Better monitoring of forests according to FAO’s definitions through map integration: Significance and limitations in the context of global environmental goals
Bibliographic record
Abstract
National monitoring of forests is essential for tracking progress towards various global environmental goals, including those of the Kunming-Montreal Global Biodiversity Framework and the Paris Agreement. Inconsistent national definitions of “forest”, however, can complicate the tracking of global progress towards achieving these goals. The FAO’s (Food and Agricultural Organization of the UN) definition of “Forest” is well-known and broad enough to be applicable globally, but it is difficult for countries to produce national forest maps according to this definition using only a single source of remote sensing data. Here, we developed an approach to integrate multiple existing land use/land cover (LULC) maps and generate an integrated map of forests and “Other land with tree cover” that is more consistent with FAO definitions. The proposed approach is based on merging thematic information from the global “PALSAR-2 Forest/Non-forest map”, a global forest/non-forest map, with that of a national map containing more detailed LULC classes. By applying the map integration approach at the national level in the Philippines as a case study, we identified 5.937 ± 0.217 Mha of “Missing forest” that were not included in the country’s national LULC map, mainly forest patches in areas that were predominantly “Brush/shrub”, “Grassland”, or “Marshland/swamp” lands. We also identified 4.294 ± 0.258 Mha of land corresponding to FAO’s definition of “Other land with tree cover” that were previously unmapped; specifically, patches of tree cover on predominantly agricultural and urban lands. Based on these additional areas of “Forest” and “Other land with tree cover” identified, we further estimated an additional 145,480 GgCO2/year of carbon sinks. Our approach is generalizable enough to potentially be applied in other countries for more standardized forest and ecosystem services monitoring.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".