1. Volcanic ash retrieval from IR multispectral measurements by means of Neural Networks: an analysis of the Eyjafjallajokull eruption Matteo Picchiani 1, Marco Chini 2, Stefano Corradini 2, Luca Merucci 2, Pasquale Sellitto 3, Fabio Del Frate 1, Alessandro Piscini 2 and Salvatore Stramondo 2 1 Earth Observation Laboratory – Tor Vergata University, Rome, Italy 2 Istituto Nazionale di Geofisica e Vulcanologia, Rome, Italy 3 Laboratoire Inter-universitaire des Systèmes Atmosphériques (LISA), Universités Paris-Est et Paris Diderot, CNRS, Créteil, France

2. 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. 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

4. 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 14 th, to May 23 rd, 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. 5 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). Motivations of NN approach:

6. 6 Problems Problem : Volcanic Ash Detection (discriminate ash from meteorological clouds). Problem : Volcanic Ash Retrieval.

7. 7 Modis Spectral Bands 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. MODIS Channel n° Center Wavelength (  m) NEDT (K) Spatial Resolution (km) 287.30.251 3111.00.051 3212.00.051

8. 8 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= Ψ w (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). Artificial Neural Networks

9. 9 The most popular ANN architecture is the Multilayer Perceptron (MLP): Neurons are organized in layers One input layer, containing the inputs to the net. One or more hidden layers, consisting of non linear neurons. One output layer, which produces the output signal. MLP are feedforward Neural Network: the signal is propagated forward through the layers (no recurrent connections).

10. 10 The training data set consist of pairs {(x i,t i )}, where x i is an input signal and t i 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 Neural Networks Training Problem: We cannot directly measure the ash quantity in the atmosphere. A forward models is needed.

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

12. 12 TOA Radiance computation MODTRAN RTM Plume geometry Spectral surface emissivity and temperature Volcanic ash Optical Proprties R i (AOD, r e ) 9 values of AOD (0 to 10, constant step in a logarithmic scale) 8 values of r e (0.7 to 10  m, constant step in a logarithmic scale) Sat. geometry P, T, H

13. 13 Data Set Three MODIS images acquired on April the 19 th, 2010, May the 6 th 2010 and May the 7 th 2010 have been considered for this NNs based Eyjafjallajokull eruption analysis. The channel 31 of MODIS, affected by the ash absorption: April 19 th, 2010 May 6 th, 2010May 7 th, 2010

14. 14 Neural Networks Training E Training time E on Training set E on Test set A trade off between accuracy and generalization capability of the networks are reached when the error function on the test set reaches the global minimum. When to stop Training? Training: 65% Test: 20% Validation: 15%

15. 15 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. Methodology DataTrTsVTot AshTot April 19 th, 20101650075006009300092250000 May 6 th 2010303731380611046552552250000 May 7 th 2010--485651476662250000

16. 16 Ch. 28Ch. 31 Ch. 32 Methodology: NN for Ash Detection NN -Inputs BTD NN – Target Outputs

17. 17 Inputs:CH 28 CH 31 CH 32 CH 32 CH 31 Ch 28 Neural Network for Ash Detection Output: Ash Detection Map Methodology: NN for Ash Detection 0 1: Not Ash 1 0 : Ash Tr, Ts an V sets have been extracted from the ash plume (Ash class) and the remaining zone of the images (Not Ash class). Uniform Sampling

18. 18 Ch. 28Ch. 31 Ch. 32 Methodology: NN for Ash Retrieval NN -Inputs BTD - MODTRAN NN – Target Outputs

19. 19 Output: Ash Mass Map Neural Network for Ash Mass Retrieval CH 32 CH 31 CH 28 Inputs:CH 28 CH 31 CH 32 Methodology: NN for Ash Retrieval Tr, Ts an V sets have been extracted from the ash plume. Uniform Sampling

20. 20 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. Methodology: Processing Chain NN for Ash Mass Retrieval Inputs:CH 28 CH 31 CH 32 CH 32 CH 31 Ch 28 NN for Ash Detection A region growing approach can be further applied to avoid the false positive ash pixels, due to high meteorological clouds.

21. 21 Ash Detection Results April 19 th 2010 BTD AshNot Ash NN Ash99.53%0.46% Not Ash0.74%99.25% Overall Accuracy= 0.993 K Coefficient= 0.987 May 6 th 2010 BTD AshNot Ash NN Ash99.57%0.42% Not Ash0.760%99.23% Overall Accuracy= 0.994 K Coefficient= 0.988 May 7 th 2010 BTD AshNot Ash NN Ash99.40%0.60% Not Ash19.60%80.40% Overall Accuracy= 0.901 K Coefficient= 0.803 April 19 th 2010 Confusion Matrix computed onto the V sets:

22. 22 Ash Retrieval Results Scatter plots computed onto the V sets:

23. 23 NN procedure – MODTRAN based procedure results comparison April 19 th 2010 BTD – MODTRAN Ash Retrieval NN Ash Retrieval

24. 24 NN procedure – MODTRAN based procedure results comparison May 6 th 2010 BTD – MODTRAN Ash Retrieval NN Ash Retrieval

25. 25 NN procedure – MODTRAN based procedure results comparison May 7 th 2010 BTD – MODTRAN Ash Retrieval NN Ash Retrieval

26. 26 The Grismvotn Eruption NN Ash Retrieval BTD – MODTRAN Ash Retrieval 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 22 nd 2011 eruption.

27. 27 NN Ash Retrieval BTD – MODTRAN Ash Retrieval The Grismvotn Eruption

28. 28 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 (r e ). Conclusion and Future Investigations

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