Nowadays, digital terrain models (DTM) are an important source of spatial data for various applications in many scientific disciplines. Therefore, special attention is given to their main characteristic - accuracy. At it is well known, the source data for DTM creation contributes a large amount of errors, including gross errors, to the final product. At present, the most effective method for detecting gross errors in DTM source data is to make a statistical analysis of surface height variation in the area around an interested location. In this paper, the method has been tested in two DTM projects with various parameters such as interpolation technique, size of neighboring area, thresh...
