Most of the papers in this issue of Polar Record are a result of research performed by a cadre of scientists specifically dealing with remote sensing applications in the Arctic environments. These studies, and other similar activities, were presented originally at the 9th Circumpolar Remote Sensing Symposium held in Seward, Alaska, from 15–19 May 2006. The symposium provided a forum to the 40 international scientists attending it for the exchange of current applied research, the presentation of new technology, and the advancement of internal co-operation in the circumpolar Arctic and Antarctic regions. Oral presentations in the 10 plenary sessions focused on snow, ice, and cryosphere studies, climate and the environment, environmental monitoring, arctic vegetation inventory, monitoring and analysis, databases, data processing and GIS. In addition there was a special plenary session on remote sensing for the International Polar Year. At the end of the symposium, there was a special panel discussion on recommendations for future sensors for boreal and polar remote sensing.

Throughout northern Eurasia, the presence of reindeer is a complicating factor in the consideration of interactions between vegetation and climate. The ability to interpret future changes in these interactions will depend on access to maps of sufficient detail to represent aspects of vegetation distribution relevant to reindeer grazing, amongst which we particularly identify lichens and shrubs. Such maps, if they are to have circumpolar coverage, can only feasibly be produced on a routine basis using satellite imagery having wide swaths but comparatively coarse resolution. This paper presents qualitative and quantitative comparisons between three such maps, and two more detailed vegetation maps compiled from fieldwork and from recent field-trained satellite image analysis, all for a study area in the Nenets Autonomous Okrug, Russia. It is shown that, despite its high degree of generalisation, the Circumpolar Arctic Vegetation Map provides the best representation of the vegetation in the study area amongst the three circumpolar land-cover maps that were examined, but that none of the three is entirely satisfactory. While the adequate representation of shrubs and lichens currently continues to depend on the analysis of field data or high-resolution satellite imagery which is unsuitable for circumpolar mapping, it is suggested that the prospects for satellite-based circumpolar vegetation mapping capable of including these components is promising.

The United States National Snow and Ice Data Center (NSIDC) initiated an outreach project to enhance the visibility of and interest in cryospheric images. Methods were utilised to convert cryospheric data into a projection and image format compatible with Google Earth™. The word ‘image’ should be emphasised since raster data in a native polar projection and format cannot be overlaid on the Earth without prior data conversions. The project focused on reaching out to a diverse audience by integrating images from key components of the cryosphere into a single compressed Keyhole Markup Language (KMZ) file. As a result, users can visualise glacier photographs, permafrost type and extent, sea ice concentration and extent, and snow extent superimposed on the Earth. Those interested in browsing the NSIDC collection of over 3,000 glacier photographs have the option of zooming into Alaska for a majority of the images and accessing both the photograph and the associated metadata. For a current perspective of global snow and ice coverage, one could look at satellite imagery derived from passive microwave Special Sensor Microwave/Imager (SSM/I) data. Another option is to select the permafrost layer and observe the various types and extent of permafrost. This paper explores the project by describing the data, methodologies and results and concludes with future implications on how to improve the processing and functionality of polar data in Google Earth.

The changing surface geometry of the glacier Midre Lovénbreen on Svalbard was investigated using LiDAR data acquired on 9 August 2003 and again on 5 July 2005. The data were processed to generate Digital Elevation Models (DEMs) of unprecedentedly high spatial resolution (2 m) and accuracy (better than 0.15 m). Comparison of the two DEMs allowed the mass balance of the glacier to be determined as more negative than −0.62 m yr−1 water equivalent, about twice as negative as the value estimated from in situ measurements. Comparison of the DEMs also showed that the area of the glacier decreased by around 0.3%, and the position of its margin retreated by around 14 m, from 2003 to 2005. It was also possible to track the motion of fine-scale features in the surface geometry such as meltwater channels, and hence to determine the glacier's surface velocity, in some areas. Typical average speeds were around 1–2 cm per day.

Alaska currently relies on the Canadian Fire Weather Index (FWI) System for the assessment of the potential for wildfire and although it provides invaluable information it is designed as a single system that does not account for the varied fuel types and drying conditions (day length, permafrost, decomposition rate, and soil type) that occur across the North American boreal forest. The FWI System is completely weather-based using noontime measurements of precipitation, relative humidity, temperature and wind speed. The most common problem observed with the FWI system is in the initialisation and need for calibration of one of the moisture codes that make up the FWI system, the Drought Code (DC), which is representative of the deeper organic soil layers and has a 53 day lag period. SAR data represent an innovative tool to improve the current weather-based fire danger system of interior Alaska by initialising the spring values of DC, calibrating the codes throughout the season and providing additional point-source data. Using radar backscatter values from several recently burned boreal forests, an algorithm was developed that related backscatter to DC. The authors then demonstrated the application and validation of this algorithm at independent test sites with good correlation to in situ soil moisture and rainfall variations.

This paper describes a new technique simultaneously to estimate topography and motion of polar glaciers from multi-temporal SAR interferograms. The approach is based on a combination of several SAR interferograms in a least-squares adjustment using the Gauss-Markov model. For connecting the multi-temporal data sets, a spatio-temporal model is proposed that describes the properties of the surface and its temporal evolution. Rigorous mathematical modelling of functional and stochastic relations allows for a systematic description of the processing chain. It is also an optimal tool to set parameters for the statistics of every individual processing step, and the propagation of errors into the results. Within the paper theoretical standard deviations of the unknowns are calculated depending on the configuration of the data sets. The influence of gross errors in the observations and the effect of non-modelled error sources on the unknowns are estimated. A validation of the approach based on real data concludes the paper.

The Arctic treeline, or more precisely the tundra-taiga interface (TTI) region, is poorly defined and characterised despite its high climatological significance. The international coordinated research programme ‘PPS Arctic’, under the auspices of the International Polar Year, represents one response to this gap in our knowledge. This paper presents preliminary work within one of the four principal research areas of PPS Arctic, the characterisation of spatial variations in vegetation, land cover and land use in the TTI using remote sensing methods. Airborne remote sensing data were collected from a 120 km2 TTI study site near Porsangmoen, Finnmark, Norway in 2004 and 2005. Three datasets were acquired: two sets of multispectral visible-infrared imagery with spatial resolutions of around 3 m, and airborne scanning LiDAR data with a horizontal resolution of 2 m and a vertical precision of around 0.2 m. While some difficulties were experienced in processing and analysing the imagery, the LiDAR data proved exceptionally well suited to the task of characterising the structure of the forest edge. Preliminary analyses were strongly suggestive of fractal characteristics, with corresponding consequences for the scale-dependence of descriptors such as canopy density and the location of the forest edge.

The climate and its components (temperature and precipitation) are organised according to different spatial scales that are structured hierarchically. The aim of this paper is to explore the dependence between temperature and deterministic factors at different scales on a 10 km2 study area on the northwestern coast of Svalbard. A GIS was developed which contained three sources of information: temperature, remotely sensed imagery and digital elevation models (DEM), and derived raster data layers. The first layer, temperatures, was acquired at regularly observed temporal intervals from 53 stations. The second layer comprised remotely sensed images (aerial photography and SPOT imagery) and DEM data at 2 m and 20 m resolution, respectively. From these, a windowing procedure was applied to derive several spatial subsets of different spatial resolutions (6, 14, 30, 60, 140, and 300 m). The third layer comprised slope, aspect, and a theoretical solar radiation value derived from the DEM, and a vegetation index derived from the remotely sensed imagery. Linear regressions were then systematically conducted on the datasets, with temperature as the dependent variable, and each of the other data layers as the independent variables. By using graphical analysis, we link the correlation coefficients obtained for each factor, from the smallest spatial resolution (6 m) to the largest resolution (300 m). The results indicated that each explanatory variable and scale brings a specific contribution to changes in temperature. For example, the effect of elevation remains constant for all spatial resolutions, reflecting a quasi ‘non-scalar’ pattern of this variable. For other variables however, the effect of spatial scale can have a strong effect. In the case of solar radiation, a maximum of explanation was obtained for spatial resolutions of 14 m and 60 m; for vegetation index the optimum contribution was related to the 300 m resolution. Thus, different environment characteristics may have significant effects on changes in temperature when differences in spatial scale are taken into account.

Radiocarbon age determinations of 32 walrus (Odobenus rosmarus) remains show that the species has been a member of the Greenland fauna since at least the middle Holocene. The oldest date is 7280–7160 calendar years BP. However, most dated remains come from archaeological sites and are of late Holocene age

Since the development of photography in the mid-nineteenth century, exploration has created iconic images of the polar regions. A new two-year research project, entitled Freeze Frame, using the world-class collections at the Scott Polar Research Institute, University of Cambridge, will bring this remarkable visual culture forward for new audiences.