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Meltwater drainage systems beneath ice sheets are a poorly understood, yet fundamentally important environment for understanding glacier dynamics, which are strongly influenced by the nature and quantity of meltwater entering the subglacial system. Contemporary sub-ice sheet meltwater drainage systems are notoriously difficult to study, but we can utilise exposed beds of palaeo-ice sheets to further our understanding of subglacial drainage. In particular, eskers record deposition in glacial drainage channels and are widespread on the exposed beds of former ice sheets. This paper presents a 1:5,000,000 scale map of >20,000 large eskers (typically > 2 km long) deposited by the Laurentide Ice Sheet (LIS), mapped from Landsat imagery of Canada, in order to establish a dataset suitable for analysis of esker morphometry and drainage patterns at the ice sheet scale. Comparisons between eskers mapped from Landsat imagery and aerial photographs indicate that, in most areas, approximately 75% of eskers are detected using Landsat. The data presented in this map build on and extend previous work in providing a consistent map of an unprecedented sample of eskers for quantitative analysis. It offers an alternative perspective on the problems surrounding ice-sheet meltwater drainage and can be used for: (i) detailed investigations of esker morphometry and distribution from a large sample size; (ii), testing of numerical models of meltwater drainage routing that predict esker characteristics (e.g. channel spacing, sinuosity), (iii) assessment of the factors that control esker location and formation; and (iv), a refined understanding of ice margin configurations during retreat of the LIS. [ABSTRACT FROM AUTHOR]

Information on biomass distribution is needed to estimate GHG emissions and removals from land use changes in Canada's north for UNFCCC reporting. This paper reports aboveground biomass measurements along the Dempster Highway transect in 2004, and around Yellowknife and the Lupin Gold Mine in 2005. The measured aboveground biomass ranges are 10-100 t ha-1 for woodlands, 1-100 t ha-1 for shrub sites, and 0.5-10 t ha-1 for grass/herbs sites. The root mean squared error (RMSE) of measurements is 21%, and the median absolute percentage error (MedAPE) is 14%. The combination of JERS backscatter and Landsat TM4/TM5 gives the best biomass equation for the Dempster Highway transect, with r 2 = 0.72 when using a one-step approach (i.e. using all points) and 0.78 when using a two-step approach (i.e. stratifying data into three classes: grass, shrub, and woodlands). The two-step approach reduces the MedAPE from 53% to 33%. The validation against Yellowknife & Lupin data indicates that the equations have good transferability. The improvement of two-step approach over the one-step approach, however, is not significant for the validation dataset, suggesting that the one-step approach is as good as the two-step approach when applied over areas outside where the equations are developed. The relationships and error analysis of this study, as well as the final estimate of GHG emission/removal over Canada's north have been incorporated into Canada's 2006 UNFCCC report. [ABSTRACT FROM AUTHOR]

This paper describes single-date and multi-date land-cover classification accuracy results using segment-based, gap-filled Landsat 7 Enhanced Thematic Mapper data compared with Landsat 5 Thematic Mapper data captured one day apart. Maximum likelihood and Decision tree classification algorithms were evaluated. The same training and verification sets of ground data were used for each classification evaluation. For the comparison with the single-date classification, an average decrease of 2.8% in the classification accuracy was obtained with the use of the gap-filled Landsat data. Area estimates for the mid-summer images differed, on average, from 0.6% to 1.9% for a four-class and eight-class classification, respectively. A multi-date land-cover classification was also completed with the addition of a late spring Landsat 5 image, resulting in an average decrease in classification accuracy of 1.8%. [ABSTRACT FROM AUTHOR]