多變的氣候環境及嚴峻的地形，加上頻繁的土砂災害使得臺灣的河川流域經營管理成為一項極具挑戰的工作；尤其災後的沉積物及流水，直接影響河川的動態平衡，甚至導致二次災害的發生，因此河川的管理是尚今一項重要的課題。
高解析度地形資料具有判釋及評估災害型態與規模的能力，能夠提供更為詳細且完整的地表災害。本研究期以高解析度的地形資料提供災害發生前後之河床粗糙度的變異型態。此外，由於傳統調查方式，往往受限於災區交通及天候因素，而無法提供即時且完整的地表災害資訊，因此本研究中藉由遙測資料輔助，分析山崩沖積扇的幾合形態變化，其成果也可延伸應用於災害敏感區的劃設。
另外，本研究亦嘗試分析比較常用的規則網格，以及點雲資料在粗糙度計算及應用的差異；爰此，本研究利用標準樣本資料，分析不同資料格式間的穩定度及其可靠度，經過半變異元圖的分析成果，發現利用規則網格資料進行粗糙度運算，可能會導致成果較點雲資料更為粗糙的結果，也可能會導致在實際應用上較為不穩定的成果。

Unpredictable weather condition and highly complex topography; in addition with frequent land mass disaster, which have made river management a substantial challenge in Taiwan. Especially, the sediment and incoming flow will directly affect the dynamic balance of the river. Even more, to induce the occurrence of secondary disaster. Namely, river management has currently become an important issue for today’s disaster prevention and mitigation.

High-resolution topographic data are capable of describing the morphological features and evaluating the magnitude of a disaster; thus, providing more detailed and more complete information of the disaster on the land surface.

Purposes of this study were to demonstrate the implementation of high resolution topographic data to show the multitemporal variability of river bed morphology through roughness mapping; before and after the disaster. In addition, due to the limitation of weather condition and abruption of transportation in the disaster region, the traditional investigation method would not be able to provide a real-time and complete information of the disaster. Therefore, this study has adopted the aids of remote sensing data to analyze the change of various geometric landform for a colluvium fan; results of the study may be extensively applicable for regional mapping of the vulnerable area.

Besides, this study have also tried to analyze and compare the variability of roughness calculation and application between the commonly used regular grid raster data and the point cloud data. Thus, for a greater understanding of the spatial scale-dependent roughness at different scales and resolutions, semivariograms were adopted to determine the effectiveness of data in representing roughness in this study; the range of semivariograms can be a clear identification that raster data generate “rougher” results compared with point cloud data. In a smooth area, the results demonstrated low similarity among point cloud data, indicating that point cloud data are smoother than raster data.

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