Seasonal Comparison of Velocity of the Eastern Tributary Glaciers, Amery Ice Shelf, Antarctica, Using Sar Offset Tracking (original) (raw)
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Czech Polar Reports, 2019
Glaciers play a crucial role in the study of the climate change pattern of the Earth. Remote sensing with access to large archives of data has the ability to monitor glaciers frequently throughout the year. Therefore, remote sensing is the most beneficial tool for the study of glacier dynamics. Fed by many tributaries from different sides, the Amery Ice Shelf (AIS) is one of the largest ice shelves that drains ice from the Antarctic ice sheet into the Southern Ocean. This study focuses on the eastern and the western tributaries of the AIS. The primary objective of the study was to derive the velocity of the tributary glaciers and the secondary objective was to compare variations in their velocities between the summer and winter season. This study was carried on using the European Space Agency’s (ESA) Sentinel-1 satellite’s Synthetic Aperture Radar (SAR) data acquired from the Sentinel data portal. Offset tracking method was applied to the Ground Range Detected (GRD) product of the S...
Estimation of Velocity of the Polar Record Glacier, Antarctica Using Synthetic Aperture Radar (SAR)
The 2nd International Electronic Conference on Remote Sensing, 2018
The ice flow velocity is a critical variable in understanding the glacier dynamics. The Synthetic Aperture Radar Interferometry (InSAR) is a robust technique to monitor Earth’s surface mainly to measure its topography and deformation. The phase information from two or more interferogram further helps to extract information about the height and displacement of the surface. We used this technique to derive glacier velocity for Polar Record Glacier (PRG), East Antarctica, using Sentinel-1 Single Look Complex images that were captured in Interferometric Wide mode. For velocity estimation, Persistent Scatterer interferometry (PS-InSAR) method was applied, which uses the time coherent of permanent pixel of master images and correlates to the same pixel of the slave image to get displacement by tracking the intensity of those pixels. C-band sensor of European Space Agency, Sentinel-1A, and 1B data were used in this study. Estimated average velocity of the PRG is found to be approximately ≈...
Earth System Science Data Discussions
We present a 14 year record of in situ glacier surface velocities determined by repeated GNSS measurements at a dense net of 52 stakes distributed across two glaciers, Johnsons (tidewater) and Hurd (land-terminating), located on Livingston Island, South Shetland Islands, Antarctica. The measurements cover the period 2000–2013 and were done at the beginning and end of each austral summer season. A second-degree polynomial approximation is calculated for each stake, which allows estimating the approximate velocities at intermediate times. This dataset can be useful as input data to numerical models of glacier dynamics, or for calibration and validation of remotely sensed velocities such as D-inSAR or SAR offset/coherence tracking velocities, for a region where very scarce in situ glacier surface velocity measurements are available. <br><br> Link to the data repository: <a href="http://doi.pangaea.de/10.1594/PANGAEA.846791" target ="_blank">h...
Changes in Velocity of Fisher Glacier, East Antarctica Using Pixel Tracking Method
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2018
Glacier movement is a crucial factor for assessing cryospheric climate change. Traditional methods of field surveys for studying glacier movement and velocity are often not possible owing to inaccessibility and harsh terrains. Furthermore, as it is not feasible to physically monitor and survey many glaciers around the globe, these traditional methods are limited in their global coverage. Remote sensing is an ideal tool to study such phenomena on a global scale. Optical remote sensing employs techniques such as feature tracking and pixel tracking, whereas, microwave remote sensing uses intensity tracking, speckle tracking, Interferometric SAR and Differential InSAR (DInSAR). This study focuses on estimation of glacier velocity and its seasonal variations using the imagematching technique for optical images for the Fisher glacier, a tributary glacier of the Amery ice shelf in Antarctica. The tool used in this study is the COSI-Corr module embedded in ENVI which provides the velocity in both azimuth and range resolution. The principle of estimating velocity using this tool is pixel tracking wherein similar pixels on two images are tracked where one is the master image and the other is a slave. This technique correlates the master and slave images over a time period and generates three outputs: displacements in the East-West and North-South directions and signal-to-noise ratio (SNR) image. Landsat 8 image pairs were used for cross correlation over a time span of four years spanning 2013-2017. With a resolution of 15m, the panchromatic band (Band 8) was the ideal choice for pixel tracking as the resolution of other bands is coarser. The initial window size for correlation was 64 while the final window size was 16. The resolution of the displacement images produced is dependent on the value assigned for the step size, which was set to 8. The resultant velocity was derived using the result of the two displacement images. The SNR image was used to remove all the pixels from the velocity output having the value of SNR less than 0.9, in order to reduce the effect of noise. The annual velocity of the Fisher glacier was estimated to be around 600 to 650 myr-1 near the tongue where it merges with the Amery Ice Shelf, which was reduced to 150 myr-1 as it recedes. The resultant velocity images have a resolution of 120 m. The seasonal variation in velocity for the year 2013-2014 is 1.8 myr-1 , while in the succeeding year 2014-2015 it subdued to 1.7 myr-1. The seasonal variation for the year 2015-2016 was estimated to be 7.9 myr-1. The seasonal variation for 2016-2017 was 17.4 myr-1 .
Mapping Velocity of the Potsdam Glacier, East Antarctica Using LANDSAT-8 Data
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019
Most of the glaciers have been retreating and thinning globally due to climate change. Glacier velocity is one such important parameter of glacier dynamics, which helps to understand the mass balance. The variations in velocity at different areas of the glacier can be used to identify the zones of ablation and accumulation. Zones of accumulation are identified as areas with higher velocity. This data is useful to incorporate in the glacier mass balance analysis. This study aims to derive the glacier velocity, using feature tracking technique for Potsdam glacier, east Antarctica. Feature tracking is an efficient way to derive glacier velocity, which is based on a cross-correlation algorithm that seeks offsets of the maximal correlation window on repeated satellite images. In this technique, two temporally different images are acquired for the same area and a distinct feature on both images is identified and the velocity is calculated with respect to the movement of that particular feature from one image to the other. Landsat-8 data for the year 2016 was used to derive velocity. Finer resolution promotes better feature tracking so the panchromatic band (band 8) of Landsat-8 OLI with a resolution of 15 m was utilized for deriving velocity. This technique was performed using COSI-Corr module in ENVI. This tool calculates displacement between the east-west and north-south directions, and the resultant velocity is calculated using the displacement in both directions and the temporal difference of two images. The velocity map generated at a resolution of 240 m showed that the resultant velocity ranged between 18.60 and 285.28 ma-1. Bias and root mean square error (RMSE) have been calculated with respect to the point-by-point MEaSUREs data provided by National Snow and Ice Data Centre at 1000 m resolution. The RMSE was found to be 78.06 ma-1 for 2016. The velocity for Potsdam glacier was also pictorially validated with the DGPS measurements from literature.
Spatiotemporal Changes in Velocity of Mellor Glacier, East Antarctica Using LANDSAT-8 Data
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2018
Glaciers all over the world are experiencing changes at varying stages due to changing climatic conditions. Minuscule changes in the glaciers in Antarctica can thus have major implications. The velocity of glaciers is important in several aspects of glaciology. A glacier's movement is caused by different factors such as gravity, internal deformation present in the ice, pressure caused by accumulation of snow, basal sliding etc. The velocity of a glacier is an important factor governing mass balance and the stability of the glacier. A glacier which moves fast generally brings more ice towards the terminus than a slow moving glacier. Thus, the glacier velocity can determine its load carrying capacity and gives indication on the 'health' of the glacier. Measurement of the ice flow velocity can help model glacier dynamics and thus provide increasing insights on different glacier subtleties. However, field measurements of velocity are limited in spatial and temporal domains because these operations are manual, tedious and logistically expensive. Remote sensing is a tool to monitor and generate such data without the need for physical expeditions. This study uses optical satellite imagery to understand the mechanisms involved in the movement of a glacier. Optical image correlation method (COSI-Corr module) is chosen here as the promising method to derive displacement of a moving glacier. The principle involved in this technique is that two images acquired at different times are correlated to find the shift in the position of moving ice, which is then treated as displacement in the time interval. Employing this technique we estimated the velocity of Mellor glacier (73°30′S, 66°30′E), a tributary glacier of the Amery Ice Shelf, Antarctica, over a span of four years from 2014 to 2017. Correlation is performed using Landsat-8 panchromatic images of 15 m resolution. Optical images from Landsat 8 often have noise due to atmospheric conditions such as cloud cover, so we used only those images cloud with cloud cover less than 10%. The glacier is covered in 128 path frame and 112 by Landsat-8. The correlation frequency was calculated using the correlator engine. Window size taken here is 256 and step sizes is 64 for both x and y dimensions. Once the correlation is calculated for an image pair for a specific time-period, we obtain three different outputs. Two of them indicated displacement (one in x direction and another in y direction) and the remaining output provided signal to noise ratio. The band math tool using displacement outputs in ENVI software performed velocity calculations. This gives us a raster image showing velocity at each point or pixel. Some errors such as noise persist and their correction is performed in ArcGIS software. In order to get pure signals, we removed all the signals with a signal to noise ratio less than 0.9 and this was carried out using raster calculator tool. All the resultant velocity rasters were interpolated and bias was calculated between seasons of two consecutive years. Two maps were generated for each year, one for early summer i.e. from January to April and one from September to December using the resultant velocity raster. The mean values of velocities found for Mellor glacier from
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2014
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Science, 1991
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Annals of Glaciology, 1998
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2014
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