Gino Dardanelli
University of Palermo
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Featured researches published by Gino Dardanelli.
Remote Sensing | 2018
Claudia Pipitone; Antonino Maltese; Gino Dardanelli; Mauro Lo Brutto; Goffredo La Loggia
Remote sensing allowed monitoring the reservoir water level by estimating its surface extension. Surface extension has been estimated using different approaches, employing both optical (Landsat 5 TM, Landsat 7 ETM+ SLC-Off, Landsat 8 OLI-TIRS and ASTER images) and Synthetic Aperture Radar (SAR) images (Cosmo SkyMed and TerraSAR-X). Images were characterized by different acquisition modes, geometric and spectral resolutions, allowing the evaluation of alternative and/or complementary techniques. For each kind of image, two techniques have been tested: The first based on an unsupervised classification and suitable to automate the process, the second based on visual matching with contour lines with the aim of fully exploiting the dataset. Their performances were evaluated by comparison with water levels measured in situ (r2 = 0.97 using the unsupervised classification, r2 = 0.95 using visual matching) demonstrating that both techniques are suitable to quantify reservoir surface extension. However ~90% of available images were analyzed using the visual matching method, and just 37 images out of 58 using the other method. The evaluation of the water level from the water surface, using both techniques, could be easily extended to un-gauged reservoirs to manage the variations of the levels during normal operation. In addition, during the period of investigation, the use of Global Navigation Satellite System (GNSS) allowed the estimation of dam displacements. The advantage of using as reference a GNSS permanent station positioned relatively far from the dam, allowed the exclusion of any interaction with the site deformations. By comparing results from both techniques, relationships between the orthogonal displacement component via GNSS, estimated water levels via remote sensing and in situ measurements were investigated. During periods of changing water level (April 2011–September 2011 and October 2011–March 2012), the moving average of displacement time series (middle section on the dam crest) shows a range of variability of ±2 mm. The dam deformation versus reservoir water level behavior differs during the reservoir emptying and filling periods indicating a hysteresis-kind loop.
Remote Sensing for Agriculture, Ecosystems, and Hydrology XVI | 2014
Gino Dardanelli; Goffredo La Loggia; Nicola Perfetti; Fulvio Capodici; Luigi Puccio; Antonino Maltese
This paper shows the results of a scientific research in which a GNSS continuous monitoring system for earth-dam deformations has been developed, then, deformations have been related with reservoir water surface and level. The experiment was conducted near Bivona (Sicily, Italy), on the Castello dam (Magazzolo Lake). On the top of the dam three control points were placed and three GNSS permanent stations were installed. The three stations continuously transmitted data to the control centre of the University of Palermo. The former has been determined using freely available satellite data (specifically Landsat 7 SLC-Off) collected during the whole study period (DOYs 101 to 348 2011). Issues related with the un-scanned rows filling and to better distinguish water from land pixels on the shoreline. The aim of this work is various: first of all, we want to evaluate whether the GPS post processing techniques can provide static results comparable to other monitoring techniques, such as spirit levelling. The study could take a significant importance given that the Italian legislation until today does not provide for the use of this technology to manage or monitor dams displacements or other civil engineering constructions. The use of GPS data in structural monitoring could in fact reduce some management costs. Usually the conventional GPS monitoring methods, where a base station GPS receiver must be located near the dam, did not ensure that the accuracy of results have been independent from the displacement of the crown (top end of dam). In this paper, a new approach in the area of study of the GNSS permanent network has been engaged to solve these problems. Field-testing results show that the new GNSS approach has excellent performances, and the monitoring of different section of the dam could reveal important information on its deformation, that its not operationally possible to retrieve elsewhere. The post-processing accuracy positioning is around 1–5 mm for the deformations monitoring of the Castello dam. Displacements of different sections of the dam reveal different behaviour (in time and periodicity) that looks to be related with water surface (and level) retrieved from remote sensing.
Remote Sensing for Agriculture, Ecosystems, and Hydrology XX | 2018
Claudia Pipitone; Antonino Maltese; Gino Dardanelli; Fulvio Capodici; Jan-Peter Muller; Goffredo La Loggia
Monitoring dam displacements using different techniques allows an evaluation of their structural behaviour over time. In this study, dam displacements (for the Castello dam, Agrigento, Italy) have been investigated using different Interferometric Synthetic Aperture Radar (InSAR) techniques exploiting a freely available dataset from the EU Copernicus Sentinel-1 SAR built by the European Space Agency (ESA). The dataset includes Sentinel 1A (S1A) images acquired in dual-polarization and Interferometric Wide (IW) swath using the Terrain Observation with Progressive Scans SAR (TOPSAR) mode. Three main Multi-Baseline Construction methods based on the identification of Persistent Scatterers (PS) have been tested, within a scene including an extra-urban area surrounding the dam. The evaluation of the best strategy is carried out over few images (7) with a constant acquisition time-span of 12 days, except for the first image, acquired 24 days before the next one. Three different multi-baseline construction methods have been investigated in this preliminary research to test the capability of these InSAR techniques in finding a time series of displacements with high accuracy in extra-urban areas also. The star graph results in displacement appear to be more in agreement with GNSS measurements than other techniques.
PROCEEDINGS OF SPIE, THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING | 2017
Claudia Pipitone; Antonino Maltese; Gino Dardanelli; Fulvio Capodici; M. Lo Brutto; G. La Loggia
The matching between reservoirs’ water edge and digital elevation model’s (DEM) contour lines allowed determining the water level at the acquisition date of satellite images. A preliminary study was conducted on the Castello dam (Magazzolo Lake), between Alessandria della Rocca and Bivona (Agrigento, south-Italy). The accuracy assessment of the technique was than evaluated from the comparison between classified and reference objects using similarity metrics about the shape, theme, edge and position, through the plugin STEP of open source software GIS. Moreover, an independent GIS technique was implemented to evaluate the water level, based on a distances’ array between existing contour lines and nodes extracted from vectorised classification images. Results have shown the potentiality of the techniques when applied on an ideal case; advantages and disadvantages when the images are characterized by clear sky, and limits when images are acquired during not ideal atmospheric conditions.
Archive | 2004
Benedetto Villa; Vincenzo Franco; Patrizia Midulla; Mauro Lo Brutto; Pietro Orlando; Gino Dardanelli; Davide Emmolo; R Amato
Periodica Polytechnica-civil Engineering | 2016
Gino Dardanelli; Claudia Pipitone
Archive | 2009
Vincenzo Franco; Mauro Lo Brutto; Gino Dardanelli
Archive | 2008
Benedetto Villa; Gino Dardanelli; Rita Corsale; Alessio Ammoscato; Ammoscato A; Corsale R; Dardanelli G; Andrea Scianna; Villa B
Quaternary Science Reviews | 2017
Paolo Stocchi; Fabrizio Antonioli; Paolo Montagna; Fabrizio Pepe; Valeria Lo Presti; Antonio Caruso; Marta Corradino; Gino Dardanelli; Pietro Renda; Norbert Frank; Eric Douville; François Thil; Bas de Boer; Rosario Ruggieri; R. Sciortino; Catherine Pierre
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences | 2017
M. Lo Brutto; Gino Dardanelli; D. Ebolese; G. Milazzo; Claudia Pipitone; R. Sciortino