Assessing the structure of primeval and managed beech forests in the Ukrainian Carpathians using remote sensing
Bibliographic record
Abstract
Forest structure reflects the forest disturbance regime and can provide important information about the rate of human impact. A better understanding of the structural variability and large-scale dynamics of natural forests is crucial for “close to nature” forest management planning. In this study, we developed a partly automated approach to assess the structure of potential primeval and managed beech forests in the Ukrainian Carpathians using WorldView-2 imagery. We analyzed the local (50 m × 50 m scale) canopy closure of these forests by extracting the canopy gaps and determined four forest structure types ranging from very closed to low density. The occurrence and frequencies of these structure types were significantly different in the primeval and managed beech forests. The four forest structure types were predicted and mapped using multinomial logistic regression based on the textural features derived from the original image bands and two vegetation indices. A 10-fold cross-validation resulted in an overall accuracy of 83% and a kappa coefficient of 75%, with the highest agreement for the very closed structure type (87%) and the lowest agreement for the medium density and low density structure types (79%). The forest structure type maps can be helpful for planning management activities in beech 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.000 | 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".