一般偵測地表變形常使用不同時期數值模型相減，但此方法在經由內插及相減兩個過程後，誤差可能被疊加而導致與現況不符。本研究提出，將共軛點雲直接相減得到其高程差後再進行內插，以縮小其誤差量。
本研究使用光達點雲資料，在阿里山選取三個不同地表性質之小區域即崩塌區、非崩塌區及模糊區進行比對。首先進行共軛點雲匹配，共軛點雲為不同時期但具相同位置(即相同x,y坐標)之點雲，將共軛點雲高程差與由光達資料內插產製DEM之差量進行比對，分析使用此兩方法之精確度及其優缺點。崩塌區部分點雲密度高、匹配結果良好達70%以上，非崩塌區則匹配不佳成果未達15%，模糊區則介於兩者約有50%。因此在進行比較後，崩塌區成果較接近真實地表變形，非崩塌區則因匹配結果不佳不予考慮。由於因點雲分布為不規則，比對結果仍以點雲密度較高區域較為合理。在DEM解析度選擇方面，若外業操作程序相同情況下，則以1m網格資料較合適，使用0.5m間距資料做為此類地形變化偵測與其它並無明顯差異。
除此之外，本研究也比對單期點雲之分布位置及由光達產製DEM之差異，討論在點雲製作DEM過程中，平均其鄰近點雲高程之結果是否引致部分真實地表特徵被忽略。此部分使用不同解析度DEM與點雲較合適之解析度，小區域特徵線在此取樣間隔下可被發現。
為滿足前後期由光達資料對於地形變化偵測之準確性，在事件前掃描之背景資料屬於低共軛點分布之屬性，為確保對未來事件變異的偵測能力，目前採用光達掃描參數仍過於粗略。

It is a traditional way to subtract digital models of various time for surface deformation detecting. However the error becomes larger by the two necessary procedures: interpolation and subtraction. Therefore this study suggests a method that subtract the elevation difference of conjugate point clouds then perform the grid interpolation to minimize the errors of unevenly distribution of raw data.
The LiDAR point cloud data from Alishan is used for the comparision purpose, and then three different land cover regions: landslide area, non-landslide area, and fuzzy area are chosen. First step is to match the conjugate point cloud, the conjugate means the point cloud gathered in different period of time but on the same position(the same x,y coordinate). The purpose of this routine is to compare the elevation difference of conjugate point cloud to the subtraction of different period LiDAR DEM thus to discuss the accuracy, advantage and disadvantage between two methods. The matching result shows that in landslide area, the density of point clouds is the highest and the matching result is the best, and the percentage of overlapped points is 70%; in non-landslide area, the matching result is worse, and the value didn’t above 15%;in fuzzy area, the matching result lies between these two, and the matching value is about 50%. Therefore the result of landslide area is close to ground truth, and non-landslide area isn’t. The position of point clouds is irregular dispersed, so the area with high density data is suitable for this application. Under the same operational procedure, the grid spacing of 1m DEM is suitable for this task since the difference of 0.5m DEM doesn’t provide obvious difference than 1m DEM.
In addition, this study compares the difference between single tasked point cloud and LiDAR DEM, and discusses whether the effect of averaging the elevation of point cloud when producing LiDAR DEM would neglect the surface feature. By using different grid spacing of DEM to compare with the raw point cloud and to find out the most suitable model. This result shows that the sampling interval of 0.5m DEM is suitable for the capability in showing the small surface feature.
To ensure the precision of deformation detecting by different period of LiDAR data, the pre-disaster data should be as low density conjugate category. In order to detect any upcoming deformation event, operated LiDAR scanning parameter now is not full fill to such requirement.

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