We present Bedmap2, a new suite of gridded products describing surface elevation, ice-thickness and the seafloor and subglacial bed elevation of the Antarctic south of 60° S. We derived these products using data from a variety of sources, including many substantial surveys completed since the original Bedmap compilation (Bedmap1) in 2001. In particular, the Bedmap2 ice thickness grid is made from 25 million measurements, over two orders of magnitude more than were used in Bedmap1. In most parts of Antarctica the subglacial landscape is visible in much greater detail than was previously available and the improved data-coverage has in many areas revealed the full scale of mountain ranges, valleys, basins and troughs, only fragments of which were previously indicated in local surveys. The derived statistics for Bedmap2 show that the volume of ice contained in the Antarctic ice sheet (27 million km³) and its potential contribution to sea-level rise (58 m) are similar to those of Bedmap1, but the mean thickness of the ice sheet is 4.6% greater, the mean depth of the bed beneath the grounded ice sheet is 72 m lower and the area of ice sheet grounded on bed below sea level is increased by 10%. The Bedmap2 compilation highlights several areas beneath the ice sheet where the bed elevation is substantially lower than the deepest bed indicated by Bedmap1. These products, along with grids of data coverage and uncertainty, provide new opportunities for detailed modelling of the past and future evolution of the Antarctic ice sheets

We present Bedmap2, a new suite of gridded products describing surface elevation, ice-thickness and the seafloor and subglacial bed elevation of the Antarctic south of 60° S. We derived these products using data from a variety of sources, including many substantial surveys completed since the original Bedmap compilation (Bedmap1) in 2001. In particular, the Bedmap2 ice thickness grid is made from 25 million measurements, over two orders of magnitude more than were used in Bedmap1. In most parts of Antarctica the subglacial landscape is visible in much greater detail than was previously available and the improved coverage of data has in many areas revealed the full scale of mountain ranges, valleys, basins and troughs, only fragments of which were previously indicated in local surveys. The derived statistics for Bedmap2 show that the volume of ice contained in the Antarctic ice sheet (27 million km³) and its potential contribution to sea-level rise (58 m) are similar to those of Bedmap1, but the mean thickness of the ice sheet is 4.6 % greater, the mean depth of the bed beneath the grounded ice sheet is 72 m lower and the area of ice sheet grounded on bed below sea level is increased by 10 %. The Bedmap2 compilation highlights several areas beneath the ice sheet where the bed elevation is substantially lower than the deepest bed indicated by Bedmap1. These products, along with grids of data coverage and uncertainty, provide new opportunities for detailed modelling of the past and future evolution of the Antarctic ice sheets

The Antarctic ice sheet has been losing mass over the past decades through the accelerated flow of its glaciers conditioned by ocean temperature and bed topography. Glaciers retreating along retrograde slopes (i.e., bed elevation drops in the inland direction) are potentially unstable, whereas subglacial ridges slow down the glacial retreat. Despite major advances in mapping subglacial bed topography, significant sectors of Antarctica remain poorly resolved and critical spatial details are missing. Here we present a novel, high-resolution, and physically-based description of Antarctic bed topography using mass conservation. Our results reveal previously unknown basal features with major implications for glacier response

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Our aim was to estimate the population of emperor penguins (Aptenodytes fosteri) using a single synoptic survey. We examined the whole continental coastline of Antarctica using a combination of medium resolution and Very High Resolution (VHR) satellite imagery to identify emperor penguin colony locations. Where colonies were identified, VHR imagery was obtained in the 2009 breeding season. The remotely-sensed images were then analysed using a supervised classification method to separate penguins from snow, shadow and guano. Actual counts of penguins from eleven ground truthing sites were used to convert these classified areas into numbers of penguins using a robust regression algorithm.We found four new colonies and confirmed the location of three previously suspected sites giving a total number of emperor penguin breeding colonies of 46. We estimated the breeding population of emperor penguins at each colony during 2009 and provide a population estimate of ∼238,000 breeding pairs (compared with the last previously published count of 135,000–175,000 pairs). Based on published values of the relationship between breeders and non-breeders, this translates to a total population of ∼595,000 adult birds.There is a growing consensus in the literature that global and regional emperor penguin populations will be affected by changing climate, a driver thought to be critical to their future survival. However, a complete understanding is severely limited by the lack of detailed knowledge about much of their ecology, and importantly a poor understanding of their total breeding population. To address the second of these issues, our work now provides a comprehensive estimate of the total breeding population that can be used in future population models and will provide a baseline for long-term research.

No abstract

Abstract. As the accuracy and sensitivity of remote-sensing satellites improve, there is an increasing demand for more accurate and updated base datasets for surveying and monitoring. However, differentiating rock outcrop from snow and ice is a particular problem in Antarctica, where extensive cloud cover and widespread shaded regions lead to classification errors. The existing rock outcrop dataset has significant georeferencing issues as well as overestimation and generalisation of rock exposure areas. The most commonly used method for automated rock and snow differentiation, the normalised difference snow index (NDSI), has difficulty differentiating rock and snow in Antarctica due to misclassification of shaded pixels and is not able to differentiate illuminated rock from clouds. This study presents a new method for identifying rock exposures using Landsat 8 data. This is the first automated methodology for snow and rock differentiation that excludes areas of snow (both illuminated and shaded), clouds and liquid water whilst identifying both sunlit and shaded rock, achieving higher and more consistent accuracies than alternative data and methods such as the NDSI. The new methodology has been applied to the whole Antarctic continent (north of 82°40′ S) using Landsat 8 data to produce a new rock outcrop dataset for Antarctica. The new data (merged with existing data where Landsat 8 tiles are unavailable; most extensively south of 82°40′ S) reveal that exposed rock forms 0.18 % (21 745 km2) of the total land area of Antarctica: half of previous estimates.

We describe a method of identifying and counting whales using very high resolution satellite imagery through the example of southern right whales breeding in part of the Golfo Nuevo, Península Valdés in Argentina. Southern right whales have been extensively hunted over the last 300 years and although numbers have recovered from near extinction in the early 20th century, current populations are fragmented and are estimated at only a small fraction of pre-hunting total. Recent extreme right whale calf mortality events at Península Valdés, which constitutes the largest single population, have raised fresh concern for the future of the species. The WorldView2 satellite has a maximum 50 cm resolution and a water penetrating coastal band in the far-blue part of the spectrum that allows it to see deeper into the water column. Using an image covering 113 km2, we identified 55 probable whales and 23 other features that are possibly whales, with a further 13 objects that are only detected by the coastal band. Comparison of a number of classification techniques, to automatically detect whale-like objects, showed that a simple thresholding technique of the panchromatic and coastal band delivered the best results. This is the first successful study using satellite imagery to count whales; a pragmatic, transferable method using this rapidly advancing technology that has major implications for future surveys of cetacean populations.