Updating Forest Stand Inventories: Integration of Photo-Interpreted and Airborne Laser Scanning Forest Attributes Using Generic Region Merging Segmentation and kNN Imputation
Bibliographic record
Abstract
Integrating airborne laser scanning (ALS) forest attributes with photo-interpreted forest stand age and species attributes can provide managers with the best information to drive estate planning, growth and yield projections, and forest operations. Photo-interpreted forest inventories provide certain forest attributes that are difficult to measure with ALS, yet are subjective and irregularly updated. ALS is objective and provides detailed estimates of forest structure attributes, but poorly estimates age and species composition. We used Generic Region Merging segmentation and k-nearest neighbor imputation to integrate photo-interpreted and ALS-derived forest attributes into a contemporary stand-based forest inventory. We first segmented gridded ALS attributes into forest stand polygons across a ∼630,000 ha managed forest in Ontario, Canada. We next applied imputation to a photo-interpreted inventory, assessing the influence of model parameters and imputed vs. observed values of age and species using leave-one-out cross-validation. Compared to the photo-interpreted inventory, the optimal imputation model estimated age with a mean absolute and mean bias difference of 16.06 and −0.14 years, and classified leading species with 65.46% accuracy. We lastly integrated imputed age and species attributes into the automatically segmented forest stand polygons, finding similar age and species distributions across the landscape when compared to the photo-interpreted inventory.
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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 | 0.000 | 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 | 0.000 |
| 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 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".