SYNOPTIC OBSERVATIONS OF CALVING EVENTS IN ANTARCTICA USING SPACEBORNE IMAGES

Iceberg calving is the detachment of ice from ice shelves or glaciers. Although calving is a natural phenomenon, an abnormal rate of calving can be a threat to ice shelves. Some of the events were so large, that an iceberg of approximately 150 × 50 km area was calved in a single event. The most recent reported iceberg calving event was Larsen C and it took place in July 2017. In addition to the large and widely reported calving events, there are several small calving events, which are also of great significance and contribute to the overall mass loss from Antarctica. This study focuses on small calving events in Antarctica along various coasts. Three calving events are studied here, all of them have occurred in the past. This study was performed using Google Earth and Landsat satellite imageries. The first event is identified to have occurred at the Knox coast in 2016. Even after the icebergs were calved, they remained intact with the ice shelf due to ice fronts. The second event took place at the Queen Mary Coast in the year 2014. This event was studied from 2009 to 2016 using Landsat satellite images and many rifts were observed. The third event took place at the Princess Astrid Coast in the year 2016. This event was monitored from 2014 and three icebergs were calved between the years 2014 to 2016. This study emphasizes the exploitation of optical satellite data for studying calving events in Antarctica. Various crevasses and rifts are observed on Landsat imageries, which can be the first sign of a calving process

EXTRACTION OF BLUE ICE AREA USING ALBEDO VALUE DERIVED FROM LANDSAT-8 SATELLITE DATA

Blue Ice Areas (BIAs) or bare ice areas are zones of glacier where surface mass balance is negative, sublimation forms the major ablation process and surface albedo is relatively small. Exceptionally dry and windy meteorological conditions over Antarctica favor the formation of large areas of net ablation on the ice sheet leading to formation of BIRs (Schytt, 1961). BIAs are major source of drinking water to research stations and serve as runways for airplanes in Antarctica. This study has been conducted on the Polar Record Glacier (PRG), Princess Elizabeth Land, East Antarctica, where more than 30% of area is covered by BIAs. The BIAs are extracted and estimated using the value of albedo which is the fraction of solar energy reflected from the surface back to space. A surface having a higher (lower) reflectivity occupies higher (lower) albedo. With an average value of blue ice albedo (also known as bare ice) of 0.55, it ranges from 0.52 to 0.66, due to its geographical area, katabatic wind and wind patterns, the direction of ice flow, rate of sublimation and ablation, surface temperature, etc. The extent of BIAs also depends upon climate and seasonal changes. Albedo is calculated using the Level-1 product of Landsat, this data product (images) is processed according to standard parameters such as Geo-referencing, re-sampling, re-projection and north-up image re-orientation. These data (Digital Numbers) were further calibrated to standard pixel value using multiplicative and additive rescaling factors from metadata provided with the Level-1 product and scaled for absolute reflectance. A further algorithm was applied to get albedo from Landsat-8 dataset. After processing the data, we detected some error in a few pixels, (∼20) which was normalized by using band math. Our result indicates that the range of albedo for the BIAs is decreasing (more surface absorption of solar radiation), which subsequently could promote warming of surface due to increase in the surface temperature. The decreasing rate of albedo suggest the possibility of less reflection of radiation to the atmosphere, more melting which leads to depletion in the BIAs. The carry home message is that the variation in different parameters like albedo of the glacier causes significant variation in the surface area and spatial extent of BIAs

CHANGES IN VELOCITY OF FISHER GLACIER, EAST ANTARCTICA USING PIXEL TRACKING METHOD

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 image-matching 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 15 m, 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

COMPARISON OF PIXEL AND OBJECT-BASED CLASSIFICATION TECHNIQUES FOR GLACIER FACIES EXTRACTION

Glacier facies are zones of snow on a glacier that have certain specific spectral characteristics that enable their characterization. The accuracy of their extraction will determine the end accuracy of the distributed mass balance model calibrated by this information. Therefore, coarse to medium resolution satellites are not preferable for this particular function as the data derived from such sensors will potentially blur out the minute spatial variations on the surface of a glacier. Very high resolution (VHR) sensors (such as, WorldView (WV)-1, 2, 3) are thus much more suited for this particular task. Hence, this study aims to extract the available glacier facies on the Sutri Dhaka glacier, Himalayas, using very high-resolution WorldView-2 (WV-2) imagery. Extensive pre-processing of the imagery was performed to prepare the data for this purpose. The steps incorporated for this purpose consist of 1) Data Calibration, 2) Mosaicking, 3) Pan Sharpening, 4) Generation of 3D surface, and 5) Digitization. Using image classification as the primary method of information extraction, this study tests the ever-popular pixel-based classification technique against the uprising object-based classification technique. In doing so, this study aims to determine the most accurate technique of information extraction for the WV-2 imagery in the given scenario. The presence of unique bands (Coastal (0.40–0.45 μm), Red Edge (0.705–0.745 μm), NIR-1 (0.770–0.895 μm) and NIR-2 (0.86–1.04 μm) in the multispectral range of WV-2, allows this study to perform facies classification through the development of customized spectral index ratios (SIRs) in the object-based domain. Establishment of thresholds was hence necessitated for information extraction through the developed SIRs. Three supervised classifiers, namely, a) Mahalanobis distance, b) Maximum likelihood, and c) Minimum distance to mean, were then used to perform classification, thereby allowing a comparative analysis between the classification schemes. Accuracy assessment for each classification scheme was performed using error matrices. The object-based approach achieved an overall accuracy of 90% (κ = 0.88) and the highest overall accuracy among the pixel-based classification methods is 78.57% (κ = 0.75). The results clearly portray that the object-based method delivered much higher accuracy than the pixel-based methods. The carry home message is that future studies must examine the transferability and accuracy of the customized SIRs in varying scenarios, as different scenarios will require varying threshold adjustments. Forthcoming studies can also develop sensor specific and unique indices for other sensors that are suitable for such applications

SPATIOTEMPORAL CHANGES IN VELOCITY OF MELLOR GLACIER, EAST ANTARCTICA USING LANDSAT-8 DATA

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 Jan-April 2014, 2015, 2016 and 2017 were 374.06 ma−1, 413.59 ma−1, 278.62 ma−1 and 406.66 ma−1, respectively. Velocities for September-December 2014, 2015, 2016 and 2017 were found to be 334.63 ma−1, 334.43 ma−1, 367.37 ma−1 and 381.31 ma−1, respectively. The biases are computed for all the seasons of four years and root mean square (RMSE) values are estimated. These RMSE values signify the season-wise variations in the velocities. RMSE values for season of Jan–April 2014–15, 2015–16 and 2016–17 were 75.92 ma−1, 147.82 ma−1, and 133.33 ma−1, respectively. Similarly, RMSE values for season of September-December 2014–15, 2015–16 and 2016–17 are 35.7 ma−1, 51.29 ma−1 and 35.84 ma−1 respectively. The results showed variations in velocities for different seasons. We plan to integrate this data with the discharge rates, to estimate mass balance and melting rates of the glacier to decipher mechanisms at work for the Mellor glacier

SEASONAL COMPARISON OF VELOCITY OF THE EASTERN TRIBUTARY GLACIERS, AMERY ICE SHELF, ANTARCTICA, USING SAR OFFSET TRACKING

Antarctica and Greenland are two major Earth’s continental ice shelves which play an important role in influencing Earth’s energy balance through their high albedo. The ice sheets comprise of grounded ice or the continental glaciers and their associated ice shelves. Surface velocity is an important parameter that needs to be monitored to understand the glacier dynamics. Marine terminating glaciers have higher velocity than land terminating glaciers. Therefore, ice shelves are generally observed to have higher velocity as compared to continental glaciers. The focus of this study is Amery ice shelf (AIS) which is the third largest ice shelf located in east Antarctica terminating into the Prydz Bay on the eastern Antarctica. The surface ice-flow velocity of AIS is very high compared to its surrounding glaciers which flows at a rate of 1400 m a−1 and drains about 8% of the Antarctic ice sheet. AIS is fed by different glaciers and ice streams at the head, as well as from the western and eastern side of the ice shelf before it terminates into the ocean. The primary objective of this study was to compute velocity of the eastern tributary glaciers of AIS using SAR from Sentinel-1 data. The secondary objective was to compare the winter and summer velocities of the glaciers for 2017–2018. The offset tracking method has been applied to the ground range detected (GRD) product obtained from Sentinel-1 satellite. This method is suitable for regions with higher glacier velocity where interferometry is generally affected by the loss of coherence. The offset tracking method works by tracking the features on the basis of another feature and calculates the offset between the two features in the images. Two tributary glaciers near the Clemence massif and another glacier near the Pickering Nunatak feed into this ice shelf from the eastern glacial basin region that drains ice from the American Highland, east Antarctica. The glaciers near the Clemence massif showed low annual velocity which ranged from 100 m a−1 at the head to ∼300 m a−1 near the end of the glacier, where it merges with AIS. The glaciers flowing near the Pickering Nunatak exhibited moderate velocity ranging from 150 m a−1 at its head and reaching up to 450 m a−1 near the tongue. The summer velocity (March 2018) was observed to be higher than the velocity in winter (July 2017) and the difference between the summer and the winter velocities was found to be between 50 m a−1 and 130 m a−1. The results for the velocity were obtained at 120 m resolution and were compared with the previous MEaSUREs (Making Earth System Data Records for Use in Research Environments) yearly velocity at 450 m and 1 km resolution provided by National Snow and Ice Data Center portal. The results were evaluated using statistical measure- bias and the accuracy was derived using the root mean square error. The bias did not exceed 20 m a−1 for the three glaciers and the accuracy was observed to be more than 85% for most of the regions. The accuracy of the results suggests that the offset tracking technique is useful for future velocity estimation in the regions of high glacier velocity

Innovative methods to monitor rock and mountain slope deformation

Displacement rates of mountain slope deformations that can affect entire valley mountain flanks are often measured spatially distributed in‐situ without spatial significance. The spatially explicit measurement and recording of time series of slope deformations is a challenge, as the unstable slopes are often disintegrated into several subdomains, which move with different deformation rates. The current state‐of‐the‐art monitoring systems detect slow to very slow deformation rates between mm/a and several m/a. Using the examples of slope deformations in Saalbach‐Hinterglemm and the deep rock slide Marzellkamm in Austria this paper presents the results of terrestrial laser scans, extensometer measurements, Spaceborne InSAR data, unmanned Aerial System Photogrammetry (UAS‐P), and fixed‐point measurements. The different measurements complement each other and are optimally aligned for different application areas. InSAR data can help to identify hot spots on regional and local scale, while UAS‐P enables for spatially high level accuracy in the detection of subdomains moving at different speeds. For local warning systems TLS, extensometers and GBInSAR deliver higher accuracy