Revealing Growth Patterns in White Spruce Genotypes using UAV LiDAR Time-Series Data
Bibliographic record
Abstract
The increasing frequency of extreme climatic events such as drought, heatwaves, and insect infestations highlights the need to monitor tree growth and identify resilient genotypes for forest management. Traditional methods of measuring tree height, which involve manual fieldwork using tools like measuring poles or clinometers, are labor-intensive, time-consuming, and impractical for large-scale studies. In this context, we utilized high-resolution Light Detection and Ranging (LiDAR) data, acquired via a low-flying drone equipped with Real-Time Kinematic (RTK) GNSS sensor to ensure high positional accuracy. We tested this approach by monitoring the height growth of 5293 juvenile white spruce trees representing 1799 genotypes throughout the 2022 growing season. LiDAR-derived tree heights were validated against field-measured data, resulting in a low standard error of 11 cm, confirm the effectiveness of the system. Our findings demonstrate that drone-based LiDAR, when post-processed with the RTK-GNSS to minimize GPS positional errors, offers an efficient and precise method for tracking individual tree height increments. The time-series LiDAR data of the white spruce trees, collected using the system, enabled their classification into slow-, medium-, and fast-growing genotypes, with average height increments quantified at 15 cm, 28 cm, and 39 cm, respectively, thus demonstrating the system’s effectiveness for tree growth-rate estimation in large-scale phenotypic studies.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 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".