An Integrated Algorithm for Extracting Terrain Feature-Point Clusters Based on DEM Data
<p>Principle of the integrated point cluster extraction: the black triangles, green squares and blue dots represent peaks, saddles and runoff nodes, respectively; the red lines represent ridgelines, and the black dashed boxes represent the mountain control areas.</p> "> Figure 2
<p>Study area and typical sample areas: (<b>a</b>) the overview of the study area; (<b>b</b>) the locations of the six typical sample areas in the DEM imageries.</p> "> Figure 3
<p>Processes of the positive terrain-constrained ridgeline extraction.</p> "> Figure 4
<p>Processes of the terrain feature-point cluster extraction [<a href="#B33-remotesensing-14-02776" class="html-bibr">33</a>].</p> "> Figure 5
<p>Correspondence between peaks and saddles in original and anti-terrain: (<b>a</b>) the peaks and saddles in the original terrain; (<b>b</b>) the peaks and saddles in the corresponding anti-terrain.</p> "> Figure 6
<p>The response of the LLNV and potential ridgelines’ length to changes in the window size of neighborhood analysis and flow accumulation threshold, respectively: the blue triangles represent the response of the LLNV to changes in the window size of the neighborhood analysis, the blue dashed lines represent the quadratic polynomial regression curves of the two, and the blue vertical dashed lines represent the optimal window sizes of the neighborhood analysis obtained by the proposed method. The orange stars represent the response of the extracted ridgeline’s length to changes in flow accumulation thresholds in the runoff simulation, and the orange dashed lines represent the logistic regression curves of the two.</p> "> Figure 7
<p>The results of positive terrain extracted with different window sizes of neighborhood analysis in Ningshan County: (<b>a</b>–<b>f</b>) 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 (<b>a</b>) to subplot (<b>f</b>) are 3, 11, 21, 31, 41 and 51, respectively. Among them, subplot (<b>c</b>) shows positive terrain distribution corresponding to the optimal analysis window size (s = 21) extracted using the proposed method.</p> "> Figure 8
<p>Validation of extracted point clusters by the ridgelines: (<b>a</b>–<b>f</b>) 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.</p> "> Figure 9
<p>Validation of extracted point clusters by the optical image data: (<b>a</b>–<b>f</b>) 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.</p> "> Figure 10
<p>Relative localization error distribution of extracted point clusters: the different colored boxes represent different methods; the upper and lower quartiles of the data are depicted by the box’s upper and lower boundaries, respectively; the median and average values are depicted by the inner horizontal lines and the red triangles, respectively; the whiskers extending from the ends of the boxes are used to represent variables other than the upper and lower quartiles.</p> "> Figure 11
<p>Response of mountain control area size and mountain undulation to HDPS changes in the six typical sample areas: the blue triangles represent the relationship between the HDPS and the mountain control area size; the orange stars represent the relationship between the HDPS and the mountain undulation; the blue and orange dashed lines represent the regression curves of the two.</p> "> Figure 12
<p>Spatial distribution of grid cells in Shaanxi Province: the blue dot represents the centroid of each gird; colored rectangles indicate the spatial extent of these cells, and different colors are adopted for different geomorphological regions.</p> "> Figure 13
<p>Relationship between the HDPS and mountain control area size and mountain undulation in each sample area of Shaanxi Province: (<b>a</b>,<b>b</b>) the coefficient distribution histogram of the exponential regression between the HDPS and the mountain control area size; (<b>c</b>,<b>d</b>) the coefficient distribution histogram of the linear regression between the HDPS and the mountain undulation, while coefficient <span class="html-italic">c</span> and <span class="html-italic">d</span> are the gradient and intercept of the regression line, respectively; six colors in the figures correspond to six geomorphological zones in Shaanxi Province.</p> "> Figure 14
<p>Spatial distribution of the HDPS and SDI: (<b>a</b>) spatial distribution of the HDPS in Shaanxi Province; (<b>b</b>) spatial distribution of the SDI in Shaanxi Province.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area and Data
2.2. Methods
2.2.1. Positive Terrain-Constrained Ridgeline Extraction
2.2.2. Terrain Feature-Point Cluster Extraction
3. Results and Discussion
3.1. Results of Optimal Threshold Determination
3.2. Extraction Results and Validations of Point Clusters
3.3. Statistics of Point Cluster Properties
3.4. Point Cluster Properties and Geomorphological Regionalization
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Sample Area Name | Landform Type | Geomorphological Region |
---|---|---|
Ningshan County | Qinling middle and high mountains | Qinling Mountains |
Zhen’an County | Qinling middle mountains | Qinling Mountains |
Zhenba County | Daba middle mountains | Daba Mountains |
Hanyin County | Low hills and mountains | Hanzhong low hill and basin area |
Yanchuan County | Loess ridge | Loess Plateau |
Mizhi County | Loess hill | Loess Plateau |
Point Cluster Type | Method Name | Avg./m | Std./m | PFM/% | PMM/% |
---|---|---|---|---|---|
Peak | Proposed Method | 21.87 | 58.21 | 48.15 | 87.99 |
Wood | 55.32 | 112.68 | 37.63 | 73.38 | |
Xiong et al. | 84.28 | 129.80 | 26.58 | 54.23 | |
Saddle | Proposed Method | 4.11 | 5.81 | 60.23 | 100.00 |
Wood | 97.68 | 103.81 | 20.20 | 37.66 | |
Xiong et al. | 52.59 | 116.97 | 26.78 | 74.45 | |
Runoff Node | Proposed Method | 0.00 | 0.02 | 99.92 | 100.00 |
Wood | 58.74 | 291.74 | 88.31 | 92.81 | |
Xiong et al. | 99.16 | 415.83 | 84.94 | 89.62 |
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Hu, J.; Luo, M.; Bai, L.; Duan, J.; Yu, B. An Integrated Algorithm for Extracting Terrain Feature-Point Clusters Based on DEM Data. Remote Sens. 2022, 14, 2776. https://doi.org/10.3390/rs14122776
Hu J, Luo M, Bai L, Duan J, Yu B. An Integrated Algorithm for Extracting Terrain Feature-Point Clusters Based on DEM Data. Remote Sensing. 2022; 14(12):2776. https://doi.org/10.3390/rs14122776
Chicago/Turabian StyleHu, Jinlong, Mingliang Luo, Leichao Bai, Jinliang Duan, and Bing Yu. 2022. "An Integrated Algorithm for Extracting Terrain Feature-Point Clusters Based on DEM Data" Remote Sensing 14, no. 12: 2776. https://doi.org/10.3390/rs14122776
APA StyleHu, J., Luo, M., Bai, L., Duan, J., & Yu, B. (2022). An Integrated Algorithm for Extracting Terrain Feature-Point Clusters Based on DEM Data. Remote Sensing, 14(12), 2776. https://doi.org/10.3390/rs14122776