Nonparametric estimation of stem volume using airborne laser scanning, aerial photography, and stand-register data
Bibliographic record
Abstract
In forest management planning and forestry decision-making there is a continuous need for higher quality information on forest resources. The aim of this study was to improve the quality of forest resource information acquired by airborne laser scanning by combining it with aerial images and current stand-register data. A k-MSN (most similar neighbor) application was constructed for the prediction of the plot and stand volumes of standing trees. The application constructed used various data sources, including laser scanner data, aerial digital photographs, class variables describing a stand, and updated old stand volumes. The ability of these data sources to predict stem volume was tested together and separately. In the airborne laser scanner data based k-MSN application, characteristics of canopy quantiles were used as independent variables. The results show that with respect to individual plot and stand volume estimation approaches, the laser-based technique is a superior one. The results were improved further when other information sources were used together with the laser scanner data. Using a combination of laser scanner data, aerial images, and class variables (on the grounds of the current forest database) improved the root mean square error (RMSE) of the estimated plot volume by 15% (from 16% to 13%) as compared to using laser scanner data on their own. When the results were averaged at the stand level, the accuracy improved considerably, but the use of other information sources together with airborne laser scanner data did not further improve the results as it did at the plot level. The RMSE of stand volume was about 6% in all data combinations where airborne laser scanning information was used. One conclusion is that making use of additional available data sources together with laser material improves the reliability of plot volume estimates. As these additional data typically mean no extra material costs (since they are available in any case), making combined use of these data and laser scanner data improves the cost efficiency of a forest inventory.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".