From the abstract of some of the referenced papers: An expert system is being developed which will apply knowledge-based techniques to the automated interpretation of remotely sensed sea-ice images taken over East Antarctica by the NOAA series of meteorological satellites. It is capable of accepting satellite images, deriving characteristic features from them and then performing knowledge-based reasoning to identify regions of cloud, land, open water and various categories of sea-ice. XXXXXXXXXXXXX This paper describes the system design of SPARTEX, a system developed to use information from remote sensing and geographic information systems linked to expert systems. It aims to automate the process of classifying information about the actual or potential use of part of the earth's surface. See the link below for public details on this project.

This data set contains the results from a study of the behaviour of Weddell seals (Leptonychotes weddelli) at the Vestfold Hills, Prydz Bay, Antarctica. Three satellite transmitters were deployed on tagged female Weddell seals at the Vestfold Hills mid-winter (June) 1999. The transmitters were recovered in December, late in the pupping season. In total, the three transmitters were deployed and active 170 days, 175 days and 180 days. I used the first two classes of data to get fixes with a standard deviation less than 1 km. Most seal holes were more that 1 km apart (see Entry: wed_survey) so at this resolution we can distinguish between haul-out sites. We examine the number and range of locations used by the individual seals. We use all data collectively to look at diurnal and seasonal changes in haul-out bouts. None of the seals were located at sites outside the area of fast ice at the Vestfold Hills, although one seal was sighted on new fast-ice (20 - 40 cm thick). Considering the long bouts in the water, and that we only tracked haul-out locations, the results do not eliminate the possibility that the seals made long trips at sea. The original data are stored by the Australian Antarctic Division in the ARGOS system on the mainframe Alpha. The transmitter numbers are 23453, 7074 and 7075.

Despite being a ubiquitous and abundant component of the Southern Ocean ecosystem, pack-ice seals (crabeater, Ross and leopard seals) are notoriously difficult to census as they are sparsely distributed over large regions of remote pack-ice. Historically, population censuses have been made from ship- or helicopter-based surveys, which are expensive and logistically difficult, and this inevitably leads to data which are limited, in time and space. High resolution images allow us now to accurately census seals e.g. elephant and Weddell seals at unprecedented spatial and temporal scales. Using this technology promises to provide regular estimates of the numbers of pack-ice seals in important regions such as Prydz Bay This study will develop techniques to survey pack-ice seals from high resolution satellite images, including automatic detection functions and a preliminary habitat model based on the characteristics of the ice contained in the images.

An occupancy survey on 21 January 2011 found a total of 7 islands along the Wilkes Land coastline had populations of breeding Adelie penguins. The survey was conducted from a fixed wing aircraft and oblique aerial photographs were taken of each occupied site except Haswell Island. The aerial photographs were geo-referenced to a satellite image and the boundaries of penguin colonies were digitised from the geo-referenced photos. Details for each island are: Adams: Photographs taken on 21 January 2011 and geo-referenced to a Quickbird satellite image taken on 30 January 2009 Fulmar: Photographs taken on 21 January 2011 and geo-referenced to a WorldView2 satellite image taken on 6 February 2011 Zykov: Photographs taken on 21 January 2011 and geo-referenced to a WorldView2 satellite image taken on 6 February 2011 Buromskiy: Photographs taken on 21 January 2011 and geo-referenced to a WorldView2 satellite image taken on 6 February 2011 Stroitley: Photographs taken on 21 January 2011 and geo-referenced to a WorldView2 satellite image taken on 6 February 2011 Tokarev: Photographs taken on 21 January 2011 and geo-referenced to a WorldView2 satellite image taken on 6 February 2011 Haswell: No photographs taken, no penguin colonies were digitised Note there are two colony boundary layers in each folder except Adams. One is the original layer mapped as above. The second is an adjusted layer that was created so that the mapped boundaries would land on the exposed rock layer. Mapping of some of the islands contained within the coast layer had been coarsely done using imagery available at the time. Now with more accurate satellite imagery the island mapping could potentially be updated which would more accurately locate these islands. If this occurred, the original colony boundary mapping may be a more appropriate fit. Please refer to the Seabird Conservation Team Data Sharing Policy for use, acknowledgement and availability of data prior to downloading data.

An occupancy survey in December 2009-February 2010 and January 2011 found a total of 6 islands along the Knox coast had populations of breeding Adelie penguins. The survey in 2009/10 was conducted from a fixed wing aircraft and oblique aerial photographs were taken of occupied sites. The aerial photographs were geo-referenced to satellite images or the coastline shapefile from the Landsat Image Mosaic of Antarctica (LIMA, tile E157) and the boundaries of penguin colonies were digitised from the geo-referenced photos. Details for each island are: Merrit: Photographs taken on 1 February 2010 and geo-referenced to LIMA tile E157 Cape Nutt: Photographs taken on 5 January 2010 and geo-referenced to a Quickbird satellite image taken on 17 February 2011 Ivanoff Head: Photographs taken on 27 December 2009 and geo-referenced to LIMA tile E157 Please refer to the Seabird Conservation Team Data Sharing Policy for use, acknowledgement and availability of data prior to downloading data.

The demographic performance of high level antarctic predators is ultimately determined by the oceanic processes that influence the spatial and temporal distribution of primary productivity. This study will quantify the links between the foraging performance of southern elephant seals and a range of oceanographic parameters, including sea surface temperature, productivity and bathymetry. These data are a crucial component in understanding how antarctic predators will respond to changes in the distribution of marine and will be an important contribution to our understanding of the on-going decline in elephant seal numbers. Data were originally collected on Time Depth Recorders (TDRs), and stored in hexadecimal format. Hexadecimal files can be read using 'Instrument Helper', a free download from Wildlife Computers (see the URL given below). However, these data have been replaced by an Access Database version, and have also been loaded into the Australian Antarctic Data Centre's ARGOS tracking database. The database can be accessed at the provided URLs.

Satellite derived tracks of humpback whales tagged on their Antarctic feeding grounds. Data can be found here: https://data.aad.gov.au/aadc/argos/display_campaign.cfm?campaign_id=83 Satellite tags were deployed on adult humpback whales with a modified version of the Air Rocket Transmitter System (ARTS, Restech) and a purpose-designed projectile carrier at a pressure of 7.5 – 10 bar. A custom-designed, 80mm anchor section is attached to a stainless steel cylindrical housing containing a location-only transmitter (SPOT-5 by Wildlife Computers, Redmond, Washington, USA and Kiwisat 202 Cricket by Sirtrack, Havelock North, New Zealand). This superseded anchor design resulted in the anchor section disarticulating upon deployment in order to achieve improved tag retention times while minimising impact. The tags were sterilised with ethylene oxide prior to deployment and implanted up to 290mm into the skin, blubber, interfacial layers and outer muscle mass of the whale. Tags were programmed to transmit to the Argos satellite system at various duty cycles and repetition rates for a maximum of 720 transmissions per day. These transmissions are relayed to processing centres which calculate the transmitter’s location by measuring the Doppler Effect on transmission frequency.

This csv details the raw Argos locations generated from satellite tags attached to pygmy blue whales in order to describe their migratory movements through Australian waters as described in: Double MC, Andrews-Goff V, Jenner KCS, Jenner M-N, Laverick SM, et al. (2014) Migratory Movements of Pygmy Blue Whales (Balaenoptera musculus brevicauda) between Australia and Indonesia as Revealed by Satellite Telemetry. PLoS ONE 9(4): e93578. doi:10.1371/journal.pone.0093578 This csv includes the following data fields - ptt: the unique Argos identifier assigned to each satellite tag gmt: the date and time in gmt with the format 'yyyy-mm-dd hh:mm:ss' class: the Argos assigned location class (see paper for details) latitude longitude deploydate: deployment date and time in gmt for each tag with the format 'yyyy-mm-dd hh:mm:ss' filt: the outcome of the sdafilter (see paper for details) - either "removed" (location removed by the filter), "not" (location not removed) or "end_location" (location at the end of the track where the algorithm could not be applied)

This metadata record is a modified child record of an original parent record originating from custodians of data associated with Geoscience Australia (The identifier of the parent record is ANZCW0703009248, and can be found on the Australian Spatial Data Directory website - see the URL given below). Taken from the report: A bathymetric grid of the Heard Island-Kerguelen Plateau Region (Longitudes 68 degrees E - 80 degrees E, Latitudes 48 degrees S - 56 degrees S) is produced. In doing so, the individual datasets used have been closely examined and any deficiencies noted for further follow up or have been rectified immediately and the changes documented. These datasets include modern multibeam data, coastline data obtained from the World Vector Shoreline, echosounder data from research, fishing and Customs vessels and satellite derived bathymetric data. A hierarchical system was employed whereby the best and most extensive datasets were gridded first and applied as a mask to the next best dataset. A new masking grid would be formed from these datasets to pass non-overlapping data in the next best dataset. This procedure was employed until finally the satellite data were masked. All the various levels of masked data were then brought together by the gridding algorithm (Intrepid - Desmond Fitzgerald Associates) and an ERMapper format grid produced. A grid cell size of 0.005 degrees (nominal 500m) was used with many iterations of minimum curvature gridding and several passes of smoothing. The final grid is available in ERMapper, ArcInfo and ASCII xyz formats.

A summary of landfast sea ice coverage and the changes in the distance between the penguin colony at Point Geologie and the nearest span of open water on the Adelie Land coast in East Antarctica. The data were derived from cloud-free NOAA Advanced Very High Resolution Radiometer (AVHRR) data acquired between 1-Jan-1992 and 31-Dec-1999. The areal extent and variability of fast ice along the Adelie Land coast were mapped using time series of NOAA AVHRR visible and thermal infrared (TIR) satellite images collected at Casey Station (66.28 degrees S, 110.53 degrees E). The AVHRR sensor is a 5-channel scanning radiometer with a best ground resolution of 1.1 km at nadir (Cracknell 1997, Kidwell 1997). The period covered began in 1992 due to a lack of sufficient AVHRR scans of the region of interest prior to this date and ended in 1999 (work is underway to extend the analysis forward in time). While cloud cover is a limiting factor for visible-TIR data, enough data passes were acquired to provide sufficient cloud-free images to resolve synoptic-scale formation and break-up events. Of 10,297 AVHRR images processed, 881 were selected for fast ice analysis, these being the best for each clear (cloud-free) day. The aim was to analyse as many cloud-free images as possible to resolve synoptic-scale variability in fast ice distribution. In addition, a smaller set of cloud-free images were obtained from the Arctic and Antarctic Research Center (AARC) at Scripps Institution of Oceanography, comprising 227 Defense Meteorological Satellite Program (DMSP) Operational Linescan Imager (OLS) images (2.7 km resolution) and 94 NOAA AVHRR images at 4 km resolution. The analysis also included 2 images (spatial resolution 140 m) from the US Argon surveillance satellite programme, originally acquired in 1963 and obtained from the USGS EROS Data Center (available at: edcsns17.cr.usgs.gov/EarthExplorer/). Initial image processing was carried out using the Common AVHRR Processing System (CAPS) (Hill 2000). This initially produces 3 brightness temperature (TB) bands (AVHRR channels 3 to 5) to create an Ice Surface Temperature (IST) map (after Key 2002) and to enable cloud clearing (after Key 2002 and Williams et al. 2002). Fast ice area was then calculated from these data through a multi-step process involving user intervention. The first step involved correcting for anomalously warm pixels at the coast due to adiabatic warming by seaward-flowing katabatic winds. This was achieved by interpolating IST values to fast ice at a distance of 15 pixels to the North/South and East/ West. The coastline for ice sheet (land) masking was obtained from Lorenzin (2000). Step 2 involved detecting open water and thin sea ice areas by their thermal signatures. Following this, old ice (as opposed to newly-formed ice) was identified using 2 rules: the difference between the IST and TB (band 4, 10.3 to 11.3 microns) for a given pixel is plus or minus 1 K and the IST is less than 250 K. The final step, i.e. determination of the fast ice area, initially applied a Sobel edge-detection algorithm (Gonzalez and Woods 1992) to identify all pixels adjacent to the coast. A segmentation algorithm then assigned a unique value to each old ice area. Finally, all pixels adjacent to the coast were examined using both the segmented and edge-detected images. If a pixel had a value (i.e. it was segmented old ice), then this segment was assumed to be attached to the coast. This segment's value was noted and every pixel with the same value was classified as fast ice. The area was then the product of the number of fast ice pixels and the resolution of each pixel. A number of factors affect the accuracy of this technique. Poorly navigated images and large sensor scan angles detrimentally impact image segmentation, and every effort was taken to circumvent this. Moreover, sub-pixel scale clouds and leads remain unresolved and, together with water vapour from leads and polynyas, can contaminate the TB. In spite of these potential shortcomings, the algorithm gives reasonable and consistent results. The accuracy of the AVHRR-derived fast ice extent retrievals was tested by comparison with near- contemporary results from higher resolution satellite microwave data, i.e. from the Radarsat-1 ScanSAR (spatial resolution 100 m over a 500 km swath) obtained from the Alaska Satellite Facility. The latter were derived from a 'snapshot' study of East Antarctic fast ice by Giles et al. (2008) using 4 SAR images averaged over the period 2 to 18 November 1997. This gave an areal extent of approximately 24,700 km2. The comparative AVHRR-derived extent was approximately 22,240 km2 (average for 3 to 14 November 1997). This is approximately 10% less than the SAR estimate, although the estimates (images) were not exactly contemporary. Time series of ScanSAR images, in combination with bathymetric data derived from Porter-Smith (2003), were also used to determine the distribution of grounded icebergs. At the 5.3 GHz frequency (? = 5.6 cm) of the ScanSAR, icebergs can be resolved as high backscatter (bright) targets that are, in general, readily distinguishable from sea ice under cold conditions (Willis et al. 1996). In addition, an estimate was made from the AVHRR derived fast ice extent product of the direct-path distance between the colony at Point Geologie and the nearest open water or thin ice. This represented the shortest distance that the penguins would have to travel across consolidated fast ice in order to reach foraging grounds. A caveat is that small leads and breaks in the fast ice remain unresolved in this satellite analysis, but may be used by the penguins. We examine possible relationships between variability in fast ice extent and the extent and characteristics of the surrounding pack ice (including the Mertz Glacier polynya to the immediate east) using both AVHRR data and daily sea ice concentration data from the DMSP Special Sensor Microwave/Imager (SSM/I) for the sector 135 to 145 degrees E. The latter were obtained from the US National Snow and Ice Data Center for the period 1992 to 1999 inclusive (Comiso 1995, 2002). The effect of variable atmospheric forcing on fast ice variability was determined using meteorological data from the French coastal station Dumont d'Urville (66.66 degrees S, 140.02 degrees E, WMO #89642, elevation 43 m above mean sea level), obtained from the SCAR READER project ( www.antarctica.ac.uk/met/READER/). Synoptic- scale circulation patterns were examined using analyses from the Australian Bureau of Meteorology Global Assimilation and Prediction System, or GASP (Seaman et al. 1995).