Dr. Alessandro Piscini Profile

Dr. Alessandro Piscini

at Istituto Nazionale di Geofisica e Vulcanologia

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Publications (5)

Proceedings Article | 9 October 2018 Presentation + Paper

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

Vito Romaniello, Marco Polcari, Stefania Amici, Alessandro Piscini, Christian Bignami, Salvatore Stramondo

Proceedings Volume 10788, 107880H (2018) https://doi.org/10.1117/12.2327128

KEYWORDS: Synthetic aperture radar, Vegetation, Satellites, Spatial resolution, Sensors, Data acquisition, Satellite imaging

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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 (γ_pre) and the second pair (γ_post) and the difference γ_post - γ_pre 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.

SPIE Journal Paper | 30 March 2017 Open Access

Earthquake damage mapping by using remotely sensed data: the Haiti case study

Vito Romaniello, Alessandro Piscini, Christian Bignami, Roberta Anniballe, Salvatore Stramondo

JARS, Vol. 11, Issue 01, 016042, (March 2017) https://doi.org/10.1117/12.10.1117/1.JRS.11.016042

KEYWORDS: Synthetic aperture radar, Earthquakes, Chromium, Satellites, Buildings, Associative arrays, Satellite imaging, Statistical analysis, Algorithm development, Earth observing sensors

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This work proposes methodologies aimed at evaluating the sensitivity of optical and synthetic aperture radar (SAR) change features obtained from satellite images with respect to the damage grade due to an earthquake. The test case is the Mw 7.0 earthquake that hit Haiti on January 12, 2010, located 25 km west–south–west of the city of Port-au-Prince. The disastrous shock caused the collapse of a huge number of buildings and widespread damage. The objective is to investigate possible parameters that can affect the robustness and sensitivity of the proposed methods derived from the literature. It is worth noting how the proposed analysis concerns the estimation of derived features at object scale. For this purpose, a segmentation of the study area into several regions has been done by considering a set of polygons, over the city of Port-au-Prince, extracted from the open source open street map geo-database. The analysis of change detection indicators is based on ground truth information collected during a postearthquake survey and is available from a Joint Research Centre database. The resulting damage map is expressed in terms of collapse ratio, thus indicating the areas with a greater number of collapsed buildings. The available satellite dataset is composed of optical and SAR images, collected before and after the seismic event. In particular, we used two GeoEye-1 optical images (one preseismic and one postseismic) and three TerraSAR-X SAR images (two preseismic and one postseismic). Previous studies allowed us to identify some features having a good sensitivity with damage at the object scale. Regarding the optical data, we selected the normalized difference index and two quantities coming from the information theory, namely the Kullback–Libler divergence (KLD) and the mutual information (MI). In addition, for the SAR data, we picked out the intensity correlation difference and the KLD parameter. In order to analyze the capability of these parameters to correctly detect damaged areas, two different classifiers were used: the Naive Bayes and the support vector machine classifiers. The classification results demonstrate that the simultaneous use of several change features from Earth observations can improve the damage estimation at object scale.

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Proceedings Article | 14 October 2015 Paper

Fire detection from hyperspectral data using neural network approach

Alessandro Piscini, Stefania Amici

Proceedings Volume 9637, 963721 (2015) https://doi.org/10.1117/12.2194911

KEYWORDS: Neural networks, Hyperspectral imaging, Remote sensing, Data modeling, Potassium, Sensors, Infrared sensors, Infrared radiation, Satellites, Short wave infrared radiation

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This study describes an application of artificial neural networks for the recognition of flaming areas using hyper- spectral remote sensed data. Satellite remote sensing is considered an effective and safe way to monitor active fires for environmental and people safeguarding. Neural networks are an effective and consolidated technique for the classification of satellite images. Moreover, once well trained, they prove to be very fast in the application stage for a rapid response. At flaming temperature, thanks to its low excitation energy (about 4.34 eV), potassium (K) ionize with a unique doublet emission features. This emission features can be detected remotely providing a detection map of active fire which allows in principle to separate flaming from smouldering areas of vegetation even in presence of smoke. For this study a normalised Advanced K Band Difference (AKBD) has been applied to airborne hyper spectral sensor covering a range of 400-970 nm with resolution 2.9 nm. A back propagation neural network was used for the recognition of active fires affecting the hyperspectral image. The network was trained using all channels of sensor as inputs, and the corresponding AKBD indexes as target output. In order to evaluate its generalization capabilities, the neural network was validated on two independent data sets of hyperspectral images, not used during neural network training phase. The validation results for the independent data-sets had an overall accuracy round 100% for both image and a few commission errors (0.1%), therefore demonstrating the feasibility of estimating the presence of active fires using a neural network approach. Although the validation of the neural network classifier had a few commission errors, the producer accuracies were lower due to the presence of omission errors. Image analysis revealed that those false negatives lie in "smoky" portion fire fronts, and due to the low intensity of the signal. The proposed method can be considered effective both in terms of classification accuracy and generalization capability. In particular our approach proved to be robust in the rejection of false positives, often corresponding to noisy or smoke pixels, whose presence in hyperspectral images can often undermine the performance of traditional classification algorithms. In order to improve neural network performance, future activities will include also the exploiting of hyperspectral images in the shortwave infrared region of the electromagnetic spectrum, covering wavelengths from 1400 to 2500 nm, which include significant emitted radiance from fire.

Proceedings Article | 6 November 2014 Paper

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

Alessandro Piscini, Elisa Carboni, Fabio Del Frate, Roy Gordon Grainger

Proceedings Volume 9242, 924213 (2014) https://doi.org/10.1117/12.2066321

KEYWORDS: Neural networks, Clouds, Infrared radiation, Satellites, Thermography, Error analysis, Atmospheric optics, Sensors, Atmospheric physics, Sulfur

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Proceedings Article | 21 October 2014 Paper

Identifying volcanic endmembers in hyperspectral images using spectral unmixing

Alessandro Piscini, Elisa Carboni, Fabio Del Frate, Roy Gordon Grainger

Proceedings Volume 9242, 924215 (2014) https://doi.org/10.1117/12.2066649

KEYWORDS: Clouds, Infrared radiation, Hyperspectral imaging, Infrared signatures, Aerosols, Infrared imaging, Satellites, Sulfur, Remote sensing, Atmospheric sensing

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