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Volcanic ash retrieval from IR multispectral measurements by means of Neural Networks: an analysis of the Eyjafjallajokull eruption. - PowerPoint PPT Presentation
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Volcanic ash retrieval from IR multispectral measurements by means of Neural Networks:
an analysis of the Eyjafjallajokull eruptionMatteo 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 Systèmes Atmosphériques (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 BTD Ash RetrievalNN Ash Retrieval NN Ash Retrieval
4
ScenarioThe 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
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
Problems
Problem :Volcanic Ash Detection (discriminate ash from meteorological clouds).
Problem :Volcanic Ash Retrieval.
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.
MODISChannel n°
Center Wavelength
(mm)NEDT (K)
Spatial Resolution
(km)
28 7.3 0.25 1
31 11.0 0.05 1
32 12.0 0.05 1
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= Ψw (x)
x : vector of independent variablesw : free adjustable parameters
In ANNs Ψ is a linear combination of a large number of non-linear functions (sigmoid functions).
Artificial Neural Networks
10
• 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
Neural Networks Training
p
i
M
jjj iyitE
1 1
2)]()([
Problem: We cannot directly measure the ash quantity in the atmosphere.A forward models is needed.
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 = Tb(11mm) - Tb(12mm)
The retrieval is based on computing the simulated inverted arches curves “BTD vs Tb(11mm)” varying the AOD (t) and the particles effective radius (re)
[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
)(34
,
,
iee
iiei rQ
rSM
t
iiTOT MM
12
TOA Radiance computation
MODTRANRTM
Plume geometry
Spectral surface emissivity and
temperature
Volcanic ash Optical
Proprties
Ri (AOD, re )
9 values of AOD (0 to 10, constant step in a logarithmic scale)
8 values of re (0.7 to 10 mm, constant step in a logarithmic scale)
Sat. geometry
P, T, H
13
Data SetThree 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:
April 19th, 2010 May 6th, 2010 May 7th, 2010
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
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
Data Tr Ts V Tot Ash Tot
April 19th, 2010 16500 7500 6009 30009 2250000
May 6th 2010 30373 13806 11046 55255 2250000
May 7th 2010 - - 48565 147666 2250000
16
Ch. 28 Ch. 31 Ch. 32
Methodology: NN for Ash DetectionN
N -I
nput
s
BTD
NN
– T
arge
t O
utpu
ts
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 Ash1 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
Ch. 28 Ch. 31 Ch. 32
Methodology: NN for Ash RetrievalN
N -I
nput
s
BTD - MODTRAN
NN
– T
arge
t O
utpu
ts
19Output: 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
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
RetrievalInputs: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
Ash Detection Results
April 19th 2010BTD
Ash Not AshNN Ash 99.53% 0.46%
Not Ash 0.74% 99.25%
Overall Accuracy= 0.993K Coefficient= 0.987
May 6th 2010BTD
Ash Not AshNN Ash 99.57% 0.42%
Not Ash 0.760% 99.23%
Overall Accuracy= 0.994K Coefficient= 0.988
May 7th 2010BTD
Ash Not AshNN Ash 99.40% 0.60%
Not Ash 19.60% 80.40%
Overall Accuracy= 0.901K Coefficient= 0.803 April 19th 2010
Confusion Matrix computed onto the V sets:
22
Ash Retrieval Results
Scatter plots computed onto the V sets:
23
NN procedure – MODTRAN based procedure results comparison
April 19th 2010
BTD – MODTRAN Ash Retrieval
NN Ash Retrieval
24
NN procedure – MODTRAN based procedure results comparison
May 6th 2010
BTD – MODTRAN Ash Retrieval
NN Ash Retrieval
25
NN procedure – MODTRAN based procedure results comparison
May 7th 2010
BTD – MODTRAN Ash Retrieval
NN Ash Retrieval
26
The Grismvotn Eruption
NN Ash RetrievalBTD – 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 22nd 2011 eruption.
27
NN Ash RetrievalBTD – MODTRAN Ash Retrieval
The Grismvotn Eruption
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 (re).
Conclusion and Future Investigations
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