Mapping sub-pixel forest cover in Europe using AVHRR data and national and regional statistics
Bibliographic record
Abstract
There are few examples of satellite-derived land cover maps dedicated specifically to the pan-European area, and even less to Europe's forested land. In an effort to remedy this situation and produce a consistent and comparable forest database for the pan-European area, attempts are made to utilize both spatial information derived from satellite data and more traditional statistical data. This paper discusses the results of combining spectral information derived from the National Oceanic and Atmospheric Administration advanced very high resolution radiometer (NOAA AVHRR) and official statistical forest data acquired at national and regional levels. Forest probability estimates derived from an AVHRR mosaic of Europe are conjoined with official statistics in an iterative calibration procedure. The resulting database consists of maps of forest‐non-forest for the European Union (EU) and a more detailed database distinguishing the probable proportion of coniferous forest, broadleaf forest, and mixed woodland within each AVHRR pixel for France and Finland. In the latter case, regional statistics are used in the calibration procedure. An area-weighted root mean square error of 7.3% and 4.3%, respectively, was found when comparing the calibrated estimates with the Coordination of Information on the Environment (CORINE) Land Cover and the CORINE with the original AVHRR mosaic (before calibration) for 14 countries of the EU. It was found that for France, the AVHRR-derived forest database tended to underestimate the total forest area for the temperate zones, whereas Mediterranean regions (dominated by so-called other wooded land) tend to be overestimated. It appeared that overestimates of the total forest area in Finland were likely to arise from overestimates in the area of broadleaf woodland. The technique provides an innovative approach to combining statistical data with spatial information in a way so as to add value to both satellite-derived and "ground-based" statistical information.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".