Prioritizing commercial thinning: quantification of growth and competition with high-density drone laser scanning
Bibliographic record
Abstract
Abstract Laser scanning sensors mounted on drones enable on-demand quantification of forest structure through the collection of high-density point clouds (500+ points m−2). These point clouds facilitate the detection of individual trees enabling the quantification of growth-related variables within a stand that can inform precision management. We present a methodology to link incremental growth data obtained from tree cores with crown models derived from drone laser scanning, quantifying the relative growth condition of individual trees and their neighbours. We stem-mapped 815 trees across five stands in north-central British Columbia, Canada of which 16% were cored to quantify recent basal area growth. Point clouds from drone laser scanning and orthomosaic imagery were used to locate trees, model three-dimensional crown features, and derive competition metrics describing the relative distribution of crown sizes. Local access to water and light were simulated using topographic wetness and potential solar irradiance indices derived from high-resolution terrain and surface models. Wall-to-wall predictions of recent basal area growth were produced from the best-performing model and summarized across a grid alongside a tree-level competition index. Overall, crown volume was most strongly correlated with observed differences in 5-year basal area increment (R2 = 0.70, P < .001). Competition and solar irradiance metrics were significant as univariate predictors (P < .001) but nonsignificant when included in multivariate models with crown volume. Using predictions from the best-performing model and laser-scanning-derived competition metrics, we present a newly developed growth competition index to assess variability and inform commercial thinning prescription prioritization. Growth predictions, competition metrics, and the growth competition index are summarized into maps that could be used in an operational workflow. Our methodology presents a new capacity to capture and quantify intra-stand variation in growth by combining competition metrics and measures of recent growth with high-density drone laser scanning data.
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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 | 0.001 | 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 | 0.000 |
| 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 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".