From plots to landscape: A k-NN-based method for estimating stand-level merchantable volume in the Province of Québec, Canada
Bibliographic record
Abstract
Estimation of forest attributes at the stand or polygon level across the forest domain is a basic component of forest inventory programs. We tested a “k-Nearest Neighbours” (k-NN)-based method for imputing merchantable volume. Our target dataset consisted of a discrete set of forest polygons within two large forest management units, and our reference dataset was a large historical database of temporary sample plots measured over the past three decades. The linkage between the target and reference datasets was provided by polygon-level photo-interpreted forest attributes. Measurements in temporary sample plots located in all target polygons enabled us to estimate fit statistics between imputed and measured merchantable volumes. A parallel imputation exercise was also done using the current operational method used by the Province of Québec to map forest attributes over the publicly owned forest lands. Results show that the volumes estimated using the historical k-NN method show fit statistics similar to those of the operational method, with a slightly higher bias that is largely within the error term of the estimates. For both methods, the coefficient of determination between measured and imputed merchantable volume is between 0.16 and 0.19 for total volume, increases substantially when the volume is partitioned between hardwoods and softwoods, but then decreases when the volume is further distributed among species. The results underline the importance of photo-interpretation uncertainties in bounding the accuracy of volume imputation as well as the value of the k-NN procedure for imputation purposes in the context of natural forests.Key words: Forest inventory; non-parametric methods; photo-interpretation; pre-stratification; natural 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".