Improved DEM Reconstruction Method Based on Multibaseline InSAR

Zhang, Wenting; Zhu, Wu; Tian, Xudong; Zhang, Qin; Zhao, Chaoying; Niu, Yufen; Wang, Chisheng

doi:10.1109/lgrs.2021.3069239

Cited by 18 publications

(15 citation statements)

References 20 publications

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“…Notably, the negative values (represented by using red) indicated that the surface moved away from the satellite, whereas the positive values (represented by using blue) indicated that the surface moved toward the satellite sensor (Figure 5) [35]. It could be inferred that the coherence of the Ya'an-Kangding and Luolong-Linzhi sections was relatively low compared to other regions, which might have been caused by the complex environment [36,37]. Meanwhile, according to the annual deformation rate map, we found that large-scale errors (such as atmospheric and unwrapping errors) could be effectively removed.…”

Section: Deformation Rate Map Acquired Via Insarmentioning

confidence: 99%

Landslide Detection in the Linzhi–Ya’an Section along the Sichuan–Tibet Railway Based on InSAR and Hot Spot Analysis Methods

et al. 2021

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Construction of the 998.64-km Linzhi–Ya’an section of the Sichuan‒Tibet Railway has been influenced by landslide disasters, threatening the safety of Sichuan‒Tibet railway projects. Landslide identification and deformation analysis in this area are urgently needed. In this context, it was the first time that 164 advanced land-observing satellite-2 (ALOS-2) phased array type L-band synthetic aperture radar-2 (PALSAR-2) images were collected to detect landslide disasters along the entire Linzhi‒Ya’an section. Interferogram stacking and small baseline interferometry methods were used to derive the deformation rate and time-series deformation from 2014‒2020. After that, the hot spot analysis method was introduced to conduct spatial clustering analysis of the annual deformation rate, and the effective deformation area was quickly extracted. Finally, 517 landslide disasters along the Linzhi‒Ya’an route were detected by integrating observed deformation, Google Earth optical images, and external geological data. The main factors controlling the spatial landslide distribution were analyzed. In the vertical direction, the spatial landslide distribution was mainly concentrated in the elevation range of 3000‒5000 m, the slope range of 10‒40°, and the aspect of northeast and east. In the horizontal direction, landslides were concentrated near rivers, and were also closely related to earthquake-prone areas, fault zones, and high-precipitation areas. In short, rainfall, freeze–thaw weathering, seismic activity, and fault zones are the main factors inducing landslides along this route. This research provides scientific support for the construction and operation of the Linzhi‒Ya’an section of the Sichuan‒Tibet Railway.

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Section: Deformation Rate Map Acquired Via Insarmentioning

confidence: 99%

Landslide Detection in the Linzhi–Ya’an Section along the Sichuan–Tibet Railway Based on InSAR and Hot Spot Analysis Methods

et al. 2021

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“…The terrain-related atmospheric phase was removed from the best-fitting linear relation between the phase delay and topography [34]. (IV) The DEM error is estimated based on the relation between the perpendicular baseline and the unwrapped phase [36].…”

Section: / 12mentioning

confidence: 99%

Near Real-Time InSAR Deformation Time Series Estimation With Modified Kalman Filter and Sequential Least Squares

Wang¹,

Zhang²,

Zhao³

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The current and planned synthetic aperture radar (SAR) sensors mounted on satellite platforms will continue to operate over the coming years, providing unprecedented SAR data for monitoring wide-range surface deformations. The near real-time processing of SAR interferometry (InSAR) data for the retrieval of ground-deformation time series is urgently required in the current era of big data. The state-of-the-art Kalman filter (KF) and sequential least squares (SLS) algorithms have been proposed to update an InSAR-driven ground-deformation time series. As a contribution of this study, we customize the conventional KF and SLS for big InSAR data for near real-time processing. The development of an accurate prediction model for KF-based InSAR processing is a challenge owing to the large scale of the targets for surface monitoring. We developed a modified KF algorithm, abbreviated as npKF, that does not require any prediction information, abbreviated as npKF. In this context, to avoid occupying a large storage space in SLS-based InSAR processing, we developed a modified SLS algorithm with a truncated cofactor matrix, abbreviated as TSLS. Using both simulated and actual SAR data, we evaluated the performance of these methods under three different aspects: accuracy, computation, and storage performance. With big data, the proposed method can estimate the deformation time series in near real time. It will be a reliable and effective tool for producing near real-time InSAR deformation products in the coming era of processing big SAR data and will play a part in the geologic hazard routine monitoring and early warning system.

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“…The Shuttle Radar Topography Mission (SRTM) 1-arcsecond (~30 m) spacing digital elevation model (DEM) was used to correct the topographic phase contribution in the original interferograms (http://srtm.csi.cgiar.org). The DEM had also been transformed into an ellipsoidal height datum (Li et al, 2012;Zhang et al, 2021). Minimum Cost Flow (MCF) algorithm was used to unwrap the interferograms to obtain line-of-sight (LOS)…”

Section: Interferometric Processingmentioning

confidence: 99%

Coseismic and postseismic deformation of the 2016 Mw 6.0 Petermann ranges earthquake from satellite radar observations

Han

Yang

et al. 2022

Advances in Space Research

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Improved DEM Reconstruction Method Based on Multibaseline InSAR

Cited by 18 publications

References 20 publications

Landslide Detection in the Linzhi–Ya’an Section along the Sichuan–Tibet Railway Based on InSAR and Hot Spot Analysis Methods

Landslide Detection in the Linzhi–Ya’an Section along the Sichuan–Tibet Railway Based on InSAR and Hot Spot Analysis Methods

Near Real-Time InSAR Deformation Time Series Estimation With Modified Kalman Filter and Sequential Least Squares

Coseismic and postseismic deformation of the 2016 Mw 6.0 Petermann ranges earthquake from satellite radar observations

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