空載光達資料目前已成為生產大範圍數值高程模型(Digital Elevation Model, DEM)的主要來源之一。藉由點雲的過濾程序，點雲即可被分類為地面點和非地面點，而分類後的地面點即可用以生產DEM。欲評估DEM的成果，過去經常使用的方法為計算點雲分類後之分類精度；另一方法則利用一個已知的DEM或是已知的控制點當作檢核資料，再與所生產的DEM進行高程差異比較。在這二個方法中，當採用分類精度的指標時，其缺點在於無法顯示點雲分類的成果是否有過度濾除之現象(即代表地面重要特徵的點位被濾除了)，或是地面點群中仍然存在極大的高程差異之非地面點，此分類精度的缺點雖然可以利用高程差異比較的方法來彌補，但高程差異法在實務的檢核應用上，往往僅適用於平坦地，不適於地勢陡峭之測區，此乃因地勢陡峭之區域，其地面點和非地面點之分類往往容易混淆，造成有較大的誤差存在，若以直接高程差異的方式來評估DEM，這些區域通常難以通過檢核之標準，實務上，我們希望判斷的標準能夠隨著地勢之起伏而有所調整，且此標準能夠適當且合理的評估DEM之品質。因此本研究以實際應用面的層面來考量，提出一個正規化高程差異指標，此指標可適用於不同坡度之地形，研究中並透過此指標來評估自行所產製的DEM，實驗結果顯示，正規化高程差異指標在實際的檢核上，能有效的運用在市區和郊區中，在考慮坡度的因素後，能夠快速提供DEM品值判斷上之依據。

The airborne laser scanning point clouds have become one of the primary data sources for DEM generation. By applying filtering algorithms to point clouds, the points can be classified into non-ground points and ground points. The DEM is then produced from the ground points. To assess the quality of DEM generation, a traditional method is to compute the classification accuracy of filtering results. Another method is to check the elevation differences between the produced DEM and a reliable reference DEM or some control points. However, the classification accuracy cannot reveal the over-filtering situations or any distinct non-ground points still remaining in the filtered ground data set. Although those disadvantages can be complemented by using the elevation difference method, the elevation difference method still needs to further take the topography relief into considerations for the use in a practical application. Usually those higher elevation differences occur in slope surfaces because the points on a slope surface are not easier to be classified by most filters compared with the points in a planar surface. For this reason, this paper presents a normalized elevation difference method which takes account of the surface slopes. The basic idea is using the slope as the weights for elevation difference computing. In a slope surface, the elevation difference toleration will raise while decrease in a planar surface. The experiment results show that our proposed method can be considered as a new assessment indicator especially in a practical application.