Volcanic Diffuse Volatile Emissions Tracked by Plant Responses Detectable From Space
Bibliographic record
Abstract
Abstract Volcanic volatile emissions provide information about volcanic unrest but are difficult to detect with satellites. Volcanic degassing affects plants by elevating local CO 2 and H 2 O concentrations, which may increase photosynthesis. Satellites can detect plant health, or a reaction to photosynthesis, through a Normalized Difference Vegetation Index (NDVI). This can act as a potential proxy for detecting changes in volcanic volatile emissions from space. We tested this method by analyzing 185 Landsat 5 and 8 images of the Tern Lake thermal area (TLTA) in northeast Yellowstone caldera from 1984 to 2022. We compared the NDVI values of the thermal area with those of similar nearby forests that were unaffected by hydrothermal activity to determine how hydrothermal activity impacted the vegetation. From 1984 to 2000, plant health in the TLTA steadily increased relative to the background forests, suggesting that vegetation in the TLTA was fertilized by volcanic CO 2 and/or magmatic water. Hydrothermal activity began to stress plants in 2002, and by 2006, large swathes of trees were dying in the hydrothermal area. Throughout most of the 1990s, the least healthy plants were located in the area which became the epicenter of hydrothermal activity in 2000. These findings suggest that plant‐focused measurements are sensitive to minor levels of volcanic unrest which may not be detected by other remote sensing methods, such as infrared temperature measurements. This method could be a safe and effective new tool for detecting changes in volatile emissions in volcanic environments which are dangerous or difficult to access.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".