Canopy Temperature Differences between Liana-Infested and Non-Liana Infested Areas in a Neotropical Dry Forest
Bibliographic record
Abstract
Lianas (woody vines) are important non-structural elements of all tropical forests. Current field observations across the Neotropics suggest that liana abundance is rising as a result of forest disturbance, increasing atmospheric CO2, and more frequent extreme climate events. Lianas can cause mechanical stress on their host trees, thus increasing mortality, in addition to potentially reducing carbon storage capacity. Furthermore, previous studies have suggested that liana leaves have an overall higher temperature than tree leaves, which presents the question of whether these differences can be extended from the leaf to the canopy. In this context, the ability to detect these temperature differences from a remote sensing platform has so far not been put into test, despite the importance such knowledge can have in large-scale land surface modeling studies and liana extent monitoring. To partially fill this knowledge gap, we acquired thermal infrared data using an unmanned aerial vehicle (UAV) system over an intermediate tropical dry forest in Costa Rica, Central America. Classification results from a previous study in the same area were used to subset the thermal infrared images into liana-infested areas, non-liana infested areas, and forest gaps. The temperature differences between these three image components were then investigated using the Welch and Games–Howell post-hoc statistical tests. Our results suggest that liana-infested areas have, on average, a statistically significant higher temperature than non-liana infested areas. Shadowed forest gaps, used as reference, have a cooler temperature than forest canopies. Our findings on the temperature differences between liana-infested and non-liana infested areas support previous leaf-level observations and open the door to the use of new approaches for the classification and modeling of liana infestation in tropical ecosystems.
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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".