In August 2010 a 265 km2 ice island calved from the Petermann Glacier innorthern Greenland. Soon after the initial calving event the mass broke intoseveral pieces, some of which exited Baffin Bay and drifted south toward theLabrador coast. By June 2011 PII-A, a large fragment of the initial PetermannIce Island, was situated offshore Labrador and in one week it had moved 225 kmdown the coast. Concern arose that if PII-A continued its trajectory it couldreach the Grand Banks by August 2011, posing a potential risk for existinginfrastructure in the offshore region of Newfoundland. To properly assess thepotential risk a realistic estimate of ice mass was necessary. This in turnrequired field measurements of the ice islands thickness. A three-day field program was carried out on the Petermann Ice Islands, PII-A and PII-A-a, from June 17–19, 2011. At this time PII-A and PII-A-a weresituated offshore Labrador, Canada, approximately 100 km northeast of the townof Rigolet. Geophysical survey methods, including Ground Penetrating Radar(GPR) and Seismic Reflection, were used to identify the base of the islands andobtain ice thickness measurements at various locations. Eight satellitetracking beacons were deployed on PII-A and one was deployed on PII-A-a. Ablation data, photographs and video footage were also obtained during theprogram. On July 22, 2011, PII-A was revisited while it was situated off thesouthern Labrador coast. GPR measurements were acquired at the pre-existingstations; the measurements allowed for deterioration rates due to surface andbasal melting to be calculated for PII-A. Results of the field measurementsindicate that ice thickness varied between 50 to 80 m on PII-A; the thicknessof PII-A-a was 30 m at a single survey location. Surface melt rates of 2.7–6.3cm day-1 were observed over a 1-day period in June. For the 35-day periodbetween June and July visits, average surface and basal melt of 5.0 cm day-1and 3.4 cm day-1, respectively, were calculated.
Information on the locations and characteristics of extreme sea ice features (such as, hummocks, ridges, stamukhas and icebergs) is important for various marine applications. Imagery acquired by high resolution optical satellites was previously used for qualitative image interpretation to identify various sea ice features and it is especially valuable when detailed ground validation is not available. Current optical satellites, such as GeoEye-1, are able to acquire images with very high resolution of 0.5m. This work addresses the problem of quantitative retrieval of ice feature parameters from very high resolution optical imagery. The developed algorithms facilitate extraction of ice feature height from shadow and derivation of statistical information on ice deformation parameters. Automated processing of GeoEye-1 image demonstrated capabilities of retrieval of ridge frequency and segmentation of rubble fields.
Well sites, including both well pads and exploratory core holes, are small polygonal landscape disturbance features approximately one half to one hectare (0.5-1 ha) in area, resulting from oil and gas exploration activities. Automatic extraction and monitoring of such small features using remote-sensing technology at regional scales has always been desirable for wildlife habitat monitoring and environmental planning and modelling. Due to the vast disturbances of well sites in a province like Alberta, Canada, high-resolution imagery is not practical for well site extraction. For operational purposes, mid-resolution and cost-effective satellite imagery such as Landsat is the choice. However, automatic well site extraction using midresolution satellite imagery is a challenging task. Wells are typically less than three pixels in width and length in a Landsat multispectral image. Furthermore, the spectral contrast between the well site pixels and the surrounding areas is low due to vegetation regrowth and the spectral complexity of the surrounding environment. This article presents a novel methodology for automatic extraction of well sites from Landsat-5 TM imagery. The method combines both pixel-and objectbased image analyses and contains three major steps: geometric enhancement, segmentation, and well site extraction. The method was applied to Landsat-5 TM images acquired over Fort McMurray, Alberta, Canada. For accuracy assessment, four regions of interest were selected and the results of the proposed automatic method were evaluated against visual inspection of the Landsat-8 pan-sharpened image. The method results in a total average correctness, completeness, and quality measures of about 80, 96, and 77%, respectively over the four sites. In addition, the method is very fast as an entire Landsat scene is processed in less than 10 minutes. The method is an operational approach for automatic detection of well sites over the entire province and can dramatically reduce the labour cost of manual digitization for monitoring and updating well site maps.
The Canadian Arctic is a highly dynamic environment that has the oldest and thickest sea ice in the world. The ice includes various features hazardous for shipping and offshore operations. The paper describes a technology addressing the problem of satellite based monitoring of hazardous features, which include ice ridges, hummocks and rubble fields. Additional attention was paid to identifying glacier ice (ice islands and icebergs). The technology was demonstrated using images collected over the Canadian Arctic in 2013-2014 and ice features were verified by analyzing high resolution satellite optical images that overlap spatially and temporally with the synthetic aperture radar (SAR) data. Various satellite images and data fusion techniques have been explored for identifying ice features and retrieving their characteristics. Ice parameters being studied include height, size and frequency of ice features.
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