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.