Significant warming in the Arctic is anticipated for doubled-CO2 scenarios, but temperatures in the eastern Canadian Arctic have not yet exhibited that trend in the last few decades. The spatial juxtaposition of the winter station in 1822–1823 of William Edward Parry's Northwest Passage expedition with the modern Igloolik Research Centre of the Science Institute of the Northwest Territories affords an opportunity for historical reconstruction and comparison. Parry's data are internally consistent. The association of colder temperatures with westerly and northerly winds, and wanner temperatures with easterly and southerly winds, is statistically significant. Temperatures are not exactly comparable between the two time periods because of differences in instrumentation, exposure, and frequency of readings. Nevertheless, in 1822–1823, November and December appear to have been cold and January to March mild compared to modern experience. Anomalously, winds were more frequently northerly (and less frequently westerly) in the latter months than in recent observations. Parry recorded two warm episodes in mid-winter, but, overall, it appears that the winter of 1822–1823 was not outside the range of modern experience.

UK-based operations that range from ship routing and resource exploration to weather forecasting and glaciology have direct and growing interests in the oceans of the polar regions. Typically, information describing sea-ice conditions in localised regions is required on short time scales. To explore this market, the UK's Defence Research Agency, as part of a programme of the British National Space Centre, has commissioned the development of a prototype sea-ice workstation by a consortium led by Earth Observation Sciences Ltd.

The sea-ice workstation (SIWS) uses data from several current earth observation sensors, thereby combining the advantages of regional survey, all-weather capability, and high-resolution imagery. The workstation has been designed to run with a minimum of operator intervention in order to optimise speed of operation and ensure consistency of results. The geophysical processing chains generate charts of the ice edge, ice type, ice concentration, ice-motion vectors, and sea-surface temperatures.

Although taking full advantage of developments made elsewhere, the project has also made significant progress in research into the automated mapping of ice types. Existing ice-motion algorithms have been significantly enhanced as well. Considerable emphasis is being placed on the validation of the results from the system in order to assess their quality, this being one of the major concerns of potential users. The sea-ice workstation was completed in July 1994 and will form the basis for a series of evaluations that are intended to assess the value of the system for mapping and monitoring sea ice.

Systematic image-classification methods were applied to ALMAZ, ERS-1, and JERS-1 synthetic aperture radar (SAR) and Landsat-TM multispectral satellite images to evaluate the relative information content of the satellite data for discriminating wet tundra habitats in northern Alaska. Results suggest that SAR data can be used to concurrently detect a maximum of four or five landcover classes using the methods of this study. Combining two or more SAR images from different satellites improved the detection of some classes, particularly water bodies. Combining full-resolution SAR data with Landsat-TM did not improve the detection capabilities of Landsat-TM alone. Further research is needed to assess other image-classification and SAR data processing methods.

The patterns of the normalized difference vegetation index (NDVI) on three glacial surfaces of different ages in the vicinity of Toolik Lake, Alaska, were examined. NDVI was derived from SPOT multispectral digital data, and the images were stratified according to boundaries on glacial geology and vegetation maps. Ground-level measurements of NDVI from common vegetation types were also collected, using a portable spectrometer. Late Pleistocene glacial surfaces have lower image-NDVI than older Middle Pleistocene surfaces, and the mean NDVI is correlated with approximate time since deglaciation. The trends are related to differences in NDVI associated with vegetation growing on mineral vs peaty substrates. Nonacidic mineral substrates are more common on the younger landscapes, and acidic peaty soils are more common on the older surfaces. The field-NDVIs of acidic dry, moist, and wet tundra are consistently higher than those of corresponding nonacidic tundra types. These same trends are seen when the SPOT NDVI image is stratified according to vegetation boundaries appearing on two detailed vegetation maps in the region. Above-ground biomass of moist and wet acidic tundra is significantly greater than corresponding nonacidic types. Vegetation species composition was examined along two transects on the oldest and youngest glacial surfaces. Shrub cover is the most important factor affecting the spectral signatures and biomass. Older surfaces have greater cover of shrub-rich tussock tundra and shrub-filled water tracks, and the younger surfaces have more dry, well-drained sites with low biomass and relatively barren nonsorted circles and stripes. These trends are related to paludification and modification of the terrain by geomorphic and geochemical processes. Similar patterns of spectral reflectance have been noted in association with a variety of large-scale natural disturbances in northern Alaska. However, extrapolation of these results to much broader regions of the circumpolar Arctic will require the use of sensors covering larger areas, such as the AVHRR aboard the NOAA satellites.

Advanced Very High Resolution Radiometer (AVHRR) satellite data were acquired and composited into twice-a-month periods from 1 May 1991 to 15 October 1991 in order to map vegetation characteristics of the Alaskan landscape. Unique spatial and temporal qualities of the AVHRR data provide information that leads to a better understanding of regional biophysical characteristics of vegetation communities and patterns. These data provided synoptic views of the landscape and depicted phenological diversity, temporal vegetation phenology (green-up, peak of green, and senescence), photosynthetic activity, and regional landscape patterns. Products generated from the data included a phenological class map, phenological composite maps (onset, peak, and duration), and photosynthetic activity maps (mean and maximum greenness). The time-series data provide opportunities to study phenological processes at small landscape scales over time periods of weeks, months, and years. Regional patterns identified on some of the maps are unique to specific areas; others correspond to biophysical or ecoregional boundaries. The data provide new insights to landscape processes, ecology, and landscape physiognomy that allow scientists to look at landscapes in ways that were previously difficult to achieve.

In the future, data from the moderate resolution imaging spectroradiometer (MODIS) will be employed to map snow in an automated environment at a resolution of 250 m to 1 km. Using Landsat thematic mapper (TM) data, an algorithm, SNOMAP, has been developed to map snow-covered area. This algorithm will be used, with appropriate modification, with MODIS data following the launch of the first Earth Observing System (EOS) platform in 1998. SNOMAP has been shown to be successful in mapping snow in a variety of areas using TM data. However, significant errors may be present in mountainous areas due to effects of topography. To increase the accuracy of mapping global snow-covered area in the future using MODIS data, digital elevation model (DEM) data have been registered to TM data for parts of Glacier National Park, Montana, so that snow cover on mountain slopes can be mapped. This paper shows that the use of DEM data registered to TM data increases the accuracy of mapping snow-covered area. Using SNOMAP on a subscene within the 14 March 1991 TM scene of northwestern Montana, 215 km2 of snow is mapped when TM data are used alone to map the snow cover. We show that about 1062 km2 of snow are actually present as measured when the TM and DEM data are registered. Approximately five times more snow is present when the effects of topography are considered for this subscene, which is in a rugged area in Glacier National Park. A simple model has been developed to determine the relationship between terrain relief and the amount of correction that must be applied to map actual snow-covered area in Glacier National Park using satellite data alone.