Identification of Water Bodies in a Landsat 8 OLI Image Using a J48 Decision Tree
"> Figure 1
<p>Overall flowchart adopted in this study.</p> "> Figure 2
<p>Location of the test site in Korea, with lakes shown in a Landsat 8 natural colour composite image taken from 11 February 2013. Each box includes the lake name.</p> "> Figure 3
<p>J48 decision tree model for water identification at the test site using OLI bands.</p> "> Figure 4
<p>Water body identification results at the test site: (<b>a</b>) density slicing; (<b>b</b>) NDWI; (<b>c</b>) MNDWI; (<b>d</b>) ML; (<b>e</b>) SVM; and (<b>f</b>) JDT. Red boxes show smaller river network ends, whereas the light blue box inside the images shows the area with a complex urban area, smaller water bodies and a lake with bridges.</p> "> Figure 4 Cont.
<p>Water body identification results at the test site: (<b>a</b>) density slicing; (<b>b</b>) NDWI; (<b>c</b>) MNDWI; (<b>d</b>) ML; (<b>e</b>) SVM; and (<b>f</b>) JDT. Red boxes show smaller river network ends, whereas the light blue box inside the images shows the area with a complex urban area, smaller water bodies and a lake with bridges.</p> "> Figure 5
<p>Enlarged section of the study area with smaller water bodies in a complex urban area and a lake with bridges: (<b>a</b>) Density Slicing; (<b>b</b>) NDWI; (<b>c</b>) MNDWI; (<b>d</b>) ML; (<b>e</b>) SVM; and (<b>f</b>) JDT. The red dotted boxes, red oval, red dotted circles and light blue dotted circles show bridges, a dam, unidentified water bodies and identified water bodies, respectively.</p> "> Figure 6
<p>Comparison of classes derived with respect to JDT: (<b>a</b>) Water bodies identified by JDT with water boundaries extracted from digital topographic map ver. 2.0 (<b>b</b>) JDT and Density Slicing (<b>c</b>) JDT and NDWI; (<b>d</b>) JDT and MNDWI; (<b>e</b>) JDT and ML and (<b>f</b>) JDT and SVM. Similar to <a href="#sensors-16-01075-f005" class="html-fig">Figure 5</a>, the red dotted boxes, red oval, red dotted circles and light blue dotted circles show bridges, a dam, unidentified water bodies and identified water bodies, respectively.</p> "> Figure 6 Cont.
<p>Comparison of classes derived with respect to JDT: (<b>a</b>) Water bodies identified by JDT with water boundaries extracted from digital topographic map ver. 2.0 (<b>b</b>) JDT and Density Slicing (<b>c</b>) JDT and NDWI; (<b>d</b>) JDT and MNDWI; (<b>e</b>) JDT and ML and (<b>f</b>) JDT and SVM. Similar to <a href="#sensors-16-01075-f005" class="html-fig">Figure 5</a>, the red dotted boxes, red oval, red dotted circles and light blue dotted circles show bridges, a dam, unidentified water bodies and identified water bodies, respectively.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Test Site
2.2. Data
2.3. Methods
3. Results and Discussion
4. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
Abbreviations
AWEI | Automated Water Extraction Index |
JDT | J48 Decision Tree |
MNDWI | Modified Normalized Difference Water Index |
NDWI | Normalized Difference Water Index |
OLI | Operational Land Imager |
TIRS | Thermal Infrared Sensor |
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Band Name | Band Number | Differences in Landsat 8 | ||
---|---|---|---|---|
Landsat 8 | Landsat 7 | Landsat 5 | ||
Deep Blue | 1 | - | - | new |
Blue | 2 | 1 | 1 | more narrow |
Green | 3 | 2 | 2 | more narrow |
Red | 4 | 3 | 3 | more narrow |
Near Infrared (NIR) | 5 | 4 | 4 | more narrow |
Short-wave Infrared 1 (SWIR_1) | 6 | 5 | 5 | more narrow |
Short-wave Infrared 2 (SWIR_2) | 7 | 7 | 7 | more narrow |
Panchromatic | 8 | 8 | - | more narrow |
only visible (red-green) | ||||
Cirrus | 9 | - | - | new |
Long-wave Infrared (LWIR) 1 | 10 | 6 | 6 | two bands instead of one |
Long-wave Infrared (LWIR) 2 | 11 | 6 | 6 | two bands instead of one |
Row/Path | Band Name | Wavelength (μm) | Resolution (m) |
---|---|---|---|
115/34 | Deep Blue | 0.435–0.451 | 30 |
Blue | 0.452–0.512 | ||
Green | 0.533–0.590 | ||
Red | 0.636–0.673 | ||
Near Infrared (NIR) | 0.851–0.879 | ||
Short-wave Infrared 1 (SWIR_1) | 1.566–1.651 | ||
Short-wave Infrared 2 (SWIR_2) | 2.107–2.294 |
Multiband Index | Equation | Remark | Reference |
---|---|---|---|
Normalized Difference Water Index | NDWI = (Green − NIR)/(Green + NIR) | Water has positive value | [10] |
Modified Normalized Difference Water | MNDWI = (Green − SWIR_1)/(Green + SWIR_1) | Water has positive value | [12] |
Classified Image | Reference Data | |||
---|---|---|---|---|
Class A | Class B | Class C | Row Total | |
Class A | nAA | nAB | nAC | nA+ |
Class B | nBA | nBB | nBC | nB+ |
Class C | nCA | nCB | nCC | nC+ |
Column Total | n+A | n+B | n+C | N |
Class | Water | Non-Water | Sum | User’s Accuracy |
---|---|---|---|---|
Water | 1533 | 16 | 1549 | 0.98967 |
Non-Water | 4 | 1533 | 1537 | 0.99740 |
Sum | 1537 | 1549 | 3086 | |
Producer’s Accuracy | 0.99740 | 0.98967 | ||
Overall Accuracy | 99.35% | Kappa coefficient | 0.9870 |
Class | Water | Non-Water | Sum | User’s Accuracy |
---|---|---|---|---|
Water | 1516 | 0 | 1516 | 1.00000 |
Non-Water | 33 | 1516 | 1549 | 0.97870 |
Sum | 1549 | 1516 | 3065 | |
Producer’s Accuracy | 0.97870 | 1.00000 | ||
Overall Accuracy | 98.92% | Kappa coefficient | 0.9785 |
Class | Water | Non-Water | Sum | User’s Accuracy |
---|---|---|---|---|
Water | 1516 | 0 | 1516 | 1.00000 |
Non-Water | 48 | 1501 | 1549 | 0.96901 |
Sum | 1564 | 1501 | 3065 | |
Producer’s Accuracy | 0.96931 | 1.00000 | ||
Overall Accuracy | 98.43% | Kappa coefficient | 0.9687 |
Class | Water | Non-Water | Sum | User’s Accuracy |
---|---|---|---|---|
Water | 1496 | 2 | 1498 | 0.99866 |
Non-Water | 20 | 1547 | 1567 | 0.98724 |
Sum | 1516 | 1549 | 3065 | |
Producer’s Accuracy | 0.98681 | 0.99871 | ||
Overall Accuracy | 99.28% | Kappa coefficient | 0.9856 |
Class | Water | Non-Water | Sum | User’s Accuracy |
---|---|---|---|---|
Water | 1531 | 0 | 1531 | 1.00000 |
Non-Water | 18 | 1531 | 1549 | 0.98838 |
Sum | 1549 | 1531 | 3080 | |
Producer’s Accuracy | 0.98838 | 1.00000 | ||
Overall Accuracy | 99.41% | Kappa coefficient | 0.9883 |
Class | Water | Non-Water | Sum | User’s Accuracy |
---|---|---|---|---|
Water | 1504 | 14 | 1518 | 0.99078 |
Non-Water | 12 | 1535 | 1547 | 0.99224 |
Sum | 1516 | 1549 | 3065 | |
Producer’s Accuracy | 0.99208 | 0.99096 | ||
Overall Accuracy | 99.15% | Kappa coefficient | 0.9830 |
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Acharya, T.D.; Lee, D.H.; Yang, I.T.; Lee, J.K. Identification of Water Bodies in a Landsat 8 OLI Image Using a J48 Decision Tree. Sensors 2016, 16, 1075. https://doi.org/10.3390/s16071075
Acharya TD, Lee DH, Yang IT, Lee JK. Identification of Water Bodies in a Landsat 8 OLI Image Using a J48 Decision Tree. Sensors. 2016; 16(7):1075. https://doi.org/10.3390/s16071075
Chicago/Turabian StyleAcharya, Tri Dev, Dong Ha Lee, In Tae Yang, and Jae Kang Lee. 2016. "Identification of Water Bodies in a Landsat 8 OLI Image Using a J48 Decision Tree" Sensors 16, no. 7: 1075. https://doi.org/10.3390/s16071075