Flood Proxy Mapping with Normalized Difference Sigma-Naught Index and Shannon’s Entropy
Rainfall-induced floods often cause significant loss of life as well as damage to infrastructure and crops. Synthetic Aperture Radar (SAR) Earth Observation Satellites (EOS) can be used to determine the extent of flooding over large geographical areas. Unlike optical sensors, SAR instruments are sui...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/9/1384 |
| _version_ | 1797569497466404864 |
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| author | Noel Ivan Ulloa Shou-Hao Chiang Sang-Ho Yun |
| author_facet | Noel Ivan Ulloa Shou-Hao Chiang Sang-Ho Yun |
| author_sort | Noel Ivan Ulloa |
| collection | DOAJ |
| description | Rainfall-induced floods often cause significant loss of life as well as damage to infrastructure and crops. Synthetic Aperture Radar (SAR) Earth Observation Satellites (EOS) can be used to determine the extent of flooding over large geographical areas. Unlike optical sensors, SAR instruments are suitable for cloudy weather conditions, making them suitable for flood detection and mapping during extreme weather events. In this study, we explore the application of the Normalized Difference Sigma-Naught Index (NDSI) and Shannon’s entropy of NDSI (SNDSI) of Sentinel-1 data for open water flooding detection, based on automatic thresholding and Bayesian probability. The proposed methodology was tested using the floods in Sofala province, Mozambique, caused by cyclone Idai on March 14–19 of 2019. Results show that thresholding of the NDSI Vertical Transmit-Horizontal Receive (VH) can produce results with Overall Accuracy above 90%, and Kappa higher than 0.6. Considerable performance improvements were obtained by our thresholding method over the entropy of NDSI, yielding results with Kappa of 0.70–0.77. Additionally, it was found that Weibull distribution can properly describe the properties of flooded pixels within the histogram of SNDSI, which allows us to generate a flood probability raster using a Bayesian approach. The final per-pixel flooding probability is useful to indicate certainty in the classification results. The SNDSI Bayesian model produced an AUC (Area Under the Receiver Operating Characteristic Curve) of 0.93–0.97, with cross-polarized data yielding the most accurate results. |
| first_indexed | 2024-03-10T20:11:26Z |
| format | Article |
| id | doaj.art-6a324734f3a649ce81209fa24e74e117 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T20:11:26Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-6a324734f3a649ce81209fa24e74e1172023-11-19T22:52:00ZengMDPI AGRemote Sensing2072-42922020-04-01129138410.3390/rs12091384Flood Proxy Mapping with Normalized Difference Sigma-Naught Index and Shannon’s EntropyNoel Ivan Ulloa0Shou-Hao Chiang1Sang-Ho Yun2Department of Civil Engineering, National Central University, Taoyuan City 32001, TaiwanDepartment of Civil Engineering, National Central University, Taoyuan City 32001, TaiwanNASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USARainfall-induced floods often cause significant loss of life as well as damage to infrastructure and crops. Synthetic Aperture Radar (SAR) Earth Observation Satellites (EOS) can be used to determine the extent of flooding over large geographical areas. Unlike optical sensors, SAR instruments are suitable for cloudy weather conditions, making them suitable for flood detection and mapping during extreme weather events. In this study, we explore the application of the Normalized Difference Sigma-Naught Index (NDSI) and Shannon’s entropy of NDSI (SNDSI) of Sentinel-1 data for open water flooding detection, based on automatic thresholding and Bayesian probability. The proposed methodology was tested using the floods in Sofala province, Mozambique, caused by cyclone Idai on March 14–19 of 2019. Results show that thresholding of the NDSI Vertical Transmit-Horizontal Receive (VH) can produce results with Overall Accuracy above 90%, and Kappa higher than 0.6. Considerable performance improvements were obtained by our thresholding method over the entropy of NDSI, yielding results with Kappa of 0.70–0.77. Additionally, it was found that Weibull distribution can properly describe the properties of flooded pixels within the histogram of SNDSI, which allows us to generate a flood probability raster using a Bayesian approach. The final per-pixel flooding probability is useful to indicate certainty in the classification results. The SNDSI Bayesian model produced an AUC (Area Under the Receiver Operating Characteristic Curve) of 0.93–0.97, with cross-polarized data yielding the most accurate results.https://www.mdpi.com/2072-4292/12/9/1384flood proxy mappingSynthetic Aperture RadarNormalized Difference Sigma-Naught IndexBayesian probabilityShannon’s entropy |
| spellingShingle | Noel Ivan Ulloa Shou-Hao Chiang Sang-Ho Yun Flood Proxy Mapping with Normalized Difference Sigma-Naught Index and Shannon’s Entropy Remote Sensing flood proxy mapping Synthetic Aperture Radar Normalized Difference Sigma-Naught Index Bayesian probability Shannon’s entropy |
| title | Flood Proxy Mapping with Normalized Difference Sigma-Naught Index and Shannon’s Entropy |
| title_full | Flood Proxy Mapping with Normalized Difference Sigma-Naught Index and Shannon’s Entropy |
| title_fullStr | Flood Proxy Mapping with Normalized Difference Sigma-Naught Index and Shannon’s Entropy |
| title_full_unstemmed | Flood Proxy Mapping with Normalized Difference Sigma-Naught Index and Shannon’s Entropy |
| title_short | Flood Proxy Mapping with Normalized Difference Sigma-Naught Index and Shannon’s Entropy |
| title_sort | flood proxy mapping with normalized difference sigma naught index and shannon s entropy |
| topic | flood proxy mapping Synthetic Aperture Radar Normalized Difference Sigma-Naught Index Bayesian probability Shannon’s entropy |
| url | https://www.mdpi.com/2072-4292/12/9/1384 |
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