Volcanic ash detection and retrievals using MODIS data by means of

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.