Inundated Vegetation Mapping Using SAR Data: A Comparison of Polarization Configurations of UAVSAR L-Band and Sentinel C-Band
Flood events have become intense and more frequent due to heavy rainfall and hurricanes caused by global warming. Accurate floodwater extent maps are essential information sources for emergency management agencies and flood relief programs to direct their resources to the most affected areas. Synthe...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/24/6374 |
| _version_ | 1797455421313646592 |
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| author | Abdella Salem Leila Hashemi-Beni |
| author_facet | Abdella Salem Leila Hashemi-Beni |
| author_sort | Abdella Salem |
| collection | DOAJ |
| description | Flood events have become intense and more frequent due to heavy rainfall and hurricanes caused by global warming. Accurate floodwater extent maps are essential information sources for emergency management agencies and flood relief programs to direct their resources to the most affected areas. Synthetic Aperture Radar (SAR) data are superior to optical data for floodwater mapping, especially in vegetated areas and in forests that are adjacent to urban areas and critical infrastructures. Investigating floodwater mapping with various available SAR sensors and comparing their performance allows the identification of suitable SAR sensors that can be used to map inundated areas in different land covers, such as forests and vegetated areas. In this study, we investigated the performance of polarization configurations for flood boundary delineation in vegetated and open areas derived from Sentinel1b, C-band, and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) L-band data collected during flood events resulting from Hurricane Florence in the eastern area of North Carolina. The datasets from the sensors for the flooding event collected on the same day and same study area were processed and classified for five landcover classes using a machine learning method—the Random Forest classification algorithm. We compared the classification results of linear, dual, and full polarizations of the SAR datasets. The L-band fully polarized data classification achieved the highest accuracy for flood mapping as the decomposition of fully polarized SAR data allows land cover features to be identified based on their scattering mechanisms. |
| first_indexed | 2024-03-09T15:53:14Z |
| format | Article |
| id | doaj.art-132d03db4ddb4796b9293f758407300a |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T15:53:14Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-132d03db4ddb4796b9293f758407300a2023-11-24T17:48:40ZengMDPI AGRemote Sensing2072-42922022-12-011424637410.3390/rs14246374Inundated Vegetation Mapping Using SAR Data: A Comparison of Polarization Configurations of UAVSAR L-Band and Sentinel C-BandAbdella Salem0Leila Hashemi-Beni1Applied Science and Technology Program, North Carolina A&T State University, Greensboro, NC 27411, USAGeomatics Program, Department of Built Environment, North Carolina A&T State University, Greensboro, NC 27411, USAFlood events have become intense and more frequent due to heavy rainfall and hurricanes caused by global warming. Accurate floodwater extent maps are essential information sources for emergency management agencies and flood relief programs to direct their resources to the most affected areas. Synthetic Aperture Radar (SAR) data are superior to optical data for floodwater mapping, especially in vegetated areas and in forests that are adjacent to urban areas and critical infrastructures. Investigating floodwater mapping with various available SAR sensors and comparing their performance allows the identification of suitable SAR sensors that can be used to map inundated areas in different land covers, such as forests and vegetated areas. In this study, we investigated the performance of polarization configurations for flood boundary delineation in vegetated and open areas derived from Sentinel1b, C-band, and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) L-band data collected during flood events resulting from Hurricane Florence in the eastern area of North Carolina. The datasets from the sensors for the flooding event collected on the same day and same study area were processed and classified for five landcover classes using a machine learning method—the Random Forest classification algorithm. We compared the classification results of linear, dual, and full polarizations of the SAR datasets. The L-band fully polarized data classification achieved the highest accuracy for flood mapping as the decomposition of fully polarized SAR data allows land cover features to be identified based on their scattering mechanisms.https://www.mdpi.com/2072-4292/14/24/6374inundated vegetationUAVhigh-resolution dataremote sensingdisaster management |
| spellingShingle | Abdella Salem Leila Hashemi-Beni Inundated Vegetation Mapping Using SAR Data: A Comparison of Polarization Configurations of UAVSAR L-Band and Sentinel C-Band Remote Sensing inundated vegetation UAV high-resolution data remote sensing disaster management |
| title | Inundated Vegetation Mapping Using SAR Data: A Comparison of Polarization Configurations of UAVSAR L-Band and Sentinel C-Band |
| title_full | Inundated Vegetation Mapping Using SAR Data: A Comparison of Polarization Configurations of UAVSAR L-Band and Sentinel C-Band |
| title_fullStr | Inundated Vegetation Mapping Using SAR Data: A Comparison of Polarization Configurations of UAVSAR L-Band and Sentinel C-Band |
| title_full_unstemmed | Inundated Vegetation Mapping Using SAR Data: A Comparison of Polarization Configurations of UAVSAR L-Band and Sentinel C-Band |
| title_short | Inundated Vegetation Mapping Using SAR Data: A Comparison of Polarization Configurations of UAVSAR L-Band and Sentinel C-Band |
| title_sort | inundated vegetation mapping using sar data a comparison of polarization configurations of uavsar l band and sentinel c band |
| topic | inundated vegetation UAV high-resolution data remote sensing disaster management |
| url | https://www.mdpi.com/2072-4292/14/24/6374 |
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