全球氣候變化增加了野火、極端高溫/降水與乾旱等極端氣候事件發生的機率，而
乾旱將導致民生和工業用水缺乏，連帶引發衛生危機、疾病及經濟蕭條，因此，良好
的水資源管理策略對世界各國的經濟發展和人口增長具有重要意義。在分析和管理水
庫、河流和湖泊等地表水資源時，水位與蓄水量是兩個重要的指標，然而，偏遠地區
或低度開發國家缺乏現地測量，難以進行即時且連續的水資源監測。衛星測高具有全
天候運行的優勢，能夠持續監測人類無法到達的內陸水體之水位，而遙測衛星影像可
成功地用於推求水域表面積。因此，本研究旨在整合Landsat 影像和衛星測高觀測量，
以台灣西南部曾文水庫為例，求得2003 至2020 年間長期表面積、水位與蓄水量變
化。首先，藉由計算Landsat 影像之改良常態差異水體指標 (Modified Normalized
Difference Water Index, MNDWI) 來提取水體邊界與表面積；為提升受雲遮蔽之遙測
衛星影像使用率，本研究利用二階多項式迴歸模型建構出部份水域表面積 (受雲遮蔽
影像) 與完整水域表面積 (無雲影像) 之相關性。接著使用衛星測高觀測的水位，建
立表面積與水位之間的線性迴歸模型，可將表面積時間序列轉換為水位時間序列。最
後，結合水域表面積和水位可計算水庫蓄水量。為評估推求之水位及蓄水量精度，我
們使用曾文水庫現地監測儀器記錄的水位和蓄水量來驗證。研究結果顯示Landsat 影
像的資料使用率從23%提高至43%，而表面積與水位迴歸模型的?2為0.97-0.99，水
位估值與現地水位的差值之均方根誤差 (root-mean-square error, RMSE) 約為2.947-
5.557 m，相關係數為0.93-0.99。此外，蓄水量估值與現地蓄水量之間的相關係數為
0.88-0.97，皆顯示本研究方法求得的估值與現地資料的一致性。綜上所述，整合多元
的遙感技術可有效地進行水資源監測工作，該技術也為長期的水位和蓄水量提供一種
新的監測方法，並可協助地方政府制定適當的水資源管理計劃。

Global climate change increases the frequency and intensity of extreme climate events, and it leads to water shortage which affects our lives including environment, ecosystem and human society. Hence, a sound management strategy for water resources is important for all countries worldwide. When analyzing and managing surface water resources, water area (WA), water level (WL) and water volume (WV) are three important indicators. However, remote regions or undeveloped countries have less water information due to the lack of insitu water records. Remote sensing has the ability to continuously monitor inland water bodies in human-inaccessible areas. In this research, we aimed to integrate Landsat satellite imagery and satellite altimetry to derive long-term WA, WL and WV of Tsengwen Reservoir located in the southwestern Taiwan from 2003-2020. First, water boundary and WA are extracted from Landsat satellite imagery by Modified Normalized Difference Water Index (MNDWI). Because data availability of optical imagery will be affected by cloud cover effects, a regression model for establishing the relationship between a portion of WA and entire WA was adopted to estimate entire WA of cloud-covered image. Then, combining the WA and WL provided by satellite altimetry to build a regression model which is used to transfer WA time series to WL time series. Finally, WV was computed from the extracted WA and WL. The local water gauge data was used to validate and evaluate the estimated WL and WV. The results showed that the data availability of Landsat imagery can be increased from 23% to 43%. Moreover, the statistics demonstrated the good agreement between the retrieved WL/WV measurements and the local water gauge data with the correlation coefficients of 0.88-0.99. To sum up, the results indicated that the integration of multi-source remote sensing technologies can effectively provide a novel method for long-term WA, WL and WV monitoring works to assist local governments with appropriate plan of water resources management.

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