Retrospective deformation of the Baige landslide using optical remote sensing images

Yang, Wentao; Wang, Yujie; Wang, Yunqi; Ma, Chao; Ma, Yixin

doi:10.1007/s10346-019-01311-7

Cited by 34 publications

(33 citation statements)

References 19 publications

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“…Previous works show that uncertainties in deriving slope deformation from optical images could result from DEM errors during image orthorectification, different viewing angles of the sensors and illumination differences of the landscape during image acquisitions [ 20 ]. Yang et al [ 8 ] hypothesize that using similar dates spanning different years could lead to lower uncertainties. However, we know little about the temporal patterns of uncertainties, as few studies used long-term observations to study temporal patterns of uncertainties.…”

Section: Discussionmentioning

confidence: 99%

“…Standard deviations of displacement in the stable zone are widely regarded as a reliable measure of uncertainty [ 8 , 10 , 11 ], with the logic being that displacement within a stable zone should exhibit the same values in all pixels and any inconsistency regarded as uncertainty in a pair. However, derived ground displacements and uncertainties can be influenced by window sizes.…”

Section: Discussionmentioning

confidence: 99%

“…The initial window was set no smaller than the final window. An earlier study indicated that larger window sizes led to clearer spatial patterns by depressing background noise, but smaller windows were more sensitive in detecting slope deformation [ 8 ]. As the investigation of the window size was not the scope of this work, a medium window size combination of 64–32 was used, which provided merits of both smaller and larger window sizes.…”

Section: Methodsmentioning

confidence: 99%

“…Optical remote sensing has been increasingly used to monitor slope deformations [ 5 , 6 , 7 , 8 ]. Image correlation is the most frequently used methods to derive slope deformation from optical images [ 5 , 6 , 8 , 9 , 10 , 11 ]. The principle of deformation detection in optical images is to examine texture similarities with sliding windows in a pair of two images taken on different dates [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

“…Lacroix et al [ 10 ] used 22 Sentinel-2 images over nine months, and found that the uncertainties were correlated with temporal intervals between both images in a pair. Yang et al [ 8 ] selected seven Sentinel-2 images from 2015 to 2018, and found that the uncertainties were correlated with solar zenith angles. Bontemps et al [ 11 ] used displacement redundancy from image pairs to reduce uncertainty in the image time series.…”

Section: Introductionmentioning

confidence: 99%

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Selecting the Best Image Pairs to Measure Slope Deformation

Yang

2020

Sensors

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Optical remote sensing images can be used to monitor slope deformation in mountain regions. Abundant optical sensors onboard various platforms were designed to provide increasingly high spatial–temporal resolution images at low cost; however, finding the best image pairs to derive slope deformation remains difficult. By selecting a location in the east Tibetan Plateau, this work used the co-registration of optically sensed images and correlation (COSI-Corr) method to analyze 402 Sentinel-2 images from August 2015 to February 2020, to quantify temporal patterns of uncertainty in deriving slope deformation. By excluding 66% of the Sentinel-2 images that were contaminated by unfavorable weather, uncertainties were found to fluctuate annually, with the least uncertainty achieved in image pairs of similar dates in different years. Six image pairs with the least uncertainties were selected to derive ground displacement for a moving slope in the study area. Cross-checks among these image pairs showed consistent results, with uncertainties less than 1/10 pixels in length. The findings from this work could help in the selection of the best image pairs to derive reliable slope displacement from large numbers of optical images.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Selecting the Best Image Pairs to Measure Slope Deformation

Yang

2020

Sensors

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Slope deformation detection using subpixel offset tracking and an unsupervised learning technique based on unmanned aerial vehicle photogrammetry data

et al. 2023

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Detecting slope deformation is an important issue in engineering. Timely deformation detection can effectively avoid catastrophic slope failure and ensure the safety of a project and engineering personnel. In this study, deformation detection for a quarry slope is implemented using the integration of subpixel offset tracking (sPOT) and unsupervised change detection algorithms based on unmanned aerial vehicle (UAV) image datasets. The sPOT algorithm is used to give the surface displacement field of the slope with subpixel accuracy, and the unsupervised change detection algorithm yields the ground object reconstruction area of the slope to verify and explain the sPOT result. The integrated analysis method in this paper is highly applicable and only requires a minimum of two UAV datasets as raw data. Combining the advantages of the sPOT and unsupervised change detection algorithms, the proposed method has the ability to detect and analyse slow and rapid slope deformation with good accuracy.

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Kinematic process and mechanism of the two slope failures at Baige Village in the upper reaches of the Jinsha River, China

Chen

Gao

Zhao

et al. 2021

Bull Eng Geol Environ

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The two landslides are located in the upper reaches of the Jinsha River and both dammed the river. Immediately since the slides, the authors have been working on the slides and help disaster reduction. Based on the data collected by April 2020, this paper is aimed at clarifying the geological condition of the slides and at explaining why the slides occurred and what the whole sliding process was. Conclusions are summarized as follows. First, the two landslides occurred in the suture belt of the Jinsha River and the rocks are composed of tectonic mélange slices of mainly gneiss intermingled with carboniferous slate and marble and with intruded serpentine and granite porphyry. The gneiss generally bears a schistosity plane with an averaged attitude of N47°W/47°, dipping into the slope. Secondly, long-term geomorphological evolution of the bank slope due to river incision contributed to the progressive slope deformation for the development of the "10.10" rockslide. No preferential joints exist in the slope, but alteration and weathering played important roles in its occurrences. Rainfall and earthquakes may also accelerate its deformation. Thirdly, the "10.10" rockslide is of high-speed wedge-like slope failure with a high-position and a high-shear outlet. Its sliding and deposition process demonstrate special features as initial speed, collision between debris, surging waterjet, and second slipping. Fourthly, the whole process of the "10.10" rockslide can be divided into 6 steps, i.e., startup of the major sliding and sliding resistance zones, sliding initiation of the trailing zone, formation of debris-eroded zones, collision of debris and triggering waterjet and mist, secondary slip of the landslide dam, and surface flush in the deposition area. The estimated speed may reach as high as 67 m/s. Fifthly, the "11.3" rockslide follows a different mode, i.e., wedge cleaving effect. And finally, the cracked zones still have the risk to constitute a potential landslide and to dam the river again.

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Retrospective deformation of the Baige landslide using optical remote sensing images

Cited by 34 publications

References 19 publications

Selecting the Best Image Pairs to Measure Slope Deformation

Selecting the Best Image Pairs to Measure Slope Deformation

Slope deformation detection using subpixel offset tracking and an unsupervised learning technique based on unmanned aerial vehicle photogrammetry data

Kinematic process and mechanism of the two slope failures at Baige Village in the upper reaches of the Jinsha River, China

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