Quantitative Assessment of Vertical and Horizontal Deformations Derived by 3D and 2D Decompositions of InSAR Line-of-Sight Measurements to Supplement Industry Surveillance Programs in the Tengiz Oilfield (Kazakhstan)

Bayramov, Emil; Buchroithner, Manfred F.; Kada, Martin; Zhuniskenov, Yermukhan

doi:10.3390/rs13132579

Cited by 25 publications

(17 citation statements)

References 41 publications

(58 reference statements)

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“…To minimize errors caused by laboratory experiments, the cosine similarity [45][46][47][48] is used to evaluate the similarity between the model and the actual parameters. The wind direction and slope values of the model form the vector (α w1 , α s1 ), and the actual values of wind direction and slope form the vector (α w2 , α s2 ).…”

Section: Cosine Correction For the Results Of S-lstmmentioning

confidence: 99%

Simulating Forest Fire Spread with Cellular Automation Driven by a LSTM Based Speed Model

Zhang

et al. 2022

Fire

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The simulation of forest fire spread is a key problem for the management of fire, and Cellular Automata (CA) has been used to simulate the complex mechanism of the fire spread for a long time. The simulation of CA is driven by the rate of fire spread (ROS), which is hard to estimate, because some input parameters of the current ROS model cannot be provided with a high precision, so the CA approach has not been well applied yet in the forest fire management system to date. The forest fire spread simulation model LSTM-CA using CA with LSTM is proposed in this paper. Based on the interaction between wind and fire, S-LSTM is proposed, which takes full advantage of the time dependency of the ROS. The ROS estimated by the S-LSTM is satisfactory, even though the input parameters are not perfect. Fifteen kinds of ROS models with the same structure are trained for different cases of slope direction and wind direction, and the model with the closest case is selected to drive the transmission between the adjacent cells. In order to simulate the actual spread of forest fire, the LSTM-based models are trained based on the data captured, and three correction rules are added to the CA model. Finally, the prediction accuracy of forest fire spread is verified though the KAPPA coefficient, Hausdorff distance, and horizontal comparison experiments based on remote sensing images of wildfires. The LSTM-CA model has good practicality in simulating the spread of forest fires.

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Section: Cosine Correction For the Results Of S-lstmmentioning

confidence: 99%

Simulating Forest Fire Spread with Cellular Automation Driven by a LSTM Based Speed Model

Zhang

et al. 2022

Fire

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“…The displacement velocities measured from the times-series InSAR are along the radar Line-of-Sight (LoS) direction. Therefore, the displacement measured from the time-series InSAR is a composite of vertical, easting, and northing displacement components, which can be written as [62][63][64][65][66]:…”

Section: Time-series Insar Analysis Methodsmentioning

confidence: 99%

Displacement Characterization and Spatial-Temporal Evolution of the 2020 Aniangzhai Landslide in Danba County Using Time-Series InSAR and Multi-Temporal Optical Dataset

Kuang

Liu

2021

Remote Sensing

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On 17 June 2020, a large ancient landslide over the Aniangzhai (ANZ) slope, Danba County, Sichuan Province, China, was reactivated by a series of multiple phenomena, including debris flow triggered by heavy rainfall and flooding. In this study, Synthetic Aperture Radar (SAR) images acquired by the Sentinel-1A/B satellite and optical images captured by the PlanetScope satellites were jointly used to analyze and explore the deformation characteristics and the Spatial-Temporal evolution of the ANZ landslide before and after the multi-hazard chain. Several areas of pre-failure movements were found from the multi-temporal optical images analysis before the reactivation of the ANZ landslide. The large post-failure surface deformation over the ANZ slope was also retrieved by the optical pixel offset tracking (POT) technique. A major northwest movement with the maximum horizontal deformation of up to 14.4 m was found. A time-series InSAR technique was applied to analyze the descending and ascending Sentinel-1A/B datasets spanning from March 2018 to July 2020, showing that the maximum magnitudes of the Line of Sight (LoS) displacement velocities were −70 mm/year and 45 mm/year, respectively. The Spatial-Temporal evolution over the ANZ landslide was analyzed based on the time-series results. No obvious change in acceleration (precursory deformation) was detected before the multi-hazard chain, while clear accelerated deformation can be observed over the slope after the event. This suggested that heavy rainfall was the most significant triggering factor for the generation and reactivation of the ANZ landslide. Other preparatory factors, including the deformation behavior, the undercutting and erosion of the river and the outburst flood, the local terrain conditions, and earthquakes, might also have played an important role in the generation and reactivation of the landslide.

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“…The geometry of the two platforms was different: ascending datasets with an incidence angle of 41.5 • for Sentinel-1A and descending datasets with an incidence angle of 27.4 • for Radarsat2. Hence, the LOS deformation can be decomposed to east-west (horizontal) and up-down (vertical) direction based on the assumption that there is no obvious deformation in the north-south direction [32,34,[51][52][53][54], as shown in Equation ( 6):…”

Section: Accuracy Evaluationmentioning

confidence: 99%

Continued Monitoring and Modeling of Xingfeng Solid Waste Landfill Settlement, China, Based on Multiplatform SAR Images

Liu

et al. 2021

Remote Sensing

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Continued settlement monitoring and modeling of landfills are critical for land redevelopment and safety assurance. This paper adopts a MTInSAR technique for time-series monitoring of the Xingfeng landfill (XFL) settlement. A major challenge is that the frequent and significant settlement in the initial stage after the closure of landfills can affect the coherence of interferograms, thus hindering the monitoring of settlement by MTInSAR. We analyzed the factors that can directly affect the temporal decorrelation of landfills and adopted a 3D phase unwrapping approach to correct the phase unwrapping errors caused by such deformation gradient. SAR images from four platforms, including 50 Sentinel-1A, 12 Radarsat-2, 4 ALOS-2, and 2 TerraSAR-X/TanDEM-X images, are collected to measure the settlement and thickness of the landfill. The settlement accuracy is evaluated by a cross-evaluation between Radarsat-2 and Sentinel-1A that have similar temporal coverages. We analyzed the spatial characteristics of settlement and the relationship between the settlement and thickness. Further, we modeled the future settlement of the XFL with a hyperbolic function model. The results showed that the coherence in the initial stage after closure of the XFL is primarily affected by temporal decorrelation caused by considerable deformation gradient compared with spatial decorrelation. Settlement occurs primarily in the forward slope of the XFL, and the maximum line-of-sight (LOS) settlement rate reached 0.808 m/year from August 2018 to May 2020. The correlation between the settlement and thickness is 0.62, indicating an obvious relationship between the two. In addition, the settlement of younger areas is usually greater than that of older areas.

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Quantitative Assessment of Vertical and Horizontal Deformations Derived by 3D and 2D Decompositions of InSAR Line-of-Sight Measurements to Supplement Industry Surveillance Programs in the Tengiz Oilfield (Kazakhstan)

Cited by 25 publications

References 41 publications

Simulating Forest Fire Spread with Cellular Automation Driven by a LSTM Based Speed Model

Simulating Forest Fire Spread with Cellular Automation Driven by a LSTM Based Speed Model

Displacement Characterization and Spatial-Temporal Evolution of the 2020 Aniangzhai Landslide in Danba County Using Time-Series InSAR and Multi-Temporal Optical Dataset

Continued Monitoring and Modeling of Xingfeng Solid Waste Landfill Settlement, China, Based on Multiplatform SAR Images

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