臺灣博碩士論文加值系統

由於臺灣傳統邊坡崩塌監測系統有空間上的局限，無法提供大範圍的監測。近年來為了實現大範圍的監測，遙感探測技術之合成孔徑雷達干涉技術(Interferometric Synthetic Aperture Radar, InSAR)、永久散射體雷達干涉技術(Persistent Scatterer InSAR, PS-InSAR)與短基線子集差分干涉法(Small Baseline Subset-InSAR, SBAS−InSAR)都是日漸成熟的新型監測技術，可提供高空間分辨率和大範圍的快速、例行性觀測，為地表變位監測的新興利器。本研究以臺灣南投縣仁愛鄉為例，比較多時序PS-InSAR與SBAS−InSAR監測其地表變位的適用性，期提出適用於監測山區地表變位之方法。
本研究採用Sentinel-1A之2017年1月3日至2017年12月29日的上升軌道雷達影像共30張，進行PS-InSAR與SBAS-InSAR兩種技術對仁愛鄉地表變位監測分析，並探討兩種技術所得到的變位量與全球導航衛星系統(Global Navigation Satellite Systems, GNSS)資料所產製的變位量之相關性。初步結果顯示，PS-InSAR與SBAS-InSAR兩種技術比對GNSS量測成果差異之最高相關係數分別為0.554、0.792，而均方根誤差(Root Mean Square Error, RMSE)整體平均分別為20.48mm、12.60mm，得知SBAS-InSAR技術反映出較為吻合GNSS量測成果的地表變位情形，較適宜監測仁愛鄉地表變位，未來可進一步應用SBAS-InSAR技術提升山區地表變位預警之防災效益。

Due to spatial limitations in Taiwan's traditional landslide monitoring system, it cannot provide extensive monitoring coverage. In recent years, to achieve large-scale monitoring, remote sensing techniques such as Interferometric Synthetic Aperture Radar (InSAR), Persistent Scatterer InSAR (PS-InSAR), and Small Baseline Subset-InSAR (SBAS-InSAR) have matured as new monitoring technologies. They offer high spatial resolution and wide-ranging rapid and routine observations, serving as emerging tools for surface deformation monitoring.
This study takes Ren'ai Township in Nantou County, Taiwan, as an example to compare the applicability of multi-temporal PS-InSAR and SBAS-InSAR for monitoring surface deformation, aiming to propose methods suitable for monitoring surface deformation in mountainous areas. Using 30 ascending radar images from Sentinel-1A spanning from January 3, 2017, to December 29, 2017, the study performs PS-InSAR and SBAS-InSAR analyses for surface deformation monitoring in Ren'ai Township. It explores the correlation between the displacement values obtained from the two techniques and those derived from Global Navigation Satellite Systems (GNSS) data. Preliminary results reveal that the highest correlation coefficients between PS-InSAR and SBAS-InSAR against GNSS measurements are 0.554 and 0.792, respectively. The overall root mean square errors (RMSE) are 20.48 mm for PS-InSAR and 12.60 mm for SBAS-InSAR. It is evident that SBAS-InSAR better reflects the surface deformation in accordance with GNSS measurements, making it more suitable for monitoring surface deformation in Ren'ai Township. In the future, SBAS-InSAR can be further applied to enhance disaster prevention benefits by improving mountainous surface deformation early warning systems.

摘要 i
Abstract ii
目錄 iii
表目次 vi
圖目次 vii
第一章 前言 1
1.1 研究動機 1
1.2 研究目的 3
第二章 文獻回顧 4
2.1 傳統監測方法 4
2.1.1 監測方式 4
2.1.2 常見的坡地監測儀器 4
2.1.3 遙感技術監測坡地 5
2.2 合成孔徑雷達干涉技術 6
2.2.1 合成孔徑雷達差分干涉技術 6
2.2.2 永久散射體雷達干涉技術(PS-InSAR) 7
2.2.3 短基線子集差分干涉法(SBAS−InSAR) 9
2.3 合成孔徑雷達干涉技術用於監測地表 10
第三章 研究區域及資料來源 11
3.1 研究區域 11
3.2 研究資料 13
3.2.1 Sentinel-1衛星 13
3.2.2 DEM數據 16
3.2.3 GNSS連續接收站 16
3.2.4 崩塌目錄 18
第四章 研究流程與方法 19
4.1 研究流程 19
4.2 PS-InSAR 20
4.2.1 PS選點 20
4.2.2 相位解纏 22
4.2.3 空間相關之誤差估算 23
4.2.4 SARscape處理 23
4.3 SBAS-InSAR 25
4.3.1 SBAS-InSAR原理 25
4.3.2 SARscape處理 25
4.4 GNSS觀測量轉換 27
4.5 相關性分析 28
4.6 均方根誤差 28
第五章 研究結果 29
5.1 GNSS位移量轉換 29
5.2 地表位移平均速率 30
5.2.1 PS-InSAR處理結果 30
5.2.2 SBAS-InSAR處理結果 31
5.3 時間序列比較 31
5.3.1 PS-InSAR時間序列成果 31
5.3.2 SBAS-InSAR時間序列成果 33
5.3.3 GNSS站時間序列比較 34
5.3.4 相關性分析 37
5.3.5 均方根誤差 37
5.3.6 GNSS連續接收站位置 38
5.4 討論 39
5.4.1 InSAR與崩塌目錄的疊加 39
5.4.2 0601豪雨春陽村的崩塌事件 40
5.4.3 0601豪雨榮興村的崩塌事件 44
第六章 結論與建議 48
6.1 結論 48
6.2 建議 49
參考文獻 50
附錄 54

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