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Remote sensing has long been identified as a technology capable of supporting the development of wildlife habitat maps over large areas. However, progress has been constrained by underdeveloped linkages between resource managers with extensive knowledge of ecology and remote sensing scientists with backgrounds in geography. This article attempts to traverse that gap by (i) clarifying the imprecise and commonly misunderstood concept of ‘habitat’, (ii) exploring the recent use of remote sensing in previous habitat-mapping exercises, (iii) reviewing the remote sensing toolset developed for extracting information from optical satellite imagery, and (iv) outlining a framework for linking ecological information needs with remote sensing techniques.

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Much attention has been given to the challenges of handling massive data volumes in modern data-intensive science. This paper examines an equally daunting challenge -the diversity of interdisciplinary data, notably research data, and the need to interrelate these data to understand complex systemic problems such as environmental change and its impact. We use the experience of the International Polar Year 2007-8 (IPY) as a case study to examine data management approaches seeking to address issues around complex interdisciplinary science. We find that, while technology is a critical factor in addressing the interdisciplinary dimension of the data intensive science, the technologies developing for exa-scale data volumes differ from those that are needed for extremely distributed and heterogeneous data. Research data will continue to be highly heterogeneous and distributed and will require technologies to be much simpler and more flexible. More importantly, there is a need for both technical and cultural adaptation. We describe a vision of discoverable, open, linked, useful, and safe collections of data, organized and curated using the best principles and practices of information and library science. This vision provides a framework for our discussion and leads us to suggest several short-and long-term strategies to facilitate a socio-technical evolution in the overall science data ecosystem.

In this work we quantify the vertical geophysical and electrical properties of a snow cover on landfast first-year sea ice observed during the Seasonal Sea Ice Monitoring and Modelling Site (SIMMS'92) experiment. Snow grain morphology, density, salinity, temperature and wetness were measured; the volume fractions of air, ice, brine, and the complex dielectric constant of the snow were modeled over a 3-cm vertical resolution spanning a seasonal period from April to June. Our results show that over the vertical dimension the snow grain morphology, salinity, density, and fractional volumes of brine, ice, and air covary. The statistical characterization of the vertical grain morphology indicates that two distinct layers occurred under cold (-20øC) and three layers under warm (-5øC) atmospheric temperatures. Over the seasonal period it was shown that new snow was deposited at about 250 kg.m -3 and quickly compacted to 375 kg.m -3' Snow grains grew at different rates within the snow cover because of differing metamorphic conditions. Dielectrically, the snow volume followed closely the seasonal and vertical patterns of grain morphology, salinity, temperature, density and the phase proportions of water within the snow volume. As the season evolved, the increasing brine volumes and presence of water in liquid phase caused the dielectric properties to increase over several factors (e•ix) and orders of magnitude introduction Snow has received the vast majority of scientific inquiry when it occurs over a land cover type, rather than over a sea ice volume. Of the studies which have been conducted on a sea ice snow cover [e.g., Matzler et al., 1984a; Kim et al.