# An Integrated Algorithm for Extracting Terrain Feature-Point Clusters Based on DEM Data
## 3 Results and Discussion
### Extraction Results and Validations of Point Clusters
Instead, we evaluate the relative localization accuracy of the point clusters from the perspective of element consistency. The relative localization errors of peaks and saddles are evaluated by the distance between the points and the nearest ridgelines, and the runoff nodes' errors are assessed by the distance between the point and the boundaries of mountain control areas. For the extraction of terrain feature points, [PERSON] proposed using the second derivative to obtain the six types of terrain feature points [54], which are widely used because of their simplicity and efficiency. [PERSON] et al. extracted peaks by searching the local maximum point of watershed boundaries based on the DEM data and achieved good results in the Tibetan Plateau [55]. Therefore, these two methods were performed to extract the terrain feature points in order to verify the relative position accuracy of the extracted point clusters with respect to others. The relative localization errors of the point clusters extracted from the six typical sample areas were calculated. The average localization errors are shown in Table 2, and their distributions are shown by the boxplots (Figure 10). For the peak extraction, the proposed method's average (avg.) and standard deviation (std.) values of localization error are 21.46 m and 54.08 m, respectively, which is less than half of the other methods; the percent of fully matching (PFM) (relative localization error of 0 m) of the proposed method is 47.32%, and the percent of mostly matching (PMM) (relative localization error less than or equal to the DEM spatial resolution) of the proposed method is 87.10%, nearly 50% higher than other methods. For the saddle extraction, the upper quartile and median value of the proposed method are close to 1/4 of the other methods; the average error is 4.11 m, close to 1/10 of the other methods. As for runoff node extraction, the PFM and the PMM are both close to 100%. In summary, the relative accuracy of the peak locations extracted using our method is better than the other methods. The saddles and runoff nodes extracted using our method have natural high consistency with the ridgelines and mountain control area boundaries, since they are extracted by intersecting the terrain feature lines. To sum up, the terrain feature points extracted by the proposed method not only have a good coupling relationship with each other, but also have high relative localization accuracy, which is consistent with geomorphological and hydrological cognition. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Point Cluster Type** & **Method Name** & **Avg./m** & **Std./m** & **PFM/\%** & **PMM/\%** \\ \hline \multirow{3}{*}{Peak} & Proposed Method & 21.87 & 58.21 & 48.15 & 87.99 \\  & Wood & 55.32 & 112.68 & 37.63 & 73.38 \\  & [PERSON] et al. & 84.28 & 129.80 & 26.58 & 54.23 \\ \hline \multirow{3}{*}{Saddle} & Proposed Method & 4.11 & 5.81 & 60.23 & 100.00 \\  & Wood & 97.68 & 103.81 & 20.20 & 37.66 \\  & [PERSON] et al. & 52.59 & 116.97 & 26.78 & 74.45 \\ \hline \multirow{3}{*}{Runoff Node} & Proposed Method & 0.00 & 0.02 & 99.92 & 100.00 \\  & Wood & 58.74 & 291.74 & 88.31 & 92.81 \\ \cline{1-1}  & [PERSON] et al. & 99.16 & 415.83 & 84.94 & 89.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Relative localization errors of extracted point clusters.