臺灣雖然屬於高年降雨量的島嶼，但因為山高平原短的特性使然，雨水不易保留，所以水庫成了台灣主要民生及工業的重要用水來源，所以監測水庫水質參數以利維持水庫運作就顯得特別重要。但是現有的監測方式，如傳統方法的架設水質測站，雖然已經有高時間解析度的自動水質測站，但是在空間分布上仍明顯不足以反應快速變化的水質；以及遙測方法的水色衛星，對於時間及空間解析度上均顯不足，而利用飛機遙測的成本太高，不適合長期使用。
為了克服現有平台對於水庫水質即時監測需求的不足，本研究以一般消費型數位相機為基礎，藉由加裝特殊光學濾鏡，並在取像範圍中舖設已知反射率之標準色板，建置一套低成本、高時空解析度之遙測水質平台，可從水庫集水區合適之制高點位置，持續穩定地拍攝水庫重點區域之多頻譜彩色（紅、綠、藍）影像。並利用本研究所發展的程式進行一系列的校正及處理，分別為；1.各波段影像重新合成。2.修正影像邊緣因為光線衰退所造成的失真。3.修正不同波段影像間的偏移。4.修正因為環境光源改變而造成輻射值比例的改變。5.修正因為大氣成分改變而造成對光線不同程度的干擾。
本研究以石門水庫大壩附近，裝設了自動水質檢測儀的測站為實驗區域。分別於104 年5 月27 日到5 月29 日，使用所建置之遙測平台，以對應自動水質測站的時間間隔拍攝自動水質測站附近的影像。再以本研究所提出之一系列影像處理方式，將這些時間序列的影像完成輻射校正處理，最後與自動水質測站同步記錄的葉綠素a濃度進行迴歸分析，建立水庫水質反算關係式，平均相對誤差為13.24%。

SUMMERY

In order to overcome the lack of existing platforms for real-time monitoring of water quality needs, this study uses consumer digital cameras to been based, with the installation of special optical filters and laying targets of color plank which are known reflectance in the image-taking range. Construction set a low-cost, high temporal and spatial resolution of the remote sensing of water quality platform, which can be installed on the commanding heights appropriate for reservoir catchment area, steady shooting color (red, green, blue) images which contain key areas in the reservoir. And use a series of correction and processing program which developmented in the study. In this study, near the Shihmen Reservoir dam, installation of automatic water quality sensor of the station for the experimental area. Respectively in 2015 years May 27 to May 29, using the build of remote sensing platform to correspond to the time interval with automatic water quality stations shoot images which’s range contains area near the automatic water quality stations. Then deal with a series of image processing mode of this study. Then these time-series images radiometric correction process is completed, the final water quality parameters and automatic water quality stations were simultaneously recorded regression analysis, reservoir water quality inverse relationship between the average relative error 13.24%.

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