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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 莊昀叡 | zh_TW |
| dc.contributor.advisor | Ray Y. Chuang | en |
| dc.contributor.author | 邱正標 | zh_TW |
| dc.contributor.author | Jeng-Biau Chiou | en |
| dc.date.accessioned | 2025-09-01T16:05:30Z | - |
| dc.date.available | 2025-09-02 | - |
| dc.date.copyright | 2025-09-01 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-08-08 | - |
| dc.identifier.citation | 中央氣象署（2024）。颱風資料庫。https://rdc28.cwa.gov.tw/TDB/public/typhoon_detail?typhoon_id=202403
曾志民、曾仁彥、張國楨、黃美甄、陳振宇（2023）。應用無人載具高解析影像及三維點雲於山區河床表層粒徑之判釋研究。土木水利，50（5），91-98。https://doi.org/10.6653/MoCICHE.202310_50(5).0012 經濟部地質調查及礦業管理中心（2019）。1/50000臺灣地質圖［地圖］。https://geomap.gsmma.gov.tw/gsb108-1/list_service.cfm 詹勳全、林柏瑋、鄭卉君（2019）。由uav影像自動萃取河床表面粒徑分佈。中華水土保持學報，50（3），102-115。https://doi.org/10.29417/jcswc.201909_50(3).0002 陳嘉欣、邵允銓、王驥魁、吳富春（2008）。河床質粒徑分布之數位影像光篩分析。農業工程學報，54（4），16-32。https://doi.org/10.29974/jtae.200812.0002 陳毅青（2019）。整合近景攝影測量和影像分析之河川沉積物搬運和侵蝕作用之研究（研究計劃編號MOST107-2119-M018-001）。國立彰化師範大學地理學系暨研究所。https://www.grb.gov.tw/search/planDetail?id=12670859 Agisoft. (2024). Agisoft Metashape User Manual: Professional Edition, Version 2.1. https://www.agisoft.com/pdf/metashape-pro_2_1_en.pdf Berra, E. F., & Peppa, M. V. (2020). Advances and Challenges of UAV SFM MVS Photogrammetry and Remote Sensing: Short Review. 2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS), 533-538. https://doi.org/10.1109/LAGIRS48042.2020.9285975 Bertin, S., & Friedrich, H. (2016). Field application of close‐range digital photogrammetry (CRDP) for grain‐scale fluvial morphology studies. Earth Surface Processes and Landforms, 41(10), 1358-1369. https://doi.org/10.1002/esp.3906 Brasington, J., Vericat, D., & Rychkov, I. (2012). Modeling river bed morphology, roughness, and surface sedimentology using high resolution terrestrial laser scanning. Water Resources Research, 48(11). https://doi.org/10.1029/2012WR012223 Buffington, J. M., & Montgomery, D. R. (2022). Geomorphic Classification of Rivers: An Updated Review. In Treatise on Geomorphology (pp. 1143-1190): Elsevier. 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. http://dx.doi.org/10.2737/RMRS-GTR-74 Canny, J. (1986). A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8(6), 679-698. https://doi.org/10.1109/TPAMI.1986.4767851 Chen, Z., Scott, T. R., Bearman, S., Anand, H., Keating, D., Scott, C., Arrowsmith, J. R., & Das, J. (2020). Geomorphological Analysis Using Unpiloted Aircraft Systems, Structure from Motion, and Deep Learning. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 1276-1283. https://doi.org/10.1109/IROS45743.2020.9341354 CloudCompare. (2022). Roughness - CloudCompareWiki. https://www.cloudcompare.org/doc/wiki/index.php/Roughness Dadson, S. J., Hovius, N., Chen, H., Dade, W. B., Lin, J.-C., Hsu, M.-L., Lin, C.-W., Horng, M.-J., Chen, T.-C., Milliman, J., & Stark, C. P. (2004). Earthquake-triggered increase in sediment delivery from an active mountain belt. Geology, 32(8), 733-736. https://doi.org/10.1130/g20639.1 Eltner, A., Kaiser, A., Castillo, C., Rock, G., Neugirg, F., & Abellán, A. (2016). Image-based surface reconstruction in geomorphometry – merits, limits and developments. Earth Surface Dynamics, 4(2), 359-389. https://doi.org/10.5194/esurf-4-359-2016 Folk, R. L., & Ward, W. C. (1957). Brazos River bar [Texas]; a study in the significance of grain size parameters. Journal of Sedimentary Research, 27(1), 3-26. https://doi.org/10.1306/74d70646-2b21-11d7-8648000102c1865d Fonstad, M. A., Dietrich, J. T., Courville, B. C., Jensen, J. L., & Carbonneau, P. E. (2013). Topographic structure from motion: a new development in photogrammetric measurement. Earth Surface Processes and Landforms, 38(4), 421-430. https://doi.org/10.1002/esp.3366 FRIEDMAN, G. M. (1979). Address of the retiring President of the International Association of Sedimentologists: Differences in size distributions of populations of particles among sands of various origins. Sedimentology, 26(1), 3-32. https://doi.org/10.1111/j.1365-3091.1979.tb00336.x Gomez, B. (1993). Roughness of stable, armored gravel beds. Water Resources Research, 29(11), 3631-3642. https://doi.org/10.1029/93WR01490 Graham, D. J., Reid, I., & Rice, S. P. (2005a). Automated Sizing of Coarse-Grained Sediments: Image-Processing Procedures. Mathematical Geology, 37(1), 1-28. https://doi.org/10.1007/s11004-005-8745-x Graham, D. J., Rice, S. P., & Reid, I. (2005b). A transferable method for the automated grain sizing of river gravels. Water Resources Research, 41(7). https://doi.org/10.1029/2004WR003868 Heritage, G. L., & Milan, D. J. (2009). Terrestrial Laser Scanning of grain roughness in a gravel-bed river. Geomorphology, 113(1), 4-11. https://doi.org/10.1016/j.geomorph.2009.03.021 Hjulström, F. (1935). Studies of the morphological activity of rivers as illustrated by the River Fyris (Publication Number XXV) [Doctoral thesis, monograph, The Geological institution of the University of Upsala]. DiVA. Uppsala. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-481786 Ibbeken, H., & Schleyer, R. (1986). Photo-sieving: A method for grain-size analysis of coarse-grained, unconsolidated bedding surfaces. Earth Surface Processes and Landforms, 11(1), 59-77. https://doi.org/10.1002/esp.3290110108 Iglhaut, J., Cabo, C., Puliti, S., Piermattei, L., O’Connor, J., & Rosette, J. (2019). Structure from Motion Photogrammetry in Forestry: a Review. Current Forestry Reports, 5(3), 155-168. https://doi.org/10.1007/s40725-019-00094-3 James, M. R., Chandler, J. H., Eltner, A., Fraser, C., Miller, P. E., Mills, J. P., Noble, T., Robson, S., & Lane, S. N. (2019). Guidelines on the use of structure‐from‐motion photogrammetry in geomorphic research. Earth Surface Processes and Landforms, 44(10), 2081-2084. https://doi.org/10.1002/esp.4637 Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W.-Y., Dollár, P., & Girshick, R. (2023). Segment Anything. arXiv:2304.02643. https://doi.org/10.48550/arXiv.2304.02643 Limerinos, J. T. (1970). Determination of the manning coefficient from measured bed roughness in natural channels [Report](1898B). (Water Supply Paper, Issue. U. S. G. P. Off. https://pubs.usgs.gov/publication/wsp1898B Mair, D., Do Prado, A. H., Garefalakis, P., Lechmann, A., Whittaker, A., & Schlunegger, F. (2022). Grain size of fluvial gravel bars from close-range UAV imagery – uncertainty in segmentation-based data. Earth Surf. Dynam., 10(5), 953-973. https://doi.org/10.5194/esurf-10-953-2022 Mair, D., Witz, G., Do Prado, A. H., Garefalakis, P., & Schlunegger, F. (2024). Automated detecting, segmenting and measuring of grains in images of fluvial sediments: The potential for large and precise data from specialist deep learning models and transfer learning. Earth Surface Processes and Landforms, 49(3), 1099-1116. https://doi.org/10.1002/esp.5755 Mancini, F., Dubbini, M., Gattelli, M., Stecchi, F., Fabbri, S., & Gabbianelli, G. (2013). Using Unmanned Aerial Vehicles (UAV) for High-Resolution Reconstruction of Topography: The Structure from Motion Approach on Coastal Environments. Remote Sensing, 5(12), 6880-6898. https://doi.org/10.3390/rs5126880 Marc, O., Turowski, J. M., & Meunier, P. (2021). Controls on the grain size distribution of landslides in Taiwan: the influence of drop height, scar depth and bedrock strength [Article]. Earth Surface Dynamics, 9(4), 995-1011. https://doi.org/10.5194/esurf-9-995-2021 Miazza, R., Pascal, I., & Ancey, C. (2024). Automated grain sizing from uncrewed aerial vehicles imagery of a gravel‐bed river: Benchmarking of three object‐based methods. Earth Surface Processes and Landforms, 49(5), 1503-1514. https://doi.org/10.1002/esp.5782 Pearson, E., Smith, M. W., Klaar, M. J., & Brown, L. E. (2017). Can high resolution 3D topographic surveys provide reliable grain size estimates in gravel bed rivers? Geomorphology, 293, 143-155. https://doi.org/10.1016/j.geomorph.2017.05.015 Pedersen, M. L., & Friberg, N. (2007). Two lowland stream riffles – linkages between physical habitats and macroinvertebrates across multiple spatial scales. Aquatic Ecology, 41(3), 475-490. https://doi.org/10.1007/s10452-004-1584-x Purinton, B., & Bookhagen, B. (2019). Introducing PebbleCounts: a grain-sizing tool for photo surveys of dynamic gravel-bed rivers. Earth Surf. Dynam., 7(3), 859-877. https://doi.org/10.5194/esurf-7-859-2019 Riebe, C. S., Sklar, L. S., Overstreet, B. T., & Wooster, J. K. (2014). Optimal reproduction in salmon spawning substrates linked to grain size and fish length. Water Resources Research, 50(2), 898-918. https://doi.org/10.1002/2013WR014231 Roda-Boluda, D. C., D'Arcy, M., McDonald, J., & Whittaker, A. C. (2018). Lithological controls on hillslope sediment supply: insights from landslide activity and grain size distributions. Earth Surface Processes and Landforms, 43(5), 956-977. https://doi.org/10.1002/esp.4281 Schönberger, J. L., & Frahm, J. M. (2016). Structure-from-Motion Revisited. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 4104-4113. https://doi.org/10.1109/CVPR.2016.445 Shen, H. W., & Lu, J. Y. (1983). Development and Prediction of Bed Armoring. Journal of Hydraulic Engineering, 109(4), 611-629. https://doi.org/10.1061/(ASCE)0733-9429(1983)109:4(611) Smith, M. W., Carrivick, J. L., & Quincey, D. J. (2016). Structure from motion photogrammetry in physical geography [Article]. Progress in Physical Geography-Earth and Environment, 40(2), 247-275. https://doi.org/10.1177/0309133315615805 Steer, P., Guerit, L., Lague, D., Crave, A., & Gourdon, A. (2022). Size, shape and orientation matter: fast and semi-automatic measurement of grain geometries from 3D point clouds. Earth Surface Dynamics, 10(6), 1211-1232. https://doi.org/10.5194/esurf-10-1211-2022 Stringer, C., Wang, T., Michaelos, M., & Pachitariu, M. (2021). Cellpose: a generalist algorithm for cellular segmentation. Nature Methods, 18(1), 100-106. https://doi.org/10.1038/s41592-020-01018-x Strom, K. B., Kuhns, R. D., & Lucas, H. J. (2010). Comparison of Automated Image-Based Grain Sizing to Standard Pebble-Count Methods. Journal of Hydraulic Engineering, 136(8), 461-473. https://doi.org/10.1061/(ASCE)HY.1943-7900.0000198 Sylvester, Z., Stockli, D. F., Howes, N., Roberts, K., Malkowski, M. A., Poros, Z., Martindale, R. C., & Bai, W. (2025). Segmenteverygrain: A Python module for segmentation of grains in images. In https://github.com/zsylvester/segmenteverygrain Vázquez-Tarrío, D., Borgniet, L., Liébault, F., & Recking, A. (2017). Using UAS optical imagery and SfM photogrammetry to characterize the surface grain size of gravel bars in a braided river (Vénéon River, French Alps). Geomorphology, 285, 94-105. https://doi.org/10.1016/j.geomorph.2017.01.039 Westoby, M. J., Brasington, J., Glasser, N. F., Hambrey, M. J., & Reynolds, J. M. (2012). ‘Structure-from-Motion’ photogrammetry: A low-cost, effective tool for geoscience applications. Geomorphology, 179, 300-314. https://doi.org/10.1016/j.geomorph.2012.08.021 Westoby, M. J., Dunning, S. A., Woodward, J., Hein, A. S., Marrero, S. M., Winter, K., & Sugden, D. E. (2015). Sedimentological characterization of Antarctic moraines using UAVs and Structure-from-Motion photogrammetry [Article]. Journal of Glaciology, 61(230), 1088-1102. https://doi.org/10.3189/2015JoG15J086 Wolman, M. G. (1954). A method of sampling coarse river-bed material. Eos, Transactions American Geophysical Union, 35(6), 951-956. https://doi.org/10.1029/TR035i006p00951 Woodget, A. S., & Austrums, R. (2017). Subaerial gravel size measurement using topographic data derived from a UAV-SfM approach. Earth Surface Processes and Landforms, 42(9), 1434-1443. https://doi.org/10.1002/esp.4139 Woodget, A. S., Carbonneau, P. E., Visser, F., & Maddock, I. P. (2015). Quantifying submerged fluvial topography using hyperspatial resolution UAS imagery and structure from motion photogrammetry. Earth Surface Processes and Landforms, 40(1), 47-64. https://doi.org/10.1002/esp.3613 Yanites, B. J., Tucker, G. E., Hsu, H. L., Chen, C. C., Chen, Y. G., & Mueller, K. J. (2011). The influence of sediment cover variability on long-term river incision rates: An example from the Peikang River, central Taiwan [Article]. Journal of Geophysical Research-Earth Surface, 116, 13, Article F03016. https://doi.org/10.1029/2010jf001933 Zimmerman, T., Jansen, K., & Miller, J. (2020). Analysis of UAS Flight Altitude and Ground Control Point Parameters on DEM Accuracy along a Complex, Developed Coastline. Remote Sensing, 12(14), 2305. https://doi.org/10.3390/rs12142305 | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/99325 | - |
| dc.description.abstract | 河道沉積物粒徑是了解河流作用的重要元素之一，除了反映侵蝕、搬運、沉積的狀態之外，在水文模型的計算中也有重大的影響，例如曼寧係數、河流水力侵蝕模型等。此外，在颱風、土石流等事件過後，河道沉積物粒徑的變化也有助於我們評估事件帶來的影響。作為一個重要的地形特徵，如何有效地蒐集準確的粒徑資料也成為值得探討的議題。過去粒徑調查多以現地採樣為主，然而人為操作下的誤差可能會使採樣結果略大於實際粒徑分布，且需要在野外工作較長時間。隨著遙測技術與電腦科學的發展，近年來不乏有使用無人機空拍，並結合機器學習以達到影像自動判釋粒徑的研究，惟前人研究多以提出新的粒徑判釋方法為主，較少針對現有的方法進行比較，尤其是以點雲、正射影像作為判釋的對象時，在資料本質上便有二維與三維的差異。本研究中首先使用無人機在河道低空飛行拍攝毫米級解析度影像，透過運動回復結構與多視立體產出點雲，並且得到相機參數後將原始影像校正為正射影像，以不同的程式由這兩種資料判釋粒徑，再與現地測量的粒徑資料進行比較。以使用5公尺航高拍攝、解析度1.88 mm/pix的影像產製出的點雲與正射影像進行分析，在D50 < 15公分的區塊，影像辨識可以獲得與現地測量相近的結果，RMSE約為D50的10-15%，最小可以辨識的粒徑約為8 mm；點雲則是僅在D50 ~ 10-15公分、礫石緊密排列下有較好的分割結果，在細顆粒與粗顆粒中分別有欠分割（under-segmentation）和過度分割（over-segmentation）的現象。而在數十公分的大顆粒當中，透過影像和點雲測得的結果皆與現地測得的粒徑有較大差異。除了礫石本身大小之外，彼此之間的排列情形也是影響分割的因素。此外，在不同時期的資料中，透過大範圍的影像辨識和傳統採樣方式獲得的粒徑分布相比，前者可以觀察到更細微的變化。這些不同方法之間的誤差與限制可以作為後續粒徑調查研究之參考。 | zh_TW |
| dc.description.abstract | Sediment grain size in stream beds is an important factor for understanding fluvial process. Grain size is one geomorphological feature of a river reflecting river dynamics, such as transport and deposition processes, stream power, flow history and the origin of sediments. Besides, the change of grain size after natural disaster, such as typhoon and landslide, is another issue worth to concern. Therefore, how to acquire grain size data efficiently and accurately is an important work. In previous studies, high resolution image-based grain size analyses are limited in patch scale, while site scale studies are limited in resolution to few centimeters. On the other hand, grain size detected in images is the “2D” grain size, and the difference between 2D grain size and real 3D grain size are rarely discussed. In this research, UAV (Unmanned Aerial Vehicle) is used flying at a low altitude to capture high resolution images in gravel- and cobble-bed rivers. During the process of structure-from-motion photogrammetry and multi-view stereo algorithm, point cloud and orthoimages are generated firstly, then grain size is detected from these two outputs respectively. Using point cloud and orthoimage generated from images captured at 5-m flight height, with resolution = 1.88, as input data to analysis, in patches with D50 < 50 cm, grain size detected from image is close to grain sized measured in the field. RMSE is 10-15% of D50 approximately, and the minimum grain axis length can be identified is about 8 mm. As for point cloud, a preferred detection results only showed in patch with D50 ~ 10-15 cm, and grains are tightly arranged. In patches with finer or courser grains, under-segmentation and over-segmentation are happened respectively. However, both image and point cloud detected grain sizes have a larger difference for grains in few decimeters large. In addition to the size of grains itself, the arrangement among grains will affect the result of segmentation in point cloud. Besides, in different period of data, grain size distribution derived from image detection is able to catch more detailed changes comparing with traditional sampling method, which is able to serve as a reference for future studies on particle size analysis using imagery and point cloud. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-09-01T16:05:30Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-09-01T16:05:30Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員審定書 i
誌謝 ii 摘要 iii Abstract iv Terminology and Abbreviations vi Table of contents vii List of figures x List of tables xiii Chapter 1 Introduction 1 1.1. Motivation 1 1.2. Research questions 3 1.3. Research purpose 3 Chapter 2 Literature review 5 2.1. Stream types and river bed sediment 5 2.2. Surface grain size measurement methods 6 2.3. Structure-from-Motion photogrammetry 9 Chapter 3 Research area and methods 13 3.1. Research area 13 3.2. Methods 16 3.2.1. Design of experience 16 3.2.2. Data acquisition 19 3.2.3. Photogrammetric processing 25 3.2.4. Grain size detection 26 Chapter 4 Results 29 4.1. Photogrammetric processing results 29 4.2. Grain size measurement 34 4.2.1. Manual 2D and 3D grain size 35 4.2.2. Patch-scale grains detected by Segmenteverygrain and G3Point 37 4.2.3. 2D grain size and segmenteverygrain 40 4.2.4. 3D grain size and segmenteverygrain 42 4.2.5. 3D grain size and G3Point 44 4.2.6. Point cloud and orthoimage with / without control points 46 4.2.7. Point cloud and orthoimage generated from different flight height image (different spatial resolution) 47 4.2.8. Site-scale measurement 50 4.2.9. Grain size distribution of 2024 and 2025 52 Chapter 5 Discussion 53 5.1. Photogrammetric processing 53 5.1.1. Flight altitude, GSD and image resolution 53 5.1.2. Control points error 54 5.2. Definition of grain axis in each program 55 5.3. Differences in grain counts of each method 56 5.4. Differences between Wolman pebble counts and segmenteverygrain in site-scale measurements 57 5.5. Limitations of using segmenteverygrain and G3Point 58 5.6. Comparisons of grain size distribution between 2024 and 2025 59 Chapter 6 Conclusion 61 Chapter 7 Future works 63 References 65 Appendix 71 a. Ground control point coordinates 71 b. Metashape workflow 73 | - |
| dc.language.iso | en | - |
| dc.subject | 無人機攝影測量 | zh_TW |
| dc.subject | 河床表面粒徑 | zh_TW |
| dc.subject | 正射影像 | zh_TW |
| dc.subject | 點雲 | zh_TW |
| dc.subject | 運動回復結構 | zh_TW |
| dc.subject | Structure-from-Motion | en |
| dc.subject | Point Cloud | en |
| dc.subject | Orthoimage | en |
| dc.subject | UAV Photogrammetry | en |
| dc.subject | Surface Grain Size | en |
| dc.title | 利用無人機攝影測量進行河床表面粒徑調查 | zh_TW |
| dc.title | Surface Grain Size Analysis of Gravel Bed Rivers by UAV Photogrammetry | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 王昱;葉恩肇;陳毅青;詹鈞評 | zh_TW |
| dc.contributor.oralexamcommittee | Yu Wang;En-Chao Yeh;Yi-Chin Chen;Jyun-Ping Jhan | en |
| dc.subject.keyword | 河床表面粒徑,無人機攝影測量,運動回復結構,點雲,正射影像, | zh_TW |
| dc.subject.keyword | Surface Grain Size,UAV Photogrammetry,Structure-from-Motion,Point Cloud,Orthoimage, | en |
| dc.relation.page | 74 | - |
| dc.identifier.doi | 10.6342/NTU202504310 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2025-08-13 | - |
| dc.contributor.author-college | 理學院 | - |
| dc.contributor.author-dept | 地理環境資源學系 | - |
| dc.date.embargo-lift | 2026-08-08 | - |
| 顯示於系所單位： | 地理環境資源學系 | |
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