Mapping old-growth forests using airborne lidar data and satellite images: how do plot size and rarity affect accuracy?
Bibliographic record
Abstract
Old-growth forests have become rare and fragmented in the boreal biome. Their precise locations are not currently known with sufficient accuracy to support forest conservation and forest management. We studied the mapping of old-growth forests using airborne lidar data and satellite images in the Finnish coniferous forests. We investigated how plot size and the rarity of old-growth forests affect the accuracy of old-growth forest detection. We employed a Gaussian process classifier to distinguish old-growth forests from managed forests. Our field data consisted of 176 old-growth and 1082 managed forest plots. The results showed that an increase in plot size from 20 m × 20 m to 60 m × 60 m improved the performance of the classifier, because the larger plots more likely contain spatial patterns of trees and crown features indicative of forest naturalness. The largest F1-score (0.74) was achieved by data augmentation that generates additional training plots located inside forest boundaries. We also showed that the detection accuracy of old-growth forests decreases as they become rarer in the population. This rarity effect is crucial to understand, because the occurrence of old-growth forests can vary regionally due to different land use pressures. The mapping procedure proposed here can assist in the planning of field-based inventories of old-growth forests.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".