學術產出-學位論文
文章檢視/開啟
書目匯出
-
題名 應用遙感探測技術偵測海岸變遷情形----以臺中市大安區為例
Application of remote sensing technique in coastal changes detection—A case study of Da’an District in Taichung City作者 何昀儒
Ho, Yun-Ru貢獻者 詹進發
Jan, Jihn-Fa
何昀儒
Ho, Yun-Ru關鍵詞 海岸變遷
機器學習
影像分類
Google Earth Engine
遙感探測技術
Coastal changes
Machine learning
Image classification
Google Earth Engine
Remote sensing technology日期 2024 上傳時間 4-九月-2024 14:28:44 (UTC+8) 摘要 海岸提供人類休憩、建立經濟活動之地理環境。但海岸變遷一直以來是人類對於環境保育議題所關注之焦點，特別是臺灣本島西部砂岸地形，藉由自然與人為因素，其改變地貌之幅度大於岩岸地形，海岸地形之變遷除了破壞海岸地景之美貌，更容易對當地經濟活動產生影響。 本研究旨在應用遙感探測技術和機器學習方法，分析臺中市大安區海岸地區於2002年至2022年間之變遷情形。本研究利用2002年Landsat 7、2014年Landsat 8及2022年Sentinel-2A衛星影像，選擇支持向量機、隨機森林和極限梯度提升三種機器學習方法進行監督式影像分類，並選出各年度分類精度較高之成果，進而分析海岸土地利用/土地覆蓋（LULC）變遷及海岸線變遷情形。 研究結果顯示，於2002年至2022年之間，大安區海岸之LULC類別，主要以水體與濕地，及植生與人工建物之間之轉變較明顯。而海岸線於2002年至2014年，皆有明顯的前進趨勢，主要集中在溫寮溪口及大安沙丘一帶；而2014年至2022年，海岸線則出現了退縮現象，特別是在大安濱海樂園、北汕海堤一帶。依據上述結果，推測大安區海岸二十年間人為開發興盛，但也因此使海岸地貌產生明顯變化。 本研究之結果可為有關部門提供參考依據，協助其制定更有效之海岸管理和保育策略，並作為未來海岸變遷相關研究之參考。
The coast provides a geographic environment for human recreation and economic activities. However, coastal changes have always been a focal point in environmental conservation issues, especially on the sandy coastlines of western Taiwan. These changes, driven by both natural and human factors, are more significant than those on rocky coastlines. Coastal geomorphological changes not only damage the beauty of coastal landscapes but also easily affect local economic activities. This study aims to analyze the changes in the coastal area of Da’an District, Taichung City, from 2002 to 2022 using remote sensing technology and machine learning methods. The study uses satellite images obtained by Landsat 7 (2002), Landsat 8 (2014), and Sentinel-2A (2022), employing three machine learning methods—Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost)—for supervised image classification. The classification results with higher accuracy for each year were selected to analyze the coastal changes in Land Use/Land Cover (LULC) and coastline. The results indicate that between 2002 and 2022, the LULC categories in the coastal area of Da’an District showed significant changes, primarily between water bodies and wetlands, and vegetation and artificial structures. The coastline exhibited a noticeable advancing trend from 2002 to 2014, mainly located around Wenliao Creek and Da’an Sand Dunes. However, from 2014 to 2022, the coastline showed signs of retreat, especially in the areas of Da’an Seaside Paradise and Beishan Seawall. Based on these findings, it is inferred that the flourishing human development in the past two decades has significantly altered the coastal geomorphology of Da’an District. The results of this study can provide a reference for relevant departments to assist in formulating more effective coastal management and conservation strategies, and serve as a reference for future research on coastal changes.參考文獻 一、中文參考文獻 內政部國土管理署，2021，「海岸管理白皮書」。 洪維屏、張維庭，2021，「臺中港鄰近海岸地形變遷分析及研究」。 高瑞卿、伍淑惠、張元聰，2010，『台灣海濱植物圖鑑』，臺中：晨星出版有限公司。 陳永森、林孟龍，2004，『台灣的國家風景區』，新北：遠足文化。 張憲國、賴羿齊、陳蔚瑋，2017，「應用衛星影像的濱線辨識於外傘頂洲的灘線變遷」，『中華民國航空測量及遙感探測學刊』，22(4)：243-262。 張巧琳，2022，應用機器學習方法於SPOT7衛星影像之土地利用分類-以南投縣名間鄉為例，國立中興大學土木工程學系碩士論文：臺中。 黃爾強、葉純甄、黃偉柏、吳乃光、邊孝倫，2022，「臺灣離島海岸災害風險評估與調適策略之探討」，『水土保持學報』，52(1)：2927-2940。 臺灣地形研究室，2013，「臺灣海岸地帶面對氣候變遷的衝擊與挑戰」，『地景保育通訊』，36：8-13。 歐鐙元，2015，「應用隨機森林（Random Forest）演算法於WorldView-2衛星影像大蒜分類判釋之研究」，逢甲大學土地管理學系碩士論文：臺中。 蕭淩瑄，2013，「遙測影像分類之不確定性評估」，國立臺灣大學生物資源暨農學院生物環境系統工程學系碩士論文：臺北。 二、外文參考文獻 Abdullah, A. Y. M., Masrur, A., Adnan, M. S. G., Baky, M. A. A., Hassan, Q. K., and Dewan, A., 2019, “Spatio-temporal patterns of land use/land cover change in the heterogeneous coastal region of Bangladesh between 1990 and 2017”, Remote Sensing, 11(7): 1-26, 790. Adam, E., Mutanga, O., Odindi, J., and Abdel-Rahman, E. M., 2014, “Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers”, International Journal of Remote Sensing, 35(10): 3440-3458. Andreatta, D., Gianelle, D., Scotton, M., and Dalponte, M., 2022, “Estimating grassland vegetation cover with remote sensing: A comparison between Landsat-8, Sentinel-2 and PlanetScope imagery” Ecological Indicators, 141: 109102. Aryal, B., Escarzaga, S. M., Vargas Zesati, S. A., Velez-Reyes, M., Fuentes, O., and Tweedie, C., 2021, “Semi-Automated Semantic Segmentation of Arctic Shorelines Using Very High-Resolution Airborne Imagery, Spectral Indices and Weakly Supervised Machine Learning Approaches” Remote Sensing, 13: 4572. Atayi, J., Twumasi, Y. A., Ning, Z. H., and Asare-Ansah, A. B., 2022, “A STUDY ON THE SHORELINE CHANGES AND LAND USE/LAND COVER ALONG THE KETA COASTAL ZONE”, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVI-M-2-2022, 39–45 Banko, G., 1998, “A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data and of Methods Including Remote Sensing Data in Forest Inventory.” Interim report: International Institute for Applied Systems Analysis. Bayram, B., Erdem, F., Akpinar, B., Ince, A. K., Bozkurt, S., Catal Reis, H., and Seker, D. Z., 2017, “The Efficiency of Random Forest Method for Shoreline Extraction from LANDSAT-8 and GOKTURK-2 Imageries”, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-4/W4: 141–145. Belgiu, M., and Dragut, L., 2016, “Random Forest in Remote Sensing: A Review of Applications and Future Directions”, ISPRS Journal of Photogrammetry and Remote Sensing, 114: 24-31. Belov, A. M., and Denisova, A. Y., 2019, “Spatial interpolation methods for spectral-spatial remote sensing image super-resolution algorithm based on gradient descent approach”, Journal of Physics: Conference Series, 1368(3):032006. Boak, E. H., and Turner, I. L., 2005, “Shoreline Definition and Detection: A Review”, Journal of Coastal Research, 21(21): 688-703. Breiman, L., 1997, “Arcing The Edge”, Technical Report 486, Statistics Department, University of California, Berkeley. Breiman, L., 2001, “Random Forest”, Machine Learning, 45: 5-32. Chen, T., and Guestrin, C., 2016, “XGBoost: A Scalable Tree Boosting System”, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining: 785-794. Cortes, C., and Vapnik, V., 1995, “Support-vector networks”, Machine Learning, 20(3): 273–297. Dolan, R., Fenster, MS., and Holme, SJ., 1991, “Temporal analysis of shoreline recession and accretion”, Journal of Coastal Research, 7(3): 723–744. Friedman, J. H., 2001, “ Greedy function approximation: A gradient boosting machine”, Annals of Statistics, 29(5): 1189-1232. Fukushima, K., 1980 “Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position”, Biological Cybernetics, 36: 193–202. Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R., 2017. “Google Earth Engine: Planetary-scale geospatial analysis for everyone. ” Remote Sensing of Environment, 202:18–27. Goutte, C., and Gaussier, E., 2005, “A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation”, 27th European Conference on IR Research: 345–359. Gunn, S. R., 1998, Support Vector Machines for Classification and Regression, Department of Electronics and Computer Science, University of Southampton. Hao, L., He, S., Zhou, J., Zhao, Q., and Lu, X., 2022, “Prediction of the landscape pattern of the Yancheng Coastal Wetland, China, based on XGBoost and the MCE-CA-Markov model”, Ecological Indicators, 145: 109735. Hasmadi, M., Pakhriazad, H., and Shahrin, M., 2019, “Evaluating supervised and unsupervised techniques for land cover mapping using remote sensing data. ” Malaysian Journal of Society and Space, 5: 1–10. Ho, T. K., 1995, “Random decision forests”, Proceedings of 3rd International Conference on Document Analysis and Recognition, 1: 278-282. Hoffmann, F., Bertram, T., Mikut, R., Reischl, M., and Nelles, O., 2019, “Benchmarking in classification and regression”, WIREs Data Mining and Knowledge Discovery, 9(5): 1318. Konko, Y., Okhimambe, A., Nimon, P., Asaana, J., Rudant, J. P., and Kokou, K., 2021, “Coastline Change Modelling Induced by Climate Change Using Geospatial Techniques in Togo (West Africa)”, Advances in Remote Sensing, 9(2): 85-100. Kumar, L., and Mutanga, O., 2018, “Google Earth Engine Applications Since Inception: Usage, Trends, and Potential”, Remote Sensing, 10(10): 1509. Mitchell, T. M., 1997, Machine Learning, New York: McGraw-Hill Science. Pohl, C., and Van Genderen, J., 1998, “Review article Multisensor image fusion in remote sensing: Concepts, methods and applications”, International Journal of Remote Sensing, 19(5): 823-854. Ren, Y., Zhang, L., and Suganthan, P. N., 2016, “Ensemble Classification and Regression-Recent Developments, Applications and Future Directions”, IEEE Computational Intelligence Magazine, 11(1): 41-53. Rogers, M. S. J., 2021, Machine Learning and remote sensing applications to shoreline dynamics, Department of Geography, University of Cambridge. Rouse Jr, J. W., Haas, R. H., Schell, J. A., and Deering, D. W., 1974, "Monitoring vegetation systems in the Great Plains with ERTS." In: Third Earth Resources Technology Satellite-1 Symposium-Volume I: Technical Presentations. NASA SP-351. 309-317. Samuel, A. L., 1959, “Some Studies in Machine Learning Using the Game of Checkers”, IBM Journal of Research and Development, 3(3): 210-229. Streiner, D. L., and Norman, G. R., 2006, “’Precision’ and ‘Accuracy’: Two Terms That Are Neither”, Journal of Clinical Epidemiology, 59: 327–330. Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Ergul, A., 2009, “The Digital Shoreline Analysis System (DSAS) Version 4.0—An ArcGIS Extension for Calculating Shoreline Change.” Open-File Report, US Geological Survey Report No. 2008-1278. Tiwari, S. P., Reshi, O. R., and Rahman, S. M., 2023, “Understanding Land use Land Cover and Shoreline Changes Along Arabian Gulf Using Geospatial Technology”, 2023 IEEE International Geoscience and Remote Sensing Symposium. DOI: 10.1109/IGARSS52108.2023.10282734 Wang, Y., Chen, Z., Cheng, L., Li, M., and Wang, J., 2013, “Parallel scanline algorithm for rapid rasterization of vector geographic data”, Computers & Geosciences, 59: 31-40. Wang, Y., Pan, Z., Zheng, J., Qian, L., and Li, M., 2019, “A hybrid ensemble method for pulsar candidate classification”, Astrophysics and Space Science, 364: 139. Wu, N. T., 1987, The Politics of a Regime Patronage: Mobilization and Control within An Authoritarian Regime, Unpublished doctoral dissertation, Department of Political Science, University of Chicago, Chicago, IL. Xu, B., Chen, J., and Yu, P., 2017, “Vectorization of classified remote sensing raster data to establish topological relations among polygons”, Earth Science Informatics, 10: 99-103. Xu, H., 2007, “Extraction of urban built-up land features from Landsat imagery using a thematic-oriented index combination technique.” Photogrammetric Engineering & Remote Sensing, 73: 1381-1391. Zhang, H., Jiang, Q., and Xu, j., 2013, “Coastline Extraction Using Support Vector Machine from Remote Sensing Image”, Journal of Multimedia, 8(2): 175-182. Zhang, X., Wang, M., and Jiang, S., 2008, “A Novel Approach for Raster Data Vectorization”, Journal of Geo-information Science, 10(6): 730-735. Zhong, Y., and El-Diraby, T., 2022, “Shoreline Recognition Using Machine Learning Techniques” IOP Conference Series: Earth and Environmental Science, 1101: 022025. 三、網頁參考文獻 內政部國土測繪中心，2023，國土測繪圖資服務雲網站。https://maps.nlsc.gov.tw/，取用日期：2023年10月25日。 內政部國土管理署，2017，整體海岸管理計畫。https://reurl.cc/m0ejEG，取用日期：2023年7月3日。 中央研究院，2024，臺灣百年歷史地圖。https://gissrv4.sinica.edu.tw/gis/twhgis.aspx，取用日期：2024年1月18日。 交通部中央氣象署，2023，潮位統計。https://www.cwa.gov.tw/V8/C/C/MMC_STAT/sta_tide.html，取用日期：2023年7月5日。 法務部，2015，海岸管理法，全國法規資料庫。https://law.moj.gov.tw/LawClass/LawAll.aspx?pcode=D0070222，取用日期：2023年12月3日。 陳世宗，2022，使用率低 五甲漁港輔導漁筏轉籍，中時新聞網，https://www.chinatimes.com/newspapers/20220503000485-260107?chdtv，取用日期：2024年3月30日。 陳淑娥，2020，北汕海堤侵蝕嚴重 明年3月改善，中時新聞網，https://tw.news.yahoo.com/%E5%8C%97%E6%B1%95%E6%B5%B7%E5%A0%A4%E4%BE%B5%E8%9D%95%E5%9A%B4%E9%87%8D-%E6%98%8E%E5%B9%B43%E6%9C%88%E6%94%B9%E5%96%84-201000484.html，取用日期：2024年3月30日。 張軒哲，2023，驚！台中大安沙雕節辦不成 沙灘「8年流失1個人高」，自由時報，https://news.ltn.com.tw/news/life/breakingnews/4401635，取用日期：2024年3月30日。 臺灣地形研究室，2013，臺灣海岸地帶面對氣候變遷的衝擊與挑戰，台灣地景保育網，http://140.112.64.54/zh_tw/LandscapeNews/communication/-8765501?mode=chapter，取用日期：2023年9月15日。 A Level Geography (2023, Oct. 20), Landforms of deposition, Retrieved October 20, 2023 from A Level Geography on the World Wide Web: https://www.alevelgeography.com/landforms-of-deposition/ ASUS (2022), ASUS TUF Gaming F15 (2022), Retrieved August 4, 2023 from ASUS on the World Wide Web: https://www.asus.com/tw/laptops/for-gaming/tuf-gaming/asus-tuf-gaming-f15-2022/techspec/ Dmlc XGBoost (2024, May 11), XGBoost Parameters (2.0.3), Retrieved May 14, 2024 from Dmlc XGBoost on the World Wide Web: https://xgboost.readthedocs.io/en/stable/parameter.html ESA (2023, Sep. 22), SENTINEL-2 MISSION GUIDE, Retrieved September 22, 2023 from ESA on the World Wide Web: https://sentinel.esa.int/web/sentinel/missions/sentinel-2 Esri (2023, Aug. 7), Make a layout, Retrieved August 7, 2023 from Esri on the World Wide Web:https://pro.arcgis.com/en/pro-app/latest/get-started/add-maps-to-a-layout.htm Esri (2024, Feb. 26), Resample function, Retrieved February 26, 2024 from Esri on the World Wide Web:https://pro.arcgis.com/en/pro-app/latest/help/analysis/raster-functions/resample-function.htm Google Colaboratory (2023, Oct. 18), What is Colab? Retrieved October 18, 2023 from Google Colaboratory on the World Wide Web: https://colab.research.google.com/?utm_source=scs-index#scrollTo=-Rh3-Vt9Nev9 Google for Developers (2023, Oct. 16), Google Earth Engine, Retrieved October 16, 2023 from Google for Developers on the World Wide Web: https://developers.google.com/earth-engine/guides/playground Javatpoints (2023, Oct. 2), Regression vs. Classification in Machine Learning, Retrieved October 2, 2023 from Javatpoints on the World Wide Web:https://www.javatpoint.com/regression-vs-classification-in-machine-learning McCarthy, J., Minsky, M., Rochester, N., and Shannon, Cl. (1955, Aug. 31), A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence. Retrieved September 23, 2023 from the original on the World Wide Web:http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html Pun:Chat (2022, Apr. 29), Quick guide to understanding image resolution, June 9, 2023 from Pun:Chat on the World Wide Web: https://www.punchat.in/funpun-1/quick-guide-to-understanding-image-resolutionnbsp QSCAT (2024, Apr. 7), A QGIS plugin for shoreline change analysis, Retrieved April 7, 2024 from Github on the World Wide Web:https://qscat.github.io/ Sanderson, L. (2024), Modify Raster Cell Size by Resampling, Retrieved May 3, 2024 from FME Support Center on the World Wide Web: https://support.safe.com/hc/en-us/articles/25407477353741-Modify-Raster-Cell-Size-by-Resampling Tondak, A. (2022, Jun. 25), Deep Learning Vs Machine Learning. Retrieved October 7, 2023 from K21Academy on the World Wide Web: https://k21academy.com/datascience-blog/deep-learning/dl-vs-ml/ USGS (2023, Sep. 22), Landsat 7. Retrieved September 22, 2023 from USGS on the World Wide Web: https://www.usgs.gov/landsat-missions/landsat-7 USGS (2023, Sep. 22), Landsat 8. Retrieved September 22, 2023 from USGS on the World Wide Web: https://www.usgs.gov/landsat-missions/landsat-8 Yehoshua, R. (2023, Mar. 25)，Random Forests, Retrieved Oct 6, 2023 from Medium on the World Wide Web: https://medium.com/@roiyeho/random-forests-98892261dc49 描述 碩士
國立政治大學
地政學系
111257031資料來源 http://thesis.lib.nccu.edu.tw/record/#G0111257031 資料類型 thesis dc.contributor.advisor 詹進發 zh_TW dc.contributor.advisor Jan, Jihn-Fa en_US dc.contributor.author (作者) 何昀儒 zh_TW dc.contributor.author (作者) Ho, Yun-Ru en_US dc.creator (作者) 何昀儒 zh_TW dc.creator (作者) Ho, Yun-Ru en_US dc.date (日期) 2024 en_US dc.date.accessioned 4-九月-2024 14:28:44 (UTC+8) - dc.date.available 4-九月-2024 14:28:44 (UTC+8) - dc.date.issued (上傳時間) 4-九月-2024 14:28:44 (UTC+8) - dc.identifier (其他 識別碼) G0111257031 en_US dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/153256 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 地政學系 zh_TW dc.description (描述) 111257031 zh_TW dc.description.abstract (摘要) 海岸提供人類休憩、建立經濟活動之地理環境。但海岸變遷一直以來是人類對於環境保育議題所關注之焦點，特別是臺灣本島西部砂岸地形，藉由自然與人為因素，其改變地貌之幅度大於岩岸地形，海岸地形之變遷除了破壞海岸地景之美貌，更容易對當地經濟活動產生影響。 本研究旨在應用遙感探測技術和機器學習方法，分析臺中市大安區海岸地區於2002年至2022年間之變遷情形。本研究利用2002年Landsat 7、2014年Landsat 8及2022年Sentinel-2A衛星影像，選擇支持向量機、隨機森林和極限梯度提升三種機器學習方法進行監督式影像分類，並選出各年度分類精度較高之成果，進而分析海岸土地利用/土地覆蓋（LULC）變遷及海岸線變遷情形。 研究結果顯示，於2002年至2022年之間，大安區海岸之LULC類別，主要以水體與濕地，及植生與人工建物之間之轉變較明顯。而海岸線於2002年至2014年，皆有明顯的前進趨勢，主要集中在溫寮溪口及大安沙丘一帶；而2014年至2022年，海岸線則出現了退縮現象，特別是在大安濱海樂園、北汕海堤一帶。依據上述結果，推測大安區海岸二十年間人為開發興盛，但也因此使海岸地貌產生明顯變化。 本研究之結果可為有關部門提供參考依據，協助其制定更有效之海岸管理和保育策略，並作為未來海岸變遷相關研究之參考。 zh_TW dc.description.abstract (摘要) The coast provides a geographic environment for human recreation and economic activities. However, coastal changes have always been a focal point in environmental conservation issues, especially on the sandy coastlines of western Taiwan. These changes, driven by both natural and human factors, are more significant than those on rocky coastlines. Coastal geomorphological changes not only damage the beauty of coastal landscapes but also easily affect local economic activities. This study aims to analyze the changes in the coastal area of Da’an District, Taichung City, from 2002 to 2022 using remote sensing technology and machine learning methods. The study uses satellite images obtained by Landsat 7 (2002), Landsat 8 (2014), and Sentinel-2A (2022), employing three machine learning methods—Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost)—for supervised image classification. The classification results with higher accuracy for each year were selected to analyze the coastal changes in Land Use/Land Cover (LULC) and coastline. The results indicate that between 2002 and 2022, the LULC categories in the coastal area of Da’an District showed significant changes, primarily between water bodies and wetlands, and vegetation and artificial structures. The coastline exhibited a noticeable advancing trend from 2002 to 2014, mainly located around Wenliao Creek and Da’an Sand Dunes. However, from 2014 to 2022, the coastline showed signs of retreat, especially in the areas of Da’an Seaside Paradise and Beishan Seawall. Based on these findings, it is inferred that the flourishing human development in the past two decades has significantly altered the coastal geomorphology of Da’an District. The results of this study can provide a reference for relevant departments to assist in formulating more effective coastal management and conservation strategies, and serve as a reference for future research on coastal changes. en_US dc.description.tableofcontents 第一章 緒論 1 第一節 研究背景與動機 1 第二節 研究目的與問題 3 第三節 研究架構 4 第四節 研究限制 5 第二章 文獻回顧 6 第一節 遙感探測影像處理 6 第二節 機器學習應用於遙感探測影像分類 13 第三節 海岸變遷研究概述 21 第三章 研究方法 25 第一節 研究範圍概述 25 第二節 研究材料與工具 26 第三節 研究設計 35 第四節 研究流程 37 第四章 結果與分析 44 第一節 影像分類成果與精度評估 44 第二節 海岸變遷情形分析 52 第五章 結論與建議 65 第一節 結論 65 第二節 建議 67 參考文獻 68 zh_TW dc.format.extent 6848749 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0111257031 en_US dc.subject (關鍵詞) 海岸變遷 zh_TW dc.subject (關鍵詞) 機器學習 zh_TW dc.subject (關鍵詞) 影像分類 zh_TW dc.subject (關鍵詞) Google Earth Engine zh_TW dc.subject (關鍵詞) 遙感探測技術 zh_TW dc.subject (關鍵詞) Coastal changes en_US dc.subject (關鍵詞) Machine learning en_US dc.subject (關鍵詞) Image classification en_US dc.subject (關鍵詞) Google Earth Engine en_US dc.subject (關鍵詞) Remote sensing technology en_US dc.title (題名) 應用遙感探測技術偵測海岸變遷情形----以臺中市大安區為例 zh_TW dc.title (題名) Application of remote sensing technique in coastal changes detection—A case study of Da’an District in Taichung City en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 一、中文參考文獻 內政部國土管理署，2021，「海岸管理白皮書」。 洪維屏、張維庭，2021，「臺中港鄰近海岸地形變遷分析及研究」。 高瑞卿、伍淑惠、張元聰，2010，『台灣海濱植物圖鑑』，臺中：晨星出版有限公司。 陳永森、林孟龍，2004，『台灣的國家風景區』，新北：遠足文化。 張憲國、賴羿齊、陳蔚瑋，2017，「應用衛星影像的濱線辨識於外傘頂洲的灘線變遷」，『中華民國航空測量及遙感探測學刊』，22(4)：243-262。 張巧琳，2022，應用機器學習方法於SPOT7衛星影像之土地利用分類-以南投縣名間鄉為例，國立中興大學土木工程學系碩士論文：臺中。 黃爾強、葉純甄、黃偉柏、吳乃光、邊孝倫，2022，「臺灣離島海岸災害風險評估與調適策略之探討」，『水土保持學報』，52(1)：2927-2940。 臺灣地形研究室，2013，「臺灣海岸地帶面對氣候變遷的衝擊與挑戰」，『地景保育通訊』，36：8-13。 歐鐙元，2015，「應用隨機森林（Random Forest）演算法於WorldView-2衛星影像大蒜分類判釋之研究」，逢甲大學土地管理學系碩士論文：臺中。 蕭淩瑄，2013，「遙測影像分類之不確定性評估」，國立臺灣大學生物資源暨農學院生物環境系統工程學系碩士論文：臺北。 二、外文參考文獻 Abdullah, A. Y. M., Masrur, A., Adnan, M. S. G., Baky, M. A. A., Hassan, Q. K., and Dewan, A., 2019, “Spatio-temporal patterns of land use/land cover change in the heterogeneous coastal region of Bangladesh between 1990 and 2017”, Remote Sensing, 11(7): 1-26, 790. Adam, E., Mutanga, O., Odindi, J., and Abdel-Rahman, E. M., 2014, “Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers”, International Journal of Remote Sensing, 35(10): 3440-3458. Andreatta, D., Gianelle, D., Scotton, M., and Dalponte, M., 2022, “Estimating grassland vegetation cover with remote sensing: A comparison between Landsat-8, Sentinel-2 and PlanetScope imagery” Ecological Indicators, 141: 109102. Aryal, B., Escarzaga, S. M., Vargas Zesati, S. A., Velez-Reyes, M., Fuentes, O., and Tweedie, C., 2021, “Semi-Automated Semantic Segmentation of Arctic Shorelines Using Very High-Resolution Airborne Imagery, Spectral Indices and Weakly Supervised Machine Learning Approaches” Remote Sensing, 13: 4572. Atayi, J., Twumasi, Y. A., Ning, Z. H., and Asare-Ansah, A. B., 2022, “A STUDY ON THE SHORELINE CHANGES AND LAND USE/LAND COVER ALONG THE KETA COASTAL ZONE”, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVI-M-2-2022, 39–45 Banko, G., 1998, “A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data and of Methods Including Remote Sensing Data in Forest Inventory.” Interim report: International Institute for Applied Systems Analysis. Bayram, B., Erdem, F., Akpinar, B., Ince, A. K., Bozkurt, S., Catal Reis, H., and Seker, D. Z., 2017, “The Efficiency of Random Forest Method for Shoreline Extraction from LANDSAT-8 and GOKTURK-2 Imageries”, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-4/W4: 141–145. Belgiu, M., and Dragut, L., 2016, “Random Forest in Remote Sensing: A Review of Applications and Future Directions”, ISPRS Journal of Photogrammetry and Remote Sensing, 114: 24-31. Belov, A. M., and Denisova, A. Y., 2019, “Spatial interpolation methods for spectral-spatial remote sensing image super-resolution algorithm based on gradient descent approach”, Journal of Physics: Conference Series, 1368(3):032006. Boak, E. H., and Turner, I. L., 2005, “Shoreline Definition and Detection: A Review”, Journal of Coastal Research, 21(21): 688-703. Breiman, L., 1997, “Arcing The Edge”, Technical Report 486, Statistics Department, University of California, Berkeley. Breiman, L., 2001, “Random Forest”, Machine Learning, 45: 5-32. Chen, T., and Guestrin, C., 2016, “XGBoost: A Scalable Tree Boosting System”, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining: 785-794. Cortes, C., and Vapnik, V., 1995, “Support-vector networks”, Machine Learning, 20(3): 273–297. Dolan, R., Fenster, MS., and Holme, SJ., 1991, “Temporal analysis of shoreline recession and accretion”, Journal of Coastal Research, 7(3): 723–744. Friedman, J. H., 2001, “ Greedy function approximation: A gradient boosting machine”, Annals of Statistics, 29(5): 1189-1232. Fukushima, K., 1980 “Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position”, Biological Cybernetics, 36: 193–202. Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R., 2017. “Google Earth Engine: Planetary-scale geospatial analysis for everyone. ” Remote Sensing of Environment, 202:18–27. Goutte, C., and Gaussier, E., 2005, “A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation”, 27th European Conference on IR Research: 345–359. Gunn, S. R., 1998, Support Vector Machines for Classification and Regression, Department of Electronics and Computer Science, University of Southampton. Hao, L., He, S., Zhou, J., Zhao, Q., and Lu, X., 2022, “Prediction of the landscape pattern of the Yancheng Coastal Wetland, China, based on XGBoost and the MCE-CA-Markov model”, Ecological Indicators, 145: 109735. Hasmadi, M., Pakhriazad, H., and Shahrin, M., 2019, “Evaluating supervised and unsupervised techniques for land cover mapping using remote sensing data. ” Malaysian Journal of Society and Space, 5: 1–10. Ho, T. K., 1995, “Random decision forests”, Proceedings of 3rd International Conference on Document Analysis and Recognition, 1: 278-282. Hoffmann, F., Bertram, T., Mikut, R., Reischl, M., and Nelles, O., 2019, “Benchmarking in classification and regression”, WIREs Data Mining and Knowledge Discovery, 9(5): 1318. Konko, Y., Okhimambe, A., Nimon, P., Asaana, J., Rudant, J. P., and Kokou, K., 2021, “Coastline Change Modelling Induced by Climate Change Using Geospatial Techniques in Togo (West Africa)”, Advances in Remote Sensing, 9(2): 85-100. Kumar, L., and Mutanga, O., 2018, “Google Earth Engine Applications Since Inception: Usage, Trends, and Potential”, Remote Sensing, 10(10): 1509. Mitchell, T. M., 1997, Machine Learning, New York: McGraw-Hill Science. Pohl, C., and Van Genderen, J., 1998, “Review article Multisensor image fusion in remote sensing: Concepts, methods and applications”, International Journal of Remote Sensing, 19(5): 823-854. Ren, Y., Zhang, L., and Suganthan, P. N., 2016, “Ensemble Classification and Regression-Recent Developments, Applications and Future Directions”, IEEE Computational Intelligence Magazine, 11(1): 41-53. Rogers, M. S. J., 2021, Machine Learning and remote sensing applications to shoreline dynamics, Department of Geography, University of Cambridge. Rouse Jr, J. W., Haas, R. H., Schell, J. A., and Deering, D. W., 1974, "Monitoring vegetation systems in the Great Plains with ERTS." In: Third Earth Resources Technology Satellite-1 Symposium-Volume I: Technical Presentations. NASA SP-351. 309-317. Samuel, A. L., 1959, “Some Studies in Machine Learning Using the Game of Checkers”, IBM Journal of Research and Development, 3(3): 210-229. Streiner, D. L., and Norman, G. R., 2006, “’Precision’ and ‘Accuracy’: Two Terms That Are Neither”, Journal of Clinical Epidemiology, 59: 327–330. Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Ergul, A., 2009, “The Digital Shoreline Analysis System (DSAS) Version 4.0—An ArcGIS Extension for Calculating Shoreline Change.” Open-File Report, US Geological Survey Report No. 2008-1278. Tiwari, S. P., Reshi, O. R., and Rahman, S. M., 2023, “Understanding Land use Land Cover and Shoreline Changes Along Arabian Gulf Using Geospatial Technology”, 2023 IEEE International Geoscience and Remote Sensing Symposium. DOI: 10.1109/IGARSS52108.2023.10282734 Wang, Y., Chen, Z., Cheng, L., Li, M., and Wang, J., 2013, “Parallel scanline algorithm for rapid rasterization of vector geographic data”, Computers & Geosciences, 59: 31-40. Wang, Y., Pan, Z., Zheng, J., Qian, L., and Li, M., 2019, “A hybrid ensemble method for pulsar candidate classification”, Astrophysics and Space Science, 364: 139. Wu, N. T., 1987, The Politics of a Regime Patronage: Mobilization and Control within An Authoritarian Regime, Unpublished doctoral dissertation, Department of Political Science, University of Chicago, Chicago, IL. Xu, B., Chen, J., and Yu, P., 2017, “Vectorization of classified remote sensing raster data to establish topological relations among polygons”, Earth Science Informatics, 10: 99-103. Xu, H., 2007, “Extraction of urban built-up land features from Landsat imagery using a thematic-oriented index combination technique.” Photogrammetric Engineering & Remote Sensing, 73: 1381-1391. Zhang, H., Jiang, Q., and Xu, j., 2013, “Coastline Extraction Using Support Vector Machine from Remote Sensing Image”, Journal of Multimedia, 8(2): 175-182. Zhang, X., Wang, M., and Jiang, S., 2008, “A Novel Approach for Raster Data Vectorization”, Journal of Geo-information Science, 10(6): 730-735. Zhong, Y., and El-Diraby, T., 2022, “Shoreline Recognition Using Machine Learning Techniques” IOP Conference Series: Earth and Environmental Science, 1101: 022025. 三、網頁參考文獻 內政部國土測繪中心，2023，國土測繪圖資服務雲網站。https://maps.nlsc.gov.tw/，取用日期：2023年10月25日。 內政部國土管理署，2017，整體海岸管理計畫。https://reurl.cc/m0ejEG，取用日期：2023年7月3日。 中央研究院，2024，臺灣百年歷史地圖。https://gissrv4.sinica.edu.tw/gis/twhgis.aspx，取用日期：2024年1月18日。 交通部中央氣象署，2023，潮位統計。https://www.cwa.gov.tw/V8/C/C/MMC_STAT/sta_tide.html，取用日期：2023年7月5日。 法務部，2015，海岸管理法，全國法規資料庫。https://law.moj.gov.tw/LawClass/LawAll.aspx?pcode=D0070222，取用日期：2023年12月3日。 陳世宗，2022，使用率低 五甲漁港輔導漁筏轉籍，中時新聞網，https://www.chinatimes.com/newspapers/20220503000485-260107?chdtv，取用日期：2024年3月30日。 陳淑娥，2020，北汕海堤侵蝕嚴重 明年3月改善，中時新聞網，https://tw.news.yahoo.com/%E5%8C%97%E6%B1%95%E6%B5%B7%E5%A0%A4%E4%BE%B5%E8%9D%95%E5%9A%B4%E9%87%8D-%E6%98%8E%E5%B9%B43%E6%9C%88%E6%94%B9%E5%96%84-201000484.html，取用日期：2024年3月30日。 張軒哲，2023，驚！台中大安沙雕節辦不成 沙灘「8年流失1個人高」，自由時報，https://news.ltn.com.tw/news/life/breakingnews/4401635，取用日期：2024年3月30日。 臺灣地形研究室，2013，臺灣海岸地帶面對氣候變遷的衝擊與挑戰，台灣地景保育網，http://140.112.64.54/zh_tw/LandscapeNews/communication/-8765501?mode=chapter，取用日期：2023年9月15日。 A Level Geography (2023, Oct. 20), Landforms of deposition, Retrieved October 20, 2023 from A Level Geography on the World Wide Web: https://www.alevelgeography.com/landforms-of-deposition/ ASUS (2022), ASUS TUF Gaming F15 (2022), Retrieved August 4, 2023 from ASUS on the World Wide Web: https://www.asus.com/tw/laptops/for-gaming/tuf-gaming/asus-tuf-gaming-f15-2022/techspec/ Dmlc XGBoost (2024, May 11), XGBoost Parameters (2.0.3), Retrieved May 14, 2024 from Dmlc XGBoost on the World Wide Web: https://xgboost.readthedocs.io/en/stable/parameter.html ESA (2023, Sep. 22), SENTINEL-2 MISSION GUIDE, Retrieved September 22, 2023 from ESA on the World Wide Web: https://sentinel.esa.int/web/sentinel/missions/sentinel-2 Esri (2023, Aug. 7), Make a layout, Retrieved August 7, 2023 from Esri on the World Wide Web:https://pro.arcgis.com/en/pro-app/latest/get-started/add-maps-to-a-layout.htm Esri (2024, Feb. 26), Resample function, Retrieved February 26, 2024 from Esri on the World Wide Web:https://pro.arcgis.com/en/pro-app/latest/help/analysis/raster-functions/resample-function.htm Google Colaboratory (2023, Oct. 18), What is Colab? Retrieved October 18, 2023 from Google Colaboratory on the World Wide Web: https://colab.research.google.com/?utm_source=scs-index#scrollTo=-Rh3-Vt9Nev9 Google for Developers (2023, Oct. 16), Google Earth Engine, Retrieved October 16, 2023 from Google for Developers on the World Wide Web: https://developers.google.com/earth-engine/guides/playground Javatpoints (2023, Oct. 2), Regression vs. Classification in Machine Learning, Retrieved October 2, 2023 from Javatpoints on the World Wide Web:https://www.javatpoint.com/regression-vs-classification-in-machine-learning McCarthy, J., Minsky, M., Rochester, N., and Shannon, Cl. (1955, Aug. 31), A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence. Retrieved September 23, 2023 from the original on the World Wide Web:http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html Pun:Chat (2022, Apr. 29), Quick guide to understanding image resolution, June 9, 2023 from Pun:Chat on the World Wide Web: https://www.punchat.in/funpun-1/quick-guide-to-understanding-image-resolutionnbsp QSCAT (2024, Apr. 7), A QGIS plugin for shoreline change analysis, Retrieved April 7, 2024 from Github on the World Wide Web:https://qscat.github.io/ Sanderson, L. (2024), Modify Raster Cell Size by Resampling, Retrieved May 3, 2024 from FME Support Center on the World Wide Web: https://support.safe.com/hc/en-us/articles/25407477353741-Modify-Raster-Cell-Size-by-Resampling Tondak, A. (2022, Jun. 25), Deep Learning Vs Machine Learning. Retrieved October 7, 2023 from K21Academy on the World Wide Web: https://k21academy.com/datascience-blog/deep-learning/dl-vs-ml/ USGS (2023, Sep. 22), Landsat 7. Retrieved September 22, 2023 from USGS on the World Wide Web: https://www.usgs.gov/landsat-missions/landsat-7 USGS (2023, Sep. 22), Landsat 8. Retrieved September 22, 2023 from USGS on the World Wide Web: https://www.usgs.gov/landsat-missions/landsat-8 Yehoshua, R. (2023, Mar. 25)，Random Forests, Retrieved Oct 6, 2023 from Medium on the World Wide Web: https://medium.com/@roiyeho/random-forests-98892261dc49 zh_TW
