Estimating the accurate position of the shoreline over time can be challenging. We propose a new approach to delimit the shoreline from remotely sensed images simply and accurately. This approach treats pixels as simultaneous radiometric measurements and, by examining the distance between iso-radiometry lines, assumes the shoreline in the position where the radiometric behavior changes more suddenly. The analyses conducted applying this coastline extraction approach underscore its significant flexibility. Specifically, the approach yields promising outcomes even if applied to a variety of different and dissimilar types of remotely sensed products. Indeed, the approach’s intrinsic straightforwardness and low computational load qualify it as a promising tool for time-series production.
Estimating the accurate position of the shoreline over time can be challenging. We propose a new approach to delimit the shoreline from remotely sensed images simply and accurately. This approach treats pixels as simultaneous radiometric measurements, and by examining the distance between iso-radiometry lines, it assumes the shoreline in the position where the radiometric behaviour changes more suddenly. The analyses carried out applying this coastline extraction approach underscore its significant flexibility. Specifically, the approach yielded promising outcomes even if applied to a variety of different and dissimilar types of remotely sensed products. Indeed, the intrinsic straightforwardness and low computational load of the approach qualify it as a promising tool for time-series production.
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.
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.
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.
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