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請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/7568
完整後設資料紀錄
DC 欄位值語言
dc.contributor.advisor韓仁毓
dc.contributor.authorJo-Yao Hsuen
dc.contributor.author徐若堯zh_TW
dc.date.accessioned2021-05-19T17:46:40Z-
dc.date.available2023-07-19
dc.date.available2021-05-19T17:46:40Z-
dc.date.copyright2018-07-19
dc.date.issued2018
dc.date.submitted2018-07-10
dc.identifier.citationBretar, F., Chauve, A., Bailly, J. S., Mallet, C., & Jacome, A. (2009). Terrain surfaces and 3-D landcover classification from small footprint full-waveform lidar data: application to badlands. Hydrology and Earth System Sciences, 13(8), 1531-1544.
Bunte, K., & Abt, S. R. (2001). Sampling surface and subsurface particle-size distributions in wadable gravel-and cobble-bed streams for analyses in sediment transport, hydraulics, and streambed monitoring. Gen. Tech. Rep. U. S. Dep. Agric., RMRS‐GTR‐74, 428 pp.
Cavalli, M., & Marchi, L. (2008). Characterisation of the surface morphology of an alpine alluvial fan using airborne LiDAR. Natural Hazards and Earth System Science, 8(2), 323-333.
Chow, V. T. (1959). Open Channel Hydraulics. McGraw-Hill Book Company, Inc; New York.
Fernandez-Diaz, J. C. (2011). Lifting the canopy veil: Airborne LiDAR for archeology of forested areas. Imaging Notes Magazine, 26(2), 31-34.
Hollaus, M., Aubrecht, C., Höfle, B., Steinnocher, K., & Wagner, W. (2011). Roughness Mapping on Various Vertical Scales Based on Full-Waveform Airborne Laser Scanning Data. Remote Sensing, 3(12), 503-523.
Horn, B.K.P. (1981). Hill shading and the reflectance map. Proceedings of the Institute of Electrical and Electronics Engineers IEEE, 69(1), pp. 14-47.
Jutzi, B., & Gross, H. (2009). Normalization of LiDAR intensity data based on range and surface incidence angle. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 38, 213-218.
Jutzi, B., & Stilla, U. (2006). Range determination with waveform recording laser systems using a Wiener Filter. ISPRS Journal of Photogrammetry and Remote Sensing, 61(2), 95-107.
Kao, D. L., Kramer, M. G., Love, A. L., Dungan, J. L., & Pang, A. T. (2005). Visualizing distributions from multi-return lidar data to understand forest structure. The Cartographic Journal, 42(1), 35-47.
Kukko, A., Kaasalainen, S. & Litkey, P. (2008). Effect of incidence angle on laser scanner intensity and surface data. Applied Optics, Vol. 47, No. 7, pp. 986-992.
Lin, Y. C., Mills, J. P., & Lin, C. L. (2014). Reliability Assessment of Gaussian Estimates Derived from Small-footprint Waveform Lidar. Journal of Photogrammetry and Remote Sensing, 19(2), 93-106.
Mallet, C., & Bretar, F. (2009). Full-waveform topographic lidar: State-of-the-art. ISPRS Journal of Photogrammetry and Remote Sensing, 64(1), 1-16.
Mallet, C., Bretar, F., & Soergel, U. (2008). Analysis of full-waveform lidar data for classification of urban areas. Photogrammetrie Fernerkundung GeoInformation (PFG), 5, 337-349.
Mallet, C., Bretar, F., Roux, M., Soergel, U., & Heipke, C. (2011). Relevance assessment of full-waveform lidar data for urban area classification. ISPRS Journal of Photogrammetry and Remote Sensing, 66(6), S71-S84.
Ozdemir, H., Sampson, C., de Almeida, G. A., & Bates, P. D. (2013). Evaluating scale and roughness effects in urban flood modelling using terrestrial LIDAR data. Hydrology and Earth System Sciences, 10, 5903-5942.
Renslow, M. S. (2012). Manual of Airborne Topographic Lidar. American Society for Photogrammetry Remote Sensing, Bethesda, Maryland.
Sankey, J. B., Glenn, N. F., Germino, M. J., Gironella, A. I. N., & Thackray, G. D. (2010). Relationships of aeolian erosion and deposition with LiDAR-derived landscape surface roughness following wildfire. Geomorphology, 119(1-2), 135-145.
Thenkabail, P. S. (2015). Remotely Sensed Data Characterization, Classification, and Accuracies. CRC Press, 362-363.
Vain, A., & Kaasalainen, S. (2011). Correcting Airborne Laser Scanning Intensity Data. In Laser Scanning, Theory and Applications. InTech.
Vain, A., Kaasalainen, S., Pyysalo, U., Krooks, A., & Litkey, P. (2009). Use of naturally available reference targets to calibrate airborne laser scanning intensity data. Sensors, 9(4), 2780-2796.
Wagner, W., Ullrich, A., Ducic, V., Melzer, T., & Studnicka, N. (2006). Gaussian decomposition and calibration of a novel small-footprint full-waveform digitising airborne laser scanner. ISPRS Journal of Photogrammetry and Remote Sensing, 60(2), 100-112.
Wagner, W., Ullrich, A., Melzer, T., Briese, C., Kraus, K. (2004). From single-pulse to full-waveform airborne laser scanners: potential and practical challenges. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 35 (Part B3), 201–206.
Whelley, P. L., Glaze, L. S., Calder, E. S., & Harding, D. J. (2014). LiDAR-derived surface roughness texture mapping: application to Mount St. Helens pumice plain deposit analysis. IEEE Transactions on Geoscience and Remote Sensing, 52(1), 426-438.
呂悅來、李廣毅,1992。地表粗糙度與土壤風蝕,土壤學進展,20(06):38-42。
李廣賀,1998。水資源利用工程與管理,清華大學出版社有限公司。
林郁珊,2012。應用空載全波形光達資料於波形分析與地物分類,國立交通大學土木工程學系碩士論文:新竹市。
洪子敏,2015。空載光達於森林地區之反射強度值校正,國立成功大學測量與空間資訊學系碩士論文:台南市。
洪宇佳,2013。全波形空載光達資料之波形特徵分析與分類,國立成功大學測量與空間資訊學系碩士論文:台南市。
張宏、溫永寧、劉愛利,2006。地理資訊系統演算法基礎,科學出版社。
黃俊翔,2012。空載光達於內插方法與不同地形類別精度分析,國立成功大學地球科學研究所碩士論文。
經濟部水利署水利規劃試驗所,2006。河床質調查方法之比較研究專題報告書。
經濟部水利署水利規劃試驗所,2007。河床質調查作業參考手冊(草案)。
經濟部水利署第十河川局,2011。三峽河治理規劃檢討報告(東眼橋至大漢溪匯流口河段) (含橫溪由成福橋至三峽河匯流口止)。
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/7568-
dc.description.abstract河床粗糙度是了解河流特性的一項重要的指標,常用於水利工程建設等項目之規劃;藉由河床上的粒徑大小、分布型態計算河床的粗糙度,進而可以分析河流流速與河床沖積等特性。本研究主要使用全波形光達之波形資訊輔助得到小於一個足跡範圍內的粒徑尺寸,作為河床之次足跡表面變化程度;本研究首先根據感測器的參數設定、地表面的幾何建立和河床粗糙度與粒徑大小相關等特性,進行參考波形的重建,用以作為特定航高和特定粒徑尺寸的標準波形。另一方面,將全波形光達之回波進行強度值的改正,消除在發射和接收過程中會造成能量耗損的影響因子,將回波回復至只有受到表面起伏影響的狀態。接著,將標準參考波形與改正後的回波進行波形匹配,殘差平方和最小之類別,即為該足跡的河床粗糙度值,並以正射影像、河床特性、自然灘地規則性和河床質調查報告書等資訊進行分類成果檢核與分析。
研究成果顯示,分類成果與河川之水理特性具有高度相關,粒徑的分布符合邊灘、沙洲的礫石堆積特性,且波形匹配之平均相似率達到85%~90%、加權計算後之平均粒徑值與現地調查量測結果僅有約3cm之差異,表示本研究所提出的方法可以作為獲得河床粒徑大小與粗糙度的另一種有效率之可行方案,並且將新式空間資訊技術與環境分析應用具體結合,解決現地河床質調查之人力與時間成本問題,可以用於輔助歷年來河床變化與沖積特性之分析。
zh_TW
dc.description.abstractSurface roughness is an important indicator to understand river morphology. It is possible to estimate the roughness of riverbeds by surveying their grain size, distribution and type, so that the flow rate and the alluvial characteristics of the riverbeds can be analyzed. This study aims to use the waveform information of a full-waveform LiDAR data to assist in obtaining the grain size within a footprint. First, the reconstruction of the reference waveform is performed as a standard waveform based on the parameters of the sensor and the geometry of the ground surface. On the other hand, an intensity correction of observed full-waveform LiDAR data is applied. This study uses Lambertian reflectance model and DEM information to eliminate the effects of energy loss during transmission. Next, the reference waveforms are used to compare with the observed waveform data and the waveform matching is performed. Finally, the grain size category can be determined by the above approach and the actual riverbed roughness value is thus identified. The experimental results show that the classification results are highly correlated with the river's hydraulic characteristics. The average similarity rate of waveform matching is 85%~90%, and the weighted average grain size value is only about 3cm difference from the on-site riverbed sampling report. It gives an evidence that the proposed approach can become an efficient alternative for evaluating the surface roughness in a river morphology analysis. It solves the problem of laborious and time-consuming of the on-site riverbed material sampling tasks, and can be used to assist the analysis of riverbed changes and alluvial characteristics over the years.en
dc.description.provenanceMade available in DSpace on 2021-05-19T17:46:40Z (GMT). No. of bitstreams: 1
ntu-107-R05521114-1.pdf: 6978579 bytes, checksum: 2ca0393d0dc76b46c87ed0cff540c9ce (MD5)
Previous issue date: 2018
en
dc.description.tableofcontents致謝 I
摘要 II
ABSTRACT III
目錄 IV
圖目錄 VI
表目錄 IX
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機與目的 3
1.3 研究方法 5
1.4 論文架構 6
第二章 文獻回顧 7
2.1 地表粗糙度之分析 7
2.1.1 地表粗糙度之應用 7
2.1.2 河床粗糙度調查作業 9
2.2 空載全波形光達系統 12
2.2.1 全波形光達原理 12
2.2.2 回波波形特徵與分析 16
2.2.3 回波強度值影響因子 19
2.3 全波形光達於地表粗糙度之應用 21
2.3.1 全波形光達應用於地表之相關研究 21
2.3.2 粗糙度之區分方法 23
2.4 小結 24
第三章 研究方法 25
3.1 次足跡波形模板之建立 25
3.1.1 足跡分割與地形模擬 25
3.1.2 標準參考波形重建 27
3.2 觀測資料之波形改正 32
3.2.1 點雲內插DEM 32
3.2.2 地表面坡度計算 34
3.2.3 回波強度值改正 36
3.3 粗糙度分類與成果評估 38
3.3.1 參考波形與回波之匹配 39
3.3.2 成果分析與指標評估 40
第四章 數值實驗成果與分析 41
4.1 不同地形模擬之波形重建與理論驗證 41
4.1.1 波形重建與理論驗證 41
4.1.2 波形模板的建立 47
4.2 研究區域與資料介紹 48
4.3 強度值改正成果與綜合比較 49
4.3.1 點雲內插與坡度圖建立 50
4.3.2 入射角與距離改正 51
4.4 粗糙度分類成果與指標評估 53
4.4.1 波形匹配成果與分析 53
4.4.2 區域分割與成果指標評估 60
第五章 結論與建議 66
5.1 結論 66
5.2 建議與未來工作 67
參考文獻 69
dc.language.isozh-TW
dc.title以全波形光達次足跡技術進行河床粗糙度之分析zh_TW
dc.titleSub-Footprint Roughness Analysis for Riverbeds using Full-Waveform LiDAR Dataen
dc.typeThesis
dc.date.schoolyear106-2
dc.description.degree碩士
dc.contributor.oralexamcommittee史天元,蔡富安,楊明德
dc.subject.keyword粗糙度分析,全波形光達,遙感探測,波形重建,次足跡法,河流型態,zh_TW
dc.subject.keywordRoughness analysis,Full-waveform LiDAR,Remote sensing,Waveform reconstruction,Sub-footprint method,River morphology,en
dc.relation.page72
dc.identifier.doi10.6342/NTU201801413
dc.rights.note同意授權(全球公開)
dc.date.accepted2018-07-10
dc.contributor.author-college工學院zh_TW
dc.contributor.author-dept土木工程學研究所zh_TW
dc.date.embargo-lift2023-07-19-
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