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Full-Text Articles in Physical Sciences and Mathematics
Volcano Infrasound: Progress And Future Directions, Jacob F. Anderson, Jeffrey B. Johnson
Volcano Infrasound: Progress And Future Directions, Jacob F. Anderson, Jeffrey B. Johnson
Geosciences Faculty Publications and Presentations
Over the past two decades (2000–2020), volcano infrasound (acoustic waves with frequencies less than 20 Hz propagating in the atmosphere) has evolved from an area of academic research to a useful monitoring tool. As a result, infrasound is routinely used by volcano observatories around the world to detect, locate, and characterize volcanic activity. It is particularly useful in confirming subaerial activity and monitoring remote eruptions, and it has shown promise in forecasting paroxysmal activity at open-vent systems. Fundamental research on volcano infrasound is providing substantial new insights on eruption dynamics and volcanic processes and will continue to do so over …
Volcano Video Data Characterized And Classified Using Computer Vision And Machine Learning Algorithms, Alex J. C. Witsil, Jeffrey B. Johnson
Volcano Video Data Characterized And Classified Using Computer Vision And Machine Learning Algorithms, Alex J. C. Witsil, Jeffrey B. Johnson
Geosciences Faculty Publications and Presentations
Video cameras are common at volcano observatories, but their utility is often limited during periods of crisis due to the large data volume from continuous acquisition and time requirements for manual analysis. For cameras to serve as effective monitoring tools, video frames must be synthesized into relevant time series signals and further analyzed to classify and characterize observable activity. In this study, we use computer vision and machine learning algorithms to identify periods of volcanic activity and quantify plume rise velocities from video observations. Data were collected at Villarrica Volcano, Chile from two visible band cameras located ~17 km from …
Lidar Aboveground Vegetation Biomass Estimates In Shrublands: Prediction, Uncertainties And Application To Coarser Scales, Aihua Li, Shital Dhakal, Nancy F. Glenn, Lucas P. Spaete
Lidar Aboveground Vegetation Biomass Estimates In Shrublands: Prediction, Uncertainties And Application To Coarser Scales, Aihua Li, Shital Dhakal, Nancy F. Glenn, Lucas P. Spaete
Geosciences Faculty Publications and Presentations
Our study objectives were to model the aboveground biomass in a xeric shrub-steppe landscape with airborne light detection and ranging (Lidar) and explore the uncertainty associated with the models we created. We incorporated vegetation vertical structure information obtained from Lidar with ground-measured biomass data, allowing us to scale shrub biomass from small field sites (1 m subplots and 1 ha plots) to a larger landscape. A series of airborne Lidar-derived vegetation metrics were trained and linked with the field-measured biomass in Random Forests (RF) regression models. A Stepwise Multiple Regression (SMR) model was also explored as a comparison. Our results …