The Phenophases of Mixed-Forest Species Are Regulated by Photo-Hydro-Thermal Conditions: An Approach Using UAV-Derived and In Situ Data
Bibliographic record
Abstract
Severe drought events have raised concerns regarding their effects on the phenological cycles of forest species. This study evaluates the correspondence between in situ phenophases and those detected by an unmanned aerial vehicle (UAV) in tree species coexisting within a mixed forest, with particular attention to their relationship with climatic variables. Based on 12 consecutive monthly field observations, we compared phenological developments with UAV-derived normalized difference vegetation index (NDVI) values, which were then correlated with environmental variables. The analysis revealed a convergence of inflection points and seasonal phenological shifts, likely driven by climatic factors, although distinct patterns emerged between coniferous and broadleaf species. Photoperiod (PP), vapor pressure deficit (VPD), maximum temperature (TMAX), and, to a lesser extent, precipitation (P) were the primary environmental variables influencing NDVI results, used here as a proxy for phenology. Photothermal conditions revealed seasonal asynchrony in NDVI responses between coniferous and broadleaf species, exerting a positive influence on conifers during summer, while having a negative impact on broadleaf species in spring. Validation of in situ observations with UAV-derived data demonstrated a biological correlation between canopy dynamics and NDVI values, supporting its use as a proxy for detecting phenophases at the level of individual trees.
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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 | 0.000 |
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".