Options
Amici, Stefania
Loading...
Preferred name
Amici, Stefania
Email
stefania.amici@ingv.it
Staff
staff
ORCID
46 results
Now showing 1 - 10 of 46
- PublicationRestrictedAirborne FTIR Measurements over the Mt. Etna Volcano. First Results from the 2003 FASA Campaign(2007)
; ; ; ; ; ;Amici, S.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione CNT, Roma, Italia ;Buongiorno, M. F.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione CNT, Roma, Italia ;Corradini, S.; Università di Modena e Reggio Emilia ;Pugnaghi, S.; Università di Modena e Reggio Emilia ;Sgavetti, M.; Università di Parma; ; ; ; From 16 to 26 July 2003 a wide field campaign was carried out around Mt. Etna (Sicily) by different research units. During the campaign a new airborne system named FASA (Fire Airborne Spectral Analyzer) was tested. FASA is a system devoted to high temperature events study. The payload on the airborne consists of the Advanced BIRD Airborne Simulator (ABAS), which is an imager, and a high resolution Michelson Interferometer with ROtating Retroreflector (MIROR) operating in the nominal infrared 600-3000 cm-1 range. The spectra calibration has been carried out by computing the instrumental response function and the instrumental offset by using two black body sources at different temperature. In this paper we describe the MIROR in flight calibration and some preliminary results from its data-set. In particular the surface temperature, the hyperspectral emissivity and gas detection from radiance spectra collected on Mount Etna are shown. The atmospheric corrections terms, needed for the retrieval procedures, have been computed using MODTRAN radiative transfer code considering the atmospheric profiles measured in situ during the field campaign. The results are compared with ASTER satellite and experimental data.625 31 - PublicationRestrictedGeological Mapping of Volcano Teide using multispectral and Hyperspectral Satellite Data(2010-12-01)
; ; ; ; ;Amici, Stefania; Istituto Nazionale di Geofisica e Vulcanologia, Sezione CNT, Roma, Italia ;Piscini, Alessandro; Istituto Nazionale di Geofisica e Vulcanologia, Sezione CNT, Roma, Italia ;Buongiorno, Maria Fabrizia; Istituto Nazionale di Geofisica e Vulcanologia, Sezione CNT, Roma, Italia ;Pieri, David C.; Jet Propulsion Laboratory; ; ; 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). The Canarian Arcipelago is made up of seven islands that represent different stages of geologic evolution. Tenerife is the central island of archipelago and has developed within the complex formed by the rifts associated with the Teide-Pico Viejo (T-PV-Lat 28° 16’ 30” Lon 16°38’ 42”) stratovolcanoes that reach a height of 3718 m, 7500 above the ocean floor. It is an active, though currently quiescent shield volcano, which last erupted in 1909. In the frame of the European Project FP6 PREVIEW-EURORISK (PREVention, Information and Early Warning pre-operational services to support the management of risks) (http://www.preview-risk.com/) a field campaign was carried out on Tenerife island to improve the retrieval algorithms and techniques, a field campaign has been realized on Pico de Teide (Tenerife island - Spain) from the 16th and 24th of September 2007. The validation campaign has been performed in order to acquire spectra used as ground truth data on the Pico de Teide in an area also know as Las Canadas Caldera (LCC). The time window was chosen taking into account different factor as: meteorological characterization, satellites scheduled passage, availability of both on Tenerife and INGV team. The measurements were localized on the summit area of the Tenerife Island and in particular within the Teide Caldera in order to identify suitable test sites both for cal/val activities and to study the geological setting of Pico the Teide volcano by image spectroscopy. Measurements in situ of reflectance and emissivity were realized very close/close the satellite passages. During the campaign atmospheric profiles and ground atmospheric measurements were acquired contemporaneously with the satellite acquisitions. A characterization of reflectance at summit crater surfaces was realised in order to complete the spectral characterization of different surfaces. The spectral measurements have been used as “ground truth” to realise the first classification map by satellite data of Teide volcano. In particular, the Support Vector Machine (SVM) supervised method has been applied to both ASTER and Hyperion data. The results are compared and discussed in this work.206 32 - PublicationOpen AccessFire detection from hyperspectral data using neural network approachThis study describes an application of artificial neural networks for the recognition of burning areas using hyperspectral 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 consolitaded 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 outputs. The neural network was validated on two independent data sets of hyperspectral images, not used during neural network training phase, in order to evaluate its generalization capabilities. 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 at the edges of fire fronts, and probably due to the detection threshold selected for discriminating pixels affected by active fires, so demonstrating that the accuracy in classification is strictly related to the sensitivity of the chosen model. 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.
84 239 - PublicationOpen AccessFire Airborne Simulator Arrangement: in progress status report(2004)
; ; ; ; ; ; ; ; ; ; ;Di Stefano, G.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione CNT, Roma, Italia ;Buongiorno, M. F.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione CNT, Roma, Italia ;Amici, S.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Roma1, Roma, Italia ;Romeo, G.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Roma1, Roma, Italia ;Badiali, L.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione CNT, Roma, Italia ;Mari, M.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione AC, Roma, Italia ;Pippi, I.; IFAC-CNR, Firenze, Italia ;Marcoionni, P.; IFAC-CNR, Firenze, Italia ;Cherubini, G.; Galileo Avionica, Campi Bisenzio Italia ;Lindermeier, E.; DLR, Germany; ; ; ; ; ; ; ; ; The FASA project in collaboration with the DLR and the financing of ASI was started in order to combine bi-spectral imager and high-resolution FTIR- spectrometer (MIROR) for airborne remote sensing and gas analyis of high temperature events such as volcanoes and wild fires.245 336 - PublicationOpen AccessWildfires Temperature Estimation by Complementary Use of Hyperspectral PRISMA and Thermal (ECOSTRESS & L8)t This paper deals with detection and temperature analysis and of wildfires using PRISMA imagery. Precursore IperSpettrale della Missione Applicativa (Hyperspectral Precursor of the Application Mission, PRISMA) is a new hyperspectral mission by ASI (Agenzia Spaziale Italiana, Italian Space Agency) launched in 2019. This mission provides hyperspectral images with a spectral range of 400–2,500 nm and an average spectral resolution less than 12 nm and a spatial resolution of 30 m/pixel. This study focuses on the wildfire temperature estimation over the Bootleg Fire, US 2021. The analysis starts by considering the Hyperspectral Fire Detection Index (HFDI) which is used to analyze the informative content of the images, along with the analysis of some specific visible, near-infrared and shortwave-infrared bands. This first analysis is used as input to perform a temperature estimation of the areas with active wildfire. Surface temperature is retrieved using PRISMA radiance and a linear mixing model based on two background components (vegetation and burn scar) and two active fire components. PRISMA temperatures are compared with LST (Land Surface Temperature) products from NASA's ECOSTRESS and Landsat 8 which imaged the Bootleg Fire before and after PRISMA. A critical discussion of the results obtained with PRISMA is presented, followed by the advantages and limitation of the proposed approach.
71 41 - PublicationRestrictedASTER temperature and emissivity validation on volcano Teide(2010-07)
; ; ; ;Amici, Stefania; Istituto Nazionale di Geofisica e Vulcanologia, Sezione CNT, Roma, Italia ;Piscini, Alessandro; Istituto Nazionale di Geofisica e Vulcanologia, Sezione CNT, Roma, Italia ;Buongiorno, Fabrizia; Istituto Nazionale di Geofisica e Vulcanologia, Sezione CNT, Roma, Italia; ; ; IEEE IGARSSThe Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER ) has operated since 19 December 1999 from NASA’s Terra Earth-orbiting, sun synchronous satellite. Emissivity and temperature standard products are based on the TES algorithms and require 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.256 39 - PublicationRestrictedThe effects of vegetation coverage, topography and coastal orientation on the 2011 Tohoku-Oki (Japan) tsunami inundation investigated by satellite data(2011-09-19)
; ; ; ; ; ;Chini, M.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione CNT, Roma, Italia ;Piscini, A.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione CNT, Roma, Italia ;Amici, S.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione CNT, Roma, Italia ;Nappi, R.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione OV, Napoli, Italia ;De Martini, P. M.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Roma1, Roma, Italia ;; ; ; We have studied the disastrous effects of the tsunami triggered by the earthquake (M 9.0) occurred on March 11th, 2011 offshore the Honshu island (Japan). The tsunami caused a huge amount of casualties and severe damages along the coastline of most of the island. The dataset used is composed of data in the visible and thermal spectral range provided by ASTER sensors, and in the microwave range from the active SAR sensors by ENVISAT mission. The processing and the analysis of this large amount of data from different sensors was performed in order to obtain the tsunami inundation map of the Sendai coastal area. Unsupervised and supervised classification algorithms have been applied to provide land cover change detection maps. The identified classes are: stressed vegetation, infrastructure and structure damage, flooded and debris areas. A maximum value of the inundation, about 6 km, is found in the central portion of the Sendai plain and the distance drops to about 1 km at the edges of the plain. The maximum inundation line has been jointly analyzed with the ASTER DTM providing the run-up, values ranging from a minimum of few meters to a maximum of 35m. We point out that the high-relief and the slope gradient are the main inland factors influencing the inundation distance in the study sector, while the vegetation cover and the coastal strike do not significantly affect run-up and wave inundation.236 23 - PublicationOpen AccessTransfer Learning Analysis For Wildfire Segmentation Using Prisma Hyperspectral Imagery And Convolutional Neural NetworksIn this work we present a segmentation study of wildfire scenarios using PRISMA hyperspectral data and a methodology based on convolutional neural networks and transfer learning. PRISMA (Precursore IperSpettrale della Missione Applicativa, Hyperspectral Precursor of the Application Mission) is the hyperspectral mission by ASI (Agenzia Spaziale Italiana, Italian Space Agency) launched in 2019 providing images with a spectral range of 0.4−2.5μm and an average spectral resolution less than 10 nm. We used the PRISMA hypercube acquired during the Australian bushfires of December 2019 in New South Wales to train a one-dimensional convolutional neural network and perform a transfer learning in the Bootleg Fire of July 2021 in the Fremont-Winema National Forest in Oregon. The generalization ability of the network is discussed and potential future applications are presented.
72 25 - PublicationOpen AccessDati iperspettrali nel Vis-Swir per l’analisi dell’emissione relativa alla banda del Potassio per lo studio di incendi(2009-03-31)
; ; ; ;Amici, Stefania; Istituto Nazionale di Geofisica e Vulcanologia, Sezione CNT, Roma, Italia ;Buongiorno, Maria Fabrizia; Istituto Nazionale di Geofisica e Vulcanologia, Sezione CNT, Roma, Italia ;Piscini, Alessandro; Istituto Nazionale di Geofisica e Vulcanologia, Sezione CNT, Roma, Italia; ; Gli incendi sono un fenomeno che colpisce ogni anno il nostro pianeta ed in particolare anche l’Italia. Oltre all’effetto immediato sul territorio, è stato riconosciuto un effetto a livello di impatto climatico. I fuochi cambiano lo stato fisico della vegetazione rilasciando nell’atmosfera gas che giocano un ruolo importante nell’effetto serra. E’ stato stimato che la biomassa bruciata in un anno, contribuisce al 38% dell’Ozono in troposfera, al 32% di monossido di Carbonio, al 20% degli altri gas (Levine, 1991; Andreae, 1991; Kaufman et al., 1998a,b). L’uso dei canali termici (8-14 micron) o relativi al vicino infrarosso (1.0 -2.5 micron) sono tradizionalmente utilizzati per la detection e lo studio di parametri fisici come il potere radiante, l’NDBR (Normalised Difference Burn Ratio), o il tasso di combustione della biomassa. Nel visibile una banda di emissione diagnostica dello stato di fiamma, è quella del Potassio (K-method) che fino ad ora non è stata molto studiata in quanto limitata dalle prestazioni degli strumenti. Nell’estate 2006, con il progetto AIRFIRE finanziato da ESA, una campagna aerea è stata effettuata su incendi non controllati utilizzanodo un prototipo di sensore iperspettrale denominato SIM-GA di Selex Galileo. Il SIM-GA è un sensore iperspettrale ad altissima risoluzione spettrale 1.2nm e 2.5 nm rispettivamente nel VISIBILE (350-1200nm) e nel vicino infrarosso(1200-2500nm) che opera in modalità pushbroom. I dati ottenuti hanno permesso di verificare e testare il metodo della panda del Potassio. I risultati ottenuti hanno mostrato come la combinazione del K-method con le analisi nel termico possa completare l’analisi dell’incendio.164 201 - PublicationRestrictedSpectral analysis of ASTER and HYPERION data for geological classification of Volcano Teide.(2010-07-24)
; ; ; ;Piscini, Alessandro; Istituto Nazionale di Geofisica e Vulcanologia, Sezione CNT, Roma, Italia ;Amici, Stefania; Istituto Nazionale di Geofisica e Vulcanologia, Sezione CNT, Roma, Italia ;Pieri, Dave; Jet Propulsion Laboratory, Pasadena, California USA; ; ; IEEE IGARSSThis 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/).155 17