A Sentinel-1 Based Processing Chain for Detection of Cyclonic Flood Impacts
Abstract
1. Introduction
2. Materials and Methods
2.1. Study Area
2.1.1. Marsh Harbour, Great Abaco, Bahamas (Dorian)
2.1.2. Beira, Mozambique (Idai)
2.2. Data
2.3. Methods
2.3.1. General Framework
2.3.2. Sentinel-1 Pre-Processing (S1-Tiling)
2.3.3. Normalized Difference Ratio
2.3.4. Data Validation and Comparison
- (1)
- The observed agreement rate or accuracy:
- (2)
- The probability of flooded (Pf), non-flooded (Pnf) and the probability of random agreement (Pe):
- (3)
- Cohen’s Kappa:
- <0.01 → no agreement
- 0.01–0.20 → slight agreement
- 0.21–0.40 → fair agreement
- 0.41–0.60 → moderate agreement
- 0.61–0.80 → substantial agreement
- 0.81–1.00 → almost perfect agreement.
3. Results
3.1. Bahamas
3.2. Beira
3.3. Time-Series Processing
3.3.1. Marsh Harbour
3.3.2. Beira
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Site | Image order | Sentinel-1 | Sentinel-2 | Pléiade/GeoEye |
---|---|---|---|---|
Marsh Harbour (Great Abaco, Bahamas) | Pre-event | 21 August 2019 | 4 December 2018 | |
Post-event | 2, 8, 14 September 2019 | 5 September 2019 | ||
Beira (Mozambique) | Pre-event | 18 February 2019 | 2 December 2018 | |
Post-event | 20–26 March to 1–7 April 2019 |
Observed | Predicted Flooded | Predicted Non-Flooded |
---|---|---|
Actual flooded | TP 1 | FN |
Actual non-flooded | FP | TN |
Observed by EMS Copernicus | Predicted by NDR | ||
---|---|---|---|
Flooded | Non-flooded | Total EMS | |
Flooded | 0.8 | 0.4 | 1.2 |
Non-flooded | 9.4 | 55.4 | 64.8 |
Total predicted | 10.2 | 55.8 | 66 |
Observed by EMS Copernicus | Predicted by NDR | ||
---|---|---|---|
Flooded | Non-flooded | Total EMS | |
Flooded | 71.6 | 23.5 | 95.1 |
Non-flooded | 19.2 | 728.7 | 747.9 |
Total predicted | 90.8 | 752.2 | 843 |
Date | 2 September 2019 | 8 September 2019 | 14 September 2019 |
---|---|---|---|
Flooded area | 10.2 | 8.4 | 3.3 |
Date | 20 March 2019 | 26 March 2019 | 1 April 2019 | 7 April 2019 |
---|---|---|---|---|
Flooded area | 90.8 | 46.1 | 65.2 | 65.4 |
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Alexandre, C.; Johary, R.; Catry, T.; Mouquet, P.; Révillion, C.; Rakotondraompiana, S.; Pennober, G. A Sentinel-1 Based Processing Chain for Detection of Cyclonic Flood Impacts. Remote Sens. 2020, 12, 252. https://doi.org/10.3390/rs12020252
Alexandre C, Johary R, Catry T, Mouquet P, Révillion C, Rakotondraompiana S, Pennober G. A Sentinel-1 Based Processing Chain for Detection of Cyclonic Flood Impacts. Remote Sensing. 2020; 12(2):252. https://doi.org/10.3390/rs12020252
Chicago/Turabian StyleAlexandre, Cyprien, Rosa Johary, Thibault Catry, Pascal Mouquet, Christophe Révillion, Solofo Rakotondraompiana, and Gwenaelle Pennober. 2020. "A Sentinel-1 Based Processing Chain for Detection of Cyclonic Flood Impacts" Remote Sensing 12, no. 2: 252. https://doi.org/10.3390/rs12020252
APA StyleAlexandre, C., Johary, R., Catry, T., Mouquet, P., Révillion, C., Rakotondraompiana, S., & Pennober, G. (2020). A Sentinel-1 Based Processing Chain for Detection of Cyclonic Flood Impacts. Remote Sensing, 12(2), 252. https://doi.org/10.3390/rs12020252