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系統識別號 U0026-0812200915365203
論文名稱(中文) 應用船載水面高光譜擷取系統遙測近岸及內陸水體水質
論文名稱(英文) Remote Sensing of Inland and Costal Waters Quality Using Shipborne Hyper Surface Acquisition System (HyperSAS)
校院名稱 成功大學
系所名稱(中) 衛星資訊暨地球環境研究所
系所名稱(英) Institute of Satellite Informatics and Earth Environment
學年度 97
學期 2
出版年 98
研究生(中文) 許華宇
研究生(英文) Hua-Yu Hsu
電子信箱 l9696402@mail.ncku.edu.tw
學號 l9696402
學位類別 碩士
語文別 中文
論文頁數 104頁
口試委員 指導教授-劉正千
口試委員-張智華
口試委員-王驥魁
中文關鍵字 GA-SA演算法  水面高光譜獲取系統(HyperSAS)  水質監測  智慧光譜影像儀(ISIS)  多平台遙測 
英文關鍵字 Hyper Surface Acquisition System (HyperSAS)  genetic algorithm and semi-analytical algorithm  Intelligent Spectral Imaging System (ISIS)  monitoring water quality  multi-platform remote sensing 
學科別分類
中文摘要 近岸及內陸水體對人們生活息息相關,但傳統方式耗時費力且資料在時空上均受到限制,不足以顯示整體水質的空間分佈,遙測方式雖可達成大範圍監測的目標,惟一般水色衛星對於觀測近岸與內陸地區水質的光譜及空間解析度均顯不足,而大量使用飛機平台又耗費過高成本。本研究使用船載水面高光譜擷取系統(Hyper Surface Acquisition System, HyperSAS)獲取光譜資料以監測水質,並做為機載拍攝影像大氣校正時之地面真實光譜。
HyperSAS經多項敏感度實驗及實測與模擬光譜驗證後,建立其穩定擷取光譜的最佳設置條件。本研究自中華民國97年3月至98年7月共進行五次野外調查,包含:曾文水庫(內陸水體)、高屏溪口(近岸水體)、台灣西南沿海(近岸水體),其中民國97年11月28日曾文水庫航次同步有機載智慧光譜影像儀( Intelligent Spectral Imaging System, ISIS )進行拍攝。將獲得之遙測反射率(remote sensing reflectance, Rrs)代入GA-SA演算法反算水質參數,包含:葉綠素a (chlorophyll-a, Chl-a)、水中懸浮固體物(suspended solids, SS)、水中無生命微粒(non-algal particle, NAP)、有色溶解性有機物質(coloured dissolved organic matter, CDOM; yellow substances; gelbstoff)等,過程中會先進行波段選取以增進其效能。

HyperSAS最佳設置條件
為取得準確的Rrs,感測器必須離水面超過1.5 m,並使太陽方位角位於135°-180°間。兩輻射儀之天頂角與天底角於30°-50°間均可,另要注意輻照度計須避免被高突物的陰影遮蔽。

以GA-SA反算水質之精度評估
HyperSAS
若架設符合最佳條件,應用在曾文水庫反算水質參數相當準確,Chl-a與SS平均絕對誤差(MAE)均在35%以內。台灣西南沿海因包含了一類與二類水體性質,因此反算結果較曾文水庫為差。但發現以Rrs之405 nm與550 nm之比值作為兩類水體的分類依據,先分類再代入相對應之生光模式(bio-optical models)進行反算,可提高水質參數的準確度,Chl-a與SS之MAE分別由137%、57%降至58%、47%。

HyperSAS之其他應用
當有HyperSAS 於ISIS拍攝時在水面進行同步光譜量測,利用同步光譜建立之回歸公式對影像進行大氣校正,能大幅提高各項水質參數之反算結果,Chl-a與SS的MAE均小於50%,便可利用ISIS來進行大範圍的水質監測。於高屏河口航次中,發現可藉由HyperSAS所測之Rrs光譜曲線對羽狀水團(plume)之泥沙含量進行分類,並能將此法應用至衛星影像上,如高空間、高時間解析度的福爾摩沙衛星二號(Formosat-2)。
英文摘要 Inland and coastal waters are the most important water resources for human beings, however, general approaches for assessing the water qualities of such an important water resource all rely on the data collected at a few sampling point. Those data are usually insufficient to identify the spatiotemporal variations of water-quality parameters. Water quality parameters derived from the remote sensing techniques may have potential to extend current monitoring results to comprehensively assess the water status. Although the progressing in remote sensing technology has enabled the observations of ocean color to be made from space, the existing spaceborne ocean color sensors are inappropriate for monitoring the water quality of inland water because of the limited spatial resolutions. Monitoring the inland water quality using airborne hyperspectral sensor is better but costly on a regular basis. The newly developed shipborne Hyper Surface Acquisition System (HyperSAS) can be deployed as a primary tool to monitor the water quality or a ground truth collector for calibrating airborne and spaceborne sensors.
Several in-situ and numerical experiments were conducted to test the data sensitivity of HyperSAS in different operation conditions and thus the standard operation procedures (SOP) were made to give reliable measurements of water-surface reflectance in ships. Five field campaigns to Tsengwen Reservoir (TWR) (inland water), Gao-Ping (Kao-Ping) River mouth (coastal water), and southwest coastal of Taiwan (SW) (coastal water) were conducted during March 2008 to July 2009. In the cruise of 11/28/2008, field campaign to TWR was conducted simultaneously with an airborne imager (Intelligent Spectral Imager System, ISIS). A newly developed water color retrieval algorithm, GA-SA, was applied to derive the concentration of chlorophyll-a (Chl-a), color dissolved organic matter (CDOM), suspended solids (SS) and non-algal particles (NAP) from the HyperSAS-measured reflectance (Rrs). Additionally, the optimal spectral bands of HyperSAS for water constituent retrieval were obtained using band selection methods for improving the efficiency of GA-SA.

SOP of HyperSAS
For a reliable measurement of Rrs, the distance between water surface and the sensor for measuring the surface radiance should be 1.5m or larger. A significant direct sun-glint effect was found in the measured Rrs when the radiance sensors are pointed at the azimuth angle between 0° and 135° to the solar plane. An optimal range 30° to 50° is suggested for the zenith angle of sky radiance measurement (also the nadir angle for measuring the surface radiance). Finally, the irradiance sensor should not be shaded when the Rrs is measuring.

Accuracy assessment of water quality inversion using GA-SA and shipborne HyperSAS
In two TWR cruises that HyperSAS were operated as the SOP given above, the measured Rrs gives good retrievals of Chl-a and SS using GA-SA and Case2 bio-optical models, as the mean absolute error (MAE) are all within 35%. The SW region contains not only optically Case 1 but also Case 2 waters in one cruise, thus the overall water quality inversion in this area is not as good as TWR where the optical properties are relatively consistent. Therefore, we developed a band ratio index using Rrs measured at 405 nm and 550 nm to determine the suitable bio-optical model for GA-SA before the retrieval. The overall accuracy for water quality inversion are improved significantly by this new approach, as the MAE for Chl-a and SS were improved from 137% to 58% and from 57% to 47%, respectively.

Other applications of HyperSAS
The shipborne HyperSAS can be deployed as a ground truth collector to provide reliable surface reflectance data for the atmospheric correction of airborne and spaceborne sensors. In the case of ISIS mission, the deviation of Chl-a and SS inversion from atmospheric corrected ISIS image were improved, as the MAE were all within 50% and the ISIS image was capable to mapping the reservoir water quality. In the case of Gao-Ping River mouth, the river plume can be classified in terms of the particle contents using the Rrs spectrum measured by HyperSAS. This newly developed classification method can be further applied in satellite imagery, such as the high temporal/spatial resolution Formosat-2 imagery.
論文目次 摘要 i
Abstract iii
致謝 vi
目錄 vii
表目錄 x
圖目錄 xi
第1章 緒論 1
1.1 研究背景 1
1.2 研究目的 4
1.3 研究架構 5
第2章 文獻回顧 8
2.1 海洋水色遙測與水質反算 8
2.2 高光譜於水色之應用 11
2.3 多平台、多感測器遙測之應用 17
2.4 小結 18
第3章 研究區域 19
3.1 曾文水庫 20
3.2 高屏溪口 23
3.3 台灣西南沿海 25
第4章 研究方法 27
4.1 HYPERSAS資料擷取及前處理 27
4.2 水質分析 31
4.3 GA-SA演算法 33
第5章 HyperSAS資料處理與分析 36
5.1 Rrs計算 36
5.2 HyperSAS敏感度測試 37
5.3 波段選取 42
5.4 水質反算 47
5.5 小結 49
第6章 HyperSAS之應用 50
6.1 內陸水體 50
6.2 近岸水體 57
第7章 結論與建議 70
7.1 結論 70
7.2 建議 72
參考文獻 74
附錄一 各航次採樣點光譜曲線 81
附錄二 各航次採樣點水質資料 85
附錄三 ISIS 88
附錄四 Formosat-2 90
附錄五 羽狀水團四類水體邊界 93
附錄六 ac-s 95
附錄七 ECO VSF3 99
附錄八 GPS 100
附錄九 DH-4 101
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