Harnessing complexity: integrating remote sensing and fuzzy expert system for evaluating land use land cover changes and identifying mangrove forest vulnerability in Bangladesh
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Purpose: This study analyzes Landsat images to examine the alterations in land cover within the Sundarbans and its surrounding regions in Bangladesh, spanning twenty-one years from 2000 to 2021. Furthermore, we develop a mangrove vulnerability map considering the combined effect of eight socioeconomic, geophysical, and climatic factors. Methods: Land use land cover (LULC) changes in the study area over a 21-year period were assessed using a random forest model, and the vulnerability analysis employed a fuzzy expert-based multicriteria decision-making (MCDM) approach. Results: The results show that a significant portion of the mangrove forest has been transformed into aquaculture practices because of the expansion of high-value shrimp cultivation. A decrease in forest areas and the expansion of aquaculture zones suggest a livelihood shift among the local population over time. This transition has adversely affected human activities within the ecosystem and the biodiversity of mangrove forests. Consequently, it is imperative to implement suitable measures to enhance the state of mangrove forests and safeguard their biodiversity. The vulnerability analysis shows that the highly vulnerable, moderately vulnerable, and low vulnerable areas cover 35.66%, 26.86%, and 19.42%, respectively. Conclusion: The vulnerability maps generated in this research could serve as a valuable resource for coastal planners seeking to ensure the sustainable stewardship of these coastal mangrove forests. These results offer a detailed understanding of coastal mangrove LULC patterns and vulnerability status, which will be useful for policymakers and resource managers to urgently incorporate into coastal land use and environmental management practices.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it