The 11th International Circumpolar Remote Sensing Symposium (ICRSS) was held at the Scott Polar Research Institute in Cambridge from 20 to 24 September 2010. The ICRSS series began in Yellowknife in 1990 and has been held biennially since then. The 2010 meeting was the sixth time it had been held in Europe and the second time in the UK, but the first time in Cambridge. 35 people attended the meeting, from 11 countries, and over 20 oral presentations were made in addition to a well-attended poster session. The majority of the oral presentations have been developed into papers and appear in this issue of Polar Record, having been subjected to the normal peer review and editorial process, and they give a fair idea of the range of topics covered at this lively meeting. Sessions at the symposium were organised around the themes of cross-platform observations, ice and snow, topography, vegetation and observations of animals. The last theme produced three fascinating presentations on the monitoring of penguins, seals and fish from spaceborne and airborne platforms. The papers in this issue address two broad areas: terrestrial ice and snow, and high-latitude vegetation (sea ice, and polar zoology, were also represented at the meeting). All of them deal to a greater or lesser extent with technological innovation in assessing, mapping and monitoring these aspects of the polar regions, and several of them focus strongly on the development of new methods, or the assessment of newly available datasets. This issue of Polar Record thus provides a limited snapshot of the ‘state of the art’ in remote sensing of polar regions. It is the result of sustained effort by the authors of the papers, and the team of anonymous reviewers. I am glad here to record my gratitude to all of them, and to the helpers at the symposium, particularly Katya Shipigina, Allen Pope and Claire Lampitt.

The volume variation of a glacier is the actual indicator of long term and short term evolution of the glacier behaviour. In order to assess the volume evolution of the Austre Lovénbreen (79° N) over the last 47 years, we used multiple historical datasets, complemented with our high density GPS tracks acquired in 2007 and 2010. The improved altitude resolution of recent measurement techniques, including phase corrected GPS and LiDAR, reduces the time interval between datasets used for volume subtraction in order to compute the mass balance. We estimate the sub-metre elevation accuracy of most recent measurement techniques to be sufficient to record ice thickness evolutions occurring over a 3 year duration at polar latitudes.

The systematic discrepancy between ablation stake measurements and DEM analysis, widely reported in the literature as well as in the current study, yields new questions concerning the similarity and relationship between these two measurement methods.

The use of Digital Elevation Model (DEM) has been an attractive alternative measurement technique to estimate glacier area and volume evolution over time with respect to the classical in situ measurement techniques based on ablation stakes. With the availability of historical datasets, whether from ground based maps, aerial photography or satellite data acquisition, such a glacier volume estimate strategy allows for the extension of the analysis duration beyond the current research programmes. Furthermore, these methods do provide a continuous spatial coverage defined by its cell size whereas interpolations based on a limited number of stakes display large spatial uncertainties. In this document, we focus on estimating the altitude accuracy of various datasets acquired between 1962 and 2010, using various techniques ranging from topographic maps to dual frequency skidoo-tracked GPS receivers and the classical aerial and satellite photogrammetric techniques.

Due to poor weather conditions including common heavy cloud cover at polar latitudes, daily satellite imaging is not always accessible. Nevertheless, fast events including heavy rainfall inducing floods appear as significant in the ice and snow budget while being ignored by satellite based studies since the slower sampling rate is unable to observe such short phenomena. We complement satellite imagery with a set of ground based autonomous automated high resolution digital cameras. The recorded oblique views, acquired at a rate of 3 images per day, are processed for comparison with the spaceborne imagery. Delaunay triangulation based mapping using a dense set of reference points provides the means for an accurate projection by applying a rubber sheeting algorithm. The measurement strategy of identifying binary information of ice and snow cover is illustrated through the example of a particular flood event. We observe a snow cover evolution from 100% to 44.5% and back to 100% over a period of 2 weeks.

Differential synthetic aperture radar interferometry (DInSAR) exploits the coherence between the phases of two or more satellite synthetic aperture radar (SAR) scenes taken from the same orbit to separate the phase contributions from topography and movement by subtracting either phase. Hence pure terrain displacement can be derived without residual height information in it, but only the component of movement in line-of-sight direction is represented in a differential interferogram. Comfortlessbreen, a recently surging glacier, flows predominantly in this direction with respect to the European Remote Sensing satellites ERS-1 and ERS-2. Four C-band SAR scenes from spring 1996 were selected because of the high coherence between the respective pairs of the 1-day repeat-pass tandem mission of the ERS sensors. 2-pass DInSAR is performed in combination with a SPOT5 (Satéllite pour l'Observation de la Terre 5) SPIRIT (SPOT5 stereoscopic survey of Polar Ice: Reference Images and Topography) digital elevation model (DEM) from 2007. The different processing steps and intermediate image products, including unwrapping and generation of displacement maps, are detailed in order to convey the DInSAR processing chain to the beginner in the field of interferometry. Maximum horizontal displacements of 18 to 20 cm d−1 in ground range direction can be detected at the glacier terminus, while a few centimetres per day characterised most of the middle and upper portions of Comfortlessbreen in spring 1996.

Very high precision satellite altimeter measurements from the Geoscience Laser Altimeter System onboard NASA's Ice, Cloud and Land Elevation Satellite (ICESat) have allowed a method of feature tracking to be developed for floating ice which relies on recording the movement of large surface undulations. This method is applied to a section of the Ross Ice Shelf downstream of the grounding line of the Beardmore Glacier, Antarctica. The altimetry method has benefits over established optical and interferometric remote sensing techniques due to high pointing accuracy for geo-location, ability to deal with tidal fluctuations and to measure velocity where visible surface features are absent. Initial processing of a single sequence of ICESat tracks gives encouraging results for unidirectional ice flow with correlations between surface profiles in consecutive years exceeding 90% and producing high internal consistency in velocity between independent tracks. Velocities of 331 ± 28 m a−1 near to the grounding line are also consistent with available ground measurements for the area.

A new source of digital elevation data, the advanced spaceborne thermal emission and reflection radiometer (ASTER) global digital elevation model (GDEM), has been freely available since 2009. It provides enormously greater coverage of the Arctic than previous satellite derived ‘global’ digital elevation models, extending to a latitude of 83 °N in contrast to 60 °N. The GDEM is described as a preliminary, research grade product. This paper investigates its accuracy in a number of specifically Arctic landscapes, including ice and snow, boreal forest, tundra and unvegetated terrain, using test sites in Svalbard, Iceland, Norway and Russia. Semivariogram analysis is used to characterise the magnitude and spatial correlation of errors in the GDEM products from the test sites. The analysis suggests that the horizontal resolution of the GDEM data is around 130 m, somewhat coarser than the sampling interval of 1 second of latitude and longitude. The vertical accuracy is variable, and the factors influencing it have not been systematically explored. However, it appears that the likely accuracy can be estimated from ‘stacking number’ data supplied with the elevation data. The stacking number is the number of independent digital elevation models averaged to generate the supplied product. Provided that this number is greater than around 6 the data have an rms accuracy of typically 5–10 m.

Due to the increased melting season in the arctic regions, especially in the seas surrounding Greenland, there has been an increased interest in utilising these waterways, both as an efficient transport route and an attractive leisure destination. However, with heavier traffic comes an increased risk of accidents. Due to the immense size and poor infrastructure of Greenland, it is not feasible to deploy ground based ship monitoring stations throughout the Greenland coastline. Thus the only feasible solution is to perform such surveillance from space. In this paper it is shown how it is possible to receive transmissions from the Automatic Identification System (AIS) from space and the quality of the received AIS signal is analysed. To validate the proposed theory, a field study, utilising a prototype of AAUSAT3, the third satellite from Aalborg University, was performed using a stratospheric balloon flight in the northern part of Sweden and Finland during the autumn of 2009. The analysis finds that, assuming a similar ship distribution as in the Barents Sea, it is feasible to monitor the ship traffic around Greenland from space with a satisfactory result.

The overall objective of this paper is to present and discuss the most recently developed vegetation map for Svalbard, Arctic Norway. The map is based on satellite images in which several Landsat TM/ETM+ images were processed through six operational stages involving: (1) automatic image classification, (2) spectral similarity analysis, (3) generation of classified image mosaics, (4) ancillary data analysis, (5) contextual correction, and (6) standardisation of the final map products. The developed map is differentiated into 18 map units interpreted from 37 spectral classes. Among the 18 units separated, six of the units comprise rivers, lakes and inland waters, glaciers, as well as non- to sparsely vegetated areas. The map unit 7 is a result of shadow effects and different types of distortions in the satellite image. The vegetation of the remaining eleven units varies from dense marshes and moss tundra communities to sparsely vegetated polar deserts and moist gravel snowbeds. The accuracy of the map is evaluated in areas were access to traditional maps have been available. The vegetation density and fertility is reflected in computed NDVI values. The map product is in digital format, which gives the opportunity to produce maps in different scales. A map sheet portraying the entire archipelago is one of the main products from this study, produced at a scale of 1:500,000.

An experimental linear mixture modelling using ground spectroradiometric measurements in the Kola Peninsula, Russia has been carried out to create a basis for mapping vegetation and non-vegetation components in the tundra-taiga ecotone using satellite imagery. We concentrated on the ground level experiment with the goal to use it further for the classification of multispectral satellite imagery through spectral unmixing. This experiment was performed on the most detailed level of remote sensing research which is free from atmospheric effects and easy to understand. We have measured typical ecotone components, including Cetraria nivalis, Betula tortuosa, Empetrum nigrum, Betula nana, Picea abies and rocks (nepheline syenite). The result of the experiment shows that the spectral mixture is indeed formed linearly but different components have different influence. Typical spectral thresholds for each component were found which are significant for vegetation mapping. Spectral unmixing of ground level data was performed and accuracy was estimated. The results add new information on typical spectral thresholds which can potentially be applied for multispectral satellite imagery when upscaling from high resolution to coarser resolution.

The state of pastures in the Finnish reindeer management area has been monitored since 1995 using remote sensing and field inventory. The first inventory was made in 1995–1996, updated in the beginning of 2000s and repeated in 2005–2008. By comparing results from 1995–1996 and 2005–2008 we can observe clear changes in forest cover, structure and ground lichen abundance. To evaluate pasture/vegetation changes on the basis of separate classifications we have used a grid approach. By implementing a 500 by 500 m grid network and summarizing pasture classes for every grid cell we can visualize and quantify intensity of change. By comparing 1995–1996 and 2005–2008 we can see a clear decrease in the number of cells classified old growth dominated forest, and also an increase of fragmentation can be detected. With a 7.5 ha threshold the amount of old growth forest was reduced by 5%, for 20 ha the decrease was 21%. This indicates a significant change in forest landscape structure, fragmentation and reduction of reindeer winter pasture value in large areas. Pixel wise comparison showed no substantial changes in pasture areas. There is a degree of uncertainty in change detection; changes in remote sensing instrument, changes in processing software and methodology, changes in field methods and ancillary data, and obviously also bias introduced by differences between analysts. When comparing reindeer lichen biomass between 1995–1996 and 2005–2008 on the basis of field site data, the measured lichen biomass has declined in 19 out of the 20 reindeer management districts. Only one district showed slight improvement, in three districts there was a notable drop in lichen biomass, from over 1500 kg/ha to about 500 kg/ha. Also amount of arboreal lichens declined due to felling of old growth forest, confirming the findings on the grid cell level. Consequently grass, shrub and sapling stands increased as felled areas start to grow graminoids and herbs.

Remote sensing has provided evidence of vegetation changes in Arctic tundra that may be attributable to recent climate warming. These changes are evident from local scales as expanding shrub cover observed in aerial photos, to continental scales as greening trends based on satellite vegetation indices. One challenge in applying conventional two date, satellite change detection in tundra environments is the short growing season observation window, combined with high inter-annual variability in vegetation conditions. We present an alternative approach for investigating tundra vegetation and surface cover changes based on trend analysis of long-term (1985-present) Landsat TM/ETM+ image stacks. The Tasseled Cap brightness, greenness, and wetness indices, representing linear transformations of the optical channels, are analysed for per-pixel trends using robust linear regression. The index trends are then related to changes in fractional shrub and other vegetation covers using a regression tree classifier trained with high resolution land cover. Fractional trends can be summarised by vegetation or ecosystem type to reveal any consistent patterns. Example results are shown for a 3 000 km2 study area in northern Yukon, Canada where index and fractional changes are related to growth of vascular plants and coastal erosion.

The boreal vegetation of the sub-Arctic comprises more than 30% of the Earth's forest area and plays a major role in controlling the global environment. In the 20th century the boreal vegetation of Fennoscandia was significantly changed by heavy industrialisation leaving many forest areas damaged or dying. Due to severe climate conditions of the sub-Arctic such vegetation changes are traceable over long periods of time. This provides an opportunity to study all types of human impact on vegetation in time and to develop methods to monitor geographical and structural changes in the vegetation cover. Here we present the first part of a larger study in which we use the remote sensing technique to investigate the dynamics of the boreal vegetation in Fennoscandia in context of human impact. We have developed a novel method for an automated analysis and mapping of vegetation and of all types of human impact based on a single support-vector-machines classifier (for the whole area). Implemented with free and open source software the method uses Landsat TM and ETM+ band data (for which it automatically performs atmospheric correction) and a number of indices like NDVI, NBR, etc. The accuracy of the 16-class classification has been assessed using field data and literature sources and determined to be 74.1%. The method has been successfully applied to a study area around Monchegorsk, Kola peninsula, Russia, the most industrialised part of northern Europe. We have characterised all major types of human impact on the boreal forest and tundra vegetation performing the change detection analysis in an area of 1750 km2 between 1986 and 2005. The analysis has confirmed industrial atmospheric pollutions as the primary type of human impact here. We have discussed the role of forest fires and uncovered temporal trends in the vegetation cover. We have found that during the 19 years covered by the study more than one third of all coniferous forest in the area was transformed primarily to wetland, deciduous forest and typical tundra vegetation. The success of the method in this area allows us to extend the study to the rest of Fennoscandia and look at large scale changes in the boreal vegetation cover.

This paper develops a simple method for the detection of ‘vegetation anomalies’, locations where the amount of vegetation, estimated through the use of the normalised difference vegetation index (NDVI), is significantly lower than expected on the basis of topographic factors alone. The method is developed and tested using satellite imagery from the area around the town of Monchegorsk on the Kola Peninsula, Russia. This area has been subject to heavy levels of airborne industrial pollution for many years and the intended purpose of the method is to allow the extent of pollution damaged vegetation to be estimated with as few operational decisions as possible by the data analyst, thus suiting it for automation and for the analysis of time-series of satellite images. While the work described in this paper is to some extent preliminary, it does establish that spatial variations in the NDVI of undisturbed vegetation can, at least in the study area, be modelled satisfactorily using topographic variables, and that negative departures from this modelled variation are very strongly associated with industrially mediated damage.