The response of Black Rapids Glacier, Alaska, to the Denali earthquake rock avalanches

We describe the impact of three simultaneous earthquake-triggered rock avalanches on the dynamics of Black Rapids Glacier, Alaska, by using spaceborne radar imagery and numerical modeling. We determined the velocities of the glacier before and after landslide deposition in 2002 by using a combination of ERS-1/ERS-2 tandem, RADARSAT-1, and ALOS PALSAR synthetic aperture radar data. Ice velocity above the debris-covered area of the glacier increased up to 14% after the earthquake but then decreased 20% by 2005. Within the area of the debris sheets, mean glacier surface velocity increased 44% within 2 years of the landslides. At the downglacier end of the lowest landslide, where strong differential ablation produced a steep ice cliff, velocities increased by 109% over the same period. By 2007, ice velocity throughout the debris area had become more uniform, consistent with a constant ice flux resulting from drastically reduced ablation at the base of the debris. Without further analysis, we cannot prove that these changes resulted from the landslides, because Black Rapids Glacier displays large seasonal and interannual variations in velocity. However, a full Stokes numerical ice flow model of a simplified glacier geometry produced a reversal of the velocity gradient from compressional to extensional flow after 5 years, which supports our interpretation that the recent changes in the velocity field of the glacier are related to landslide-induced mass balance changes

Advanced Geoscience Remote Sensing

Nowadays, advanced remote sensing technology plays tremendous roles to build a quantitative and comprehensive understanding of how the Earth system operates. The advanced remote sensing technology is also used widely to monitor and survey the natural disasters and man-made pollution. Besides, telecommunication is considered as precise advanced remote sensing technology tool. Indeed precise usages of remote sensing and telecommunication without a comprehensive understanding of mathematics and physics. This book has three parts (i) microwave remote sensing applications, (ii) nuclear, geophysics and telecommunication; and (iii) environment remote sensing investigations

Geostatistical and statistical classification of sea-ice properties and provinces from SAR data

Recent drastic reductions in the Arctic sea-ice cover have raised an interest in understanding the role of sea ice in the global system as well as pointed out a need to understand the physical processes that lead to such changes. Satellite remote-sensing data provide important information about remote ice areas, and Synthetic Aperture Radar (SAR) data have the advantages of penetration of the omnipresent cloud cover and of high spatial resolution. A challenge addressed in this paper is how to extract information on sea-ice types and sea-ice processes from SAR data. We introduce, validate and apply geostatistical and statistical approaches to automated classification of sea ice from SAR data, to be used as individual tools for mapping sea-ice properties and provinces or in combination. A key concept of the geostatistical classification method is the analysis of spatial surface structures and their anisotropies, more generally, of spatial surface roughness, at variable, intermediate-sized scales. The geostatistical approach utilizes vario parameters extracted from directional vario functions, the parameters can be mapped or combined into feature vectors for classification. The method is flexible with respect to window sizes and parameter types and detects anisotropies. In two applications to RADARSAT and ERS-2 SAR data from the area near Point Barrow, Alaska, it is demonstrated that vario-parameter maps may be utilized to distinguish regions of different sea-ice characteristics in the Beaufort Sea, the Chukchi Sea and in Elson Lagoon. In a third and a fourth case study the analysis is taken further by utilizing multi-parameter feature vectors as inputs for unsupervised and supervised statistical classification. Field measurements and high-resolution aerial observations serve as basis for validation of the geostatistical-statistical classification methods. A combination of supervised classification and vario-parameter mapping yields best results, correctly identifying several sea-ice provinces in the shore-fast ice and the pack ice. Notably, sea ice does not have to be static to be classifiable with respect to spatial structures. In consequence, the geostatistical-statistical classification may be applied to detect changes in ice dynamics, kinematics or environmental changes, such as increased melt ponding, increased snowfall or changes in the equilibrium line

Ice island thinning : rates and model calibration with in situ observations from Baffin Bay, Nunavut

Funding: Instrument development and fieldwork were supported by the Northern Transportation Adaptation Initiative of Transport Canada, the Polar Knowledge Canada Safe Passage project (no. 1516-065), and Polar Knowledge Canada's Northern Scientific Training Program. Anna J. Crawford received personal funding from the Garfield Weston Foundation, the Natural Sciences and Engineering Research Council (Canada), and Environment and Climate Change Canada.A 130 km2 tabular iceberg calved from Petermann Glacier innorthwestern Greenland on 5 August 2012. Subsequent fracturing generated manyindividual large “ice islands”, including Petermann ice island (PII)-A-1-f, which drifted between Nares Strait and the North Atlantic.Thinning caused by basal and surface ablation increases the likelihood thatthese ice islands will fracture and disperse further, thereby increasing therisk to marine transport and infrastructure as well as affecting thedistribution of freshwater from the polar ice sheets. We use a uniquestationary and mobile ice-penetrating radar dataset collected over fourcampaigns to PII-A-1-f to quantify and contextualize ice island surface andbasal ablation rates and calibrate a forced convection basal ablation model.The ice island thinned by 4.7 m over 11 months. The majority of thinning (73 %) resulted from basal ablation, but the volume loss associated withbasal ablation was ∼12 times less than that caused by arealreduction (e.g. wave erosion, calving, and fracture). However, localizedthinning may have influenced a large fracture event that occurred along asection of ice that was ∼40 m thinner than the remainder ofthe ice island. The calibration of the basal ablation model, the first knownto be conducted with field data, supports assigning thetheoretically derived value of 1.2×10−5 m2∕5 s−1/5 ∘C−1 to the model's bulk heat transfercoefficient with the use of an empirically estimated ice–ocean interfacetemperature. Overall, this work highlights the value of systematicallycollecting ice island field data for analyzing deterioration processes,assessing their connections to ice island morphology, and adequatelydeveloping models for operational and research purposes.Publisher PDFPeer reviewe

The Sentinel-1 mission for the improvement of the scientific understanding and the operational monitoring of the seismic cycle

We describe the state of the art of scientific research on the earthquake cycle based on the analysis of Synthetic Aperture Radar (SAR) data acquired from satellite platforms. We examine the achievements and the main limitations of present SAR systems for the measurement and analysis of crustal deformation, and envision the foreseeable advances that the Sentinel-1 data will generate in the fields of geophysics and tectonics. We also review the technological and scientific issues which have limited so far the operational use of satellite data in seismic hazard assessment and crisis management, and show the improvements expected from Sentinel-1 dat

SAR Remote Sensing of Canadian Coastal Waters using Total Variation Optimization Segmentation Approaches

The synthetic aperture radar (SAR) onboard Earth observing satellites has been acknowledged as an integral tool for many applications in monitoring the marine environment. Some of these applications include regional sea-ice monitoring and detection of illegal or accidental oil discharges from ships. Nonetheless, a practicality of the usage of SAR images is greatly hindered by the presence of speckle noises. Such noise must be eliminated or reduced to be utilized in real-world applications to ensure the safety of the marine environment. Thus this thesis presents a novel two-phase total variation optimization segmentation approach to tackle such a challenging task. In the total variation optimization phase, the Rudin-Osher-Fatemi total variation model was modified and implemented iteratively to estimate the piecewise smooth state by minimizing the total variation constraints. In the finite mixture model classification phase, an expectation-maximization method was performed to estimate the final class likelihoods using a Gaussian mixture model. Then a maximum likelihood classification technique was utilized to obtain the final segmented result. For its evaluation, a synthetic image was used to test its effectiveness. Then it was further applied to two distinct real SAR images, X-band COSMO-SkyMed imagery containing verified oil-spills and C-band RADARSAT-2 imagery mainly containing two different sea-ice types to confirm its robustness. Furthermore, other well-established methods were compared with the proposed method to ensure its performance. With the advantage of a short processing time, the visual inspection and quantitative analysis including kappa coefficients and F1 scores of segmentation results confirm the superiority of the proposed method over other existing methods

Synthetic aperture radar analysis of floating ice at Terra Nova Bay-an application to ice eddy parameter extraction

In the framework of a study of ice formation in Antarctica, synthetic aperture radar (SAR) image acquisitions were planned over Terra Nova Bay (TNB). Thanks to the European Space Agency (ESA) Third Party Mission program, Cosmo-SkyMed and Radarsat-2 images over TNB were obtained for the period of February 20 to March 20, 2015; in addition, available Sentinel-1 images for the same period were retrieved from the ESA scientific data hub. The first inspection of the images revealed the presence of a prominent eddy, i.e., an ice vortex presumably caused by the wind blowing from the continent. The important parameters of an eddy are its area and lifetime. While the eddy lifetime was easily obtained from the image sequence, the area was measured using a specific processing scheme that consists of nonlinear filtering and Markov random field segmentation. The main goal of our study was to develop a segmentation scheme to detect and measure "objects" in SAR images. In addition, the connection between eddy area and wind field was investigated using parametric and nonparametric correlation functions; statistically significant correlation values were obtained in the analyzed period. After March 15, a powerful katabatic wind completely disrupted the surface eddy