Detecting Rock Glacier Displacement in the Central Himalayas Using Multi-Temporal InSAR

Rock glaciers represent typical periglacial landscapes and are distributed widely in alpine mountain environments. Rock glacier activity represents a critical indicator of water reserves state, permafrost distribution, and landslide disaster susceptibility. The dynamics of rock glacier activity in alpine periglacial environments are poorly quantified, especially in the central Himalayas. Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) has been shown to be a useful technique for rock glacier deformation detection. In this study, we developed a multi-baseline persistent scatterer (PS) and distributed scatterer (DS) combined MT-InSAR method to monitor the activity of rock glaciers in the central Himalayas. In periglacial landforms, the application of the PS interferometry (PSI) method is restricted by insufficient PS due to large temporal baseline intervals and temporal decorrelation, which hinder comprehensive measurements of rock glaciers. Thus, we first evaluated the rock glacier interferometric coherence of all possible interferometric combinations and determined a multi-baseline network based on rock glacier coherence; then, we constructed a Delaunay triangulation network (DTN) by exploiting both PS and DS points. To improve the robustness of deformation parameters estimation in the DTN, we combined the Nelder–Mead algorithm with the M-estimator method to estimate the deformation rate variation at the arcs of the DTN and introduced a ridge-estimator-based weighted least square (WLR) method for the inversion of the deformation rate from the deformation rate variation. We applied our method to Sentinel-1A ascending and descending geometry data (May 2018 to January 2019) and obtained measurements of rock glacier deformation for 4327 rock glaciers over the central Himalayas, at least more than 15% detecting with single geometry data. The line-of-sight (LOS) deformation of rock glaciers in the central Himalayas ranged from −150 mm to 150 mm. We classified the active deformation area (ADA) of all individual rock glaciers with the threshold determined by the standard deviation of the deformation map. The results show that 49% of the detected rock glaciers (monitoring rate greater than 30%) are highly active, with an ADA ratio greater than 10%. After projecting the LOS deformation to the steep slope direction and classifying the rock glacier activity following the IPA Action Group guideline, 12% of the identified rock glaciers were classified as active and 86% were classified as transitional. This research is the first multi-baseline, PS, and DS network-based MT-InSAR method applied to detecting large-scale rock glaciers activity.

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Ice Segregation as an Origin for Lenses of Non-Glacial Ice in “Ice-Cemented” Rock Glaciers

Journal of Glaciology ◽

10.3189/s0022143000011564 ◽

1981 ◽

Vol 27 (97) ◽

pp. 506-510 ◽

Cited By ~ 1

Author(s):

William J. Wayne

Keyword(s):

Groundwater Flow ◽

Large Scale ◽

Water Pressure ◽

Snow Melt ◽

Total Thickness ◽

Rock Glacier ◽

Glacial Ice ◽

Substantial Loss ◽

Rock Glaciers ◽

Ice Segregation

AbstractIn order to flow with the gradients observed (10° to 15°) rock glaciers cannot be simply ice-cemented rock debris, but probably contain masses or lenses of debris-free ice. The nature and origin of the ice in rock glaciers that are in no way connected to ice glaciers has not been adequately explained. Rock glaciers and talus above them are permeable. Water from snow-melt and rain flows through the lower part of the debris on top of the bedrock floor. In the headward part of a rock glacier, where the total thickness is not great, if this groundwater flow is able to maintain water pressure against the base of an aggrading permafrost, segregation of ice lenses should take place. Ice segregation on a large scale would produce lenses of clear ice of sufficient size to permit the streams or lobes of rock debris to flow with gradients comparable to those of glaciers. It would also account for the substantial loss in volume that takes place when a rock glacier stabilizes and collapses.

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Seasonal melting simulation of permafrost rock glaciers and their potential contribution to sediment loads in Alpine catchments

10.5194/egusphere-egu2020-17504 ◽

2020 ◽

Author(s):

Lucas Reid ◽

Ulrike Scherer ◽

Erwin Zehe

Keyword(s):

Large Scale ◽

Sediment Load ◽

Series Data ◽

Rock Glacier ◽

Erosion Model ◽

Permafrost Rock ◽

Sediment Loads ◽

Rock Glaciers ◽

High Sediment ◽

Seasonal Melting

A common issue with large scale erosion modelling is that local processes are often unaccounted for, either because they haven&#8217;t been included in the model conceptually, or because they are undetected yet. On the other hand, significant deviations from such a general soil erosion model to the measurements can reveal those local processes. We compared the average yearly sediment amounts of a network of turbidity measurement stations in the catchment of the alpine River Inn to the results of the large scale erosion model RUSLE2015 (Panagos et. al.) for long term yearly erosion amounts and found a significant underestimation of sediment loads in three sub catchments. An important source of sediments in alpine rivers comes from glaciers, which explains the high loads in one of the stations, but two of the three high sediment load sub catchments are too low to have substantial valley glaciers. But another potential source of glacial sediment exists in the form of permafrost soils and in this case a specific permafrost form: rock glaciers. Rock glaciers in particular have been spotted in those two high sediment load catchments, but since they are hard to detect from remote sensing due to the surface being covered with rocks, the existence or the exact spatial extent is often unknown. But with rising temperatures in the Alps, the areas in which permafrost rock glaciers can exist decreases every year and the depth of the seasonal melting layer increases.We propose the hypothesis that the high sediment loads in those sub catchments are caused by increasingly deeper melting of permafrost rock glaciers. This process releases fine materials which have been trapped frozen since the glacial period and are now being eroded and transported to the alpine streams. To get an estimation of potential erodible material from rock glacier melting in the respective sub catchments, we developed a model to simulate the heat diffusion from the air into the frozen ground, while accommodating for the change in specific thermal capacity. The model (developed in Python) takes air temperature time series data as input and can be configured for varying ground stratification setups with different thermal diffusivity values depending on the ground properties.From the simulated melting depth of an average square meter of rock glacier we extrapolate the mass of melted material to the potential permafrost erosion material available in the River Inn sub catchments. We show that this source of sediments can be significant and needs to be factored in should an erosion model be used to calculate sediment input into the rivers. But, with the estimation of sediment load from permafrost origins narrowed down, improving a large-scale erosion model like the RUSLE2015 for this alpine mountain region by accounting for local processes like this one is possible.&#160;

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Ground deformations associated with underground coal-mining observed by Sentinel-1 SAR images in vegetated area

10.5194/egusphere-egu2020-22428 ◽

2020 ◽

Author(s):

Yonghong Zhang ◽

Hong’an Wu ◽

Yong Luo ◽

Yonghui Kang ◽

Hongdong Fan

Keyword(s):

Winter Wheat ◽

Coal Mining ◽

Large Scale ◽

Central Government ◽

Underground Mining ◽

Mining Area ◽

Least Square ◽

Whole Process ◽

Ground Deformations ◽

Multi Temporal

Coal is the largest energy source for China, and over 90% coal production in China is from underground mining. However, underground mining usually trigger large-scale ground deformations, which tend to develop as hazards. Therefore, the central government of China issued the &#8220;green mine&#8221; policy in 2017, which requires to strictly implement scientific and orderly exploitation and keeping the disturbance to the mining area and surrounding environment within the limits of sustainable development in the whole process of coal mining. This policy necessitates accurate monitoring of ground deformations induced by underground mining. Satellite Interferometric SAR (InSAR), especially the multi-temporal InSAR techniques have been successfully used to monitor deformations associated with underground mining. But temporal decorrelation still remains a big challenge because many underground mining takes place beneath farmland or forested region. Given the advantages of Sentinel-1 (S-1) in short revisit time, small baselines and free accessibility, underground mining deformations can be monitored somehow with S-1 InSAR in vegetated areas. In this research we report such an application in an underground coal-mine site located in Xuzhou, Jiangsu province of China. Four working panels are investigatedThe working panels are all beneath farmland where winter wheat is sowed before the end of October and reaped around next late May, then corn or rice is planted during the coming summer season from June to September. Therefore the C-band S-1 interferograms can keep good coherence only when both images are acquired in the period of late October to next early April (this period is called coherent period thereafter) when the newly planted winter wheat is in its early growing stage. Three subsets of S-1 images acquired during three consecutive coherent periods&#160; are used to generate mining-induced ground deformations.During each coherent period, all of the interferograms with 12-day separation and some of the interferograms with 24-day separation and good coherence are selected and phase-unwrapped. Then these two sets of unwrapped interferograms are stacked, and finally the temporal deformations along SAR line-of-sight (LOS) are calculated under the least square principle. The temporal and spatial characteristics of the LOS deformation time series (DTS) are analyzed by considering extraction stage and extraction parameters of the working panel. Based on the analysis, we can diagnose whether the underground exploitation overstepped its designed boundary, or whether the working panel has been exploited for longer time than the designed extraction period.&#160;

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Ice Segregation as an Origin for Lenses of Non-Glacial Ice in “Ice-Cemented” Rock Glaciers

Journal of Glaciology ◽

10.1017/s0022143000011564 ◽

1981 ◽

Vol 27 (97) ◽

pp. 506-510 ◽

Cited By ~ 20

Author(s):

William J. Wayne

Keyword(s):

Groundwater Flow ◽

Large Scale ◽

Water Pressure ◽

Snow Melt ◽

Total Thickness ◽

Rock Glacier ◽

Glacial Ice ◽

Substantial Loss ◽

Rock Glaciers ◽

Ice Segregation

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Conventional and UAV-Based Aerial Surveys for Long-Term Monitoring (1954–2020) of a Highly Active Rock Glacier in Austria

Frontiers in Remote Sensing ◽

10.3389/frsen.2021.732744 ◽

2021 ◽

Vol 2 ◽

Author(s):

Viktor Kaufmann ◽

Andreas Kellerer-Pirklbauer ◽

Gernot Seier

Keyword(s):

Laser Scanning ◽

Temperature Data ◽

General View ◽

Rock Glacier ◽

Aerial Surveys ◽

Multi Temporal ◽

Rock Glaciers ◽

Ice Content ◽

Creep Velocity

Rock glaciers are creep phenomena of mountain permafrost. Speed-up has been observed on several rock glaciers in recent years and attributed to climate change. Although rare, related long-term studies are nevertheless essential to bring a climate perspective to creep velocity changes. In the present study, we focused on changes both in the surface creep velocity and volume of the Leibnitzkopf rock glacier (Hohe Tauern Range, Austria) in the period 1954–2020. We applied 3D change detection using aerial images of both conventional (12 epochs between 1954 and 2018) and unmanned aerial vehicle (UAV)-based aerial surveys (2 epochs, 2019 and 2020), and combined this with ground and air temperature data. Photogrammetric processing (structure-from-motion, multi-view stereo) of the multi-temporal dataset resulted in high-resolution digital orthophotos/DOPs (5–50 cm spatial resolution) and digital elevation models/DEMs (10–50 cm grid spacing). Georeferencing was supported by five externally triangulated images from 2018, bi-temporal aerial triangulation of the image data relying on stable ground around the rock glacier, measured ground control points (2019 and 2020), and measured camera locations (PPK-GNSS) of the UAV flight in 2020. 2D displacement vectors based on the multi-temporal DOPs and/or DEMs were computed. Accuracy analyses were conducted based on geodetic measurements (2010–2020) and airborne laser scanning data (2009). Our analyses show high multi-annual and inter-annual creep velocity variabilities with maxima between 12 (1974–1981) and 576 cm/year (2019–2020), always detected in the same area of the rock glacier where surface disintegration was first observed in 2018. Our volume change analyses of the entire landform for the period 1954–2018 do not indicate any significant changes. This suggests little permafrost ice melt and/or general low ice content of the rock glacier. Analyses of the temperature data reveal a close relationship between higher temperatures and rock glacier acceleration despite the high probability of low ice content. This suggests that hydrogeological changes play an important role in the rock glacier system. The paper concludes with a summary of technical improvements and recommendations useful for rock glacier monitoring and a general view on the kinematic state of the Leibnitzkopf rock glacier.

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Measurement Based Parameters Estimation of Large Scale Wind Farm Dynamic Equivalent model

Renewable Energy ◽

10.1016/j.renene.2020.12.063 ◽

2020 ◽

Author(s):

Akhilesh Prakash Gupta ◽

A. Mohapatra ◽

S.N. Singh

Keyword(s):

Large Scale ◽

Wind Farm ◽

Parameters Estimation ◽

Equivalent Model ◽

Dynamic Equivalent

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Kernel weighted least square approach for imputing missing values of metabolomics data

Scientific Reports ◽

10.1038/s41598-021-90654-0 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Nishith Kumar ◽

Md. Aminul Hoque ◽

Masahiro Sugimoto

Keyword(s):

Missing Data ◽

Large Scale ◽

Missing Values ◽

Kernel Weight ◽

Least Square ◽

Data Matrix ◽

Data Imputation ◽

Metabolomics Data ◽

Missing Value ◽

Missing Data Imputation

AbstractMass spectrometry is a modern and sophisticated high-throughput analytical technique that enables large-scale metabolomic analyses. It yields a high-dimensional large-scale matrix (samples × metabolites) of quantified data that often contain missing cells in the data matrix as well as outliers that originate for several reasons, including technical and biological sources. Although several missing data imputation techniques are described in the literature, all conventional existing techniques only solve the missing value problems. They do not relieve the problems of outliers. Therefore, outliers in the dataset decrease the accuracy of the imputation. We developed a new kernel weight function-based proposed missing data imputation technique that resolves the problems of missing values and outliers. We evaluated the performance of the proposed method and other conventional and recently developed missing imputation techniques using both artificially generated data and experimentally measured data analysis in both the absence and presence of different rates of outliers. Performances based on both artificial data and real metabolomics data indicate the superiority of our proposed kernel weight-based missing data imputation technique to the existing alternatives. For user convenience, an R package of the proposed kernel weight-based missing value imputation technique was developed, which is available at https://github.com/NishithPaul/tWLSA.

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A Compound Wind Power Forecasting Strategy Based on Clustering, Two-Stage Decomposition, Parameter Optimization, and Optimal Combination of Multiple Machine Learning Approaches

Energies ◽

10.3390/en12183586 ◽

2019 ◽

Vol 12 (18) ◽

pp. 3586 ◽

Cited By ~ 1

Author(s):

Sizhou Sun ◽

Jingqi Fu ◽

Ang Li

Keyword(s):

Wind Power ◽

Large Scale ◽

Wind Farm ◽

Search Algorithm ◽

Interpolation Method ◽

Least Square ◽

Support Vector ◽

Two Stage ◽

Wind Power Forecasting ◽

Power Forecasting

Given the large-scale exploitation and utilization of wind power, the problems caused by the high stochastic and random characteristics of wind speed make researchers develop more reliable and precise wind power forecasting (WPF) models. To obtain better predicting accuracy, this study proposes a novel compound WPF strategy by optimal integration of four base forecasting engines. In the forecasting process, density-based spatial clustering of applications with noise (DBSCAN) is firstly employed to identify meaningful information and discard the abnormal wind power data. To eliminate the adverse influence of the missing data on the forecasting accuracy, Lagrange interpolation method is developed to get the corrected values of the missing points. Then, the two-stage decomposition (TSD) method including ensemble empirical mode decomposition (EEMD) and wavelet transform (WT) is utilized to preprocess the wind power data. In the decomposition process, the empirical wind power data are disassembled into different intrinsic mode functions (IMFs) and one residual (Res) by EEMD, and the highest frequent time series IMF1 is further broken into different components by WT. After determination of the input matrix by a partial autocorrelation function (PACF) and normalization into [0, 1], these decomposed components are used as the input variables of all the base forecasting engines, including least square support vector machine (LSSVM), wavelet neural networks (WNN), extreme learning machine (ELM) and autoregressive integrated moving average (ARIMA), to make the multistep WPF. To avoid local optima and improve the forecasting performance, the parameters in LSSVM, ELM, and WNN are tuned by backtracking search algorithm (BSA). On this basis, BSA algorithm is also employed to optimize the weighted coefficients of the individual forecasting results that produced by the four base forecasting engines to generate an ensemble of the forecasts. In the end, case studies for a certain wind farm in China are carried out to assess the proposed forecasting strategy.

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Observations on a Debris-Covered Polar Glacier “Whisky Glacier”, James Ross Island, Antarctic Peninsula, Antarctica

Journal of Glaciology ◽

10.1017/s002214300000887x ◽

1987 ◽

Vol 33 (115) ◽

pp. 300-310 ◽

Cited By ~ 25

Author(s):

T.J.H. Chinn ◽

A. Dillon

Keyword(s):

Antarctic Peninsula ◽

Rock Glacier ◽

Freezing Front ◽

Extensive Area ◽

Free Zone ◽

Rock Glaciers ◽

James Ross Island ◽

Polythermal Glacier ◽

Powerful Mechanism

Abstract“Whisky Glacier” on James Ross Island, Antarctic Peninsula, comprises anévéand clean ice trunk surrounded by an extensive area of debris-covered ice resembling a rock glacier. The debris-free trunk of the glacier abuts abruptly against the broad, totally debris-covered tongue at a number of concentric zones where debris-laden beds crop out at the surface in a manner similar to the “inner moraine” formations of many polar glaciers.Ice structures and foliation suggest that “Whisky Glacier” is a polythermal glacier which is wet-based under the debris-free zone, and dry-based under the debris-covered zone. It is surmised that the glacier sole crosses the freezing front close to where the basal debris beds are upwarped towards the surface. Here, basal water is confined, and freezes to the under side of the glacier in thick beds of regelation ice which are uplifted to the surface along with the debris-laden beds. Ablation losses effectively cease beneath the blanket of debris covering the tongue.The transition from wet-based to dry-based conditions at the glacier sole is a powerful mechanism for entraining debris into a glacier and, in the case of “Whisky Glacier”, for lifting debris to the surface. It is suggested that this may be a mechanism for forming some polar rock glaciers.

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