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1 Earth Observation Laboratory – Tor Vergata University, Rome, Italy

Volcanic ash retrieval from IR multispectral measurements by means of Neural Networks: an analysis of the Eyjafjallajokull eruption.

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1 Earth Observation Laboratory – Tor Vergata University, Rome, Italy

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  1. Volcanic ash retrieval from IR multispectral measurements by means of Neural Networks: an analysis of the Eyjafjallajokull eruption Matteo Picchiani1, Marco Chini2, Stefano Corradini2, Luca Merucci2, Pasquale Sellitto3,Fabio Del Frate1, Alessandro Piscini2 and Salvatore Stramondo2 • 1Earth Observation Laboratory – Tor Vergata University, Rome, Italy • 2Istituto Nazionale di Geofisica e Vulcanologia, Rome, Italy • 3Laboratoire Inter-universitaire des SystèmesAtmosphériques (LISA), Universités Paris-Est et Paris Diderot, CNRS, Créteil, France

  2. Scenario • The tracking of volcanic clouds is a key task for aviation safety, allowing to beware the dangerous effects of fine volcanic ash particles on aircrafts. • The procedure for the ash mass computation [Prata et al., 1989; Wen & Rose, 1994] requires many input parameters and it can be so time consuming that could prevent the utilization during the crisis phases. • A novel technique [1] based on the synergic use of MODTRAN simulations and Neural Network has shown good potentiality in the automatic development of Ash detection and Ash mass retrievals from Moderate resolution Imager Spectroradiometer (MODIS) data. [1] Picchiani, M.,  Chini, M., Corradini, S., Merucci, L., Sellitto, P., Del Frate, F. and Stramondo, S., “Volcanic ash detection and retrievals from MODIS data by means of Neural Networks”, Atmos. Meas. Tech. Discuss., 4, 2567-2598, 2011.

  3. Scenario The methodology has been developed considering several eruption of Mt. Etna [37.73°N, 15.00°E], a massive stratovolcano (3330 m a.s.l.) located in the eastern part of Sicily (Italy), showing interesting results: BTD Ash Retrieval NN Ash Retrieval BTD Ash Retrieval NN Ash Retrieval

  4. Scenario The Eyjafjallajokull volcano, located on the south of Iceland, is a stratovolcano 1666 meters high, with a caldera on its summit, 2.5 km wide. The unexpected explosive activity lasted from April 14th, to May 23rd, 2010 causing widespread and unprecedented disruption to aviation and everyday life in large parts of Europe. • A set of MODIS images collected during the Eyjafjallajokull eruption have been analyzed by means of NN algorithm. • The results of NN and BTD has been compared.

  5. Motivations of NN approach: • No need for ancillary data. • If the NN is properly trained new data can be inverted in a few minutes (instead of some hours of MODTRAN based procedure). • Possibility to employ a trained NNs to new area under specified conditions (sea surface temperature, atmospheric profile, i.e. similar latitude and longitude).

  6. Problems Problem : VolcanicAsh Detection (discriminate ash from meteorological clouds). Problem : VolcanicAshRetrieval.

  7. ModisSpectralBands MODIS is a multi-spectral instrument that covers 36 spectral bands, from visible (VIS) to thermal infrared (TIR) with a global coverage in 1 to 2 days. The spatial resolution ranges from 250 m to 1000 m, depending on the acquisition mode.

  8. Artificial Neural Networks Artificial Neural Networks (ANNs) can be seen as mathematical models for multivariate nonlinear regression or functional approximation. Functional mapping: a relationship between an input space (the space of the data) and an output space is searched : y= Ψw (x) x : vector of independent variables w : free adjustable parameters In ANNs Ψ is a linear combination of a large number of non-linear functions (sigmoid functions).

  9. Neural Networks Training • The training data set consist of pairs {(xi,ti)}, where xi is an input signal and ti is the desired response to that input. • During the training phase, the free parameters of the ANN (weights, biases) are adjusted in order to minimize a cost function, e.g. • p=number of training patterns, • M=number of output units Problem: We cannot directly measure the ash quantity in the atmosphere. A forward models is needed.

  10. Ash retrieval in the TIR spectral range BTD < 0 volcanic ash BTD > 0 meteo clouds Pixel Area Ash Density Extinction Efficiency Factor The cloud discrimination is based on Brightness Temperature Difference algorithm [Prata et al., 1989] (+ water vapor correction) BTD = Tb(11m) - Tb(12m) The retrieval is based on computing the simulated inverted arches curves “BTD vs Tb(11m)” varying the AOD (t) and the particles effective radius (re) [Wen and Rose, 1994; Prata et al., 2001] The TOA simulated Radiances LUT has been computed using MODTRAN RTM

  11. TOA Radiance computation Sat. geometry Plume geometry Spectral surface emissivity and temperature P, T, H Volcanic ash Optical Proprties MODTRAN RTM • Ri(AOD, re) • 9 values of AOD (0 to 10, constant step in a logarithmic scale) • 8 values of re (0.7 to 10 m, constant step in a logarithmic scale)

  12. Data Set Three MODIS images acquired on April the 19th, 2010, May the 6th 2010 and May the 7th 2010 have been considered for this NNs based Eyjafjallajokull eruption analysis. The channel 31 of MODIS, affected by the ash absorption: May 6th, 2010 May 7th, 2010 April 19th, 2010

  13. Neural Networks Training E Training time When to stop Training? E on Training set E on Test set Training: 65% Test: 20% Validation: 15% A trade off between accuracyandgeneralization capability of the networks are reached when the error function on the test set reaches the global minimum.

  14. Methodology • Two different NNs have been trained for the ash detection and retrieval. • Training (Tr), Test (Ts) and Validation (V) sets have been extracted from the data to train the NNs. • Input-output pairs: MODIS Ch 28-31-32 – MODTRAN based procedure results.

  15. Methodology: NN forAsh Detection NN -Inputs Ch. 32 Ch. 28 Ch. 31 BTD NN – Target Outputs

  16. Methodology: NN forAsh Detection Ch 28 0 1: NotAsh 1 0 : Ash CH 31 CH 32 Uniform Sampling Neural Network forAsh Detection Tr, Ts an V sets have been extracted from the ash plume (Ash class) and the remaining zone of the images (Not Ash class). Inputs:CH 28 CH 31 CH 32 Output: Ash Detection Map

  17. Methodology: NN for Ash Retrieval NN -Inputs Ch. 32 Ch. 28 Ch. 31 BTD - MODTRAN NN – Target Outputs

  18. Methodology: NN forAshRetrieval CH 28 CH 31 CH 32 Uniform Sampling Neural Network forAsh Mass Retrieval Tr, Ts an V sets have been extracted from the ash plume. Output: Ash Mass Map Inputs:CH 28 CH 31 CH 32

  19. Methodology: Processing Chain The two NNs have been insert in an automatic chain, processing the MODIS data to produce the ash detection and ash mass retrieved maps. The second NN is applied only where the ash is detected by the first NN. To improve the results a region growing algorithm is applied after the NN for the detection. NN forAsh Detection NN for Ash Mass Retrieval Inputs:CH 28 CH 31 CH 32 A region growing approach can be further applied to avoid the false positive ash pixels, due to high meteorological clouds. Ch 28 CH 31 CH 32

  20. Ash Detection Results Confusion Matrix computed onto the V sets: April 19th 2010

  21. Ash Retrieval Results Scatter plots computed onto the V sets:

  22. NN procedure – MODTRAN based procedure results comparison April 19th 2010 BTD – MODTRAN Ash Retrieval NN Ash Retrieval

  23. NN procedure – MODTRAN based procedure results comparison May 6th 2010 BTD – MODTRAN Ash Retrieval NN Ash Retrieval

  24. NN procedure – MODTRAN based procedure results comparison May 7th 2010 BTD – MODTRAN Ash Retrieval NN Ash Retrieval

  25. The Grismvotn Eruption The eruption events of the Icelandic Grismvotn volcano have offered an interesting opportunity to test the NN procedure. The NNs trained onto Eyjafjallajokull have been used to retrieve the Ash mass of the May 22nd 2011eruption. NN Ash Retrieval BTD – MODTRAN Ash Retrieval

  26. The Grismvotn Eruption BTD – MODTRAN Ash Retrieval NN Ash Retrieval

  27. Conclusion and Future Investigations • We investigated the possibility of applying the NNs to the problems of Ash detection and Ash mass retrieval. • A minimum set of MODIS channels have been used. • The obtained results show that the trained NNs can be used on new area under particular conditions (sea surface temperature, atmospheric profile) and can replace the BTD retrieval procedure in the crisis phase management. • Future investigations will concern the study of information content of other MODIS channels to improve the discrimination of meteorological clouds, as well as the inversion of other parameters such as the ash optical thickness (AOT) and the ash effective radius (re).

  28. Thanks for attention. Contact: picchian@disp.uniroma2.it; marco.chini@ingv.it; stefano.corradini@ingv.it

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