## 6 National- and Regional-Scale Land Cover Maps  
The main sources of regional- and/or national-scale land cover images are Landsat data. A special characteristic of such maps is that they are usually a combination of data from several years in order to achieve complete spatial coverage; e.g., [PERSON] et al. [104] compiled a Canada-wide map fuzzy K-means clustering with 30-m resolution from three years of data. To some extent, information from coarser datasets, which are also used on the global scale, is applied. Unsupervised classification with a preceding mosaicking step is commonly used for regional-scale mapping. Such an approach has been, e.g., also used for the first map of Northern Alaska based on Landsat MSS [46]. Landsat was also combined with e.g., SPOT VGT for normalization [105; 8] in order to create a land cover map for the Canadian Northern Territories. IKONOS was utilized to develop an approach that provides land cover fractions within the Landsat resolution cells [24]. The national land cover map of the U.S. (from Landsat TM/ETM+) has been assessed regionally by [PERSON] and [PERSON] [106] over Alaska. An agreement of 59.4% for Level II (highest thematic detail) was found. This result underlines the need to address high latitude environments separately. A different scheme has been adopted by [PERSON] et al. [107; 108]. NOAA-AVHRR data (1 km, including multi-temporal NDVI 10-day composites) are processed for Canada [107; 108]. Polygons were hand-drawn based on visual interpretation of variations in the AVHRR base map and interpretation of ancillary data. This strategy corresponds to the one chosen for the production of the Circum-Arctic Vegetation Map (CAVM; Figure 2) from NOAA-AVHRR [5,109], which was the first attempt to produce a consistent map with the necessary thematic content for vegetation studies. Only regional maps existed before [4]. Many studies have shown that the CAVM is the best regional-scale land cover product in the Arctic; e.g., [PERSON] and [PERSON] [110] have demonstrated its prevailing performance over global land cover maps for reindeer pasture mapping; however, it still lacks the necessary detail regarding separation of lichen- and shrub-dominated land cover types. A further constraint is its limitation to non-forested areas. Figure 2: CAVM [5] and global land cover maps for the CAVM domain. For a description, see Table 2.  
A range of approaches and sensors has been tested for Northern Eurasia, including usage of seasonal mosaics of SPOT4-VEGFATION (1 km, [111]), MODIS (500 m, [112]) and even radar data [113] from JERS-1 (L-band SAR, 100 m). This sensor is specifically suited to detect wetlands, as demonstrated by [PERSON] et al. [114]. [PERSON] et al. [111] applied unsupervised ISODATA classification to SPOT data, [PERSON] et al. [112] and [PERSON] et al. [113] a maximum likelihood supervised scheme to JERS-1 and MODIS, respectively. One major issue of SPOT-4 products for applications in the Arctic is their spatial limitation to areas south of 70 degrees latitude. The random forest approach, which also utilizes training data, has been applied by [PERSON] et al. [114] to L-band SAR data. This approach proved to be especially applicable to wetland mapping. SAR-based land cover maps have until recently only been based on summer (unfrozen condition) acquisitions. [PERSON] et al. [79] demonstrated that winter C-band backscatter reflects certain land cover characteristics, especially wetlands. The 1-km SAR data better represent wetland spatial distribution than global maps, which have been developed from MODIS or MERIS (500 m and 300 m, respectively). The SAR data provide mostly surface roughness and only to a limited amount of volume scattering in vegetation in tundra regions. Traditional global maps include only classes for flooded shrulands. There is, however, a much higher diversity in wetland types across the Arctic. A common feature is the low surface roughness with respect to C-band (5.6-cm wavelength).