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Your search keyword '"Trevisiol, Francesca"' showing total 11 results
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11 results on '"Trevisiol, Francesca"'

2. 3D Data Management and Thermographic Studies as a Knowledge Base for the Conservation of a Rationalist Architecture

Author

Barbieri, Ester, Franzoni, Elisa, Lambertini, Alessandro, Pizzigatti, Cesare, Trevisiol, Francesca, Bitelli, Gabriele, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Haddar, Mohamed, Editorial Board Member, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Furferi, Rocco, editor, Governi, Lapo, editor, Volpe, Yary, editor, and Seymour, Kate, editor

Published
2023
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5. Cross-sensors analysis of multispectral Earth observation data for environmental monitoring

Author

Bitelli, Gabriele, Trevisiol, Francesca <1991>, Bitelli, Gabriele, and Trevisiol, Francesca <1991>

Abstract

Time Series Analysis of multispectral satellite data offers an innovative way to extract valuable information of our changing planet. This is now a real option for scientists thanks to data availability as well as innovative cloud-computing platforms, such as Google Earth Engine. The integration of different missions would mitigate known issues in multispectral time series construction, such as gaps due to clouds or other atmospheric effects. With this purpose, harmonization among Landsat-like missions is possible through statistical analysis. This research offers an overview of the different instruments from Landsat and Sentinel missions (TM, ETM, OLI, OLI-2 and MSI sensors) and products levels (Collection-2 Level-1 and Surface Reflectance for Landsat and Level-1C and Level-2A for Sentinel-2). Moreover, a cross-sensors comparison was performed to assess the interoperability of the sensors on-board Landsat and Sentinel-2 constellations, having in mind a possible combined use for time series analysis. Firstly, more than 20,000 pairs of images almost simultaneously acquired all over Europe were selected over a period of several years. The study performed a cross-comparison analysis on these data, and provided an assessment of the calibration coefficients that can be used to minimize differences in the combined use. Four of the most popular vegetation indexes were selected for the study: NDVI, EVI, SAVI and NDMI. As a result, it is possible to reconstruct a longer and denser harmonized time series since 1984, useful for vegetation monitoring purposes. Secondly, the spectral characteristics of the recent Landsat-9 mission were assessed for a combined use with Landsat-8 and Sentinel-2. A cross-sensor analysis of common bands of more than 3,000 almost simultaneous acquisitions verified a high consistency between datasets. The most relevant discrepancy has been observed in the blue and SWIRS bands, often used in vegetation and water related studies. This analysis was suppo

Published
2023

10. <italic>Lacticaseibacillus casei</italic> as Anti-blowing Agents: Impact on the Evolution of Ripening and Sensory Profile of Montasio Cheese.

Author

Trevisiol, Francesca, Renoldi, Niccolò, Rossi, Anna, Di Filippo, Giulia, Marino, Marilena, and Innocente, Nadia

Subjects
*LACTIC acid bacteria, *PRINCIPAL components analysis, *CHEESEMAKING, *LYSOZYMES, *CHEESE
Abstract

Recently, the Lacticaseibacillus casei group strains have been gaining growing interest due to their potential to be used as secondary adjunct cultures in cheese. This work aimed to test autochthonous Lb. casei strains as anti-blowing agents and to evaluate their impact on the evolution of the ripening and the sensory profile of Montasio cheese. The cheesemaking trial included a control production without lysozyme (C1), a control with lysozyme (C2), and four experimental productions, each containing a different pool of autochthonous Lb. casei strains (EX1-4). Samples were taken during ripening, and physicochemical, microbiological, and sensory analyses were carried out. Lb. casei counts indicate that the selected strains survived the cheesemaking and maintained their viability of about 9 log cfu g−1 at the end of the ripening. Only EX3 showed a significant slowdown of the proteolytic index compared to controls over time. Furthermore, from the principal component analysis, it emerged that at the end of the 120-day-long ripening, C2 and the experimental samples were characterized by similar profiles of volatile compounds. The late-blowing defect (LBD) was observed exclusively in C1, whereas it was not detected in the control sample with lysozyme or in any experimental samples. These findings not only confirmed the efficacy of lysozyme in preventing LBD but also supported the effectiveness of the selected Lb. casei strains as anti-blowing agents with the ability to contribute to the final volatile profile without compromising the typicality of the product. [ABSTRACT FROM AUTHOR]

Published
2024
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11. An Object-Oriented Approach to the Classification of Roofing Materials Using Very High-Resolution Satellite Stereo-Pairs

Author

Francesca Trevisiol, Alessandro Lambertini, Francesca Franci, Emanuele Mandanici, Trevisiol, Francesca, Lambertini, Alessandro, Franci, Francesca, and Mandanici, Emanuele

Subjects
WorldView-3, machine learning, building extraction, roofing classification, OBIA classification, smart city, General Earth and Planetary Sciences
Abstract

The availability of multispectral images, with both high spatial and spectral resolution, makes it possible to obtain valuable information about complex urban environment, reducing the need for more expensive surveying techniques. Here, a methodology is tested for the semi-automatic extraction of buildings and the mapping of the main roofing materials over a urban area of approximately 100 km2, including the entire city of Bologna (Italy). The methodology follows an object-oriented approach and exploits a limited number of training samples. After a validation based on field inspections and close-range photos acquired by a drone, the final map achieved an overall accuracy of 94% (producer accuracy 79%) regarding the building extraction and of 91% for the classification of the roofing materials. The proposed approach proved to be flexible enough to catch the strong variability of the urban texture in different districts and can be easily reproducible in other contexts, as only satellite imagery is required for the mapping.

Published
2022

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