Maps of East Antarctic landfast sea-ice extent, generated from approx. 250,000 1 km visible/thermal infrared cloud-free MODIS composite imagery (augmented with AMSR-E 6.25-km sea-ice concentration composite imagery when required). Because of imperfections in the MODIS composite images (typically caused by inaccurate cloud masking, persistent cloud in a given region, and/or a highly dynamic fast-ice edge), automation of the fast-ice extent retrieval process was not possible. Each image was thus classified manually. A study of errors/biases of this process revealed that most images were able to be classified with a 2-sigma accuracy of +/- ~3%. More details are provided in Fraser et al., (2010). *Version 1.2 with extra QC around the Mawson coast and Lutzow-Holm Bay The directory named "pngs" contains browsable maps of fast-ice extent, in the form of Portable Network Graphics (PNG) images. Each of the 159 consecutive images (20-day intervals from Day Of Year (DOY) 61-80, 2000 to DOY 341-366, 2008) contains a map of fast-ice extent along the East Antarctic coast, generated from MODIS and AMSR-E imagery. The colour scale is as follows: Dark blue: Fast ice, as classified from a single 20-day MODIS composite image Red: Fast ice, as classified using the previous or next 20-day MODIS composite images Yellow: Fast ice, as classified using a single 20-day AMSR-E composite image White: Antarctic continent (including ice shelves), as defined using the Mosaic of Antarctica product. Light blue: Southern ocean/pack ice/icebergs These maps are also provided as unformatted binary fast ice images, in the directory named "imgs". These .img files are all flat binary images of dimension 4300 * 425 pixels. The data type is 8-bit byte. Within the .img files, the value for each pixel indicates its cover: 0: Southern Ocean, pack ice or icebergs, corresponding to light blue in the PNG files. 1: Antarctic continent (including ice shelves), as defined using the Mosaic of Antarctica product, corresponding to white in the PNG files. 2: Fast ice, as classified from a single 20-day MODIS composite image, corresponding to dark blue in the PNG files 3: Fast ice, as classified using a single 20-day AMSR-E composite image, corresponding to yellow in the PNG files 4: Fast ice, as classified using the previous or next 20-day MODIS composite images, corresponding to red in the PNG files To assist in georeferencing these data, files containing information on the latitude and longitude of each pixel are provided in the directory named "geo". These files are summarised as follows: lats.img: File containing the latitude of the centre of each pixel. File format is unformatted 32-bit floating point, 4300 * 425 pixels. lons.img: File containing the longitude of the centre of each pixel. File format is unformatted 32-bit floating point, 4300 * 425 pixels. The .gpd Grid Point Descriptor file used to build the projection is also included. It contains parameters which you can use for matching your projection. To refer to the time series, climatology, or maps of average persistence, please reference this paper: Fraser, A. D., R. A. Massom, K. J. Michael, B. K. Galton-Fenzi, and J. L. Lieser, East Antarctic landfast sea ice distribution and variability, 2000-08, Journal of Climate 25, 4, pp. 1137-1156, 2012 In addition, please cite the following reference when describing the process of generating these maps: Fraser, A. D., R. A. Massom, and K. J. Michael, Generation of high-resolution East Antarctic landfast sea-ice maps from cloud-free MODIS satellite composite imagery, Elsevier Remote Sensing of Environment, 114 (12), 2888-2896, doi:10.1016/j.rse.2010.07.006, 2010. To reference the techniques for generating the MODIS composite images, please use the following reference: Fraser, A. D., R. A. Massom, and K. J. Michael, A method for compositing polar MODIS satellite images to remove cloud cover for landfast sea-ice detection, IEEE Transactions on Geoscience and Remote Sensing, 47 (9), pp. 3272-3282, doi:10.1109/TGRS.2009.2019726, 2009. Please contact Alex Fraser (adfraser@utas.edu.au) for further information.

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).

An image correlation technique has been applied to RADARSAT ScanSAR images from November in 1997, and November 1999, to create the first detailed maps of fast ice around East Antarctica (75E-170E). This method is based upon searching for, and distinguishing, correlated regions of the ice-covered ocean which remain stationary, in contrast to adjacent moving pack ice. Within the overlapping longitudinal range of ~86E-150.6E, the total fast-ice area is 141,450 km2 in 1997 and 152,216 km2 in 1999. Calibrated radar backscatter data are also used to determine the distribution of two fast-ice classes based on their surface roughness characteristics. The outer boundaries of the determined fast-ice area for November in 1997 and 1999 are contained in the data files for this record. This work has been allocated to ASAC project 3024.

Metadata record for data from AAS (ASAC) Project 3024. Public The proposed research will derive improved estimates of East Antarctic fast-ice extent and thickness, and their variability, from satellite data. These will be used to explicitly test relationships between fast ice/other environmental parameters and Emperor penguin population dynamics. We shall also combine observations with a wave-ice shelf-sea ice interaction model to test the hypothesis that catastrophic ice shelf break-up events on the E. Antarctic Peninsula are linked to increased ocean wave energy associated with sea-ice extent anomalies (driven by atmospheric anomalies), and/or long-period swell from far-remote storms. This work will aid comprehension of processes responsible for recent rapid ice-shelf demise. Project objectives: 1. To measure and monitor East Antarctic fast ice areal extent and thickness, and their spatio-temporal variability, using satellite remote sensing. 2) To analyse the impact of fast ice variability on the breeding success of Emperor penguins (Aptenodytes forsteri). 3) To investigate the potential impact of sea ice on recent ice shelf break-up breakup on the Antarctic Peninsula. Taken from the 2008-2009 Progress Report: This project has shown a strong correlation between interannual fast ice variability and Emperor penguin breeding success at Dumont d'Urville, and has produced satellite-based maps of East Antarctic fast ice (radar snapshot mosaics from November 1997/98 and 20-day composite images for 2005-2008, extending back to 2000). Secondly, significant progress was made towards implicating an atmospherically-driven anomalous lack of sea ice in recent Antarctic ice-shelf disintegrations. Finally, new research highlights a previously-overlooked mechanical coupling between the floating Mertz Glacier tongue and very thick (greater than 25m) and old (greater than 20yrs) fast ice attached to it, with important implications for ice-sheet margin stability. Taken from the 2009-2010 Progress Report: Progress against objectives: 1) To measure and monitor East Antarctic fast ice areal extent and thickness, and their spatio-temporal variability, using satellite remote sensing. Considerable progress has been made against this objective, building on last year's publication of the first detailed "snapshot" maps of landfast sea ice (fast ice) extent around the East Antarctic coast from 75 degrees E-170 degrees E for the Novembers of 1997 and 1999 using RADARSAT satellite ScanSAR images (see Giles et al., 2008). The main achievements are: * The development of an improved semi-automated method to successfully derive fast ice extent (and pack ice motion) from time series of Envisat Advanced SAR images (Giles et al., in prep.), via a project with the European Space Agency and the International Space Science Institute (Berne, Switzerland). Fast ice is identified as regions of zero motion in the cross-correlation analysis of carefully co-registered pairs of satellite SAR images. * Significant progress in the PhD project (Alex Fraser) aimed at deriving longer and near-continuous time series of fast ice extent from time series of NASA MODIS visible and thermal IR imagery at 1 km resolution. A major challenge has been to address the problem of effectively 'removing' persistent cloud cover from the images. This has been achieved by compositing many thousands of MODIS images to create 20-day composite images of the entire East Antarctic coastal zone from 10W to 170E. This technique was showcased at the prestigious International Geoscience and Remote Sensing 2009 conference in South Africa in July 2009 (Fraser et al., 2009a), with subsequent publication by Fraser et al. (2009b). During the year, this work resulted in an important new time series of fast ice extent that runs from 2000 to 2008 inclusive (Fraser et al., in prep.), with techniques being described in Fraser et al. (in press). This unique dataset represents by far the most detailed estimate of East Antarctic fast ice and its spatio-temporal variability to date. It furthermore represents an important new baseline against which to gauge change, given that Antarctic fast ice is a key yet poorly understood component of the global cryosphere (and ocean freshwater budget), is of immense ecological significance (see 2 below), and is a sensitive indicator of climate change/variability. This baseline is directly comparable to the more familiar overall sea ice (pack ice) extent product. Work is underway to determine why large regional differences occur in fast ice distribution and behaviour, including analysis of the role of bathymetry, grounded icebergs and changes in wind patterns. This work also provides crucial regional-scale fast ice information in support of detailed localised fast ice measurements carried out within the Antarctic Fast Ice network at Casey and Davis (AAS 3032). * A collaborative project has been established with Drs Fricker (USA) and Legresy (France) to estimate the thickness of a large region of perennial fast ice adjacent and attached to the Mertz Glacier Tongue. This has been achieved by combining satellite imagery with surface elevation data from the NASA's ICESat laser altimeter satellite, although current unknowns include the thickness and density of the overlying snowcover. The results suggest that this fast ice is extraordinarily thick i.e. greater than 25 m, and may be at least 20 years old (Massom et al., subm., a). Work examining the glaciological significance of this extremely thick fast ice is described in 3 (below). Work is also underway to evaluate the impact on this and regional fast ice of the major calving of the Mertz Glacier in February 2010. 2) To analyse the impact of fast ice variability on the breeding success of Emperor penguins The first element of this multi-disciplinary, international study was completed last year i.e. a case study showing strong links between Emperor penguin breeding success at Dumont d'Urville and fast ice distribution along the Adelie Land coast of East Antarctica and its variability due to variability in the regional wind field. Results were published in Marine Ecology Progress Series (Massom et al., 2009a), and were also presented in a keynote address to the Xth SCAR International Biology Symposium in September 2009. Work is underway to extend this study both temporally and to other species and regions, using the new MODIS-derived time series of 20-day composite maps of fast ice extent (see 1 above). This work will include a comparison of the fast ice information with new data from French penguin scientists (Drs Barbraud, Ancel and LeMayo) on Emperor penguin mortality and other demographic parameters, with a view to discovering links between the penguin demographics and fast ice variability due to changing weather patterns. Further work is in its initial stages to study the impact of fast ice variability on i) Weddell seal foraging behaviour (with Dr Hindell's group at the Univ. of Tasmania), ii) Adelie penguin breeding success and foraging behaviour (with Drs Southwell and Emmerson, AAD), and iii) other Emperor penguin colonies in East Antarctica (with Dr Wienecke, AAD). Ongoing/future work will also evaluate the impact of abrupt change on the seals and penguins at Dumont d'Urville following the Mertz Glacier calving in February 2010.

Data from ASAC project 3030. Public summary for the project: This project will measure the sea ice thickness off East Antarctica, over spatial scales up to hundreds of kilometers. Sea ice is a likely sensitive indicator of climate variations and change. No large scale sea ice thickness measurements exist in the Antarctic. An estimation of trends of change in Antarctic sea ice thickness and volume is therefore not currently possible. To address this deficiency and to provide an independent data set for the validation of models and the calibration of remote-sensing data, we will conduct high accuracy air borne laser scanner measurements in the sea ice zone off East Antarctica. More information about the project can be found in lidar.pdf (which is available with the data).