Monitoring of an Alpine Glacier by Means of Ground-Based SAR Interferometry

Luzi, G.; Pieraccini, Massimiliano; Mecatti, D.; Noferini, L.; Macaluso, G.; Tamburini, A.; Atzeni, C.

doi:10.1109/lgrs.2007.898282

Cited by 89 publications

(49 citation statements)

References 21 publications

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“…The costs and difficulties related to the setup of onsite sensors suggest the use of terrestrial radar. The first applications on snow-covered and glacial surfaces (Luzi et al 2007;Noferini et al 2009;Martinez-Vazquez and FortunyGuasch. 2008) demonstrated the capabilities of terrestrial radar to monitor glacier and snow evolution.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Alpine glacier surface deformations with GB-SAR

Dematteis

Luzi

Giordan

et al. 2017

Remote Sensing Letters

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We present methods and results from interferometric data processing of a long-lasting survey campaign monitoring the Planpincieux glacier, located on the Italian side of the Mont Blanc, using a ground-based synthetic aperture radar (GB-SAR). Monitoring a European Alpine glacier during the winter, when the meteorological conditions are highly variable, presents some difficulties in radar data interpretation. The main issues to tackle in interferometric processing are unwrapping errors and high amplitude dispersion (DA), mainly due to the high velocity and dielectric heterogeneity of the backscattering surface. To improve the reliability of the results, a coherence-driven pixel-selection criterion for identifying the glacier area and a simple approach to reduce possible unwrapping errors in interferograms with low coherence are here proposed. The development of a new 2D polynomial regression model, as a function of elevation, for atmospheric phase screen (APS) estimation is also discussed. A comparison with the results obtained with a vision-based approach gave showed good agreement. ARTICLE HISTORY

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Section: Introductionmentioning

confidence: 99%

Monitoring Alpine glacier surface deformations with GB-SAR

Dematteis

Luzi

Giordan

et al. 2017

Remote Sensing Letters

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“…Calculation of the Rayleigh parameter proceeds in the following fashion. The curve f [x] is generated by recursive application of (1)- (5). The horizontal dimension of the curve f [x] is determined by calculating the footprint or insonified area of the sound wave which has been projected on the snow surface by the loudspeaker.…”

Section: Reflection Coefficients and Acoustic Impedancementioning

confidence: 99%

Automated Determination of Snow Water Equivalent by Acoustic Reflectometry

Kinar

Pomeroy

2009

IEEE Trans. Geosci. Remote Sensing

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Abstract-Snow water equivalent (SWE) is commonly determined using gravimetric and depth measurement techniques. Previous research has demonstrated the ability to determine SWE from the propagation and reflection of acoustic waves. Despite the advantages of the acoustic technique, it has not been adapted so that SWE can be determined in an automated fashion. This paper presents a new technique for determining SWE by the application of acoustics. A maximum-length sequence was used as the input to the layered snowpack system. Signal processing of the reflected wave and a recursive algorithm was used to model the sound pressure wave as it passed through the snowpack. Embedded systems were designed to implement the signal processing and calculations so that SWE could be quickly determined at a field location. The systems were deployed at sites near Whitehorse, YT, and at sites in the Rocky Mountains of Alberta, Canada. Comparisons were made between SWE estimated by the acoustic technique and SWE determined by gravimetric sampling. These comparisons demonstrated that the acoustic SWE measurement performed with the embedded systems and the new signal processing technique can provide SWE estimates that are of comparable accuracy to SWE calculated from gravimetric samples.

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“…Other geotechnical applications include the monitoring of slope instabilities in quarries and open pit mines [10,11]. In the cryospheric field, ground-based radar interferometry is widely used to determine for example the glacier surface velocity, e.g., [12,13], to detect changes in snow water equivalent [14], to identify wet snow avalanche precursors movements [15,16] or to map snow avalanches [17]. Various ground-based radars have also been used for the generation of digital elevation models (DEMs), e.g., [18,19].…”

Section: Introductionmentioning

confidence: 99%

Multipath Interferences in Ground-Based Radar Data: A Case Study

Lucas

Leinß

Bühler³

et al. 2017

Remote Sensing

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Abstract:Multipath interference can occur in ground-based radar data acquired with systems with a large antenna beam width in elevation in an upward looking geometry, where the observation area and the radar are separated by a reflective surface. Radiation reflected at this surface forms a coherent overlay with the direct image of the observation area and appears as a fringe-like pattern in the data. This deteriorates the phase and intensity data and therefore can pose a considerable disadvantage to many ground-based radar measurement campaigns. This poses a problem for physical parameter retrieval from backscatter intensity and polarimetric data, absolute and relative calibration on corner reflectors, the generation of digital elevation models from interferograms and in the case of a variable reflective surface, differential interferometry. The main parameters controlling the interference pattern are the vertical distance between the radar antennas and the reflective surface, and the reflectivity of this surface. We used datasets acquired in two different locations under changing conditions as well as a model to constrain and fully understand the phenomenon. To avoid data deterioration in test sites prone to multipath interference, we tested a shielding of the antennas preventing the radar waves from illuminating the reflective surface. In our experiment, this strongly reduced but did not completely prevent the interference. We therefore recommend avoiding measurement geometries prone to multipath interferences.

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Monitoring of an Alpine Glacier by Means of Ground-Based SAR Interferometry

Cited by 89 publications

References 21 publications

Monitoring Alpine glacier surface deformations with GB-SAR

Monitoring Alpine glacier surface deformations with GB-SAR

Automated Determination of Snow Water Equivalent by Acoustic Reflectometry

Multipath Interferences in Ground-Based Radar Data: A Case Study

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