Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 102-115 |
| Seitenumfang | 14 |
| Fachzeitschrift | Journal of Ocean Technology |
| Jahrgang | 16 |
| Ausgabenummer | 3 |
| Publikationsstatus | Veröffentlicht - 1 Sept. 2021 |
| Extern publiziert | Ja |
Abstract
Satellites allow users to observe ocean colour in a way that is not possible from a ship or the shore. Ocean colour depends on interactions of incident light with particles or substances in the water. These light interactions cause the ocean to be a variety of shades depending on what the water is composed of and how these materials change the reflections of the light. The ocean colour fluctuation can be caused by different compositions, such as the biomass of phytoplankton or zooplankton, and can lead to a change in ocean colour, for example, from normal clear blue into a variety of shades of green. Satellites take measurements that can be used to calculate ocean colour and concentrations of materials in the ocean. This study focused on ocean colour mapping using satellite images captured from the Mediterranean Sea. The Iterative Self Organizing Data Analysis Techniques Algorithm (ISODATA) unsupervised machine learning (ML) algorithm was employed to determine ocean colour. The produced map is a basic way of displaying ocean colour and is easy for users of any skill level to produce. Finally, it was observed that having more information about phytoplankton and applying it to the algorithm could improve the results.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Meerestechnik
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
in: Journal of Ocean Technology, Jahrgang 16, Nr. 3, 01.09.2021, S. 102-115.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Ocean colour mapping using remote sensing technology and an unsupervised machine learning algorithm
AU - Parsons, Sarah
AU - Amani, Meisam
AU - Moghimi, Armin
N1 - Publisher Copyright: © 2021, Centre for Applied Ocean Technology, Marine Institute. All rights reserved.
PY - 2021/9/1
Y1 - 2021/9/1
N2 - Satellites allow users to observe ocean colour in a way that is not possible from a ship or the shore. Ocean colour depends on interactions of incident light with particles or substances in the water. These light interactions cause the ocean to be a variety of shades depending on what the water is composed of and how these materials change the reflections of the light. The ocean colour fluctuation can be caused by different compositions, such as the biomass of phytoplankton or zooplankton, and can lead to a change in ocean colour, for example, from normal clear blue into a variety of shades of green. Satellites take measurements that can be used to calculate ocean colour and concentrations of materials in the ocean. This study focused on ocean colour mapping using satellite images captured from the Mediterranean Sea. The Iterative Self Organizing Data Analysis Techniques Algorithm (ISODATA) unsupervised machine learning (ML) algorithm was employed to determine ocean colour. The produced map is a basic way of displaying ocean colour and is easy for users of any skill level to produce. Finally, it was observed that having more information about phytoplankton and applying it to the algorithm could improve the results.
AB - Satellites allow users to observe ocean colour in a way that is not possible from a ship or the shore. Ocean colour depends on interactions of incident light with particles or substances in the water. These light interactions cause the ocean to be a variety of shades depending on what the water is composed of and how these materials change the reflections of the light. The ocean colour fluctuation can be caused by different compositions, such as the biomass of phytoplankton or zooplankton, and can lead to a change in ocean colour, for example, from normal clear blue into a variety of shades of green. Satellites take measurements that can be used to calculate ocean colour and concentrations of materials in the ocean. This study focused on ocean colour mapping using satellite images captured from the Mediterranean Sea. The Iterative Self Organizing Data Analysis Techniques Algorithm (ISODATA) unsupervised machine learning (ML) algorithm was employed to determine ocean colour. The produced map is a basic way of displaying ocean colour and is easy for users of any skill level to produce. Finally, it was observed that having more information about phytoplankton and applying it to the algorithm could improve the results.
KW - ISODATA
KW - Machine learning
KW - Ocean colour
KW - Phytoplankton
KW - Remote sensing
UR - http://www.scopus.com/inward/record.url?scp=85119053208&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:85119053208
VL - 16
SP - 102
EP - 115
JO - Journal of Ocean Technology
JF - Journal of Ocean Technology
SN - 1718-3200
IS - 3
ER -