Assessment of Landslide Pre-Failure Monitoring and Forecasting Using Satellite SAR InterferometryReport as inadecuate


Assessment of Landslide Pre-Failure Monitoring and Forecasting Using Satellite SAR Interferometry


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1

Department of Earth Sciences, Sapienza University of Rome, Rome 00185, Italy

2

NHAZCA S.r.l., Spin-off of Sapienza, University of Rome, Rome 00185, Italy





*

Author to whom correspondence should be addressed.



Academic Editors: Francesca Cigna and Jesus Martinez-Frias

Abstract In this work, the ability of advanced satellite interferometry to monitor pre-failure landslide behaviours and the potential application of this technique to Failure Forecasting Methods FFMs are analysed. Several limits affect the ability of the technique to monitor a landslide process, especially during the pre-failure phase tertiary creep. In this study, two of the major limitations affecting the technique have been explored: 1 the low data sampling frequency and 2 the phase ambiguity constraints. We explored the time series of displacements for 56 monitored landslides inferred from the scientific literature and from different in situ and remote monitoring instruments i.e., extensometers, inclinometers, distometers, Ground Base InSAR, and total station. Furthermore, four different forecasting techniques have been applied to the monitoring data of the selected landslides. To analyse the reliability of the FFMs based on the InSAR satellite data, the 56 time series have been sampled based on different satellite features, simulating the satellite revisit time and the phase ambiguity constraints. Our analysis shows that the satellite InSAR technique could be successful in monitoring the landslide’s tertiary creep phase and, in some cases, for forecasting the corresponding time of failure using FFMs. However, the low data sampling frequency of the present satellite systems do not capture the necessary detail for the application of FFMs in actual risk management problems or for early warning purposes. View Full-Text

Keywords: A-DInSAR; InSAR; landslide; failure forecasting methods; landslide forecasting A-DInSAR; InSAR; landslide; failure forecasting methods; landslide forecasting





Author: Serena Moretto 1,2,* , Francesca Bozzano 1,2, Carlo Esposito 1, Paolo Mazzanti 1,2 and Alfredo Rocca 2

Source: http://mdpi.com/



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