A numerical model for the simulation of a solitary wave in a coastal region

In this paper we propose a numerical model for the simulation of the tsunami wave propagation on coastal region. The model can simulate the wave transformation due to refraction, shoaling, diffraction and breaking phenomena that take place in the surf zone and can simulate the wet front progress on the mainland. The above mentioned model is based on the numerical integration of the Fully Non-linear Boussinesq Equations in the deep water region and of the Non-linear Shallow Water Equations in the surf zone. These equations are expressed in an integral contravariant formulation and are integrated on generalized curvilinear boundary conforming grid that can reproduce the complex morphology of the coast line. The numerical integration of the model equations is implemented by a high order Upwind WENO numerical scheme that involves an exact Riemann Solver. For the simulation of the wet front progress on the dry bed, the exact solution of the Riemann problem for the wet-dry front is used. The capacity of the proposed model to simulate the wet front progress velocity is tested by numerical reproducing the dam-break problem on a dry bed. The capacity of the proposed model to correctly simulate the tsunami wave evolution and propagation on the coastal region is tested by numerical reproducing a benchmark test case about the tsunami wave propagation on a conic island

Non-Linear Shallow Water Equations numerical integration on curvilinear boundary-conforming grids

An Upwind Weighted Essentially Non-Oscillatory scheme for the solution of the Shallow Water Equations on generalized curvilinear coordinate systems is proposed. The Shallow Water Equations are expressed in a contravariant formulation in which Christoffel symbols are avoided. The equations are solved by using a high-resolution finite-volume method incorporated with an exact Riemann Solver. A procedure developed in order to correct errors related to the difficulties of numerically satisfying the metric identities on generalized boundary-conforming grids is presented; this procedure allows the numerical scheme to satisfy the freestream preservation property on highly-distorted grids. The capacity of the proposed model is verified against test cases present in literature. The results obtained are compared with analytical solutions and alternative numerical solutions

On the use of satellite Sentinel 2 data for automatic mapping of burnt areas and burn severity

In this paper, we present and discuss the preliminary tools we devised for the automatic recognition of burnt areas and burn severity developed in the framework of the EU-funded SERV_FORFIRE project. The project is focused on the set up of operational services for fire monitoring and mitigation specifically devised for decision-makers and planning authorities. The main objectives of SERV_FORFIRE are: (i) to create a bridge between observations, model development, operational products, information translation and user uptake; and (ii) to contribute to creating an international collaborative community made up of researchers and decision-makers and planning authorities. For the purpose of this study, investigations into a fire burnt area were conducted in the south of Italy from a fire that occurred on 10 August 2017, affecting both the protected natural site of Pignola (Potenza, South of Italy) and agricultural lands. Sentinel 2 data were processed to identify and map different burnt areas and burn severity levels. Local Index for Statistical Analyses LISA were used to overcome the limits of fixed threshold values and to devise an automatic approach that is easier to re-apply to diverse ecosystems and geographic regions. The validation was assessed using 15 random plots selected from in situ analyses performed extensively in the investigated burnt area. The field survey showed a success rate of around 95%, whereas the commission and omission errors were around 3% of and 2%, respectively. Overall, our findings indicate that the use of Sentinel 2 data allows the development of standardized burn severity maps to evaluate fire effects and address post-fire management activities that support planning, decision-making, and mitigation strategies.Fil: Lasaponara, Rosa. Consiglio Nazionale delle Ricerche; ItaliaFil: Tucci, Biagio. Consiglio Nazionale delle Ricerche; ItaliaFil: Ghermandi, Luciana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina. Universidad Nacional del Comahue. Centro Regional Universitario Bariloche. Laboratorio de Ecotono; Argentin

Detection of archaeological crop marks by using satellite QuickBird multispectral imagery

The capability of satellite QuickBird imagery for the identification of archaeological crop marks is herein presented and discussed for two test sites located in the South of Italy. The selected sites, dating back to Middle Ages, were buried under surfaces covered by herbaceous plants characterized by a different phenological status (dry/green) when the satellite data were acquired. The methodological approach adopted for the enhancement and extraction of crop marks is mainly based on the use of data fusion and edge detection algorithm. The main remarkable differences found for the two archaeological sites can be suitably linked to the different state of vegetation that caused a different spectral response. In particular, near infrared (NIR) spectral channel was able to better enhance crop marks observed for dry vegetation; whereas, Normalized Difference Vegetation Index (NDVI) was found to be more capable to better enhance crop marks observed for green vegetation

Immagini satellitari ad alta risoluzione e ricerca archeologica: applicazioni e casi di studio con riprese pancromatiche e multispettrali di QuickBird

The paper concerns the research activities of the IBAM-CNR and the IMAA-CNR in the field of archaeological remote sensing with the use of very high resolution images of QuickBird, the satellite with the greatest geometrical resolution available for civil use. These images have an enormous potential in the study of ancient urban and territorial contexts and for the identification and spatial characterization of archaeological sites, particularly when aerial photos and recent detailed maps are not available. During the archaeological research in Hierapolis of Phrygia (Turkey) and in southern Italy (Monte Irsi, Monte Serico, Jure Vetere and Metaponto), the examination and the study of panchromatic and multispectral images of QuickBird made it possible to detect surface anomalies and traces linked to ancient buried structures or to paleo-environmental elements; moreover, panchromatic images were georeferenced and used as the base field maps for the survey in Hierapolis, together with GPS systems. The satellite images were analysed both for the identification of archaeological features and for the characterisation of the contexts in which these elements were found. During field work, the traces and the anomalies identified in the images were constantly verified, so as to determine their actual relevance to archaeological elements, to interpret them and, where possible, to specify their chronology, thus avoiding misunderstandings and errors. The images were used in all phases of the research (field work, documentation, data processing and management in GIS environment), in combination with the aerial photographs and the available maps; they were also used for presentation of the results and were draped on DEM for the 3D visualization of the territories and of the archaeological features. In order to highlight particular archaeological traces and anomalies some image processing methodologies were adopted: multispectral processing and algorithms of data fusion (with the integration of the high spatial resolution of panchromatic images with the spectral capability of multispectral images), of enhancement (such as PCA, NDVI and TCT) and edge detection

On the Use of Google Earth Engine and Sentinel Data to Detect 'Lost' Sections of Ancient Roads. The Case of Via Appia

The currently available tools and services as open and free cloud resources to process big satellite data opened up a new frontier of possibilities and applications including archeological research. These new research opportunities also pose several challenges to be faced, as, for example, the data processing and interpretation. This letter is about the assessment of different methods and data sources to support a visual interpretation of EO imagery. Multitemporal Sentinel 1 and Sentinel 2 data sets have been processed to assess their capability in the detection of buried archeological remains related to some lost sections of the ancient Via Appia road (herein selected as case study). The very subtle and nonpermanent features linked to buried archeological remains can be captured using multitemporal (intra- and inter-year) satellite acquisitions, but this requires strong hardware infrastructures or cloud facilities, today also available as open and free tools as Google Earth Engine (GEE). In this study, a total of 2948 Sentinel 1 and 743 Sentinel 2 images were selected (from February 2017 to August 2020) and processed using GEE to enhance and unveil archeological features. Outputs obtained from both Sentinel 1 and Sentinel 2 have been successfully compared with in situ analysis and high-resolution Google Earth images

Characterization and Mapping of Fuel Types for the Mediterranean Ecosystems of Pollino National Park in Southern Italy by Using Hyperspectral MIVIS Data

Abstract The characterization and mapping of fuel types is one of the most important factors that should be taken into consideration for wildland fire prevention and prefire planning. This research aims to investigate the usefulness of hyperspectral data to recognize and map fuel types in order to ascertain how well remote sensing data can provide an exhaustive classification of fuel properties. For this purpose airborne hyperspectral Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) data acquired in November 1998 have been analyzed for a test area of 60 km2 selected inside Pollino National Park in the south of Italy. Fieldwork fuel-type recognitions, performed at the same time as remote sensing data acquisition, were used as a ground-truth dataset to assess the results obtained for the considered test area. The method comprised the following three steps: 1) adaptation of Prometheus fuel types for obtaining a standardization system useful for remotely sensed classification of fuel types and properties in the considered Mediterranean ecosystems; 2) model construction for the spectral characterization and mapping of fuel types based on a maximum likelihood (ML) classification algorithm; and 3) accuracy assessment for the performance evaluation based on the comparison of MIVIS-based results with ground truth. Results from our analysis showed that the use of remotely sensed data at high spatial and spectral resolution provided a valuable characterization and mapping of fuel types being that the achieved classification accuracy was higher than 90%

On the Use of Google Earth Engine and Sentinel Data to Detect “Lost” Sections of Ancient Roads. The Case of Via Appia

The currently available tools and services as open and free cloud resources to process big satellite data opened up a new frontier of possibilities and applications including archeological research. These new research opportunities also pose several challenges to be faced, as, for example, the data processing and interpretation. This letter is about the assessment of different methods and data sources to support a visual interpretation of EO imagery. Multitemporal Sentinel 1 and Sentinel 2 data sets have been processed to assess their capability in the detection of buried archeological remains related to some lost sections of the ancient Via Appia road (herein selected as case study). The very subtle and nonpermanent features linked to buried archeological remains can be captured using multitemporal (intra- and inter-year) satellite acquisitions, but this requires strong hardware infrastructures or cloud facilities, today also available as open and free tools as Google Earth Engine (GEE). In this study, a total of 2948 Sentinel 1 and 743 Sentinel 2 images were selected (from February 2017 to August 2020) and processed using GEE to enhance and unveil archeological features. Outputs obtained from both Sentinel 1 and Sentinel 2 have been successfully compared with in situ analysis and high-resolution Google Earth images