在植被覆蓋的山區中，由於空載雷射掃描可穿透植被間縫隙的特性，有較高機會收集到植被下的地面資訊，因此適合作為製作植被覆蓋地區數值高程模型的資料來源，而在過濾過程中，一般僅利用點雲間的三維位置關係進行幾何過濾，而全波形空載雷射掃描可另外提供點位的波形寬、振幅值、散射截面積以及散射截面積數等波形資料，本研究將透過波形資料分析進行點雲過濾。 首先經最低點採樣後，本研究利用貝氏定理自動分析並計算得到地面點的波形資料的特徵區間範圍，採用振幅值、散射截面積以及散射截面積係數得到的特徵區間範圍開始第一階段的波形資料過濾，完成後再以第二階段的一般幾何過濾濾除剩餘之非地面點，最後的成果將與航測以及只採用幾何過濾時的成果比較。 由研究成果中顯示，不同的植被覆蓋間的單一回波波形資料的差異較明顯，最後回波類似。同一植被覆蓋下的單一回波及最後回波反應不同。而在成果的比較中，本實驗的成果與不採用波形資料輔助的成果大致相同本研究的成果在部分植被覆蓋的區域成果稍差，但透過波形過濾，可將幾何過濾所需計算的點雲數減少許多，可以增進整理過濾的效率。本研究的成果與航測相比時，在植被覆蓋區域較航測成果貼近實際的地面起伏，數值高程模型成果較為正確。 / In mountain areas covered with vegetation, discrete airborne laser scanning is an appropriate technique to produce DEMs for its laser signal is able to reach the ground beneath the vegetation. Once the scanned data was derived, point cloud filtering was performed based on the geometry relationship between the points at the processing stage. With the development of the advanced full-waveform laser scanning system, the additional waveform data has been proved useful for improving the performance of point cloud filtering. This research therefore focused on using the waveform data to extract DEM over vegetation covered area. The amplitude, backscatter cross-section and backscatter cross-section coefficient were the waveform parameters used to do the filtering. After initial waveform analysis was accomplished, an automated method to determine threshold range of each parameter representing ground points was proposed. By applying the thresholds, the original point cloud was filtered. Geometric filtering method was then used to eliminate the remained non-ground points. As a result, the DEM over the target vegetated area was derived. With the comparison against photogrammetric DEM and DEM derived from traditional filtering method, it was demonstrated that the quality of the resultant DEM was improved.

摘要 本研究主要利用誤差偵測方法發掘數值高程模型中可能出現的高程誤差，藉以提升數值高程模型之高程品質。本研究採用三種誤差偵測方法即參數統計、水流方向矩陣、坡度與變化約制等，這三種方法過去是應用在航測資料測製之格網式數值高程模型，本研究嘗試推廣至空載光達製作的數值高程模型。 利用模擬DEM資料以驗證三種偵測方法之偵測能力。首先利用多項式函數擬合出各種地形，並假設該地形無誤差。再將人為誤差隨機加入模擬DEM資料；第二部份則將誤差偵測之方法應用至真實的數值高程模型資料，並配合檢核點高程測量檢驗之。根據誤差偵測結果，參數統計和坡度變化結果類似而且皆有過度偵測之缺點，可透過提高門檻值或高通濾波改善；水流方向矩陣比較不適合誤差偵測，但可透過窪地填平最佳化地形。 關鍵字：數值高程模型、誤差偵測、參數統計法、坡度與變化約制、水流方向矩陣。 / Abstract In this study, error detection methods were proposed to find possible elevation errors in digital elevation model (DEM), and to improve the quality of DEM. Three methods were employed to detect errors in the study, i.e. parametric statistical method, flow direction matrix, and constrained slope and change. These methods can deal with grid DEM from photogrammetric approach in the past, and now the methods are used to find errors in high resolution DEM from light detection and ranging (LIDAR). The simulated DEMs were used to approve the detection capability of the proposed methods. The fitted DEMs were first obtained by polynomial functions fit the different terrains and assuming these DEMs were free of errors. Then the artificial errors were added to fitted DEMs. The proposed methods were also applied to real DEM data got from LIDAR and field check works were run to insure the results. The results of parametric statistical method and constrained slope and change are similar, and all show the over-detection of errors. These results can be improved by using high threshold or high-pass filter. Flow direction matrix is not suitable for error detection in DEM, but can be applied to fill sink to optimize terrain for watershed analysis. Keyword: digital elevation model, error detection, parametric statistical method, constrained slope and change, flow direction matrix.