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論文名稱(外文):Establishment of monitoring approach for copper ionin water using hyperspectral remote sensing technique
指導教授(外文):Lee, Meng-shan
口試委員(外文):Chen, Wei-HsiangShiau, Yo-JinLai, Yi-Chieh
外文關鍵詞:NIRHyperspectralHeavy metals-CuStepwise multiple linear regressionchlorophyll aturbidity
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摘要 I
誌謝 III
目錄 IV
圖目錄 VII
表目錄 X
第一章 前言 1
1.1 研究背景 1
1.2 研究動機與目的 2
第二章 文獻回顧 3
2.1 水中重金屬現況 3
2.2 重金屬對環境與人體影響 5
2.3 水中重金屬與環境因子影響 6
2.4 水中重金屬分析方法 8
2.4.1 傳統破壞性分析 8
2.4.2 非破壞性分析 9
2.5 遙感探測 13
2.6 高光譜資訊分析 17
2.6.1 大氣校正(Atmospheric correction) 17
2.6.2 資料預處理(Data preprocessing) 18
2.6.3 資料轉換(Data Transformation) 20
2.6.4 特徵波段選取 23
2.6.5 水質參數經驗公式 24
2.7 高光譜應用 26
2.7.1 水體 26
2.7.2 土壤 30
2.7.3 礦物 32
2.7.4 漏油 34
第三章 研究方法與步驟 35
3.1 研究方法 35
3.2 水樣配置與標準溶液配置 36
3.2.1 重金屬溶液配置方法 36
3.2.2 水中優養化模擬 37
3.2.3 水中濁度模擬 37
3.3 實驗儀器與分析方法 38
3.3.1 實驗環境 38
3.3.2 近紅外光高光譜儀(NIR) 38
3.3.3 感應耦合電漿原子發射光譜法(ICP-AES) 40
3.3.4 數據處理 40
第四章 研究結果與分析 42
4.1 重金屬-銅溶液分析方法之建立 42
4.1.1 重金屬-銅溶液之光譜圖 42
4.1.2 濃度與反射率之相關性分析 44
4.1.3 建立預測模型 45
4.2 重金屬-銅溶液添加濁度試驗 48
4.2.1 原始光譜圖 49
4.2.2 數據預處理-對數轉換 51
4.2.3 以簡單過濾去除濁度影響 52
4.2.4 濁度對預測模型之影響 54
4.3 重金屬-銅溶液添加藻水試驗 56
4.3.1 原始光譜圖 57
4.3.2 數據預處理-對數轉換 58
4.3.3 葉綠素a對預測模型之影響 59
4.4 添加不同環境之水樣 61
4.4.1 配置比例 61
4.4.2 添加人工海水 62
4.4.3 添加魚塭水 63
4.4.4 預測模型於不同環境水樣應用之結果 64
4.4.5 檢測方法之建立 67
4.5 光譜指數之建立 67
4.5.1 光譜指數預測模型 67
4.5.2 預測模型之比較 69
第五章 結論與建議 70
5.1 結論 70
5.2 建議 70
第六章 參考文獻 71
附錄一 相關性分析後選取波段 75

Bruins, M. R., Kapil, S., & Oehme, F. W. (2000). Microbial resistance to metals in the environment. Ecotoxicology and Environmental Safety, 45(3), 198-207.
Choe, E., van der Meer, F., van Ruitenbeek, F., van der Werff, H., de Smeth, B., & Kim, K.-W. (2008). Mapping of heavy metal pollution in stream sediments using combined geochemistry, field spectroscopy, and hyperspectral remote sensing: A case study of the Rodalquilar mining area, SE Spain. Remote Sensing of Environment, 112(7), 3222-3233.
Chou, J.-S., Ho, C.-C., & Hoang, H.-S. (2018). Determining quality of water in reservoir using machine learning. Ecological Informatics, 44, 57-75.
Corbet, C. A. (2007). Colored Dissolved Organic Matter (CDOM) Workshop summary.
Dörnhöfer, K., & Oppelt, N. (2016). Remote sensing for lake research and monitoring – Recent advances. Ecological Indicators, 64, 105-122.
de la Mare, W., Ellis, N., Pascual, R., & Tickell, S. (2012). An empirical model of water quality for use in rapid management strategy evaluation in Southeast Queensland, Australia. Marine Pollution Bulletin, 64(4), 704-711.
Fittschen, U. E. A., & Falkenberg, G. (2011). Trends in environmental science using microscopic X-ray fluorescence. Spectrochimica Acta Part B: Atomic Spectroscopy, 66(8), 567-580.
Galdames, F. J., Perez, C. A., Estévez, P. A., & Adams, M. (2019). Rock lithological classification by hyperspectral, range 3D and color images. Chemometrics and Intelligent Laboratory Systems, 189, 138-148.
Gholizadeh, M. H., Melesse, A. M., & Reddi, L. (2016). A comprehensive review on water quality parameters estimation using remote sensing techniques. Sensors (Basel), 16(8).
Ghosh, A., Fassnacht, F. E., Joshi, P. K., & Koch, B. (2014). A framework for mapping tree species combining hyperspectral and LiDAR data: Role of selected classifiers and sensor across three spatial scales. International Journal of Applied Earth Observation and Geoinformation, 26, 49-63.
Guo, L., Zhang, H., Shi, T., Chen, Y., Jiang, Q., & Linderman, M. (2019). Prediction of soil organic carbon stock by laboratory spectral data and airborne hyperspectral images. Geoderma, 337, 32-41.
Hacısalihoğlu, S., & Karaer, F. (2016). Relationships of heavy metals in water and surface sediment with different chemical fractions in Lake Uluabat, Turkey. Polish Journal of Environmental Studies, 25(5), 1937-1946.
Jolivet, L., Leprince, M., Moncayo, S., Sorbier, L., Lienemann, C.-P., & Motto-Ros, V. (2019). Review of the recent advances and applications of LIBS-based imaging. Spectrochimica Acta Part B: Atomic Spectroscopy, 151, 41-53.
Julian, J. P., Davies-Colley, R. J., Gallegos, C. L., & Tran, T. (2013). Optical water quality of inland waters: a landscape perspective. Annals of the Association of American Geographers, 103(2), 309-318.
Koponen, S., Pulliainen, J., & Kallio, K., & Hallikainen, M. (2002). Lake water quality classification with airborne hyperspectral spectrometer and simulated MERIS data. Remote Sensing of Environment, 79, 51-59.
Liu, J.-J., Diao, Z.-H., Xu, X.-R., & Xie, Q. (2019). Effects of dissolved oxygen, salinity, nitrogen and phosphorus on the release of heavy metals from coastal sediments. Sci Total Environ, 666, 894-901.
Liu, L., Feng, J., Rivard, B., Xu, X., Zhou, J., Han, L., Yang, J., & Ren, G. (2018). Mapping alteration using imagery from the Tiangong-1 hyperspectral spaceborne system: Example for the Jintanzi gold province, China. International Journal of Applied Earth Observation and Geoinformation, 64, 275-286.
Mather, P. M., & Koch, M. (2011). Computer Processing of Remotely-Sensed Images: An Introduction., John Wiley & Sons, Inc.
Mirzaei, M., Verrelst, J., Marofi, S., Abbasi, M., & Azadi, H. (2019). Eco-friendly estimation of heavy metal contents in grapevine foliage using in-field hyperspectral data and multivariate analysis. Remote Sensing, 11(23), 2731.
Nasrabadi, T., Ruegner, H., Sirdari, Z. Z., Schwientek, M., & Grathwohl, P. (2016). Using total suspended solids (TSS) and turbidity as proxies for evaluation of metal transport in river water. Applied Geochemistry, 68, 1-9.
Németh, T., & Kádár, I. (2005). Leaching of microelement contaminants: a long-term field study. Zeitschrift für Naturforschung C, 60, 260-264.
Onojeghuo, A. O., & Blackburn, G. A. (2011). Optimising the use of hyperspectral and LiDAR data for mapping reedbed habitats. Remote Sensing of Environment, 115(8), 2025-2034.
Pearson, D., Chakraborty, S., Duda, B., Li, B., Weindorf, D. C., Deb, S., Brevik ,E., Ray, & D. P. (2017). Water analysis via portable X-ray fluorescence spectrometry. Journal of Hydrology, 544, 172-179.
Pelta, R., Carmon, N., & Ben-Dor, E. (2019). A machine learning approach to detect crude oil contamination in a real scenario using hyperspectral remote sensing. International Journal of Applied Earth Observation and Geoinformation, 82, 101901.
Popescu, C.-M., Navi, Placencia Peña, M. I., & Popescu, M.-C. (2018). Structural changes of wood during hydro-thermal and thermal treatments evaluated through NIR spectroscopy and principal component analysis. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 191, 405-412.
Rostom, N. G., Shalaby, A. A., Issa, Y. M., Afifi, A. A. (2017). Evaluation of Mariut Lake water quality using Hyperspectral Remote Sensing and laboratory works. The Egyptian Journal of Remote Sensing and Space Science, 20, S39-S48.
Serranti, S., Palmieri, R., Bonifazi, G., & Cózar, A. (2018). Characterization of microplastic litter from oceans by an innovative approach based on hyperspectral imaging. Waste Manag, 76, 117-125.
Stehle, P., Stoffel-Wagner, B., & Kuhn, K. S. (2016). Parenteral trace element provision: recent clinical research and practical conclusions. European Journal of Clinical Nutrition, 70, 886-893.
Szili-Kovács T, Anton, A., Gulyás, F. (2000). Effect of Cd, Ni and Cu on some microbial properties of a calcareous chernozem soil. In: Kubát J, Prague (ed.) Proceedings of the 2nd Symposium on the pathways and consequences of the dissemination of pollutants in the biosphere, Prague, 88–102.
Tan, K., Wang, H., Chen, L., Du, Q., Du, P., & Pan, C. (2019). Estimation of the spatial distribution of heavy metal in agricultural soils using airborne hyperspectral imaging and random forest. Journal of Hazardous Materials, 382, 120987.
Van den Noortgate, W., López-López, J. A., Marín-Martínez, F., & Sánchez-Meca, J. (2015). Meta-analysis of multiple outcomes: a multilevel approach. Behavior Research Methods, 47(4), 1274-1294.
Wang, F., Gao, J., & Zha, Y. (2018). Hyperspectral sensing of heavy metals in soil and vegetation: Feasibility and challenges. ISPRS Journal of Photogrammetry and Remote Sensing, 136, 73-84.
Zhou, H., Deng, Z., Xia, Y., & Fu, M. (2016). A new sampling method in particle filter based on Pearson correlation coefficient. Neurocomputing, 216, 208-215.
Zhou, W., Liu, H., Xu, Q., Li, P., Zhao, L., & Gao, H. (2020). Glycerol's generalized two-dimensional correlation IR/NIR spectroscopy and its principal component analysis. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 228, 117824.
江守山. (2006). 重金屬汙染事件頻傳有害國人健康-如何檢查及治療重金屬汙染. 新光醫訊, 173.
行政院環境保護署. (2018). 107環境水質年報.
施介嵐. (2002). 以光譜混合分析法進行台灣地區Master影像之研究.
鄭森雄. (2009). 台灣之河川污染及其與生態環境之關係. 台美環保及再生研究會專題演講.

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