Earth Observation—An Essential Tool towards Effective Aquatic Ecosystems’ Management under a Climate in Change
Bibliographic record
Abstract
Numerous policies have been proposed by international and supranational institutions, such as the European Union, to surveil Earth from space and furnish indicators of environmental conditions across diverse scenarios. In tandem with these policies, different initiatives, particularly on both sides of the Atlantic, have emerged to provide valuable data for environmental management such as the concept of essential climate variables. However, a key question arises: do the available data align with the monitoring requirements outlined in these policies? In this paper, we concentrate on Earth Observation (EO) optical data applications for environmental monitoring, with a specific emphasis on ocean colour. In a rapidly changing climate, it becomes imperative to consider data requirements for upcoming space missions. We place particular significance on the application of these data when monitoring lakes and marine protected areas (MPAs). These two use cases, albeit very different in nature, underscore the necessity for higher-spatial-resolution imagery to effectively study these vital habitats. Limnological ecosystems, sensitive to ice melting and temperature fluctuations, serve as crucial indicators of a climate in change. Simultaneously, MPAs, although generally small in size, play a crucial role in safeguarding marine biodiversity and supporting sustainable marine resource management. They are increasingly acknowledged as a critical component of global efforts to conserve and manage marine ecosystems, as exemplified by Target 3 of the Kunming–Montreal Global Biodiversity Framework (GBF), which aims to effectively conserve 30% of terrestrial, inland water, coastal, and marine areas by 2030 through protected areas and other conservation measures. In this paper, we analysed different policies concerning EO data and their application to environmental-based monitoring. We also reviewed and analysed the existing relevant literature in order to find gaps that need to be bridged to effectively monitor these habitats in an ecosystem-based approach, making data more accessible, leading to the generation of water quality indicators derived from new high- and very high-resolution satellite monitoring focusing especially on Chlorophyll-a concentrations. Such data are pivotal for comprehending, at small and local scales, how these habitats are responding to climate change and various stressors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".