Limits...
Probabilistic detection of volcanic ash using a Bayesian approach.

Mackie S, Watson M - J Geophys Res Atmos (2014)

Bottom Line: Such techniques are generally specific to data from particular sensors, and most approaches result in a binary classification of pixels into "ash" and "ash free" classes with no indication of the classification certainty for individual pixels.The technique has already been successfully applied to other detection problems in remote sensing, and this work shows that it will be a useful and effective tool for ash detection.Presentation of a probabilistic volcanic ash detection schemeMethod for calculation of probability density function for ash observationsDemonstration of a remote sensing technique for monitoring volcanic ash hazards.

View Article: PubMed Central - PubMed

Affiliation: School of Earth Sciences, University of Bristol Bristol, UK.

ABSTRACT

: Airborne volcanic ash can pose a hazard to aviation, agriculture, and both human and animal health. It is therefore important that ash clouds are monitored both day and night, even when they travel far from their source. Infrared satellite data provide perhaps the only means of doing this, and since the hugely expensive ash crisis that followed the 2010 Eyjafjalljökull eruption, much research has been carried out into techniques for discriminating ash in such data and for deriving key properties. Such techniques are generally specific to data from particular sensors, and most approaches result in a binary classification of pixels into "ash" and "ash free" classes with no indication of the classification certainty for individual pixels. Furthermore, almost all operational methods rely on expert-set thresholds to determine what constitutes "ash" and can therefore be criticized for being subjective and dependent on expertise that may not remain with an institution. Very few existing methods exploit available contemporaneous atmospheric data to inform the detection, despite the sensitivity of most techniques to atmospheric parameters. The Bayesian method proposed here does exploit such data and gives a probabilistic, physically based classification. We provide an example of the method's implementation for a scene containing both land and sea observations, and a large area of desert dust (often misidentified as ash by other methods). The technique has already been successfully applied to other detection problems in remote sensing, and this work shows that it will be a useful and effective tool for ash detection.

Key points: Presentation of a probabilistic volcanic ash detection schemeMethod for calculation of probability density function for ash observationsDemonstration of a remote sensing technique for monitoring volcanic ash hazards.

No MeSH data available.


Related in: MedlinePlus

Curve (solid line) fitted to the simulated BTs at 10 µm (plus) for an ash cloud at 9755 m altitude (note that the x axis has a logarithmic scale).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4379904&req=5

fig06: Curve (solid line) fitted to the simulated BTs at 10 µm (plus) for an ash cloud at 9755 m altitude (note that the x axis has a logarithmic scale).

Mentions: We use the data set of atmospheric profiles from section 3.2 to forward model observations of ash. Multiple ash observations are simulated for each profile, corresponding to ash at different altitudes and with different concentrations. Curves are fitted to the simulated ash observations, assuming the same form for the BT relationship with ash concentration, as assumed for the BT-CWP in section 3.2; see Figure 6. Following the same interpolation as in section 3.2, BTs for ash clouds were inferred for ash clouds at 3 m vertical increments through the atmosphere, with mass concentration varying from 50 µg/m3 to 13,000 µg/m3 in 1000 equally spaced increments for each individual profile.


Probabilistic detection of volcanic ash using a Bayesian approach.

Mackie S, Watson M - J Geophys Res Atmos (2014)

Curve (solid line) fitted to the simulated BTs at 10 µm (plus) for an ash cloud at 9755 m altitude (note that the x axis has a logarithmic scale).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4379904&req=5

fig06: Curve (solid line) fitted to the simulated BTs at 10 µm (plus) for an ash cloud at 9755 m altitude (note that the x axis has a logarithmic scale).
Mentions: We use the data set of atmospheric profiles from section 3.2 to forward model observations of ash. Multiple ash observations are simulated for each profile, corresponding to ash at different altitudes and with different concentrations. Curves are fitted to the simulated ash observations, assuming the same form for the BT relationship with ash concentration, as assumed for the BT-CWP in section 3.2; see Figure 6. Following the same interpolation as in section 3.2, BTs for ash clouds were inferred for ash clouds at 3 m vertical increments through the atmosphere, with mass concentration varying from 50 µg/m3 to 13,000 µg/m3 in 1000 equally spaced increments for each individual profile.

Bottom Line: Such techniques are generally specific to data from particular sensors, and most approaches result in a binary classification of pixels into "ash" and "ash free" classes with no indication of the classification certainty for individual pixels.The technique has already been successfully applied to other detection problems in remote sensing, and this work shows that it will be a useful and effective tool for ash detection.Presentation of a probabilistic volcanic ash detection schemeMethod for calculation of probability density function for ash observationsDemonstration of a remote sensing technique for monitoring volcanic ash hazards.

View Article: PubMed Central - PubMed

Affiliation: School of Earth Sciences, University of Bristol Bristol, UK.

ABSTRACT

: Airborne volcanic ash can pose a hazard to aviation, agriculture, and both human and animal health. It is therefore important that ash clouds are monitored both day and night, even when they travel far from their source. Infrared satellite data provide perhaps the only means of doing this, and since the hugely expensive ash crisis that followed the 2010 Eyjafjalljökull eruption, much research has been carried out into techniques for discriminating ash in such data and for deriving key properties. Such techniques are generally specific to data from particular sensors, and most approaches result in a binary classification of pixels into "ash" and "ash free" classes with no indication of the classification certainty for individual pixels. Furthermore, almost all operational methods rely on expert-set thresholds to determine what constitutes "ash" and can therefore be criticized for being subjective and dependent on expertise that may not remain with an institution. Very few existing methods exploit available contemporaneous atmospheric data to inform the detection, despite the sensitivity of most techniques to atmospheric parameters. The Bayesian method proposed here does exploit such data and gives a probabilistic, physically based classification. We provide an example of the method's implementation for a scene containing both land and sea observations, and a large area of desert dust (often misidentified as ash by other methods). The technique has already been successfully applied to other detection problems in remote sensing, and this work shows that it will be a useful and effective tool for ash detection.

Key points: Presentation of a probabilistic volcanic ash detection schemeMethod for calculation of probability density function for ash observationsDemonstration of a remote sensing technique for monitoring volcanic ash hazards.

No MeSH data available.


Related in: MedlinePlus