We present methods and results from interferometric data processingof a long-lasting survey campaign monitoring thePlanpincieux glacier, located on the Italian side of the MontBlanc, using a ground-based synthetic aperture radar (GB-SAR).Monitoring a European Alpine glacier during the winter, whenthe meteorological conditions are highly variable, presents somedifficulties in radar data interpretation. The main issues to tackle ininterferometric processing are unwrapping errors and high amplitudedispersion (DA), mainly due to the high velocity and dielectricheterogeneity of the backscattering surface. To improve thereliability of the results, a coherence-driven pixel-selection criterionfor identifying the glacier area and a simple approach toreduce possible unwrapping errors in interferograms with lowcoherence are here proposed. The development of a new 2Dpolynomial regression model, as a function of elevation, for atmosphericphase screen (APS) estimation is also discussed. A comparisonwith the results obtained with a vision-based approach gaveshowed good agreement.
Monitoring Alpine glacier surface deformations with GB-SAR
Niccolò DematteisPrimo
;Daniele Giordan;Paolo Allasia
2017
Abstract
We present methods and results from interferometric data processingof a long-lasting survey campaign monitoring thePlanpincieux glacier, located on the Italian side of the MontBlanc, using a ground-based synthetic aperture radar (GB-SAR).Monitoring a European Alpine glacier during the winter, whenthe meteorological conditions are highly variable, presents somedifficulties in radar data interpretation. The main issues to tackle ininterferometric processing are unwrapping errors and high amplitudedispersion (DA), mainly due to the high velocity and dielectricheterogeneity of the backscattering surface. To improve thereliability of the results, a coherence-driven pixel-selection criterionfor identifying the glacier area and a simple approach toreduce possible unwrapping errors in interferograms with lowcoherence are here proposed. The development of a new 2Dpolynomial regression model, as a function of elevation, for atmosphericphase screen (APS) estimation is also discussed. A comparisonwith the results obtained with a vision-based approach gaveshowed good agreement.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.


