NPP-VIIRS DNB Daily Data in Natural Disaster Assessment: Evidence from Selected Case Studies
Abstract
:1. Introduction
2. Materials and Methods
2.1. Event Selection
2.2. Data
2.3. Methods
2.3.1. Data Preprocessing
2.3.2. PNL Image
2.3.3. Statistical Significance
3. Results
3.1. Earthquakes
3.2. Storms
3.3. Floods
3.4. Impact of Clouds on Assessing Natural Disasters
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Disaster Type | Event | Time | Study Area |
---|---|---|---|
Earthquake | Gorkha Nepal Earthquake | 25 April 2015 06:11 a.m. (UTC) | Kathmandu and the surrounding area (Area size: 48 km × 57 km), Nepal |
Central Italy Earthquake | 24 August 2016 01:36 a.m. (UTC) | Accumoli, Amatrice, Norcia, and Arquata Del Tronto, Italy | |
Storm | Hurricane Maria | 20 September 2017 10:15 a.m. (UTC) | Puerto Rico, U.S. |
Tropical Cyclone Hudhud | 12 October 2014 04:30 p.m. (UTC) | Visakhapatnam, India | |
Flood | Louisiana Flood | 11 August 2016–16 August 2016 | East Baton Rouge Parish and Livingston Parish, U.S. |
Yulin Flood | 25 July 2017–26 July 2017 | Suide, China |
Pre-Earthquake | Post-Earthquake | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Date | 7April | 8 April | 9 April | 18 April | 21 April | 23 April | 26 April | 2 May | 4 May | 5 May |
Days Before (After) the Disaster | 18 | 17 | 16 | 7 | 4 | 2 | 1 | 7 | 9 | 10 |
Cloud Coverage (%) | 0.1 | 0.0 | 0.0 | 0.7 | 0.6 | 2.5 | 17.1 | 1.1 | 1.1 | 0.1 |
Earthquake Event | Actual (DPM) | Predicted (NTL) | Accuracy | ||
---|---|---|---|---|---|
Damaged | Undamaged | Total | |||
Gorkha Nepal Earthquake | Damaged | 488 | 871 | 1359 | Overall accuracy = 75.5% TPR = 35.9% FPR = 8.9% Kstandard = 0.31 Kquantity = 0.82 Klocation = 0.46 |
(35.9%) | (64.1%) | ||||
Undamaged | 307 | 3150 | 3457 | ||
(8.9%) | (91.1%) | ||||
Total | 795 | 4021 | |||
Central Italy Earthquake | Damaged | 5 | 35 | 40 | Overall accuracy = 90.2% TRP = 12.5% FPR = 9.0% Kstandard = 0.01 Kquantity = 0.84 Klocation = 0.04 |
(12.5%) | (87.5%) | ||||
Undamaged | 322 | 3298 | 3620 | ||
(8.9%) | (91.1%) | ||||
Total | 327 | 3333 |
Pre-Earthquake | Post-Earthquake | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Date | 4 August | 5 August | 8 August | 9 August | 11 August | 12 August | 13 August | 14 August | 15 August | 26 August | 27 August | 28 August | 30 August |
Days Before (After) the Disaster | 20 | 19 | 16 | 15 | 13 | 12 | 11 | 10 | 9 | 2 | 3 | 4 | 6 |
Cloud Coverage (%) | 5.5 | 4.6 | 4.0 | 12.3 | 0.0 | 0.0 | 2.1 | 15.6 | 21.9 | 4.0 | 16.1 | 0.0 | 0.0 |
Study Area | Number of Buildings 1 | Total Population 2 | Average Number of People Per Building |
---|---|---|---|
Kathmandu and the surrounding area, Nepal | 220,451 | 3,566,322 | 16.2 |
Four counties in Italy | 12,234 | 9371 | 0.8 |
Pre-Storm | Post-Storm | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Date | 22 August | 23 August | 24 August | 28 August | 30 August | 1 September | 18 September | 27 September | 28 September | 29 September | 7 October | 8 October |
Days Before (After) the Disaster | 29 | 28 | 27 | 23 | 21 | 19 | 2 | 7 | 8 | 9 | 17 | 18 |
Cloud Coverage (%) | 15.2 | 8.1 | 11.4 | 11.1 | 19.9 | 19.7 | 20.3 | 41.2 | 71.8 | 48.2 | 70.8 | 34.4 |
Date Range | Dates of Available Image | Average DNB | PNL | Pnopower | |
---|---|---|---|---|---|
Before Storm | 20 August –20 September | 22 August–24 August, 28 August, 30 August, 1 September, 18 September | 4.52 | 0.0% | 0.0% |
After Storm | 21 September –10 October | 27 September –29 September, 7 October, 8 October | 0.77 | 17.1% | 94.3% |
11 October –20 October | 13 October, 15 October, 18 October, 19 October | 1.31 | 29.0% | 84.5% | |
21 October –30 October | 22 October –27 October | 1.57 | 34.8% | 77.7% | |
31 October –9 November | 31 October, 1 November, 3 November, 4 November | 1.58 | 34.9% | 63.1% | |
03 January –12 January | 3 January, 4 January, 10 January | 3.20 | 70.7% | 41.7% | |
13 January –22 January | 13 January –15 January, 18 January, 21 January, 22 January | 3.16 | 69.9% | 36.0% | |
23 January –1 February | 4 February, 9 February, 10 February, 11 February | 3.70 | 81.9% | 26.9% | |
22 February –3 March | 22 February –24 February, 27 February, 3 March | 3.24 | 71.6% | 13.2% | |
4 March –13 March | 4 March, 7 March, 11 March, 12 March, 13 March | 4.09 | 90.4% | 11.0% | |
14 March –23 March | 14 March, 15 March, 17 March, 19 March, 20 March | 3.76 | 83.2% | 7.6% | |
24 March –2 April | 24 March, 28 March | 4.32 | 95.5% | 5.8% |
Pre-Storm | Post-Storm | ||||||
---|---|---|---|---|---|---|---|
Date | 27 September | 28 September | 29 September | 14 October | 15 October | 16 October | 19 October |
Days Before (After) the Disaster | 15 | 14 | 13 | 2 | 3 | 4 | 7 |
Cloud Coverage (%) | 15.6 | 6.6 | 0.0 | 19.8 | 4.9 | 8.6 | 1.2 |
Pre-Flood | Post-Flood | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Date | 12 July | 13 July | 19 July | 22 July | 1 August | 2 August | 4 August | 21 August | 22 August | 24 August |
Days Before (After) the Disaster | 30 | 29 | 23 | 20 | 10 | 9 | 7 | 10 | 11 | 13 |
Cloud Coverage (%) | 0.0 | 0.0 | 9.0 | 0.0 | 19.9 | 0.0 | 5.6 | 22.7 | 21.3 | 0.0 |
Pre-Flood | Post-Flood | ||||||
---|---|---|---|---|---|---|---|
Date | 27 June | 29 June | 1 July | 17 July | 30 July | 31 July | 2 August |
Days Before (After) the Disaster | 29 | 27 | 24 | 8 | 4 | 5 | 7 |
Cloud Coverage (%) | 12.7 | 0.0 | 1.2 | 0.7 | 0.0 | 0.5 | 0.0 |
Disaster Type | Event | Application | Validation Data | Validation Results | Challenges |
---|---|---|---|---|---|
Earthquake | Gorkha Nepal Earthquake | Identify damaged areas | DPM | Overall accuracy = 75.5% TPR = 35.9% FPR = 8.9% Kstandard = 0.31 Kquantity = 0.82 Klocation = 0.46 | Rescue and repair activities produced extra lights |
Central Italy Earthquake | None | DPM | Overall accuracy = 90.2% TPR = 12.5% FPR = 9.0% Kstandard = 0.01 Kquantity = 0.84 Klocation = 0.04 | Rescue and repair activities produced extra lights | |
Storm | Hurricane Maria | Detect power outages | DPM, and Pnopower published by CESER in the U.S. | Strong correlation between the PNL and Pnopower (R2 = 0.94) | Cloud |
Tropical Cyclone Hudhud | Detect power outages | Power outage rate in news reports | The estimated Pnopower of 77.6% was in good agreement with the report that 80% of the power supply was not restored. | Cloud | |
Flood | Louisiana Flood | None | FPM and news reports | Mann-Whitney U-test indicates that the difference between pre- and post-flood images is not statistically significant | Cloud |
Yulin Flood | Detect power outages | None | None | Increased rescue vehicles and repair activities brought extra lights |
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Zhao, X.; Yu, B.; Liu, Y.; Yao, S.; Lian, T.; Chen, L.; Yang, C.; Chen, Z.; Wu, J. NPP-VIIRS DNB Daily Data in Natural Disaster Assessment: Evidence from Selected Case Studies. Remote Sens. 2018, 10, 1526. https://doi.org/10.3390/rs10101526
Zhao X, Yu B, Liu Y, Yao S, Lian T, Chen L, Yang C, Chen Z, Wu J. NPP-VIIRS DNB Daily Data in Natural Disaster Assessment: Evidence from Selected Case Studies. Remote Sensing. 2018; 10(10):1526. https://doi.org/10.3390/rs10101526
Chicago/Turabian StyleZhao, Xizhi, Bailang Yu, Yan Liu, Shenjun Yao, Ting Lian, Liujia Chen, Chengshu Yang, Zuoqi Chen, and Jianping Wu. 2018. "NPP-VIIRS DNB Daily Data in Natural Disaster Assessment: Evidence from Selected Case Studies" Remote Sensing 10, no. 10: 1526. https://doi.org/10.3390/rs10101526