Investigation of Slow-Moving Landslides from ALOS/PALSAR Images with TCPInSAR: A Case Study of Oso, USA

Sun, Qiliang; Zhang, Lei; Ding, Xiaoli; Hu, Jun; Liang, Hongyu

doi:10.3390/rs70100072

Cited by 35 publications

(21 citation statements)

References 35 publications

(46 reference statements)

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“…Our analyses show that our detected areas of activation within the landslide-prone slopes are located in areas characterized by a slope angle of 15-17 degrees, which is much smaller than the ALOS/PALSAR look angle of 38 degrees. Therefore, landslides can be detected without limitations [9,11,44]. Thus, in the area of the Uzgen mountain front, the landslide-prone slopes exhibit the best geometry for monitoring based on the use of L-Band ALOS/PALSAR data.…”

Section: Downslope Deformations Of Landslidesmentioning

confidence: 99%

“…With the increasing availability of suitable radar data, advanced time-series analysis techniques have been developed, which allow for the quantitative derivation of spatially variable deformation rates with increasing improvements in the temporal resolution. The resulting satellite InSAR techniques have been successfully applied for the quantitative analysis of landslide activities in a variety of environments [7][8][9][10][11][12]. A comprehensive review of the current state-of-the-art satellite InSAR-based landslide investigations can be found in Wasowski & Bovenga, 2014 [13].…”

Section: Introductionmentioning

confidence: 99%

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ALOS/PALSAR InSAR Time-Series Analysis for Detecting Very Slow-Moving Landslides in Southern Kyrgyzstan

et al. 2015

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This study focuses on evaluating the potential of ALOS/PALSAR time-series data to analyze the activation of deep-seated landslides in the foothill zone of the high mountain Alai range in the southern Tien Shan (Kyrgyzstan). Most previous field-based landslide investigations have revealed that many landslides have indicators for ongoing slow movements in the form of migrating and newly developing cracks. L-band ALOS/PALSAR data for the period between 2007 and 2010 are available for the 484 km 2 area in this study. We analyzed these data using the Small Baseline Subset (SBAS) time-series technique to assess the surface deformation related to the activation of landslides. We observed up to ±17 mm/year of LOS velocity deformation rates, which were projected along the local steepest slope and resulted in velocity rates of up to −63 mm/year. The obtained rates indicate very slow movement of the deep-seated landslides during the observation time. We also compared these movements with precipitation and earthquake records. The results suggest that the deformation peaks correlate with rainfall in the 3 preceding months and with an earthquake event. Overall, the results of this study indicated the great potential of L-band InSAR time series analysis for OPEN ACCESS Remote Sens. 2015, 7 8974 efficient spatiotemporal identification and monitoring of slope activations in this region of high landslide activity in Southern Kyrgyzstan.

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Section: Downslope Deformations Of Landslidesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ALOS/PALSAR InSAR Time-Series Analysis for Detecting Very Slow-Moving Landslides in Southern Kyrgyzstan

et al. 2015

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“…The results are detailed in Figure 4. The polynomial curve fitting method is also used to remove the orbital error in InSAR (Interferometric Synthetic Aperture Radar) interferogram [33][34][35][36][37][38][39][40][41][42][43][44]. …”

Section: Long Wavelength Orbital Errormentioning

confidence: 99%

Spatio-Temporal Error Sources Analysis and Accuracy Improvement in Landsat 8 Image Ground Displacement Measurements

et al. 2016

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Abstract:Because of the advantages of low cost, large coverage and short revisit cycle, Landsat 8 images have been widely applied to monitor earth surface movements. However, there are few systematic studies considering the error source characteristics or the improvement of the deformation field accuracy obtained by Landsat 8 image. In this study, we utilize the 2013 Mw 7.7 Balochistan, Pakistan earthquake to analyze error spatio-temporal characteristics and elaborate how to mitigate error sources in the deformation field extracted from multi-temporal Landsat 8 images. We found that the stripe artifacts and the topographic shadowing artifacts are two major error components in the deformation field, which currently lack overall understanding and an effective mitigation strategy. For the stripe artifacts, we propose a small spatial baseline (<200 m) method to avoid the stripe artifacts effect on the deformation field. We also propose a small radiometric baseline method to reduce the topographic shadowing artifacts and radiometric decorrelation noises. Those performances and accuracy evaluation show that these two methods are effective in improving the precision of deformation field. This study provides the possibility to detect subtle ground movement with higher precision caused by earthquake, melting glaciers, landslides, etc., with Landsat 8 images. It is also a good reference for error source analysis and corrections in deformation field extracted from other optical satellite images.

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“…However, these measurements have their own limitations, e.g., high cost, sensitive to working conditions or small coverage. On the other hand, InSAR technique, which has been successfully applied to investigate natural hazards such as landslide and earthquakes [17][18][19][20][21][22][23][24], can also be used to generate high quality DEM. Thanks to the notable evolution of sensors and processing techniques, the spatial coverage, resolution, and accuracy of InSAR derived DEM have been steadily improved.…”

Section: Introductionmentioning

confidence: 99%

Recent Landslide Movement in Tsaoling, Taiwan Tracked by TerraSAR-X/TanDEM-X DEM Time Series

Liu

Zhang

et al. 2017

Remote Sensing

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Abstract:The Tsaoling Landslide in Taiwan has captured attentions of researchers worldwide due to its frequent catastrophic failure and distinctive features. Thanks to the launch of TerraSAR-X/TanDEM-X (TSX/TDX) constellation, retrieval of global DEM with high spatial resolution and accuracy becomes possible, which is extremely useful for the study of natural hazards (e.g., landslides) globally. We attempt here for the first time to track recent landslide movements in Tsaoling Taiwan by analyzing DEM time series reconstructed from TSX/TDX image pairs. Quality improvement of InSAR derived DEM through an iterated differential operation is addressed during the data processing.

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Investigation of Slow-Moving Landslides from ALOS/PALSAR Images with TCPInSAR: A Case Study of Oso, USA

Abstract: Abstract:Monitoring slope instability is of great significance for understanding landslide kinematics and, therefore, reducing the related geological hazards.

Cited by 35 publications

References 35 publications

ALOS/PALSAR InSAR Time-Series Analysis for Detecting Very Slow-Moving Landslides in Southern Kyrgyzstan

ALOS/PALSAR InSAR Time-Series Analysis for Detecting Very Slow-Moving Landslides in Southern Kyrgyzstan

Spatio-Temporal Error Sources Analysis and Accuracy Improvement in Landsat 8 Image Ground Displacement Measurements

Recent Landslide Movement in Tsaoling, Taiwan Tracked by TerraSAR-X/TanDEM-X DEM Time Series

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