Radar image and data fusion for natural hazards characterisation

Lü, Zhong; Dzurisin, Daniel; Jung, Hyung-Sup; Zhang, Jixian; Zhang, Yonghong

doi:10.1080/19479832.2010.499219

Cited by 41 publications

(24 citation statements)

References 127 publications

(117 reference statements)

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“…In addition, the accuracy of InSAR measurements can be significantly reduced by atmospheric phase artifacts and orbital errors (Ferretti, Prati, & Rocca, 2001). Accordingly, advanced multi-interferogram InSAR techniques such as Persistent Scatterers InSAR (PSInSAR) and Small Baseline Subset (SBAS) InSAR have been developed to map time-series land surface displacements by minimizing artifacts plaguing the conventional InSAR method (Berardino, Fornaro, Lanari, & Sansosti, 2002;Ferretti et al, 2011Ferretti et al, , 2001Hooper, Zebker, Segall, & Kampes, 2004;Lu, Dzurisin, Jung, Zhang, & Zhang, 2010;Lu & Zhang, 2014;Mora, Mallorqui, & Broqustas, 2003;Werner, Wegmuller, Strozzi, & Wiesmann, 2003). Multi-interferogram InSAR technique has facilitated the monitoring of land subsidence in many urban areas, such as Las Vegas (Burbey, 2002), Paris (Fruneau & Sarti, 2000), Naples (Tesauro et al, 2000), Mexico City (Osmanoglu, Dixon, Wdowinski, Cabral-Cano, & Jiang, 2011), Granada Basin (Joaquim, Hooper, Hanssen, Bastos, & Ruiz, 2011), Lisbon (Heleno et al, 2011), Hong Kong (Ding, Liu, Li, Li, & Chen, 2004), Shanghai and Tianjin (Perissin & Wang, 2010).…”

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confidence: 97%

Land subsidence and ground fissures in Xi'an, China 2005–2012 revealed by multi-band InSAR time-series analysis

Zhang

Lü

et al. 2014

Remote Sensing of Environment

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confidence: 97%

Land subsidence and ground fissures in Xi'an, China 2005–2012 revealed by multi-band InSAR time-series analysis

Zhang

Lü

et al. 2014

Remote Sensing of Environment

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154

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“…The amplitude data alone can be directory utilized for visual interpretation. On the other hand, the phase data are often combined with two or more SAR data acquired from similar orbits to derive a digital elevation model (DEM) through the interferometric SAR (InSAR) technique, as well as to derive minute changes in the slant range direction through the differential InSAR (DInSAR) technique (Lu et al 2010). Moreover, polarimetric SAR data have been studied for terrain and land use classification.…”

Section: Introductionmentioning

confidence: 99%

Landslide detection based on height and amplitude differences using pre- and post-event airborne X-band SAR data

et al. 2018

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The recognition of landslides and making their inventory map are considered to be urgent tasks not only for damage estimation but also for planning rescue and restoration activities.Owing to the capability of synthetic aperture radar (SAR) for day-and-night and all-weather imaging, various studies utilizing SAR data for landslide detection have been reported to date. Among the detection methods utilizing SAR data, those based on height differences accompanying landslides are attractive and should be further improved, since they can directly contribute to damage estimation through a volumetric estimation of landslides. In this context, we propose in this paper a landslide detection method utilizing height differences derived from pre-and post-event SAR digital elevation models (DEMs) combined with amplitude differences. The proposed method was applied to the landslides triggered by the 2016 Kumamoto earthquake. The application results demonstrate that SAR DEMs with a high altitudinal resolution can improve the detection ability and that the incorporation of the amplitude differences is effective for decreasing the number of false detections. Although the reliability of the proposed method is deemed moderate when evaluated on the basis of the kappa coefficients derived through an accuracy assessment, most of the outliers are correctly filtered out and large-and medium-scale landslides are detected. Therefore, the inventory maps derived from the proposed method are thought to be effective at the initial stage of planning rescue and restoration activities.

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“…A multilook factor of two is applied to generate interferograms at a spatial spacing of about 7.5 m in both directions. Interferograms with this spacing can detect small-scale rockslides, and are suitable to map rockslides with large deformation gradients in low coherence areas [19,20]. One arc-second digital elevation model (DEM) data with a pixel size of 30 m, generated by shuttle radar topography mission (SRTM), is adopted for the topographic phase removal and result analysis.…”

Section: Datamentioning

confidence: 99%

Application of InSAR Techniques to an Analysis of the Guanling Landslide

et al. 2017

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Abstract:On the afternoon of 28 June 2010, an enormous landslide occurred in the Gangwu region of Guanling County, Guizhou Province. In order to better understand the mechanism of the Guanling landslide, archived ALOS/PALSAR data was used to acquire the deformation prior to the landslide occurrence through stacking and time-series InSAR techniques. First, the deformation structure from InSAR was compared to the potential creep bodies identified using the optical remote sensing data. A strong consistency between the InSAR detected deformed regions and the creep bodies detected from optical remote sensing images was achieved. Around 10 creep bodies were suffering from deformation. In the source area, the maximum pre-slide mean deformation rate along the slope direction reached 160 mm/year, and the uncertainty of the deformation rates ranged from 15 to 34 mm/year. Then, the pre-slide deformation at the source area was analyzed in terms of the topography, geological structure, and historical rainfall records. Through observation and analysis, the deformation pattern of one creep body located within the source area can be segmented into three sections: a creeping section in the front, a locking section in the middle, and a cracking section in the rear. These sections constitute one of the common landslide modes seen in the south-west of China. This study concluded that a sudden shear failure in the locking segment of one creeping body located within the source area was caused by a strong rainstorm, which triggered the Guanling landslide.

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Radar image and data fusion for natural hazards characterisation

Cited by 41 publications

References 127 publications

Land subsidence and ground fissures in Xi'an, China 2005–2012 revealed by multi-band InSAR time-series analysis

Land subsidence and ground fissures in Xi'an, China 2005–2012 revealed by multi-band InSAR time-series analysis

Landslide detection based on height and amplitude differences using pre- and post-event airborne X-band SAR data

Application of InSAR Techniques to an Analysis of the Guanling Landslide

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