# An Integrated Algorithm for Extracting Terrain Feature-Point Clusters Based on DEM Data
## 3 Results and Discussion
### Extraction Results and Validations of Point Clusters  
The integrated terrain feature-point clusters of six typical sample areas were extracted using the method proposed in Section 2. Since the typical sample areas are large, it is difficult to show the extraction effect directly, so the typical sample area is locally enlarged and the results are shown in Figure 8. It can be seen that the extracted peaks of the six typical sample areas are all located at the top of the slope, the saddles are mostly located between the two peaks and at the lowest local terrain, and the runoff nodes are all located in valleys. Theoretically, the three point clusters should be completely consistent. However, due to the DEM data's spatial resolution and elevation accuracy, the three point clusters obtained based on the DEM data are not completely consistent. Therefore, the overall distribution of the extracted terrain feature elements is in line with expectations. Notably, the terrain segmentation threshold (i.e., the height difference from the peak to the saddle) is a vital threshold for extracting the peaks and their control areas by space segmentation, which is set to 40m here. The influence and significance of this threshold will be further discussed in Sections 3.3 and 3.4 in this paper. The extracted terrain feature-point clusters are overlaid with the ridgeline data and optical image data (Figures 8 and 9, respectively) to verify the correctness and validity further. Figure 8 shows that the extracted peaks and saddles are mostly located on the ridgelines, and the runoff nodes are mostly located on the boundaries of the mountain control area. In addition, the saddles are mostly located in the saddle-shaped depression between two adjacent hills, and the runoff nodes are mostly located at the flat slope foot. Figure 9 shows that the extracted peaks are mostly located at the top of the slope, and the runoff nodes are mostly located in the valleys (such as roads, towns and cultivated land). Figure 7: The results of positive terrain extracted with different window sizes of neighborhood analysis in Ningshan County: (**a–f**) the positive terrain distribution of Ningxia County extracted with different window sizes of neighborhood analysis. The positive terrain areas are shown as blue, the upper left corners are the window sizes of these subplots, the analysis window sizes from subplot (**a**) to subplot (**f**) are 3, 11, 21, 31, 41 and 51, respectively. Among them, subplot (**c**) shows positive terrain distribution corresponding to the optimal analysis window size (s = 21) extracted using the proposed method. Figure 8: Validation of extracted point clusters by the ridgelines: (**a–f**) correspond to six typical sample areas, including Hanyin, Mizhi, Ningshan, Yanchuan, Zhen’an and Zhenba County, respectively; the black triangles, purple squares and blue dots represent peaks, saddles and runoff nodes, respectively; the yellow lines represent ridgelines, and the black dashed boxes represent the mountain control areas. Figure 9: Validation of extracted point clusters by the optical image data: (**a–f**) correspond to six typical sample areas, including Hanyin, Mizhi, Ningshan, Yanchuan, Zhen’an and Zhenba County, respectively; the black triangles, purple squares and blue dots represent peaks, saddles and runoff nodes, respectively. A quantitative evaluation of the extracted point clusters' localization accuracy was performed. At present, it is still difficult to extract and verify the accuracy of terrain feature points. Due to the lack of standards such as the peak or the saddle, the peak extracted by each person is determined according to their own cognition, which cannot be well matched to the real world to determine the accuracy. This is one of the reasons why many researchers have done much work and tried to unify the understanding of topographic features [48, 52, 53]. As a result, this paper does not evaluate the absolute accuracy of the point clusters' localization.