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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 莊昀叡(Ray Y. Chung) | |
dc.contributor.author | Ting-Wei Yeh | en |
dc.contributor.author | 葉庭維 | zh_TW |
dc.date.accessioned | 2021-06-16T02:25:19Z | - |
dc.date.available | 2021-02-20 | |
dc.date.copyright | 2021-02-20 | |
dc.date.issued | 2021 | |
dc.date.submitted | 2021-02-08 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/53517 | - |
dc.description.abstract | 山崩是形塑地表的重要作用之一,也是常見的自然災害,因此監測山崩非常重要。傳統上,在大地測量和地工技術的協助下,可透過多時期的觀測了解崩塌行為。近年來,無人飛行載具和運動回復結構的結合已成為地面測量的方式,透過其省力及低成本的操作流程,獲取地物的詳細表面資訊。該方法逐漸用於崩塌地的調查上,同時部分研究依攝影測量原理試驗不同的測量設計以提升量測品質。然而少有研究專注在陡峭崩塌地上,此類崩塌地常出現在活躍造山帶上,並且有較高的致災潛力,也是台灣地區常見之崩塌地型態。本研究目標為使用無人機與運動回復結構,發展一套低成本且有效偵測陡峭崩塌地細部變化的方法。本研究檢驗無人機的定位設備精度、航帶形狀、以及地面控制點分布對運動回復結構產製之模型的影響;另測試點雲密度、影像網型、以及地面控制點分布對變遷偵測的影響。本研究區位於台20線177公里處,包含兩個坡陡、表面不穩定、以及現場作業空間受限的崩塌地。本研究流程包含野外調查、運動回復結構、準確度評估、變遷偵測、以及體積估算。測試結果顯示在有地面控制點的情況下,無人機的定位設備精度非影響模型準確度的關鍵,航帶形狀亦無顯著影響,而地面控制點的分佈是影響模型準確度的關鍵。另外,點雲密度對偵測崩塌地表面變化無顯著影響,而影像網型對變遷偵測的影響非常明顯;在相對誤差可以保證的情況下,地面控制點的分佈並不是影響變遷偵測關鍵。 | zh_TW |
dc.description.abstract | Landslides are one of the fundamental processes shaping the Earth’s surface and one major natural hazard, so it is important to monitor landslides. Traditionally, with the assistance of terrestrial geodesy and geotechnical methods, one can understand landslide behaviors via multi-temporal observations. In recent years, the combination of UAV and SfM have been a topographical survey method to acquire detail 3D morphological information of landforms for its labor-saving and low-budget operating procedure. The method has been progressively applied in landslide surveys, and some studies have experimented with various survey designs to improve the quality of the UAV-SfM survey according to photogrammetry principles. However, few studies focus on steep landslides, which commonly occur in active mountain ranges with high hazardous potential and a common landslide type in Taiwan. Therefore, this study aims to use UAV-SfM workflow to develop a low-cost, efficient methodology to detect a detailed morphological change of steep landslides. This study examines how UAV's positioning equipment, shapes of flight strips, and GCP geometries affect the SfM-derived 3D models’ accuracy. Also, it examines how point cloud density, image block, and the GCP geometries affect morphological change detection. The study area is located at 177 km of the provincial highway No. 20, and there are two landslides with high steepness, dynamic surface, and limited space for fieldwork. The whole workflow includes field survey, the SfM process, accuracy assessment, change detection, and estimation of volume change. The results of the examination show that the UAV positioning equipment is not the key to the model’s accuracy when GCPs are available. The shapes of flight strips have no significant impact. The GCP geometry is a decisive factor in the model’s accuracy. Also, the point cloud density is not significantly influential in the morphological change detection of the landslides. The image blocks affect the performance of the volume change detection very obviously. When the comparative accuracy can be guaranteed, the GCP geometry becomes less important for volume change detection. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T02:25:19Z (GMT). No. of bitstreams: 1 U0001-0502202110345300.pdf: 11612059 bytes, checksum: 21aef8fb20e6345d291e6e9842174fc1 (MD5) Previous issue date: 2021 | en |
dc.description.tableofcontents | 口試委員審定書 i ACKNOWLEDGEMENT ii 摘要 iii ABSTRACT iv CONTENTS vi LIST OF FIGURES viii LIST OF TABLES xi LIST OF ABBREVIATIONS xii Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Research purpose 4 Chapter 2 Literature Review 5 2.1 Landslide 5 2.2 Unmanned Aerial Vehicle 7 2.3 Structure from Motion 9 2.3.1 Fundamental concept 9 2.3.2 Validation and errors 12 2.4 UAV and SfM in the topographic survey 16 2.4.1 The workflow of UAV-SfM for the topographic survey 16 2.4.2 UAV survey for landslide 18 2.4.3 UAV-SfM survey on different topography 21 Chapter 3 Methodology 24 3.1 Workflow and items for comparisons 24 3.1.1 Workflow 24 3.1.2 Items for comparison 27 3.2 Study Site 29 3.3 Field survey plan 36 3.3.1 UAVs mission plan 36 3.3.2 Ground control point and check point 40 3.4 Data processing 45 3.4.1 SfM processing 45 3.4.2 Point cloud processing 47 3.4.3 The technique for point cloud comparison 48 3.5 Validation and comparison 50 3.5.1 Compare by check points 50 3.5.2 Compare with LiDAR 52 3.6 Change detection and volumetric estimation 53 3.6.1 M3C2 settings 54 3.6.2 Axis-angle rotation 54 3.6.3 Alphashape geometry 56 Chapter 4 Results 57 4.1 Field survey 57 4.1.1 Implementation of Flight mission plan 57 4.1.2 Measurement of Ground control point 60 4.2 Point Cloud Models 62 4.3 Accuracy assessment 64 4.3.1 From different field survey settings 64 4.3.2 From LiDAR data 77 4.4 Volume change estimation 80 4.4.1 1st and 2nd 82 4.4.2 2nd and 3rd 87 4.5 Point density and change detection performance 92 4.6 Vertical and Oblique photograph 96 4.7 Change detection with a reduced number of GCPs 98 4.8 Multi-temporal change detection without GCPs 100 Chapter 5 Discussion 105 5.1 Factors affect change detection 105 5.2 Factors triggering landslide 107 5.3 The Limitation of the proposed method 108 5.4 Survey suggestion 111 Chapter 6 Conclusion 114 REFERENCE 115 | |
dc.language.iso | en | |
dc.title | 無人機攝影測量於陡峭崩塌地之應用 | zh_TW |
dc.title | Applying UAV Photogrammetry on Steep Landslides | en |
dc.type | Thesis | |
dc.date.schoolyear | 109-1 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 董家鈞(Jia-Jyun Dong),姜壽浩(Shou-Hao Chiang),王聖鐸(Sendo Wang),陳毅青(Yi-Chin Chen) | |
dc.subject.keyword | 無人飛行載具,運動回復結構,山崩,變遷偵測,點雲, | zh_TW |
dc.subject.keyword | UAV,SfM,Landslide,Change detection,Point cloud, | en |
dc.relation.page | 122 | |
dc.identifier.doi | 10.6342/NTU202100568 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2021-02-10 | |
dc.contributor.author-college | 理學院 | zh_TW |
dc.contributor.author-dept | 地理環境資源學研究所 | zh_TW |
顯示於系所單位: | 地理環境資源學系 |
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