Satellite-Derived Bathymetry for Selected Shallow Maltese Coastal Zones
Bathymetric information has become essential to help maintain and operate coastal zones. Traditional in situ bathymetry mapping using echo sounders is inefficient in shallow waters and operates at a high logistical cost. On the other hand, lidar mapping provides an efficient means of mapping coastal...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/9/5238 |
| _version_ | 1797603053790035968 |
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| author | Gareth Darmanin Adam Gauci Alan Deidun Luciano Galone Sebastiano D’Amico |
| author_facet | Gareth Darmanin Adam Gauci Alan Deidun Luciano Galone Sebastiano D’Amico |
| author_sort | Gareth Darmanin |
| collection | DOAJ |
| description | Bathymetric information has become essential to help maintain and operate coastal zones. Traditional in situ bathymetry mapping using echo sounders is inefficient in shallow waters and operates at a high logistical cost. On the other hand, lidar mapping provides an efficient means of mapping coastal areas. However, this comes at a high acquisition cost as well. In comparison, satellite-derived bathymetry (SDB) provides a more cost-effective way of mapping coastal regions, albeit at a lower resolution. This work utilises all three of these methods collectively, to obtain accurate bathymetric depth data of two pocket beaches, Golden Bay and Għajn Tuffieħa, located in the northwestern region of Malta. Using the Google Earth Engine platform, together with Sentinel-2 data and collected in situ measurements, an empirical pre-processing workflow for estimating SDB was developed. Four different machine learning algorithms which produced differing depth accuracies by calibrating SDBs with those derived from alternative techniques were tested. Thus, this study provides an insight into the depth accuracy that can be achieved for shallow coastal regions using SDB techniques. |
| first_indexed | 2024-03-11T04:24:58Z |
| format | Article |
| id | doaj.art-3f491f459570436f85d7a901f0689c8a |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T04:24:58Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-3f491f459570436f85d7a901f0689c8a2023-11-17T22:31:15ZengMDPI AGApplied Sciences2076-34172023-04-01139523810.3390/app13095238Satellite-Derived Bathymetry for Selected Shallow Maltese Coastal ZonesGareth Darmanin0Adam Gauci1Alan Deidun2Luciano Galone3Sebastiano D’Amico4Department of Geosciences, University of Malta, MSD2080 Msida, MaltaDepartment of Geosciences, University of Malta, MSD2080 Msida, MaltaDepartment of Geosciences, University of Malta, MSD2080 Msida, MaltaDepartment of Geosciences, University of Malta, MSD2080 Msida, MaltaDepartment of Geosciences, University of Malta, MSD2080 Msida, MaltaBathymetric information has become essential to help maintain and operate coastal zones. Traditional in situ bathymetry mapping using echo sounders is inefficient in shallow waters and operates at a high logistical cost. On the other hand, lidar mapping provides an efficient means of mapping coastal areas. However, this comes at a high acquisition cost as well. In comparison, satellite-derived bathymetry (SDB) provides a more cost-effective way of mapping coastal regions, albeit at a lower resolution. This work utilises all three of these methods collectively, to obtain accurate bathymetric depth data of two pocket beaches, Golden Bay and Għajn Tuffieħa, located in the northwestern region of Malta. Using the Google Earth Engine platform, together with Sentinel-2 data and collected in situ measurements, an empirical pre-processing workflow for estimating SDB was developed. Four different machine learning algorithms which produced differing depth accuracies by calibrating SDBs with those derived from alternative techniques were tested. Thus, this study provides an insight into the depth accuracy that can be achieved for shallow coastal regions using SDB techniques.https://www.mdpi.com/2076-3417/13/9/5238bathymetryocean remote sensingsatellite-derived bathymetryMaltese islands |
| spellingShingle | Gareth Darmanin Adam Gauci Alan Deidun Luciano Galone Sebastiano D’Amico Satellite-Derived Bathymetry for Selected Shallow Maltese Coastal Zones Applied Sciences bathymetry ocean remote sensing satellite-derived bathymetry Maltese islands |
| title | Satellite-Derived Bathymetry for Selected Shallow Maltese Coastal Zones |
| title_full | Satellite-Derived Bathymetry for Selected Shallow Maltese Coastal Zones |
| title_fullStr | Satellite-Derived Bathymetry for Selected Shallow Maltese Coastal Zones |
| title_full_unstemmed | Satellite-Derived Bathymetry for Selected Shallow Maltese Coastal Zones |
| title_short | Satellite-Derived Bathymetry for Selected Shallow Maltese Coastal Zones |
| title_sort | satellite derived bathymetry for selected shallow maltese coastal zones |
| topic | bathymetry ocean remote sensing satellite-derived bathymetry Maltese islands |
| url | https://www.mdpi.com/2076-3417/13/9/5238 |
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