Using thermal imagery and changes in stem radius to assess water stress in two coniferous tree species
Bibliographic record
Abstract
With a warming climate and greater evaporative demand, many forest ecosystems are increasingly affected by water limitation as prolonged water deficits reduce tree-level growth and survival. Water deficit can be monitored in several ways, including from daily measurements of sap flow or changes in stem radius from automated sensors mounted on individual trees. As an alternative approach, we evaluated the use of airborne thermal imagery from unmanned aerial vehicles as a rapid, scalable tool for assessing tree-level water stress. Plant water stress leads to higher leaf temperatures when soil moisture is low and evaporative demand is high. To detect this response, we modeled the difference between leaf and air temperature (∆T) as a function of local soil moisture, vapor pressure deficit, and wind speed for two tree species, lodgepole pine ( Pinus contorta var. latifolia ) and white spruce ( Picea glauca ). We used those same weather and soil conditions to model dendrometer-based measurements of daily changes in internal tree water deficit (∆TWD). While canopy leaf temperature and daily change in tree water deficit showed little direct correlation with one another, these variables both responded to soil moisture, vapor pressure deficit, and wind speed in a manner that reflects responses to water stress as soil became progressively drier over the summer months. The two species showed some differences related to species-specific strategies for drought avoidance. The application of thermal imagery to detect water stress in natural forest ecosystems can improve understanding of how trees experience water stress across species and environmental conditions.
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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 | 0.000 |
| 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 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".