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Lava flow mapping has direct relevance to volcanic hazards once an eruption has begun. Satellite remote sensing techniques are increasingly used to map newly erupted lava, thanks to their capability to survey large areas with frequent revisit time and accurate spatial resolution. Visible and infrared satellite data are routinely used to detect the distributions of volcanic deposits and monitor thermal features, even if clouds are a serious obstacle for optical sensors, since they cannot be penetrated by optical radiation. On the other hand, radar satellite data have been playing an important role in surface change detection and image classification, being able to operate in all weather conditions, although their use is hampered by the special imaging geometry, the complicated scattering process, and the presence of speckle noise. Thus, optical and radar data are complementary data sources that can be used to map lava flows effectively, in addition to alleviating cloud obstruction and improving change detection performance. Here, we propose a machine learning approach based on the Google Earth Engine (GEE) platform to analyze simultaneously the images acquired by the synthetic aperture radar (SAR) sensor, on board of Sentinel-1 mission, and by optical and multispectral sensors of Landsat-8 missions and Multi-Spectral Imager (MSI), on board of Sentinel-2 mission. Machine learning classifiers, including K-means algorithm (K-means) and support vector machine (SVM), are used to map lava flows automatically from a combination of optical and SAR images. We describe the operation of this approach by using a retrospective analysis of two recent lava flow-forming eruptions at Mount Etna (Italy) and Fogo Island (Cape Verde). We found that combining both radar and optical imagery improved the accuracy and reliability of lava flow mapping. The results highlight the need to fully exploit the extraordinary potential of complementary satellite sensors to provide time-critical hazard information during volcanic eruptions.
Claudia Corradino; Giuseppe Bilotta; Annalisa Cappello; Luigi Fortuna; Ciro Del Negro. Combining Radar and Optical Satellite Imagery with Machine Learning to Map Lava Flows at Mount Etna and Fogo Island. Energies 2021, 14, 197 .
AMA StyleClaudia Corradino, Giuseppe Bilotta, Annalisa Cappello, Luigi Fortuna, Ciro Del Negro. Combining Radar and Optical Satellite Imagery with Machine Learning to Map Lava Flows at Mount Etna and Fogo Island. Energies. 2021; 14 (1):197.
Chicago/Turabian StyleClaudia Corradino; Giuseppe Bilotta; Annalisa Cappello; Luigi Fortuna; Ciro Del Negro. 2021. "Combining Radar and Optical Satellite Imagery with Machine Learning to Map Lava Flows at Mount Etna and Fogo Island." Energies 14, no. 1: 197.
Leonardo da Vinci inventions and projects represent an intriguing starting point to remark the concept that innovation must be considered as a continuous route towards evolution in history. Some of the particular ideas and innovations presented by Leonardo da Vinci led us to formulate a link with automatic control. Selected models of the Leonardo da Vinci machines are presented in this paper, taking strictly into account the original mechanical schemes and working principles, but introducing modern low-cost control equipment, emphasizing the role of automatic control and that of electronic control devices, such as microcontrollers, sensors, and communication devices, to completely automate the Leonardo da Vinci machines. The approach outlined in the paper can be applied not only to other Leonardo machines but also to other mechanical equipment not necessarily designed by Leonardo da Vinci. Moreover, it is useful to remark that the approach followed in this paper can be very important also to introduce students, leading by example, to concepts typical of automation and for assisting in learning, keeping in mind the practical applications of advanced automation principles. The main research task of this paper is proving the efficacy of modern digital control techniques and teleoperation in strongly classical mechanical Leonardo machines, remarking that the projects of Leonardo are prompt to be efficiently controlled. This task could not be explored by Leonardo himself due to the lack of control technology. Moreover, the paper is addressed also to stimulate the young generations of engineers in joining classical mechanics with advanced technology. Therefore, the paper is also devoted to give focus on the fact that the Leonardo machines encompass all the key aspects of modern system engineering.
Maide Bucolo; Arturo Buscarino; Carlo Famoso; Luigi Fortuna; Salvina Gagliano. Automation of the Leonardo da Vinci Machines. Machines 2020, 8, 53 .
AMA StyleMaide Bucolo, Arturo Buscarino, Carlo Famoso, Luigi Fortuna, Salvina Gagliano. Automation of the Leonardo da Vinci Machines. Machines. 2020; 8 (3):53.
Chicago/Turabian StyleMaide Bucolo; Arturo Buscarino; Carlo Famoso; Luigi Fortuna; Salvina Gagliano. 2020. "Automation of the Leonardo da Vinci Machines." Machines 8, no. 3: 53.
The huge amount of information coming from remote sensors on satellites has allowed monitoring changes in the planetary environment from about 50 years. These instruments are widely adopted to observe extreme thermal events such as eruptive phenomena in volcanic areas. Although the availability of so many different infrared sensors makes these instruments suitable to observe different kind of thermal phenomena, choosing the right infrared sensor to monitor each thermal event is not straightforward. In fact, the decision should take into account both the main features of the phenomena under investigation, e.g., its size and temperatures, that are often not known a priori, and the instruments specifications, e.g., spatial resolution. Here, a smart decision support system (SDSS) is proposed to address this task. In particular, we used a SDSS to simulate remote sensors responses, collect data coming from three different classes of remote sensors, retrieve information about the main features of the observed thermal event and, consequently, select the most suitable infrared remote sensor for the specific observed phenomena. Results obtained for a real case of study at Etna volcano is shown.
Claudia Corradino; Gaetana Ganci; Giuseppe Bilotta; Annalisa Cappello; Ciro Del Negro; Luigi Fortuna. Smart Decision Support Systems for Volcanic Applications. Energies 2019, 12, 1216 .
AMA StyleClaudia Corradino, Gaetana Ganci, Giuseppe Bilotta, Annalisa Cappello, Ciro Del Negro, Luigi Fortuna. Smart Decision Support Systems for Volcanic Applications. Energies. 2019; 12 (7):1216.
Chicago/Turabian StyleClaudia Corradino; Gaetana Ganci; Giuseppe Bilotta; Annalisa Cappello; Ciro Del Negro; Luigi Fortuna. 2019. "Smart Decision Support Systems for Volcanic Applications." Energies 12, no. 7: 1216.