A Novel Urban Composition Index Based on Water-Impervious Surface-Pervious Surface (W-I-P) Model for Urban Compositions Mapping Using Landsat Imagery
Abstract
:1. Introduction
2. Materials and Methods
2.1. Datasets
2.2. Urban Composition Index (UCI)
2.2.1. Principle and Development of UCI
2.2.2. Threshold Analysis of UCI
2.3. Comparative Analysis with Other Indices
2.3.1. Separability Analysis
2.3.2. Correlation Analysis with ISA Proportion
2.3.3. Accuracy Assessment
3. Results
3.1. Applying UCI to Landsat-8 Images
3.2. Comparisons with Multiple Composition Indices
3.3. Comparisons with Single Composition Indices
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Index | Description | Urban Compositions | Reference |
---|---|---|---|
NDWI | Normalized difference water index | Water | [25] |
MNDWI | Modification normalized difference water index | Water | [26] |
NDVI | Normalized difference vegetation index | Vegetation | [27] |
NDBI | Normalized difference built-up index | ISA | [28] |
NDISI | Normalized difference impervious surface index | ISA | [5] |
BCI | Biophysical composition index | Soil, ISA and vegetation | [29] |
MNDISI | Modified normalized difference impervious surface index | ISA | [30] |
PISI | Perpendicular impervious surface index | ISA | [31] |
CBCI | Combinational biophysical composition index | Water, soil, ISA and vegetation | [32] |
ENDISI | Enhanced normalized difference impervious surfaces index | ISA | [33] |
Study Areas | Date of Landsat-8 Images | Path/Row Landsat-8 | Date of Google Earth Images | Wavelength (μm) of Landsat-8 |
---|---|---|---|---|
Harbin | 7 May 2018 | 118/28 | 14 May 2018 | Band1 (Coastal): 0.435–0.451 |
Band2 (Blue): 0.452–0.512 | ||||
Beijing | 13 May 2019 | 123/32 | 24 March 2019 | Band3 (Green): 0.533–0.590 |
Band4 (Red): 0.636–0.673 | ||||
Wuhan | 15 September 2018 | 123/39 | 16 September 2018 | Band5 (NIR): 0.851–0.879 |
Band6 (SWIR1): 1.566–1.651 | ||||
Guangzhou | 23 October 2017 | 122/44 | 17 September 2017 | Band7 (SWIR2): 2.107–2.294 |
Index | Harbin | Beijing | Wuhan | Guangzhou | Global |
---|---|---|---|---|---|
UCI | 1.60 | 1.37 | 1.39 | 1.38 | 1.20 |
PISI | 0.91 | 1.05 | 1.32 | 1.33 | 0.85 |
CBCI | 0.82 | 0.82 | 1.04 | 1.31 | 0.91 |
BCI | 1.03 | 0.81 | 1.15 | 0.50 | 0.40 |
NDBI | 0.95 | 0.44 | 0.48 | 0.86 | 0.51 |
MNDWI | 1.34 | 0.63 | 0.32 | 0.71 | 0.98 |
Index | Harbin | Beijing | Wuhan | Guangzhou | Global |
---|---|---|---|---|---|
UCI | 2.00 | 1.80 | 1.99 | 2.00 | 1.91 |
PISI | 1.06 | 1.18 | 0.38 | 0.90 | 0.63 |
CBCI | 1.87 | 1.51 | 1.35 | 1.82 | 0.73 |
BCI | 1.04 | 0.58 | 1.20 | 0.25 | 0.61 |
NDBI | 1.63 | 0.90 | 1.43 | 1.87 | 1.28 |
MNDWI | 2.00 | 1.89 | 2.00 | 2.00 | 1.93 |
Index | Harbin | Beijing | Wuhan | Guangzhou | Global |
---|---|---|---|---|---|
UCI | 2.00 | 1.98 | 2.00 | 2.00 | 2.00 |
PISI | 2.00 | 1.83 | 1.88 | 1.91 | 1.26 |
CBCI | 2.00 | 1.77 | 1.91 | 1.97 | 1.27 |
BCI | 2.00 | 0.85 | 1.17 | 1.13 | 0.92 |
NDBI | 1.94 | 0.22 | 0.85 | 0.48 | 0.52 |
MNDWI | 2.00 | 1.99 | 2.00 | 2.00 | 2.00 |
Index | Harbin | Beijing | Wuhan | Guangzhou | Global | |||||
---|---|---|---|---|---|---|---|---|---|---|
OA | Kappa | OA | Kappa | OA | Kappa | OA | Kappa | OA | Kappa | |
UCI | 97.17 | 0.96 | 93.13 | 0.89 | 91.09 | 0.85 | 93.13 | 0.89 | 94.60 | 0.91 |
PISI + | 67.11 | 0.50 | 92.22 | 0.88 | 86.72 | 0.79 | 90.61 | 0.85 | 91.58 | 0.85 |
UCI * | 98.19 | 0.97 | 97.68 | 0.96 | 94.52 | 0.91 | 95.39 | 0.93 | 95.44 | 0.93 |
CBCI * | 87.44 | 0.81 | 83.53 | 0.74 | 89.97 | 0.84 | 92.46 | 0.88 | 87.47 | 0.77 |
BCI *+ | 92.62 | 0.89 | 90.23 | 0.85 | 92.11 | 0.87 | 85.41 | 0.77 | 84.78 | 0.76 |
NDBI *+ | 51.18 | 0.26 | 73.84 | 0.60 | 76.51 | 0.62 | 84.24 | 0.75 | 73.40 | 0.53 |
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Zhang, L.; Tian, Y.; Liu, Q. A Novel Urban Composition Index Based on Water-Impervious Surface-Pervious Surface (W-I-P) Model for Urban Compositions Mapping Using Landsat Imagery. Remote Sens. 2021, 13, 3. https://doi.org/10.3390/rs13010003
Zhang L, Tian Y, Liu Q. A Novel Urban Composition Index Based on Water-Impervious Surface-Pervious Surface (W-I-P) Model for Urban Compositions Mapping Using Landsat Imagery. Remote Sensing. 2021; 13(1):3. https://doi.org/10.3390/rs13010003
Chicago/Turabian StyleZhang, Lihao, Yugang Tian, and Qingwei Liu. 2021. "A Novel Urban Composition Index Based on Water-Impervious Surface-Pervious Surface (W-I-P) Model for Urban Compositions Mapping Using Landsat Imagery" Remote Sensing 13, no. 1: 3. https://doi.org/10.3390/rs13010003
APA StyleZhang, L., Tian, Y., & Liu, Q. (2021). A Novel Urban Composition Index Based on Water-Impervious Surface-Pervious Surface (W-I-P) Model for Urban Compositions Mapping Using Landsat Imagery. Remote Sensing, 13(1), 3. https://doi.org/10.3390/rs13010003