Nonparametric estimation of stand volume using spectral and spatial features of aerial photographs and old inventory data
Bibliographic record
Abstract
The forest management planning inventories for private forests in Finland are currently carried out in stand-level field inventories. To decrease the amount of fieldwork, aerial photographs and old inventory data could be utilized. The main objectives were to test the accuracy of a method based on these data sources and the effect of stand shape on the accuracy. Median pixel values, semivariances, and old inventory data were extracted for each of the 577 stands in the study. These data were applied as indicator attributes in k-nearest-neighbor estimation of stand volume. Stand-level estimates were computed as weighted means of k most similar neighbors. When all the stands were used, a root mean square error of 29.9% was obtained. Old inventory data proved to be valuable auxiliary information. It was also found that exclusion of stands with tortuous boundaries and small area decreased the error. The accuracy of mean volume estimation just met the requirements for stand-level inventory, but the method still needs further research before the final conclusion of the applicability for management planning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".