Mapping Kuwait bathymetry using passive multispectral remote sensing
Mapping bathymetry is essential for many fields, including science, engineering and the military, among others. Bathymetry is extremely important in the scientific field because it is linked to many physical and environmental issues such as coastal erosion, sea level rise and water quality. Traditi...
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| Format: | Article |
| Language: | English |
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Elsevier
2021-08-01
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| Series: | Kuwait Journal of Science |
| Subjects: | |
| Online Access: | https://journalskuwait.org/kjs/index.php/KJS/article/view/8978 |
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| Summary: | Mapping bathymetry is essential for many fields, including science, engineering and the military, among others. Bathymetry is extremely important in the scientific field because it is linked to many physical and environmental issues such as coastal erosion, sea level rise and water quality. Traditionally, conventional methods, such as pre-measured cable passage, were used to estimate depths. Lately, echo-sounder assessments were used on hydrograph ships. This method is effective, but it is very costly in both economic and time terms. Remote sensing technology provides modern methods for mapping bathymetry, such as the use of active and passive remote sensing. Many satellite sensors cover multispectral bands. Some are commercial, such as IKONAS and WorldView, while others are freely available, such as Landsat 8 and sentainel-2. In this study, Landsat 8 (15 meters spatial resolution) was used to estimate depths of the waters of Kuwait, an Arabian Gulf country located on the Northwestern side of the gulf. Ground control points (GCPs) were used to build a bathymetric model of Kuwaiti regional water using a ratio transform algorithm (RTA) applied on Landsat 8 data. The results showed a good ability of Landsat 8 and RTA to estimate depths of Kuwait’s waters, where the relationship between the derived model from Landsat 8 and the GCPs was positive ( = 0.9634). Meanwhile, the accuracy of the derived bathymetric model was evaluated by computing the Root Mean Square Error (RMSE =1.66 meters) and Mean Absolute Error (MAE = 1.29).
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| ISSN: | 2307-4108 2307-4116 |