## 3 Results
### Results of the Test Area  
Figure 9 shows the individual differences between the newly calculated DEMs and the manually filtered DEM (DoD = Differences of DEMs). The areas with warm colour indicate a good match between the DEMs. The red areas indicate where the newly calculated DEMs are above the reference model, resulting from an insufficient filter effect. Blue areas indicate where the newly calculated DEM is lower than the reference model due to faulty filtered ground points. After the manual filtering of the point data, only a few points remained in the centre of the test area. Due to the lack of ground points, the significance of the filter effect in this area is low. Consequently, the statistical values are distorted for the entire recording area. Therefore, we have selected a specific area with a high point density for further analysis. In this area, the statistical inaccuracies are largely avoided and thus more significant results are achieved. As a result of the HOVE-wedge-filtering, we identified 23,135 ground points and 55,634 non-ground points. The mean error of the two surfaces is \(-\)0.07 m with a RMSE of 1.610 and a standard deviation of 1.609 (Table 1). Especially in the forested areas near the laser scanner, an area with high point density, we see a better filter effect, as indicated by the larger warm coloured zones in Figure 9. In the more distant forested areas, the filtering effect is reduced, recognizable by the large red areas in the centre of Figure 9.  
In this area, only the highest tops of the trees could be recorded, since it is unlikely that the laser beam penetrates to the ground of such forested areas. Therefore, effective filtering is difficult. Looking at the figure, we can see that in the part of the HOVE-wedge there are fewer dark red areas than in the other two filtering methods. This indicates that vegetation has been filtered out well. In Figure 10, some areas with great optical significance for quality of the filtering were worked out. Detail 1 shows a protruding formation sloping to three sides. In such terrain forms, the filtering is extremely difficult, since high angular differences caused by the terrain edges have influence to the calculation formula. If the threshold value is set too low, this rock ledge is incorrectly removed during filtering. The blue points in Detail 1 show the result at a threshold of 160, the yellow points show the points which are not eliminated with a threshold of 200, the red which are not eliminated at a threshold of 240. For Detail 2, the results from the calculation with a threshold of 160 (blue points) are compared with the results from the calculation with a threshold value of 240 (red points). These points are very difficult to detect as ground points by an automated analysis, since the terrain in this area drops sharply (high angle difference) and only a few neighbouring points are present (small density factor). The optical evaluation classification as probable ground points is only possible with the inclusion of \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Statistic** & **Modified Wedge-Filtering** & **Wedge Absolute-Filtering (60\({}^{\circ}\))** & **IDWMO Method** \\ \hline Groundpoints & 23,135 & 29,229 & 12,326 \\ Mean error & \(-\)0.070 & 2.908 & 1.032 \\ RMSE & 1.610 & 5.284 & 3.333 \\ Standard deviation & 1.609 & 4.412 & 3.169 \\ Median & \(-\)0.138 & 1.277 & 0.166 \\ NMAD & 1.498 & 5.085 & 3.411 \\
68.3\% quantile & 0.242 & 3.819 & 1.258 \\
95\% quantile & 2.707 & 11.765 & 7.718 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistical values for the first test area. Figure 9: DoDs of different filtering methods and image source: © Land Tirol, tiris, www.tirol.gv.at/tiris. remote environmental parameters. It is difficult to automate an inclusion of such remote environmental parameters because the inclusion of wrong (e.g., not yet properly filtered) point regions would affect the filter result negatively.