台灣受地質和地理環境影響，崩塌災害頻繁發生，目前崩塌分析的相關研究多針對單一事件並採用人工圈選的做法，較易受到主觀想法影響，也需耗費較長的分析時間。然而遙感探測具有即時、大規模、全面和非接觸式快速偵測的特點，對於環境敏感地的監測與分析就至關重要。
因此，本研究基於衛星影像時間序列的概念，透過Taiwan Data Cube平台進行衛星影像的高效率雲端計算應用，建立一個長期且大範圍的環境敏感地監測模型，並對各張影像進行常態化差異植生指標(NDVI)的計算以及最大似然分類法，再由該兩種結果組成一個新的判釋立方體，其中各時期影像中的每個像素都會含有一個NDVI值與一個土地使用的種類，藉此特性，即能以時間和空間兩個面向對該環境敏感地區進行分析探討。在時間面向中，可以針對每一時期各種類的NDVI值進行描述性統計，觀察出該地環境的長期趨勢與變化；在空間面向中，則可以統計各種類和NDVI值高低的像素數量進行比較，找出崩塌地識別之門檻值，並將不同時期之NDVI結果進行影像差分法，找到新生崩塌地的面積變化以及位置。
本研究以高雄市六龜區及南橫公路上的梅山明隧道作為試驗區，蒐集「Formosat 2」和「Landsat 8」資料集中的影像進行分析。六龜區的分析結果顯示，2007到2009年及2014年間該區的新生崩塌面積明顯上升，而在空間分布中可以發現，該區崩塌位置主要分布在荖濃溪及其支流兩側。在梅山明隧道的時間分析中則明顯呈現2008到2009年及2016到2017年期間的新生崩塌面積高於其他時間，尤其2016到2017年的新生崩塌面積更將近20公頃，而該範圍之崩塌空間分布則是從原本的河道兩側轉變到明隧道上方坡地。因此，經由衛星影像時間序列的建立，可以用來解析目標位置時空上的變化，讓地理空間資訊的應用更加全面。

Taiwan is a mountainous island surrounded by sea. Due to geographical and geological factors, typhoons hit Taiwan frequently. It may cause serious disasters, so monitoring the condition of landslide area is critical. Therefore, the purpose of this study is to establish a long-term monitoring model that can be used to find out both temporal and spatial change. To achieve our goal, a satellite image time series (SITS) is built by using Taiwan Data Cube (TWDC), a highly effictive cloud computing platform. The normalized difference vegetation index (NDVI) and maximum likelihood classification (MLC) are then calculated pixel by pixel on these satellite images, respectively. Finally, descriptive statistics and quantity statistics are used in the interpretation cube to analyze the temporal and spatial characteristics of the geologically sensitive area. This study selects “Liouguei District” and “Meishan open-cut tunnel” as the regions of interest. The result demonstrates that NDVI changes in each category over time can be detected for the temporal analysis. The identification standard, area and location of new landslide can also be found for the spatial analysis.

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