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| Titel |
Volcanic ash detection and retrievals using MODIS data by means of neural networks |
| VerfasserIn |
M. Picchiani, M. Chini, S. Corradini, L. Merucci, P. Sellitto, F. Frate, S. Stramondo |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1867-1381
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Measurement Techniques ; 4, no. 12 ; Nr. 4, no. 12 (2011-12-07), S.2619-2631 |
| Datensatznummer |
250002144
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| Publikation (Nr.) |
 copernicus.org/amt-4-2619-2011.pdf |
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| Zusammenfassung |
Volcanic ash clouds detection and retrieval represent a key issue for
aviation safety due to the harming effects on aircraft. A lesson learned
from the recent Eyjafjallajokull eruption is the need to obtain accurate and
reliable retrievals on a real time basis.
In this work we have developed a fast and accurate Neural Network (NN)
approach to detect and retrieve volcanic ash cloud properties from the
Moderate Resolution Imaging Spectroradiometer (MODIS) data in the Thermal
InfraRed (TIR) spectral range. Some measurements collected during the 2001,
2002 and 2006 Mt. Etna volcano eruptions have been considered as test cases.
The ash detection and retrievals obtained from the Brightness Temperature
Difference (BTD) algorithm are used as training for the NN procedure that
consists in two separate steps: ash detection and ash mass retrieval. The
ash detection is reduced to a classification problem by identifying two
classes: "ashy" and "non-ashy" pixels in the MODIS images. Then the ash mass
is estimated by means of the NN, replicating the BTD-based model
performances. A segmentation procedure has also been tested to remove the
false ash pixels detection induced by the presence of high meteorological
clouds. The segmentation procedure shows a clear advantage in terms of
classification accuracy: the main drawback is the loss of information on ash
clouds distal part.
The results obtained are very encouraging; indeed the ash detection accuracy
is greater than 90%, while a mean RMSE equal to 0.365 t km−2 has been
obtained for the ash mass retrieval. Moreover, the NN quickness in results
delivering makes the procedure extremely attractive in all the cases when
the rapid response time of the system is a mandatory requirement. |
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