arrow
Return

Scalable low-cost seabed landers: the missing link for sustained, integrated, long-term observations in dynamic shallow seas

delete2026-03-31
delete0
delete
OA
AI
A
Arienne Calonge *
R
Roeland Develter *
C
Carlota Muñiz
C
Clea Parcerisas
J
Jan Reubens
W
Wieter Boone
K
Klaas Deneudt
E
Elisabeth Debusschere
DOI:10.1002/rse2.70072delete
deleteOriginal
deleteShare
deleteSave
View PDF
Abstract

Abstract

En 中文
Shallow coastal seas are increasingly affected by accelerating climate change and widespread anthropogenic pressures, yet consistent, long-term monitoring data remain scarce and fragmented. Many existing observations are restricted to project-specific objectives, limiting their potential contribution to broader-scale assessments. Seabed landers, when equipped with a range of biotic and abiotic sensors, offer a non-invasive and cost-effective solution for ecosystem-scale monitoring of Essential Biodiversity Variables (EBVs) and Essential Ocean Variables (EOVs). Examples of EBVs and EOVs include marine mammal distribution, fish movement, underwater sound, plankton biomass and diversity, sea surface height, currents, and temperature. The integration of datasets improves our ability to address ecological questions across larger spatial and temporal scales. Incorporating biodiversity monitoring data into the Digital Twin of the Ocean, a high-resolution multi-dimensional virtual representation of the ocean, marks a critical step toward large-scale, standardized ecosystem-based assessments of ocean health. Strengthening infrastructure, data management capabilities, and protocols will further unlock the potential of seabed observatories to inform conservation efforts and policy development, particularly through the application of artificial intelligence. In this article, a perspective on the capacity of subsea observatories is presented, specifically on multi-purpose seabed landers, to deliver scalable, sustained, and high-resolution observations of EOVs and EBVs over large areas.
Keywords:
Digital twin
EBVs
EOVs
mooring
observatory
sensor network
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Remote Sensing in Ecology and Conservation cover
Remote Sensing in Ecology and Conservation
IF:
4.3
Papers:
447
Citations:
1.9K

Organization

F
flanders marine institute
Scholars:
26
Papers: 13
Citations: 0