Spatiotemporal Changes in Ghana's Mangrove Ecosystems and Pathways for Restoration Action
Bibliographic record
Abstract
ABSTRACT The protection and restoration of mangrove ecosystems are recognized as one of the nature‐based solutions to a changing climate. They are, however, threatened by anthropogenic and natural stresses. Efforts undertaken in the past years to develop global‐scale mangrove extent maps either do not provide up‐to‐date maps or end up missing mangrove extents at local scales. This study aims to assess the spatiotemporal changes of Ghana's mangrove extent and evaluate the key factors causing these potential changes at the country's regional level. The random forest (RF) classifier was used to develop 2015, 2021 and 2024 mangrove extent maps for Ghana and compared them with each other at the country and regional levels to assess the changes over time. With Kappa coefficients higher than 0.8, the results indicate that Ghana's mangrove extent had declined by 15.4% from 2015 (68.41 km 2 ) to 2024 (57.87 km 2 ), with the country's Western, Central and Greater Accra regions experiencing a decline in their mangrove extents. Only the Volta region experienced an increase in its mangrove extent. These significant mangrove extent changes in Ghana at the three regions, as derived from a comprehensive literature review on Ghana's mangroves, are mainly attributable to urban expansion, indiscriminate waste disposal, wildfires, uncontrolled sand and salt mining, among others. This study highlights the need for countries to have national mangrove extent maps. This will help countries to effectively achieve the Global Mangrove Alliance's goals of halting loss, doubling protection and restoring half of the world's mangroves by 2030.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".