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Abstract:To remotely monitor vegetation at temporal and spatial resolutions unobtainable with satellite-based systems, near remote sensing systems must be employed. To this extent we used Normalized Difference Vegetation Index NDVI sensors and normal digital cameras to monitor the greenness of six different but common and widespread High Arctic plant species/groups (graminoid/Salix polaris; Cassiope tetragona; Luzula spp.; Dryas octopetala/S. polaris; C. tetragona/D. octopetala; graminoid/bryophyte) during an entire growing season in central Svalbard. Of the three greenness indices (2G_RBi, Channel G% and GRVI) derived from digital camera images, GRVI showed the most significant correlations with NDVI among all vegetation types. The GRVI (Green-Red Vegetation Index) is calculated as (G DN − R DN )/(G DN + R DN ) where G DN is Green digital number and R DN is Red digital number. Both NDVI and GRVI successfully recorded timings of the green-up and plant growth periods and senescence in all six plant species/groups. Some differences in phenology between plant species/groups occurred: the mid-season growing period reached a sharp peak in NDVI and GRVI values where graminoids were present, but a prolonged period of higher values occurred with the other plant species/groups. In particular, plots containing C. tetragona experienced increased NDVI and GRVI values towards the end of the season. NDVI measured with active and passive sensors were strongly correlated (r > 0.70) for the same plant species/groups. Although NDVI recorded by the active sensor was consistently lower than that of the passive sensor for the same plant species/groups, differences were small and likely due to the differing light sources used. Thus, it is evident that GRVI and NDVI measured with active and passive sensors captured similar vegetation attributes of High Arctic plants. Hence, inexpensive digital cameras can be used with passive and active NDVI devices to establish a near remote sensing network for monitoring changing vegetation dynamics in the High Arctic.

Aims: An Arctic Vegetation Classification (AVC) is needed to address issues related to rapid Arctic-wide changes to climate, land-use, and biodiversity. Location: The 7.1 million km 2 Arctic tundra biome. Approach and conclusions: The purpose, scope and conceptual framework for an Arctic Vegetation Archive (AVA) and Classification (AVC) were developed during numerous workshops starting in 1992. The AVA and AVC are modeled after the European vegetation archive (EVA) and classification (EVC). The AVA will use Turboveg for data management. The EVC will use a Braun-Blanquet (Br.-Bl.) classification approach. There are approximately 31,000 Arctic plots that could be included in the AVA. An Alaska AVA (AVA-AK, 24 datasets, 3026 plots) is a prototype for archives in other parts of the Arctic. The plan is to eventually merge data from other regions of the Arctic into a single Turboveg v3 database. We present the pros and cons of using the Br.-Bl. classification approach compared to the EcoVeg (US) and Biogeoclimatic Ecological Classification (Canada) approaches. The main advantages are that the Br.-Bl. approach already has been widely used in all regions of the Arctic, and many described, well-accepted vegetation classes have a pan-Arctic distribution. A crosswalk comparison of Dryas octopetala communities described according to the EcoVeg and the Braun-Blanquet approaches indicates that the non-parallel hierarchies of the two approaches make crosswalks difficult above the plantcommunity level. A preliminary Arctic prodromus contains a list of typical Arctic habitat types with associated described syntaxa from Europe, Greenland, western North America, and Alaska. Numerical clustering methods are used to provide an overview of the variability of habitat types across the range of datasets and to determine their relationship to previously described Braun-Blanquet syntaxa. We emphasize the need for continued maintenance of the Pan-Arctic Species List, and additional plot data to fully sample the variability across bioclimatic subzones, phytogeographic regions, and habitats in the Arctic. This will require standardized methods of plot-data collection, inclusion of physiogonomic information in the numeric analysis approaches to create formal definitions for vegetation units, and new methods of data sharing between the AVA and national vegetation-plot databases.

Vegetation and environmental data were collected at 266 sampling points distributed in a regular manner along transects covering the Brù ggerhalvù ya peninsula, on the north-western coast of Spitsbergen. Transects with sampling points were drawn in advance on aerial photographs. The analysis of releve  s and collection of ground data along transects represent an e cient, representative and precise way of sampling. The vegetation data were classi® ed and 19 plant communities distinguished. The plant communities were subjected to detrended correspondence analysis (DCA). Among the recorded variables, moisture is the one with the highest correlation along axes one and two, and re¯ects a coincidental moisture and vegetation cover gradient. The vegetation component responsible for this positive correlation is the bryophytes. Likewise, the TWINSPAN classi® cation con® rms this gradient in a dendrogram re¯ecting the hierarchical structure of the plant communities.Plant communities constitute the base of a statistical model that links the communities and the SPOT satellite data. The model then classi® es and maps plant communities by means of satellite data, covering the entire Brù ggerhalvù ya peninsula. Satellite data and environmental data were analysed regarding their ability to distinguish the plant communities in a discriminant function analysis (DFA). The results of the DFA indicate that it may be reasonable to include all the information from the di erent satellite channels when using satellite data for vegetation classi® cation purposes. Among the satellite data the panchromatic channel is the one adding the most unique information to the power of the model in separating plant communities.The classi® cation of satellite data using the probability model indicates that plant communities with less than 30% vegetation cover could be classi® ed with the same degree of con® dence or better, as compared with plant communities with more than 30% vegetation cover. The overall percentage of correctly classi-® ed releve  s increased by 13% when using probability level two instead of level one (57.8 to 71.1%). The probability classi® cation model makes it possible

Protected area management can be highly contentious. Information about the acceptability of conservation actions can help environmental authorities design policies that are accepted locally, and identify potential areas of conflict between land users and conservation objectives. In this study, we implemented a spatially-explicit method for eliciting public preferences for land use and conservation policy (web-based public participation GIS; PPGIS). We invited randomly selected local residents in two mountainous regions in Norway to map their preferences for consumptive resource use, motorized use, land development and predator-control. We assessed whether local communities favored or opposed these human activities in nearby protected areas using mixed-effects logistic regression and controlling for landscape characteristics, accessibility and demographics. Local residents strongly favored consumptive resource use and predator control regardless of protected area status, and were more likely to oppose than favor land development inside protected areas. These preferences are largely consistent with the present protected area policy in Norway and Europe that promotes traditional consumptive use and the maintenance of cultural landscapes, but restricts land development. Our results suggest that use-based framing of conservation is more likely to resonate with these communities than narratives tied to the preservation of pristine nature and emerging conservation ideas of the rewilding of nature. Mapped community preferences can be a valuable tool for policy makers and stakeholders representing community interests in participatory processes, and for assessing the local acceptance of alternative management actions within protected areas.

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A major environmental concern regarding the Arctic is how global change effects can influence vegetation and ecosystems. The amount of summer warmth is the single most important variable for biological processes in the Arctic, and the one that is most likely to be affected by climate change. A major task is to establish how temperature conditions are modified at a very high spatial resolution.In order to build up scenarios that are as relevant as possible concerning vegetation development, the key point is to know how the spatial distribution of temperature varies and can be related to different environment factors at different spatial scales. A method is suggested for modelling the temperature distribution at a high resolution (2 × 2 m 2 ) and with high accuracy. In step one, a polynomial equation is established for modelling the distribution of temperature values measured in the field at low spatial resolution (1 × 1 km 2 ), with latitude and longitude defining the general trend surface of the temperature distribution. In step two, residue values from the previous step are explained by environment factors stored as numerical information layers in a geographical information system. These environmental data are derived from a high-resolution digital elevation model and from a digitized infrared aerial photograph. Finally, the combination of these kilometric and micro-scale models make it possible to restore the thermic field of the study area and to represent it through different maps at a very fine-grained resolution. The method is tested on two glacier forefields in Svalbard, covering an area of 8 km 2 .

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