A Novel Approach for Identifying Urban Built-Up Area Boundaries Using High-Resolution Remote-Sensing Data Based on the Scale Effect
<p>The study area and GF-2 image of Beijing within the 6th Ring Road.</p> "> Figure 2
<p>The DMSP/OLS stable nighttime light data from 2015 in the study area.</p> "> Figure 3
<p>Land cover classification of Beijing within the 6th Ring Road.</p> "> Figure 4
<p>Distribution of green spaces and water bodies within the 6th Ring Road of Beijing.</p> "> Figure 5
<p>(<b>a</b>) Distribution of buildings and roads within the 6th Ring Road; and (<b>b</b>) grid impervious surface density within the 6th Ring Road.</p> "> Figure 6
<p>Spatial unit neighborhood of the quadrilateral and hexagonal grids: (<b>a</b>) edge-neighbored quadrilateral grids; (<b>b</b>) corner-neighbored quadrilateral grids; and (<b>c</b>) edge-neighbored hexagonal grids.</p> "> Figure 7
<p>Flowchart of urban built-up area boundary extraction: (<b>a</b>) first-level vector grid; (<b>b</b>) impervious surface density classification in first-level vector grid; (<b>c</b>) first-level extraction; (<b>d</b>) second-level vector grid; (<b>e</b>) impervious surface density classification in second-level vector grid; (<b>f</b>) second-level extraction; (<b>g</b>) third-level vector grid; (<b>h</b>) impervious surface density classification in third-level vector grid; (<b>i</b>) third-level extraction; and (<b>j</b>) urban built-up area boundary smoothing.</p> "> Figure 8
<p>One thousand real sample points used to assess the accuracy of the extracted Beijing built-up area.</p> "> Figure 9
<p>Flowchart for extracting the Beijing built-up area boundary: (<b>a</b>) hexagonal vector grids with 1920 m edges; (<b>b</b>) impervious surface classification at the 1920 m scale; (<b>c</b>) extraction at the 1920 m scale; (<b>d</b>) hexagonal vector grids with 960 m edges; (<b>e</b>) impervious surface classification at the 960 m scale; (<b>f</b>) extraction at the 960 m scale; (<b>g</b>) hexagonal vector grids with 480 m edges; (<b>h</b>) impervious surface density classification at the 480 m scale; (<b>i</b>) extraction at the 480 m scale; (<b>j</b>) hexagonal vector grids with 240 m edges; (<b>k</b>) impervious surface density classification at the 240 m scale; and (<b>l</b>) extraction at the 240 m scale.</p> "> Figure 9 Cont.
<p>Flowchart for extracting the Beijing built-up area boundary: (<b>a</b>) hexagonal vector grids with 1920 m edges; (<b>b</b>) impervious surface classification at the 1920 m scale; (<b>c</b>) extraction at the 1920 m scale; (<b>d</b>) hexagonal vector grids with 960 m edges; (<b>e</b>) impervious surface classification at the 960 m scale; (<b>f</b>) extraction at the 960 m scale; (<b>g</b>) hexagonal vector grids with 480 m edges; (<b>h</b>) impervious surface density classification at the 480 m scale; (<b>i</b>) extraction at the 480 m scale; (<b>j</b>) hexagonal vector grids with 240 m edges; (<b>k</b>) impervious surface density classification at the 240 m scale; and (<b>l</b>) extraction at the 240 m scale.</p> "> Figure 10
<p>Post-treatment of the extracted urban built-up area boundary: (<b>a</b>) the hexagonal cells of the built-up area within the 6th Ring Road were fused; and (<b>b</b>) the smoothed urban built-up area of Beijing.</p> "> Figure 11
<p>The Beijing built-up boundary: (<b>a</b>) overlaid on the original remote-sensing images; and (<b>b</b>) overlaid on the land cover classification map of the area within the 6th Ring Road.</p> "> Figure 12
<p>(<b>a</b>) The Beijing built-up area boundary adapted from [<a href="#B28-ijgi-07-00135" class="html-bibr">28</a>]; and (<b>b</b>) the basic principle of neighborhood dilation.</p> "> Figure 13
<p>(<b>a</b>) Beijing built-up area boundary adapted from [<a href="#B1-ijgi-07-00135" class="html-bibr">1</a>]; (<b>b</b>) kernel density interpolation and the contour line; and (<b>c</b>) the criterion for identifying the built-up area.</p> "> Figure 14
<p>Plausibility check with nighttime light data.</p> "> Figure 15
<p>The box plots of nighttime light data for: (<b>a</b>) the inner part and (<b>b</b>) the outer part of the extracted Beijing built-up area boundary.</p> "> Figure 16
<p>The tendency of the threshold values with the measurement scales of the hexagonal vector grids.</p> "> Figure 17
<p>Visualization of the measurement scale effects on extracting the urban built-up area.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area and Data
2.2. Data Pre-Processing and Analysis
2.3. Methods
2.3.1. Defining a New Measurement Unit
2.3.2. Algorithm for Classifying Impervious Surface Densities
2.3.3. Steps for Extracting Continuous Impervious Surfaces of High Density
2.3.4. Plausibility Check and Accuracy Assessment
3. Results
4. Discussion and Conclusions
4.1. Comparison with Previous Studies
4.2. Plausibility Check and Accuracy Assesement
4.3. The Measurement Scale Effects on the Results
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Class | Buildings | Roads | Green Spaces | Water Bodies | Shadows | User’s Accuracy |
---|---|---|---|---|---|---|
Buildings | 207 | 19 | 4 | 0 | 4 | 88.46% |
Roads | 14 | 131 | 3 | 0 | 5 | 85.62% |
Green spaces | 3 | 8 | 291 | 4 | 11 | 91.80% |
Water bodies | 0 | 0 | 0 | 76 | 6 | 92.68% |
Shadows | 1 | 2 | 1 | 9 | 201 | 93.93% |
Producer’s accuracy | 92% | 81.88% | 97.32% | 85.39% | 88.55% | |
Overall accuracy: 90.60%; kappa coefficient: 87.83% |
Class | Built-Up Area | Rural Settlements | User’s Accuracy |
---|---|---|---|
Built-up area | 572 | 33 | 94.55% |
rural settlements | 26 | 369 | 93.42% |
Producer’s accuracy | 95.65% | 91.79% | |
Overall accuracy: 94.1%; kappa coefficient: 87.69% |
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Zhou, Y.; Tu, M.; Wang, S.; Liu, W. A Novel Approach for Identifying Urban Built-Up Area Boundaries Using High-Resolution Remote-Sensing Data Based on the Scale Effect. ISPRS Int. J. Geo-Inf. 2018, 7, 135. https://doi.org/10.3390/ijgi7040135
Zhou Y, Tu M, Wang S, Liu W. A Novel Approach for Identifying Urban Built-Up Area Boundaries Using High-Resolution Remote-Sensing Data Based on the Scale Effect. ISPRS International Journal of Geo-Information. 2018; 7(4):135. https://doi.org/10.3390/ijgi7040135
Chicago/Turabian StyleZhou, Yi, Mingguang Tu, Shixin Wang, and Wenliang Liu. 2018. "A Novel Approach for Identifying Urban Built-Up Area Boundaries Using High-Resolution Remote-Sensing Data Based on the Scale Effect" ISPRS International Journal of Geo-Information 7, no. 4: 135. https://doi.org/10.3390/ijgi7040135