Alessandro Piscini

The 2011 Tohoku (Japan) Tsunami Inundation and Liquefaction Investigated Through Optical, Thermal, and SAR Data

We studied the disastrous effects of the tsunami triggered by the Mw 9.0 earthquake that occurred on March 11, 2011, offshore the Honshu island (Japan). The tsunami caused a huge amount of casualties and severe damage along most of the eastern coastline of the island. The data set used is composed of images from ASTER, visible and thermal, and ENVISAT SAR sensors. The processing and the analysis of data from different sources were performed in order to obtain the tsunami inundation map of the Sendai coastal area, to analyze inland factors driving the tsunami inundation, and to detect the liquefaction effects in the Chiba bay area as well. The obtained inundation line, with a maximum value of about 6 km, has been jointly analyzed with digital elevation model providing the run-up values, which are generally below 21 m in the ca. 60-km-long study area of Sendai. Moreover, from SAR coherence and intensity correlation, a wide area of subsidence is mapped at Chiba bay, which is reasonably related to strong ground shaking and pervasive liquefaction.

Spectral analysis of aster and hyperion data for geological classification of volcano teide

This work is an evaluation, to which degree geological information can be obtained from modern remote sensing systems like the multispectral ASTER or the hyperspectral Hyperion sensor for a volcanic region like Teide Volcano (Tenerife, Canary Islands). To account for the enhanced information content these sensors provide, hyperspectral analysis methods, incorporating for example Minimum Noise Fraction-Transformation (MNF) for data quality assessment and noise reduction as well as Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) for supervised classification, were applied. Ground Truth reflectance data were obtained with a FieldSpec Pro measurements campaign conducted during later summer of 2007 in the frame of the EC project PREVIEW (http://www.preview-risk.com/).

Volcanic ash retrieval from IR multispectral measurements by means of neural networks: An analysis of the Eyjafjallajokull eruption

The great eruption of the Icelandic Eyjafjallajokull volcano that occurred from the 14th of April to the 23rd of May 2010 injected large and dense ash clouds into the atmosphere, causing major international air traffic disruption worldwide.

Exploitation of SAR data to detect burned areas in the Sila mountain area (southern Italy)

This study focuses on testing the SAR coherence changes from Sentinel-1 data to detect burned areas and to compare the results with optical Sentinel-2 derived burned area product to be used as validation. Visible Infrared Imaging Radiometer Suite (VIIRS) data at 350 m resolution was used to identify active fires locations. We focused on a sequence of wildfires that affected the Sila mountain area during the summer of the 2017. This area of the Calabria region (southern Italy) was interested by a range of fires for the second half of July and the whole month of August ([1], [2]) due also to an extremely dry and hot summer. We used a pair of optical images acquired from Sentinel- 2 satellites on 24 July 2017 (pre-events) and 23 August 2017 (post-events). Firstly, we computed the Normalized Difference Vegetation Index (NDVI) for both images and calculated the difference between these two (dNDVI) at 10m resolution; the results put in evidence several areas characterized by vegetation reduction, with dNDVI values up to 0.3-0.4. Concerning the SAR data, we evaluated the coherence changes by exploiting two pairs of Sentinel-1 SAR data over the same area. Both pairs were acquired along descending orbit, respectively before (on July, 19th and 31st) and after (on September, 5th and 17th) the fires occurred in the Sila mountain area. The coherence was computed separately for the first (γpre) and the second pair (γpost) and the difference γpost - γpre was calculated. In this way, we evaluated the difference in coherence between September, i.e. post-fires, and July, i.e. pre-fires expecting a higher coherence after burning, due to the vegetation reduction. In several areas, the coherence seems to be consistent with the fire events showing increments up to 0.20-0.25. However, the increasing of coherence difference could also be due to other reasons such as the soil moisture variations in the proximity of lakes/rivers or the seasonal cultivation changes. Further analysis integrating more information such as the SAR amplitude signal and the cross-polarized backscattering coefficient will be conducted in order to better evaluate and discriminate any contributions.

Automatic monitoring of ash and meteorological clouds by Neural Networks

Volcanic eruptions affect at different levels the population and economy of interested areas. Moreover, volcanic ash detection represents a key issue for aviation safety due to the harming effects on aircraft. For these reasons, an accurate and fast analysis of the data is needed to monitor the phenomenas evolution and to manage the risk mitigation phase. In this scenario, the introduction of an inversion approach based on Neural Networks (NNs) has significant interest to reduce the need of human interpretation of the ash detection maps as those generated by the application of brightness temperature difference approach. In this work we show that NNs algorithms are suitable for an accurate mapping of ash cloud on Moderate Resolution Imaging Spectroradiometer (MODIS) images in a very cloudy scenario as the ones of 2010 Eyjafjallajökull and 2011 Grimsvötn eruptions.

A neural network approach for simultaneous retrieval of volcanic SO 2 and plume height using hyperspectral measurements

In this study two neural networks were implemented in order to emulate a retrieval model and to estimate the sulphur dioxide (SO2) columnar content and plume height from volcanic eruption. ANNs were trained using all IASI channels in TIR as inputs, and the corresponding values of SO2 content and height of plume obtained using the Oxford SO2 retrievals as target outputs. The retrieval is demonstrated for the eruption of the Eyjafjallajökull volcano (Iceland) occured in 2010 and to three IASI images of the Grímsvötn volcanic eruption that occurred in May 2011, in order to evaluate the networks for a different eruption. The results of validation, both for Eyjafjallajökull and Grímsvötn independent datasets, provided RMSE values between neural network outputs and targets lower than 20 DU for SO2 total column and 200 mb for plume height, therefore demonstrating the feasibility to estimate SO2 values using a neural network approach, and its importance in near real time monitoring activities, owing to its fast application. Concerning the validation carried out with neural networks on images from the Grímsvötn eruption, the RMSE of the outputs remained lower than the Standard Deviation (STD) of targets, and the neural network underestimated retrieval only where target outputs showed different statistics than those used during the training phase.

A neural network approach for monitoring of volcanic SO2 and plume height using hyperspectral measurements

In this study two neural networks were implemented in order to emulate a retrieval model and to estimate the sulphur dioxide (SO2) columnar content and cloud height from volcanic eruption. ANNs were trained using all Infrared Atmospheric Sounding Interferometer (IASI) channels in Thermal Infrared (TIR) as inputs, and the corresponding values of SO2 content and height of volcanic cloud obtained using the Oxford SO2 retrievals as target outputs. The retrieval is demonstrated for the eruption of the Eyjafjallajökull volcano (Iceland) occurred in 2010 and to three IASI images of the Grímsvötn volcanic eruption that occurred in May 2011, in order to evaluate the networks for an unknown eruption. The results of validation, both for Eyjafjallajökull independent data-sets, provided root mean square error (RMSE) values between neural network outputs and targets lower than 20 DU for SO2 total column and 200 mb for cloud height, therefore demonstrating the feasibility to estimate SO2 values using a neural network approach, and its importance in near real time monitoring activities, owing to its fast application. Concerning the validation carried out with neural networks on images from the Grímsvötn eruption, the RMSE of the outputs remained lower than the Standard Deviation (STD) of targets, and the neural network underestimated retrieval only where target outputs showed different statistics than those used during the training phase.

Associative memory techniques for the exploitation of remote sensing data in the monitoring of volcanic events

The possibility offered by space-based sensors represents an irreplaceable resource for monitoring in near real time the eruption activities. The high revisit time of sensor like MODIS, seems to be the most effective way to mitigate the aviation hazard imaging the phenomenon evolution. In this work we propose a neural networks based approach to the volcanic ash mass retrieval. In comparison with the techniques based on radiative transfer models, the proposed algorithm has shown similar accuracy and faster computation. This issue can be of real interest to address the problems inherent the volcanic activity in short time. A set of MODIS images collected during the Eyjafjallajokull eruption, occurred from the 14th of April to the 23rd of May 2010, has been used to analyze the performance variations due to different selection of the algorithm inputs, i.e. the MODIS channels from visible to thermal infrared electromagnetic spectrum. The best wavelength sets for the retrieval of the ash mass, optical thickness and effective radius have been identified by means of neural network pruning algorithm.

Aster temperature and emissivity validation on Volcano Teide

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER ) has operated since 19 December 1999 from NASAs Terra Earth-orbiting, sun synchronous satellite. Emissivity and temperature standard products are based on the TES algorithms and required periodical validation campaign. In the frame of the EC project PREVIEW (http://www.preview-risk.com/) a field campaign on Volcano Teide was carried on, from the 16th to 24th of September 2007, to validate and to integrate the satellite derived products services.