Mapping Regional Urban Extent Using NPP-VIIRS DNB and MODIS NDVI Data
Abstract
:1. Introduction
2. Materials
2.1. Defining Urban Extent
2.2. Study Area
2.3. Data Acquisition and Pre-Processing
2.3.1. Generating the Maximum NDVI
2.3.2. Removing Background Noise and Filtering Extreme Values in NPP-VIIRS DNB
2.3.3. Aggregating GLC30-2010 from 30 m to 480 m
3. Methods
3.1. Sampling
3.2. Training and Predicting
3.3. Accessing Accuracy
4. Results
4.1. Urban Land Classification Accuracy on the Pixel Level
4.2. Urban Land Classification Accuracy on the City Level
5. Discussion
5.1. Comparative Analysis of Urban Extent under Different Nighttime Light Data
5.2. Case Analysis: Performance of Combining NPP-VIIRS DNB and MODIS NDVI
5.3. Reliability Analysis
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Index | Product | Time | Spatial Resolution | Description |
---|---|---|---|---|
(1) | MODIS NDVI (MOD13A1) | 2012 | 463 m | 16-day composite. |
(2) | NPP-VIIRS DNB | 2012 | 15 arc-seconds (491 m) | Composited by the NPP-VIIRS DNB data with low moonlight during 18–26 April 2012 and 11–23 October 2012. |
(3) | DMSP/OLS NLD | 2012 | 30 arc-seconds (985 m) | Yearly nighttime stable light composite. |
(4) | GLC30-2010 | 2010 | 30 m | Generated by hybrid classification from global multispectral remote sensing images of 30 m resolution (Landsat TM, ETM7 (SLC-off), HJ-1A/B, etc.) in 2010. |
Classified Data | Reference Data | User’s Accuracy | Kappa Coefficient | ||
---|---|---|---|---|---|
Non-Urban | Urban | Total | |||
Non-urban | 19,823 | 33 | 19,856 | 99.83% | 0.842 |
Urban | 15 | 129 | 144 | 89.58% | |
Total | 19,838 | 162 | 20,000 | ||
Producer’s Accuracy | 99.92% | 79.63% | 99.76% (OA 1) |
Grouped by Administrative Levels | Reference Number | Predicted Number | Omission Rate (%) |
---|---|---|---|
Municipality Directly under the Central Government | 4 | 4 | 0 |
Vice-provincial City | 15 | 15 | 0 |
Prefecture-level City | 266 | 266 | 0 |
County-level City | 362 | 358 | 1.10 |
Total | 647 | 643 | 0.62 |
Cities | Reference Samples | Urban Extent Generated by | Omission Rate (%) |
---|---|---|---|
Beijing | 99 | Group 1: NPP-VIIRS DNB and MODIS NDVI | 39.39 |
Group 2: DMSP/OLS NLD and MODIS NDVI | 27.27 | ||
Group 3: MCD12Q1 in MODIS products | 28.28 | ||
Wuhan | 41 | Group 1: NPP-VIIRS DNB and MODIS NDVI | 2.44 |
Group 2: DMSP/OLS NLD and MODIS NDVI | 4.88 | ||
Group 3: MCD12Q1 in MODIS products | 46.34 | ||
Hohhot | 39 | Group 1: NPP-VIIRS DNB and MODIS NDVI | 5.13 |
Group 2: DMSP/OLS NLD and MODIS NDVI | 35.90 | ||
Group 3: MCD12Q1 in MODIS products | 53.85 | ||
Datong | 50 | Group 1: NPP-VIIRS DNB and MODIS NDVI | 6 |
Group 2: DMSP/OLS NLD and MODIS NDVI | 16 | ||
Group 3: MCD12Q1 in MODIS products | 20 | ||
Suihua | 35 | Group 1: NPP-VIIRS DNB and MODIS NDVI | 8.57 |
Group 2: DMSP/OLS NLD and MODIS NDVI | 37.14 | ||
Group 3: MCD12Q1 in MODIS products | 57.14 |
Threshold | Region 1 | Region 2 | Region 3 | |||
---|---|---|---|---|---|---|
OA | Kappa | OA | Kappa | OA | Kappa | |
0.4 | 98.6% | 0.898 | 98.3% | 0.817 | 94.8% | 0.781266 |
0.5 | 98.2% | 0.864 | 98.5% | 0.824 | 95.1% | 0.783642 |
0.6 | 98.1% | 0.849 | 98.6% | 0.830 | 94.5% | 0.744323 |
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Wang, R.; Wan, B.; Guo, Q.; Hu, M.; Zhou, S. Mapping Regional Urban Extent Using NPP-VIIRS DNB and MODIS NDVI Data. Remote Sens. 2017, 9, 862. https://doi.org/10.3390/rs9080862
Wang R, Wan B, Guo Q, Hu M, Zhou S. Mapping Regional Urban Extent Using NPP-VIIRS DNB and MODIS NDVI Data. Remote Sensing. 2017; 9(8):862. https://doi.org/10.3390/rs9080862
Chicago/Turabian StyleWang, Run, Bo Wan, Qinghua Guo, Maosheng Hu, and Shunping Zhou. 2017. "Mapping Regional Urban Extent Using NPP-VIIRS DNB and MODIS NDVI Data" Remote Sensing 9, no. 8: 862. https://doi.org/10.3390/rs9080862
APA StyleWang, R., Wan, B., Guo, Q., Hu, M., & Zhou, S. (2017). Mapping Regional Urban Extent Using NPP-VIIRS DNB and MODIS NDVI Data. Remote Sensing, 9(8), 862. https://doi.org/10.3390/rs9080862