Mapping Mangrove Forest Land Cover Change in Kampong Som Bay, Cambodia from 2015 to 2020
Bibliographic record
Abstract
Mangrove forests are productive and biologically complex ecosystems that provide a wide range of environmental services and supply people with numerous goods. Growing in a variety of depths of salty water, mangrove forests play a significant role in supporting the coastal communities along Cambodia’s 440 kilometer-long coastline. However, mangrove forests in Cambodia have experienced great losses due to the impacts of intensive human activities. Although the human-driven mangrove losses in Cambodia have declined, the recent coverage change of mangrove forests needs to be evaluated for implementing effective regional ecological restoration programs. This study assessed the land cover change and fragmentation level of mangrove forests in Kampong Som Bay, Cambodia from 2015 to 2020. Using a combination of unsupervised and supervised classifications on Landsat 8 OLI/TIRS Level-2 satellite imageries, this study identified that 19,929 ha of mangrove forests remained unchanged, while 2,799 ha of mangrove forests were converted to other land cover types. The key driver of mangrove loss was the expansion of soil class, which could be explained by multiple human activities like rice agriculture and urban development. Based on the computation results of four landscape metrics, the mean patch size of mangrove increased but the shape of mangrove patches became more complex over time. To implement policies that conserve mangrove forests in Kampong Som Bay, it is essential to consider the deforestation occurring on the edge of mangrove patches.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".