A general framework and related procedures for multiscale analyses of DInSAR data in subsiding urban areas

Peduto, Dario; Cascini, Leonardo; Arena, Livia; Ferlisi, Settimio; Fornaro, G.; Reale, Diego

doi:10.1016/j.isprsjprs.2015.04.001

Cited by 54 publications

(44 citation statements)

References 73 publications

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“…Although various methodologies were implemented to map the ground motion areas using the average velocity detected by A-DInSAR data [12][13][14][15][16][17], ground motion areas mapping on the basis of A-DInSAR time series is still not a common practice in the scientific community.…”

Section: Discussionmentioning

confidence: 99%

“…Analysis of the hydraulic condition role on ground motion was performed using the unsaturated zone thickness, measured in October 2005 (Figure 9). The unsaturated zone thickness detected in October 2005, was [17]. Relationship between the average LOS velocity detected by ERS-1/2 asc.…”

Section: Comparison Between Ground Motion and Hydrogeological Settingsmentioning

confidence: 93%

“…Meanwhile, the ground motion areas located over engineering geological Unit 8 are due to other processes, which are explained in Section 6.3. [17]. Relationship between the average LOS velocity detected by ERS-1/2 asc.…”

Section: Comparison Between Ground Motion and Hydrogeological Settingsmentioning

confidence: 99%

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Methodology for Detection and Interpretation of Ground Motion Areas with the A-DInSAR Time Series Analysis

2016

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Recent improvement to Advanced Differential Interferometric SAR (A-DInSAR) time series quality enhances the knowledge of various geohazards. Ground motion studies need an appropriate methodology to exploit the great potential contained in the A-DInSAR time series. Here, we propose a methodology to analyze multi-sensors and multi-temporal A-DInSAR data for the geological interpretation of areas affected by land subsidence/uplift and seasonal movements.The methodology was applied in the plain area of the Oltrepo Pavese (Po Plain, Italy) using ERS-1/2 and Radarsat data, processed using the SqueeSAR™ algorithm, and covering time spans, respectively, from 1992 to 2000 and from 2003 to 2010. The test area is a representative site of the Po Plain, affected by various geohazards and characterized by moderate rates of motion, ranging from −10 to 4 mm/yr. Different components of motion were recognized: linear, non-linear, and seasonal deformational behaviors. Natural and man-induced processes were identified such as swelling/shrinkage of clayey soils, land subsidence due to load of new buildings, moderate tectonic uplift, and seasonal ground motion due to seasonal groundwater level variations.

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Section: Discussionmentioning

confidence: 99%

Section: Comparison Between Ground Motion and Hydrogeological Settingsmentioning

confidence: 93%

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Methodology for Detection and Interpretation of Ground Motion Areas with the A-DInSAR Time Series Analysis

2016

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“…The idea has attracted attention since its introduction. PS-DINSAR can obtain high coherent points by natural and artificial objects with surface scattering properties and observe surface deformation with properties of high precision and large scale in a long time [3][4][5][6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Optimized Method for Selecting Common Master Image in PS-DINSAR

Liu¹,

Cao²,

Wang³

et al. 2018

JESTR

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The differential synthetic aperture radar interferometry technique of permanent scatterers (PS-DINSAR) is an important method in the phase analysis of synthetic aperture radar data. Selection of common master image in PS-DINSAR widely influences the data processing results. Most of existing selection methods of common master image are based the statistical characteristics on the baseline of the interferogram and thus cannot ensure best selected results. A method based on fixed weights of observed values in the surveying adjustment was used in this study to extract an optimum image. The temporal baselines, effective spatial baselines, and Doppler centroid frequency difference of image pairs were incorporated in this technique. The sum of normalized weights model was utilized based on integrated correlation coefficient algorithm and minimum sum of three baseline algorithm to obtain optimal common master image. Then, the procedure of selecting common master images based on the idea of maximum sum of normalized weights was introduced and used to test the selection of common master image by using 19 images of ERS-1/2. Result shows that, in comparison with data generated separately by integrated correlation coefficient algorithm and minimum sum of three baseline algorithm, the outcome produced by maximum sum of normalized weights model in this study exhibits considerably better statistical property in temporal baselines, effective spatial baselines, and Doppler centroid frequency difference of image pairs. Moreover, the interferograms show that the maximum, average, and standard deviation values are less than those of others in temporal baselines, effective spatial baselines, and Doppler centroid frequency difference. Therefore, the selected common master is robust and stable. This study provides an effective method for the optimization of common master image and presents certain guiding significance for selecting common master image.

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“…On the other hand, several works have demonstrated the usefulness of C-band SAR images acquired by medium spatial resolution sensors such as ERS and ENVISAT satellites to monitor urban structures [22,[25][26][27][28]. They provide a free continuous archive of SAR images from 1992 to 2012 with high spatial coverage, which continues with the Sentinel-1 satellites launched in April 2014.…”

Section: Introductionmentioning

confidence: 99%

A-DInSAR Monitoring of Landslide and Subsidence Activity: A Case of Urban Damage in Arcos de la Frontera, Spain

Brú¹,

González

Mateos

et al. 2017

Remote Sensing

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A general framework and related procedures for multiscale analyses of DInSAR data in subsiding urban areas

Cited by 54 publications

References 73 publications

Methodology for Detection and Interpretation of Ground Motion Areas with the A-DInSAR Time Series Analysis

Methodology for Detection and Interpretation of Ground Motion Areas with the A-DInSAR Time Series Analysis

Optimized Method for Selecting Common Master Image in PS-DINSAR

A-DInSAR Monitoring of Landslide and Subsidence Activity: A Case of Urban Damage in Arcos de la Frontera, Spain

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