貢獻更大。此外，本研究依照不同颱風風雨特徵的空間分布特性將颱風歷史事件分類，得到TTI空間分布相關性高之颱風通常具有相似颱風路徑。
　　在本研究最後一部分提出了以隨機森林機器學習模型建立多光譜遙測影像之颱風前後崩塌地自動辨識系統建立方法，結果透過圖像差分法結合雲指標之濾雲程序，可有效地偵測出風災後大規模崩塌以及崩塌聚集區之影像像素，崩塌分類準確度為0.77，已可辨識出崩塌地之主要輪廓。

摘要(英)

The most direct hazard factor caused by typhoons is strong winds and intense rainfall. Traditionally, the basis for describing the classification of typhoon intensity is wind speed. However, when discussing typhoon disasters, rainfall is often used as the hazard factor parameter, so that the influence of strong winds may be ignored in disaster assessment. Therefore, in this study, from the perspective of the wind and rain characteristics, the typhoon is divided into "wind-type typhoon" and "rain-type typhoon." An innovative Typhoon Type Index has been developed to quantitatively describe the typhoon′s rain-wind characteristics in different regions. We further analyze the impact mechanism of crop loss and landslides disaster triggering caused by typhoons. For example, Typhoon SOUDELOR(2015) was a typical wind-type typhoon (average TTI of the whole of Taiwan = 2.66). The crop losses caused by it were mainly fruit crops such as bananas, grapefruits, and guava. Moreover, for the landslides triggered by SOUDELOR, the overall wind speed characteristics are higher than the rainfall around the site of landslides (landslide site TTI = 2.74), indicating that strong winds may mainly trigger the landslide events. Typhoon KALMEAGI(2008) was a rain-type typhoon (average TTI of the whole Taiwan = -0.62). The agricultural damage caused was mainly soaking melons, vegetables, watermelons. Furthermore, the trigger of the landslides caused by KALMEAGI was mainly influent by the rainfall dominate (landslide site TTI = -0.76). Also, this study classifies historical typhoon events according to the spatial distribution characteristics of typhoon rain-wind characteristics. The result shows that the typhoons with high TTI spatial distribution correlation usually have similar typhoon track.
In the last part of this study, the Random Forest machine learning model is built to recognize the landslide pixels in the multispectral satellite image. The classification accuracy of the landslide is 0.77, and the main outline of the landslide area can be identified.

關鍵字(中)

★ 颱風特性
★ 颱風分類模型
★ 颱風農損
★ 颱風坡地災害
★ 遙測影像辨識
★ 機器學習

關鍵字(英)

★ Typhoon characteristic
★ Typhoon classification model
★ Typhoon caused agricultural damage
★ Typhoon caused landslide
★ remote sensing image identification
★ machine learning

論文目次

目錄
摘要 i
Abstract ii
誌謝 iii
目錄 iv
表目錄 vii
圖目錄 x
第一章緒論 1
1-1 研究背景與動機 1
1-1-1 颱風的風雨特徵 1
1-1-2 颱風特性的空間變異性 3
1-2 研究目的 6
1-3 論文架構 7
第二章文獻回顧 8
2-1 颱風災害特徵 8
2-2 颱風與農損 11
2-3 颱風與崩塌 12
2-4 遙測影像與崩塌地影像判釋相關研究 13
2-4-1 植生指數於崩塌地之應用 13
2-4-2 遙測影像雲處理 16
2-4-3 機器學習模型於遙測影像辨識之應用 18
第三章研究方法 21
3-1 研究架構 21
3-2 資料蒐集及描述 23
3-2-1 颱風資料 23
3-2-2 農損資料 25
3-2-3 歷史崩塌點位資料 25
3-2-4 衛星影像 28
3-2-5 數值地形模型資料 29
3-3 颱風分類模型與分類指標 31
3-3-1 颱風分類指標建立 – 統計迴歸模型 32
3-3-2 颱風分類指標空間推估—徑向基函數內插法 37
3-3-3 颱風分類指標空間相關性分析 — 聚合式階層分群法 42
3-4 機器學習方法於衛星影像崩塌地辨識之應用 45
3-4-1 圖像差分法 46
3-4-2 崩塌地特徵萃取 46
3-4-3 雲指數( Cloud index) 47
3-4-4 隨機森林分類模型與特徵重要性評估 49
第四章結果分析與討論 53
4-1 颱風分類模型結果分析 53
4-1-1 颱風分類模型與指標 53
4-1-2 颱風分類指標空間分布 59
4-2 TTI空間分布聚類分析與颱風路徑之探討 69
4-3 颱風分類指標應用：農作物於颱風災害易損性評估 98
4-4 颱風特徵與崩塌地分布之分析 103
4-4-1 蘇迪勒颱風於烏來地區之崩塌分析 104
4-4-2 全臺尺度颱風崩塌事件分析 110
4-5 隨機森林於遙測影像崩塌地辨識結果 124
4-5-1 圖像差分法與崩塌地特徵萃取 125
4-5-2 隨機森林模型結果與特徵重要性 127
第五章結論與建議 137
5-1 結論 137
5-2 建議 138
5-3 貢獻 139
參考文獻 140
附　錄　一 151
附　錄　二 153
評審意見回覆表 155

參考文獻

參考文獻
[ 1 ] Akar, Ö., & Güngör, O. (2012). Classification of multispectral images using Random Forest algorithm. Journal of Geodesy and Geoinformation, 1(2), 105-112.
[ 2 ] Alpaydin, E. (2020). Introduction to machine learning: MIT press.
[ 3 ] Bandyopadhyay, S., Maulik, U., & Mukhopadhyay, A. (2007). Multiobjective genetic clustering for pixel classification in remote sensing imagery. IEEE Transactions on Geoscience and Remote Sensing, 45(5), 1506-1511.
[ 4 ] Bi, M., Li, T., Peng, M., & Shen, X. J. J. o. t. A. S. (2015). Interactions between Typhoon Megi (2010) and a low-frequency monsoon gyre. 72(7), 2682-2702.
[ 5 ] Caine, N. J. G. a. s. A., physical geography. (1980). The rainfall intensity-duration control of shallow landslides and debris flows. 62(1-2), 23-27.
[ 6 ] Carrara, A. J. J. o. t. I. A. f. M. G. (1983). Multivariate models for landslide hazard evaluation. 15(3), 403-426.
[ 7 ] Chang, C.-C., & Lin, C.-J. (2011). LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol., 2(3), Article 27. doi:10.1145/1961189.1961199
[ 8 ] Chang, C., Yeh, T., & Chen, J. J. M. w. r. (1993). Effects of terrain on the surface structure of typhoons over Taiwan. 121(3), 734-752.
[ 9 ] Chang, K.-T., Chiang, S.-H., & Hsu, M.-L. J. G. (2007). Modeling typhoon-and earthquake-induced landslides in a mountainous watershed using logistic regression. 89(3-4), 335-347.
[ 10 ] Chen, C.-S., Lin, Y.-L., Zeng, H.-T., Chen, C.-Y., & Liu, C.-L. J. A. r. (2013). Orographic effects on heavy rainfall events over northeastern Taiwan during the northeasterly monsoon season. 122, 310-335.
[ 11 ] Chen, W.-K., Sui, G.-J., & Tang, D. (2011). Predicting the economic loss of typhoon by case base reasoning and fuzzy theory. Paper presented at the 2011 International Conference on Machine Learning and Cybernetics.
[ 12 ] Chen, Z., Zhang, Y., Ouyang, C., Zhang, F., & Ma, J. (2018). Automated landslides detection for mountain cities using multi-temporal remote sensing imagery. Sensors, 18(3), 821.
[ 13 ] Chien, F., & Hung, C. J. J. o. G. R., D, doi:./JD. (2011). On the Extreme Rainfall of Typhoon Morakot ().
[ 14 ] Cogan, J., Gratchev, I., & Wang, G. J. L. (2018). Rainfall-induced shallow landslides caused by ex-Tropical Cyclone Debbie, 31st March 2017. 15(6), 1215-1221.
[ 15 ] Congalton, R. G. (2001). Accuracy assessment and validation of remotely sensed and other spatial information. International Journal of Wildland Fire, 10(4), 321-328.
[ 16 ] Crane, J., Balerdi, C., Campbell, R., Campbell, C., & Goldweber, S. J. H. (1994). Managing fruit orchards to minimize hurricane damage. 4(1), 21-27.
[ 17 ] Crozier, M. J. J. E. S. P., & Group, L. T. J. o. t. B. G. R. (1999). Prediction of rainfall‐triggered landslides: A test of the antecedent water status model. 24(9), 825-833.
[ 18 ] Day, W. H., & Edelsbrunner, H. (1984). Efficient algorithms for agglomerative hierarchical clustering methods. Journal of classification, 1(1), 7-24.
[ 19 ] Dean, A., & Smith, G. (2003). An evaluation of per-parcel land cover mapping using maximum likelihood class probabilities. International Journal of Remote Sensing, 24(14), 2905-2920.
[ 20 ] Du Toit, W. (2008). Radial basis function interpolation. Stellenbosch: Stellenbosch University,
[ 21 ] Elazab, A., Bort, J., Zhou, B., Serret, M. D., Nieto-Taladriz, M. T., & Araus, J. L. (2015). The combined use of vegetation indices and stable isotopes to predict durum wheat grain yield under contrasting water conditions. Agricultural Water Management, 158, 196-208.
[ 22 ] Escuin, S., Navarro, R., & Fernandez, P. (2008). Fire severity assessment by using NBR (Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) derived from LANDSAT TM/ETM images. International Journal of Remote Sensing, 29(4), 1053-1073.
[ 23 ] Gislason, P. O., Benediktsson, J. A., & Sveinsson, J. R. (2006). Random forests for land cover classification. Pattern Recognition Letters, 27(4), 294-300.
[ 24 ] Hall, T. M., & Jewson, S. (2007). Statistical modelling of North Atlantic tropical cyclone tracks. Tellus A: Dynamic Meteorology and Oceanography, 59(4), 486-498.
[ 25 ] Hardy, R. L. J. C., & Applications, M. w. (1990). Theory and applications of the multiquadric-biharmonic method 20 years of discovery 1968–1988. 19(8-9), 163-208.
[ 26 ] Hayes, M. M., Miller, S. N., & Murphy, M. A. (2014). High-resolution landcover classification using Random Forest. Remote sensing letters, 5(2), 112-121.
[ 27 ] Hong, H., Liu, J., Zhu, A.-X., Shahabi, H., Pham, B. T., Chen, W., . . . Bui, D. T. J. E. E. S. (2017). A novel hybrid integration model using support vector machines and random subspace for weather-triggered landslide susceptibility assessment in the Wuning area (China). 76(19), 652.
[ 28 ] Hossain, M., Islam, M., Sakai, T., & Ishida, M. (2008). Impact of tropical cyclones on rural infrastructures in Bangladesh. Agricultural Engineering International: CIGR Journal.
[ 29 ] Hsiang, S. M. (2010). Temperatures and cyclones strongly associated with economic production in the Caribbean and Central America. Proceedings of the National Academy of sciences, 107(35), 15367-15372.
[ 30 ] Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1-2), 195-213.
[ 31 ] Hunt, E. R., Cavigelli, M., Daughtry, C. S., Mcmurtrey, J. E., & Walthall, C. L. (2005). Evaluation of digital photography from model aircraft for remote sensing of crop biomass and nitrogen status. Precision Agriculture, 6(4), 359-378.
[ 32 ] Irish, R. R., Barker, J. L., Goward, S. N., & Arvidson, T. (2006). Characterization of the Landsat-7 ETM+ automated cloud-cover assessment (ACCA) algorithm. Photogrammetric engineering & remote sensing, 72(10), 1179-1188.
[ 33 ] Iverson, R. M. J. W. r. r. (2000). Landslide triggering by rain infiltration. 36(7), 1897-1910.
[ 34 ] Janssen, L. L., & Vanderwel, F. J. (1994). Accuracy assessment of satellite derived land-cover data: a review. Photogrammetric engineering and remote sensing;(United States), 60(4).
[ 35 ] John, G. H., Kohavi, R., & Pfleger, K. (1994). Irrelevant features and the subset selection problem. In Machine Learning Proceedings 1994 (pp. 121-129): Elsevier.
[ 36 ] Keerthi, S. S., & Lin, C.-J. (2003). Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel. Neural Computation, 15(7), 1667-1689. doi:10.1162/089976603321891855
[ 37 ] Kim, J., Jeong, S., & Regueiro, R. A. (2012). Instability of partially saturated soil slopes due to alteration of rainfall pattern. Engineering geology, 147, 28-36.
[ 38 ] Kuo, E.-D. (2019). 運用訊號分析方法於地下水資源旱災韌性與風險評估. National Central University,
[ 39 ] Lambin, E. F., Geist, H. J., & Lepers, E. (2003). Dynamics of land-use and land-cover change in tropical regions. Annual review of environment and resources, 28(1), 205-241.
[ 40 ] Lee, E. M., & Jones, D. K. (2004). Landslide risk assessment: Thomas Telford London.
[ 41 ] Lepore, C., Arnone, E., Noto, L., Sivandran, G., & Bras, R. (2013). Physically based modeling of rainfall-triggered landslides: a case study in the Luquillo forest, Puerto Rico.
[ 42 ] Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R news, 2(3), 18-22.
[ 43 ] Lin, C.-Y., Lo, H.-M., Chou, W.-C., & Lin, W.-T. (2004). Vegetation recovery assessment at the Jou-Jou Mountain landslide area caused by the 921 Earthquake in Central Taiwan. Ecological Modelling, 176(1-2), 75-81.
[ 44 ] Lin, Y.-C., Chang, T.-J., Lu, M.-M., & Yu, H.-L. (2015). A space-time typhoon trajectories analysis in the vicinity of Taiwan. Stochastic environmental research and risk assessment, 29(7), 1857-1866.
[ 45 ] Lin, Y.-C., Wang, W.-H., Lai, C.-Y., & Lin, Y.-Q. (2020). Typhoon Type Index: A New Index for Understanding the Rain or Wind Characteristics of Typhoons and Its Application to Agricultural Losses and Crop Vulnerability. Journal of Applied Meteorology and Climatology, 59(5), 973-989.
[ 46 ] Lin, Y.-L., Ensley, D. B., Chiao, S., & Huang, C.-Y. (2002). Orographic influences on rainfall and track deflection associated with the passage of a tropical cyclone. Monthly weather review, 130(12), 2929-2950.
[ 47 ] Liu, C.-C. (2015). Preparing a landslide and shadow inventory map from high-spatial-resolution imagery facilitated by an expert system. Journal of Applied Remote Sensing, 9(1), 096080.
[ 48 ] Liu, J.-K., & Shih, P. T. J. R. S. (2013). Topographic correction of wind-driven rainfall for landslide analysis in Central Taiwan with validation from aerial and satellite optical images. 5(6), 2571-2589.
[ 49 ] Lunetta, R. S., Knight, J. F., Ediriwickrema, J., Lyon, J. G., & Worthy, L. D. (2006). Land-cover change detection using multi-temporal MODIS NDVI data. Remote Sensing of Environment, 105(2), 142-154.
[ 50 ] Mondini, A. C., Chang, K.-T., & Yin, H.-Y. J. G. (2011). Combining multiple change detection indices for mapping landslides triggered by typhoons. 134(3-4), 440-451.
[ 51 ] Ologunorisa, T. E., & Tersoo, T. (2006). The changing rainfall pattern and its implication for flood frequency in Makurdi, Northern Nigeria. Journal of Applied Sciences and Environmental Management, 10(3), 97-102.
[ 52 ] Otukei, J. R., & Blaschke, T. (2010). Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. International Journal of Applied Earth Observation and Geoinformation, 12, S27-S31.
[ 53 ] Pal, M., & Mather, P. M. (2003). An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sensing of Environment, 86(4), 554-565.
[ 54 ] Perlich, C., Provost, F., & Simonoff, J. S. (2003). Tree induction vs. logistic regression: A learning-curve analysis. Journal of Machine Learning Research, 4(Jun), 211-255.
[ 55 ] Rippa, S. J. A. i. C. M. (1999). An algorithm for selecting a good value for the parameter c in radial basis function interpolation. 11(2-3), 193-210.
[ 56 ] Rogan, J., & Chen, D. (2004). Remote sensing technology for mapping and monitoring land-cover and land-use change. Progress in planning, 61(4), 301-325.
[ 57 ] Rokni, K., Ahmad, A., Selamat, A., & Hazini, S. (2014). Water feature extraction and change detection using multitemporal Landsat imagery. Remote sensing, 6(5), 4173-4189.
[ 58 ] Rwanga, S. S., & Ndambuki, J. M. (2017). Accuracy assessment of land use/land cover classification using remote sensing and GIS. International Journal of Geosciences, 8(04), 611.
[ 59 ] Sah, A., Sah, B., Honji, K., Kubo, N., & Senthil, S. (2012). Semi-automated cloud/shadow removal and land cover change detection using satellite imagery. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 39, B7.
[ 60 ] Senthilnath, J., Omkar, S., Mani, V., Tejovanth, N., Diwakar, P., & Shenoy, A. B. (2012). Hierarchical clustering algorithm for land cover mapping using satellite images. IEEE journal of selected topics in applied earth observations and remote sensing, 5(3), 762-768.
[ 61 ] Shao, G., & Wu, J. (2008). On the accuracy of landscape pattern analysis using remote sensing data. Landscape Ecology, 23(5), 505-511.
[ 62 ] Shimada, S., Matsumoto, J., Sekiyama, A., Aosier, B., & Yokohana, M. (2012). A new spectral index to detect Poaceae grass abundance in Mongolian grasslands. Advances in Space Research, 50(9), 1266-1273.
[ 63 ] Sun, L., Mi, X., Wei, J., Wang, J., Tian, X., Yu, H., . . . sensing, r. (2017). A cloud detection algorithm-generating method for remote sensing data at visible to short-wave infrared wavelengths. 124, 70-88.
[ 64 ] Tatem, A. J., Lewis, H. G., Atkinson, P. M., & Nixon, M. S. (2002). Super-resolution land cover pattern prediction using a Hopfield neural network. Remote Sensing of Environment, 79(1), 1-14.
[ 65 ] Tillman, C. W., Sivillo, J. K., Frolov, S. A. J. A., & Procedia, A. S. (2010). Managing typhoon related crop risk at WPC. 1, 204-211.
[ 66 ] Torok, M. M., Golparvar-Fard, M., & Kochersberger, K. B. (2014). Image-based automated 3D crack detection for post-disaster building assessment. Journal of Computing in Civil Engineering, 28(5), A4014004.
[ 67 ] Townshend, J., Justice, C., Li, W., Gurney, C., & McManus, J. (1991). Global land cover classification by remote sensing: present capabilities and future possibilities. Remote Sensing of Environment, 35(2-3), 243-255.
[ 68 ] Tucker, C. J. (1978). Red and photographic infrared linear combinations for monitoring vegetation.
[ 69 ] Verhegghen, A., Bontemps, S., & Defourny, P. (2014). A global NDVI and EVI reference data set for land-surface phenology using 13 years of daily SPOT-VEGETATION observations. International Journal of Remote Sensing, 35(7), 2440-2471. doi:10.1080/01431161.2014.883105
[ 70 ] Wang, S., Huang, G., Lin, Q., Li, Z., Zhang, H., & Fan, Y. J. I. J. o. C. (2014). Comparison of interpolation methods for estimating spatial distribution of precipitation in Ontario, Canada. 34(14), 3745-3751.
[ 71 ] Waske, B., & Braun, M. (2009). Classifier ensembles for land cover mapping using multitemporal SAR imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 64(5), 450-457.
[ 72 ] Wu, T., Hu, X., Zhang, Y., Zhang, L., Tao, P., & Lu, L. (2016). Automatic cloud detection for high resolution satellite stereo images and its application in terrain extraction. ISPRS Journal of Photogrammetry and Remote Sensing, 121, 143-156.
[ 73 ] Yang, W., Wang, M., & Shi, P. (2012). Using MODIS NDVI time series to identify geographic patterns of landslides in vegetated regions. IEEE Geoscience and Remote Sensing Letters, 10(4), 707-710.
[ 74 ] Yoshida, T., & Omatu, S. (1994). Neural network approach to land cover mapping. IEEE Transactions on Geoscience and Remote Sensing, 32(5), 1103-1109.
[ 75 ] Zhai, H., Zhang, H., Zhang, L., & Li, P. (2018). Cloud/shadow detection based on spectral indices for multi/hyperspectral optical remote sensing imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 144, 235-253.
[ 76 ] Zhai, H., Zhang, H., Zhang, L., Li, P. J. I. J. o. P., & Sensing, R. (2018). Cloud/shadow detection based on spectral indices for multi/hyperspectral optical remote sensing imagery. 144, 235-253.
[ 77 ] Zhu, Z., & Woodcock, C. E. (2012). Object-based cloud and cloud shadow detection in Landsat imagery. Remote sensing of Environment, 118, 83-94.
[ 78 ] Zhu, Z., & Woodcock, C. E. J. R. s. o. e. (2012). Object-based cloud and cloud shadow detection in Landsat imagery. 118, 83-94.
[ 79 ] 中央氣象局. (2004, 2020,June 20). 颱風百問. 中央氣象局. Retrieved from http://photino.cwb.gov.tw/rdcweb/lib/cd/cd08bk/typhoon.pdf
[ 80 ] 何宇麗. (2014). 降雨誘發山崩潛勢與崩塌分佈之研究. 1-75.
[ 81 ] 吳振發, & 詹士樑. (2003). 常態化差異植生指數應用於都市綠地品質管制之探討. Journal of Taiwan Land Research, 6(2), 17-42.
[ 82 ] 吳瑞賢, 蘇., 廖偉民,張志誠. (2002). 臺灣的颱風, 暴雨災害量化分析. In: 台中: 農業試驗所.
[ 83 ] 李江南, 王安宇, 杨兆礼, 李国丽, 何夏江, 彭涛涌, & 古志明. (2003). 台风暴雨的研究进展. 熱帶氣象學報, 19(S), 152-159.
[ 84 ] 李清勝, 鄭., 陳柏孚, 謝宜桓, 鄧旭峰. (2015). 侵台颱風過山期間雨帶重建之初步研究. 大氣科學, 43(1), 69-90.
[ 85 ] 李鎮洋, 賴., 陳振宇,黃效禹,郭力行. (2011). 莫拉克颱風複合型災害發生歷程的時空重建: 以小林村深層崩塌為例.
[ 86 ] 沈哲緯, 羅., 蕭震洋 (2013). 莫拉克颱風災區土石流發生因子之特性—以地形因子, 降雨強度及崩塌率進行探討. 59(1), 26-48.
[ 87 ] 林欽國, & 陳致穎. (2010). 利用高解析度模式探討地形高度與西南氣流水氣傳送對莫拉克 (2009) 颱風模擬之影響. 76.
[ 88 ] 科技部. (2015). 山崩災害歷史資料庫. Retrieved from http://dmip.tw/Lone//basicdata/historydb.aspx
[ 89 ] 紀博敏. (2004). 從位渦診斷的觀點探討納莉颱風 (2001) 的特殊路徑因子.
[ 90 ] 陳正改. (2011). 台灣的氣象災害與防災策略. 中華防災學刊, 3(2), 120-132.
[ 91 ] 陳佳正. (2007). 台灣極端降雨氣候事件判定方法. 35(2), 105-117.
[ 92 ] 陳國彥. (1981). 侵台颱風之路徑與強度. 國立臺灣師範大學地理研究報告.
[ 93 ] 蔡慧敏. (2000). 永續減災的環境教育. 環境教育, 41, 63-70.
[ 94 ] 蔡豐合. (2015). 以自組特徵映射網路分析颱風路徑與推估全洪程歷線. 臺灣大學生物環境系統工程學研究所學位論文, 1-97.
[ 95 ] 賴瑩嬛. (2012). 應用群集分析探討侵臺颱風之分類特性. 交通大學土木工程系所學位論文, 1-34.

指導教授

林遠見(Yuan-Chien Lin)

審核日期

2020-7-16

推文