臺灣位處環太平洋地震帶與亞熱帶季風區，地質條件脆弱，且頻繁受到強降雨與地震影響，極易引發山崩與邊坡滑動災害，對人民安全與基礎設施構成重大威脅。因此，發展兼具即時性、廣域性與高精度的山崩偵測與監測技術，成為災害風險管理的核心課題。本研究整合三種具代表性的監測技術與尺度架構，針對山崩變動進行多面向探討：
第一部分提出「多期雙時相影像分析法」 (Multi-Bitemporal Images, MBTIs) ，利用 Google Earth Engine (GEE) 大量匯入事件前後的衛星影像，搭配像素級雲霧遮蔽過濾與變化統計，完成山崩範圍的自動化偵測與量化。此方法可同時整合所有可用影像進行交叉驗證，有效提升變化訊號的可靠度與空間涵蓋率。在設定誤判率 1% 的條件下，MBTIs 仍能穩定輸出高一致性的變化結果，相較於傳統機器學習或深度學習方法在複雜山區中仍易產生高誤判，MBTIs 展現更佳的實用性與穩健性，在雲端運算架構下，計算720組衛星影像差分，可在15分鐘內完成偵測。
第二部分建構以條件生成對抗網路 (conditional Generative Adversarial Network, cGAN) 為核心的深度學習架構，使用數值地形模型 (Digital Elevation Models, DEMs) 與多光譜衛星影像，預測山崩後的地形變化量 (DEM of Difference, DoD) ，並估算侵蝕與堆積體積。結果顯示該模型具備空間分布重建與體積估算的能力。
第三部分則針對坡地現場監測，本研究於多處邊坡布設地基合成孔徑雷達 (GB-InSAR) ，比較旋轉式與線性式系統之特性，並於不同條件下 (降雨、工程施工、爆破事件) 分析邊坡微位移行為。結果顯示，GB-InSAR 可於短時間提供毫米級位移資訊，能清楚辨識邊坡位移趨勢、變化邊界時序反應；旋轉式系統具有部署快速與場地需求低的優勢，而線性式系統在成像解析度上表現較佳。
三項技術互補後，可形成「崩塌範圍判釋-地形變化量估算-近場位移監測」的完整地表變遷分析鏈，展現高度的資料整合與實務應用潛力。此整合式框架強化了山崩資料庫品質、改善事件後地形推估方式，並建立可用於不同地貌區與監測需求之通用型分析，對臺灣山區地形變遷研究與中長期環境監測有重要貢獻。

This study develops an integrated, multi-scale framework for comprehensive landslide management, aiming to advance the spatial completeness of landslide inventories and enhance the accurate quantification of terrain change and near-real-time deformation monitoring. Three complementary methodologies were employed: (1) A Multi-Bitemporal Images (MBTIs) approach, implemented via Google Earth Engine (GEE), was used for the automated and large-scale delineation of landslide boundaries. (2) A conditional Generative Adversarial Network (cGAN) was engineered to generate predictive DEM of Difference (DoD) maps from multispectral imagery, facilitating the rapid estimation of erosion and deposition volumes. (3) Ground-Based Synthetic Aperture Radar (GB-InSAR) systems were deployed on-site to quantify the millimeter-scale deformation that occurs in response to environmental triggers such as rainfall, excavation, and blasting. This framework provides robust, data-driven support for disaster management, achieving crucial cross-scale integration from regional detection to site-specific, high-precision quantification. The results show that MBTIs improve the spatial consistency of landslide mapping, the cGAN model provides stable terrain-change predictions, and GB-InSAR captures short-term displacement behavior. When integrated, these methods form a coherent workflow linking landslide detection, terrain change estimation, and near-field deformation monitoring.

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