Evaluating Effects of Remotely Sensed Neighborhood Crowding and Depth-to-Water on Tree Height Growth
Bibliographic record
Abstract
Previous research on the effects of neighborhood crowding and soil moisture on tree height growth have been limited by time-consuming and sometimes inaccurate ground-based measurements of tree height. Recent developments in unmanned aerial vehicles (UAVs) allow detailed 3D point clouds of the canopy surface to be generated at relatively low cost. Using UAV-derived point clouds, we obtained height measurements of 4386 trees for the years 2019 and 2021. We also calculated four neighborhood crowding indices and a topography-based moisture index (depth-to-water) for these trees. Using initial tree height, neighborhood crowding indices and the depth-to-water index, we developed Bayesian hierarchical models to predict height growth for three tree species (Picea glauca (white spruce), Populus tremoluides (trembling aspen) and Pinus contorta (lodgepole pine)) across different stands. Bayes-R2 values of the final models were highest for white spruce (35%) followed by trembling aspen (28%) and lodgepole pine (25%). Model outputs showed that the effect of crowding and depth-to-water on height growth are limited and species-dependent, adding a maximum of 7% to the Bayes-R2 metric. Comparing different neighborhood crowding indices revealed that no index is clearly superior to others across all three species, as different neighborhood crowding indices resulted in only minor differences in model performance. While height growth can be partially explained by aerially derived neighborhood crowding indices and the depth-to-water index, future studies should focus on identifying relevant site characteristics to predict tree growth with greater accuracy.
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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 | 0.001 |
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 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".