Enhancing Forest Growth and Yield Predictions with Airborne Laser Scanning Data: Increasing Spatial Detail and Optimizing Yield Curve Selection through Template Matching
Why this work is in the frame
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Bibliographic record
Abstract
Accurate information on both the current stock and future growth and yield of forest resources is critical for sustainable forest management. We demonstrate a novel approach to utilizing airborne laser scanning (ALS)-derived forest stand attributes to determine future growth and yield of six attributes at a sub-stand (25 m grid cell) level of detail: dominant height (HMAX), Lorey’s height (HL), quadratic mean diameter (QMD), basal area (BA), whole stem volume (V), and trees per hectare (TPH). The approach is designed to find the most appropriate matching yield curve and project the attributes to the age of 80 years. Comparisons to conventional plot-level projections resulted in relative mean differences of 13.4% (HMAX), −27.1% (HL), 18.8% (QMD), 12.0% (BA), 18.6% (V), and −17.5% (TPH). The respective relative root mean squared difference values were: 31.1%, 38.4%, 19.8%, 19.8%, 21.8%, and 38.4%. Differences were driven mostly by stand-level age and site index. The uncertainty of cell-level yield curve assignment was used to refine stand-level summaries. The novel contribution of this study is in the application of growth and yield models at the cell level, combined with the use of ALS-derived attributes to optimize yield curve selection via template matching.
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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 | 0.001 | 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 it