MétaCan
Menu
Back to cohort
Record W7115033563

Developing vegetation-based proxies for satellite detection of volcanic degassing

2025· dissertation· en· W7115033563 on OpenAlexfundno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2025
Typedissertation
Languageen
FieldEngineering
TopicSynthetic Aperture Radar (SAR) Applications and Techniques
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaU.S. Geological Survey
KeywordsVolcanoSatelliteVolcanic GasesLavaVulcanian eruption
DOInot available

Abstract

fetched live from OpenAlex

Detecting changes in volcanic gas emissions is essential for forecasting eruptions.Carbon dioxide (CO 2 ) is among the most important gases for identifying early signs of volcanic activity, but it remains difficult to monitor effectively.Ground-based methods often lack the spatial and temporal resolution to detect subtle flux changes, while satellite observations are hindered by high atmospheric CO 2 backgrounds that obscure localized signals.To overcome this, we developed an alternative approach that uses satellite observations of vegetation health as a proxy for volcanic degassing.Volcanic gases such as CO 2 and SO 2 affect plant physiology in different and measurable ways, making vegetation a potentially sensitive and responsive living sensor of volcanic activity.We tested this method at a dynamic hydrothermal area in Yellowstone National Park by combining four decades of Landsat vegetation indices with isotopic analyses of tree rings.Vegetation responses captured in satellite imagery corresponded with tree-ring signals of CO 2 exposure, validating this approach and revealing a transition from cold to hot degassing that had not been previously documented.We then applied the method to Taal Volcano in the Philippines, where an eruption in 2020 was preceded by significant changes in degassing.Using high-resolution daily imagery from Planet Labs, we detected increases in vegetation health during periods of elevated CO 2 emissions and sharp declines during SO 2 spikes.These patterns were consistent with known physiological responses and aligned with other monitoring data.In some cases, vegetation changes revealed degassing events missed by other volcano monitoring methods.Together, these results demonstrate that satellite vegetation monitoring offers a scalable, indirect method for tracking volcanic degassing.This approach is effective across diverse ecological and volcanological settings and holds promise as a complementary tool for global volcano monitoring.iii Résumé Détecter les variations dans les émissions de gaz volcaniques est essentiel pour prévoir les éruptions.Le dioxyde de carbone (CO 2 ) figure parmi les gaz les plus importants pour identifier les premiers signes d'activité volcanique, mais il reste difficile à surveiller efficacement.Les méthodes au sol manquent souvent de la résolution spatiale et temporelle nécessaire pour détecter des variations subtiles de flux, tandis que le observations satellitaires sont limitées par les concentrations élevées de CO 2 atmosphérique qui masquent les signaux localisés.Pour surmonter ces obstacles, nous avons développé une approche alternative utilisant les observations satellitaires de la santé de la végétation comme indicateur indirect du dégazage volcanique.Les gaz volcaniques tels que le CO 2 et le SO 2 affectent la physiologie des plantes de manières différentes et mesurables, faisant de la végétation un capteur vivant potentiel de l'activité volcanique.Nous avons testé cette méthode dans une zone hydrothermale dynamique du parc national de Yellowstone en combinant quatre décennies d'indices de végétation Landsat avec des analyses isotopiques d'anneaux de croissance d'arbres.Les réponses de la végétation capturées dans les images satellites correspondaient aux signaux d'exposition au CO 2 observés dans les cernes des arbres, validant ainsi cette approche et révélant une transition du dégazage froid au dégazage chaud qui n'avait pas été documentée auparavant.Nous avons ensuite appliqué la méthode au volcan Taal aux Philippines, où une éruption en 2020 a été précédée de changements significatifs dans le dégazage.En utilisant des images haute résolution quotidiennes fournies par Planet Labs, nous avons détecté des augmentations de la santé de la végétation lors de périodes d'émissions élevées de CO 2 , ainsi que des baisses marquées lors des pics d'émissions de SO 2 .Ces tendances étaient cohérentes avec les réponses physiologiques connues et correspondaient aux données d'autres systèmes de surveillance.Dans certains cas, les changements de la végétation ont permis de révéler des événements de dégazage passés inaperçus par les méthodes classiques de surveillance volcanique.Ensemble, ces résultats démontrent que la surveillance satellitaire de la végétation offre une méthode indirecte, extensible et efficace pour suivre le dégazage volcanique.Cette approche s'avère efficace dans une variété de contextes écologiques et volcaniques, et constitue un outil complémentaire prometteur pour la surveillance volcanique à l'échelle mondiale.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.239
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueeScholarship@McGill (McGill)Same topicSynthetic Aperture Radar (SAR) Applications and TechniquesFrench-language works237,207