Satellite-Derived Bathymetry for Selected Shallow Maltese Coastal Zones

Featured Application: The main outcome and procedure described in this paper aim to equip Public Administrations with a tool that can provide on-demand support. Bathymetric information has become essential to help maintain and operate coastal zones. Traditional in situ bathymetry mapping using echo...

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Bibliographic Details
Main Authors: D’Amico, S. (Author), Darmanin, G. (Author), Deidun, A. (Author), Galone, L. (Author), Gauci, A. (Author)
Format: Article
Language:English
Published: MDPI 2023
Subjects:
Online Access:View Fulltext in Publisher
View in Scopus
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001 10.3390-app13095238
008 230529s2023 CNT 000 0 und d
020 |a 20763417 (ISSN) 
245 1 0 |a Satellite-Derived Bathymetry for Selected Shallow Maltese Coastal Zones 
260 0 |b MDPI  |c 2023 
856 |z View Fulltext in Publisher  |u https://doi.org/10.3390/app13095238 
856 |z View in Scopus  |u https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159352309&doi=10.3390%2fapp13095238&partnerID=40&md5=4b67407dd4e0f81a868fca9248befff4 
520 3 |a Featured Application: The main outcome and procedure described in this paper aim to equip Public Administrations with a tool that can provide on-demand support. 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. © 2023 by the authors. 
650 0 4 |a bathymetry 
650 0 4 |a Maltese islands 
650 0 4 |a ocean remote sensing 
650 0 4 |a satellite-derived bathymetry 
700 1 0 |a D’Amico, S.  |e author 
700 1 0 |a Darmanin, G.  |e author 
700 1 0 |a Deidun, A.  |e author 
700 1 0 |a Galone, L.  |e author 
700 1 0 |a Gauci, A.  |e author 
773 |t Applied Sciences (Switzerland)