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題 名 | 結合監督模式與共克利金法估算河川污染指標面化圖=Study of River Pollution Index (RPI) by Using Different SPOT Image Corrections and Estimation Procedures |
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作 者 | 施明倫; 洪志豪; 林珈禎; | 書刊名 | 航測及遙測學刊 |
卷 期 | 17:4 2013.12[民102.12] |
頁 次 | 頁295-308 |
分類號 | 443.64 |
關鍵詞 | 遙測; 支撐向量機; 共克利金; 河川水質污染指標面化圖; 數值高程; Remote sensing; Support vector regression; Co-kriging; River pollution index; Atmospheric corrections; RS; SVR; CK; RPIM; DEM; |
語 文 | 中文(Chinese) |
中文摘要 | 本研究擬結合大範圍但精確性低的SPOT 衛星遙測影像(Remote sensing, RS)與小範圍但精確性高的現場水質採樣分析資料,以監督學習模式及空間統計分析方法面化建立台灣河川流域水質污染指標分布圖(River pollution index map, RPIM)。監督學習模式採用支撐向量機迴歸模式 (Support vector regression,SVR),而空間統計方法則採用地理資訊系統 (Geographic Information System, GIS)常用之共克利金內插方法(Co-kriging , CK)。利用前期歷史資料的學習建模,藉以概略推估當日衛星影像的估算值,再加入當日少數定點水質採樣資料作空間內插,兼顧兩種方法的優點建立遙測河川水質污染面化圖,最後再與單獨個別方法的估算結果作比較。 |
英文摘要 | This study concentrated on satellite image’ correction processes and its application on water pollution estimation. In addition to the existing established Geometric Correction, the image correction was evaluated by combining radiation with atmospheric corrections. In SVR model’s input variables, we optimized from not only the original four bands in SPOT 4 and 5, but also the derived bands refer to different author' recommendations, Additionally, the Digital Elevation Model (DEM) was also used as one of the input variables. Therefore, two different model estimation procedures were evaluated: average river pollution index from simulated single river pollutant's score (RPI-Si) and average river pollution index from simulated single river pollutant's concentration (RPI-C). Finally, average river pollution index which predicted from the above compared best method was Co-Kriging (CK) with calculated RPI from few measured water quality sampling stations was also evaluated. The results showed that the short wave infrared band of SPOT image also has the positive contribution on the prediction results of river pollution index. Among different river pollution index estimation procedures, RPI_Si is the best way to estimate the final average water quality index. Furthermore, if we can interpolate the data of monitoring stations with the data predicted from SVR model for the display of river pollution index maps, the results indicated more improvement than that of only using SVR model. Conclusionally, when we combine the precised traditional river water quality monitoring data with the roughly advanced image predicted data from SVR model will be the feasible way as so far in order to establish a sketch map of surface river pollution index. |
本系統中英文摘要資訊取自各篇刊載內容。