## 5 Changing Land Cover and Transition Zones  
Land cover change in high latitudes is mostly associated with shrub expansion, forest fires, thaw lake variations, grazing or settlement development (e.g., [81; 82]). Change can be identified using, e.g., post-classification approaches or assessing trends of the measured reflectance. The latter yields especially large potential, since it avoids the problem of classification accuracy. The review of [PERSON] et al. [83] in 2004 focused on land cover change in Arctic Tundra ecosystems. A number of selected studies has been summarized demonstrating various approaches with one example for each type of data: repeat terrestrial photography, aerial photography, high, medium and coarse resolution satellite image analyses. Land cover change from satellites is quantified based on indexes rather than classifications in all examples. AVHRR NDVI is commonly used for pan-Arctic assessment [84; 85], but Landsat on the regional scale. Especially the latter points to varying patterns across the Arctic. Greening is associated with lowland areas in Canada [86], but the opposite case is in Siberia where upland areas have been linked to positive NDVI trends [87]. This upland greening in the Urals, Gydan and Taymyr regions seems to be strongly connected to cryogenic disturbance processes in areas of patterned ground. According to [PERSON] et al. [87], this is not captured by AVHRR analyses due to the limited size of single disturbance sites. The applicability of various further indexes has been demonstrated by [PERSON] et al. [88], who used pixel-based trend analysis of long-term Landsat TM/ETM+ image stacks to map gradual, long-term changes to vegetation (TC brightness, greenness and wetness indexes and fractional land cover trained from IKONOS high resolution images). [PERSON] and [PERSON] [89], [PERSON] and [PERSON] [90] confirmed the suitability of such an approach in the Lena Delta and on the Alaskan North Slope, respectively. These regions coincide with two of the areas with multi-purpose land cover mapping (see Figure 1). The advantage of Landsat is its potential for regional to global mapping and records longer than 30 years, which are especially exploited for water area changes (e.g., [81; 91]). However, not only the spatial resolution, also the interpretation of water surface changes is challenging due to the inconsistent data coverage. NDVI trends are also interpreted for wetting and drying [89; 92]. A general constraint for all Landsat-based approaches is the lack of data for 1989-1998 for much of the Arctic [87]. To capture and interpret subtle changes, high resolution satellite images are required [85; 87]. A further challenging task is the detection of the tundra-taiga transition area. This is of specific interest to long-term studies on land cover change. Earlier studies, such as by [PERSON] et al. [93] in 2001, concluded that a combination of index analyses and unsupervised classification performs best. A hybrid unsupervised-supervised classification has been suggested by [PERSON] and [PERSON] [94] in 1997 to study the effect of air pollution on the Kola peninsula. Tundra and forest areas were analyzed by unsupervised classification separately. Their separation however is based on supervised classification. This approach results in classes that can be well distinguished rather than targeting predefined purpose-driven classes. Separate parametrization is however required for each scene. A similar approach was applied for the same purpose by [PERSON] et al. [95]. [PERSON] et al. [96] found that methods using multi-spectral Landsat ETM+ summer images, multi-angle MISR red band reflectance images, RADARSAT images with larger incidence angle or multi-temporal and multi-spectral MODIS data provide similar classification results (supervised) over central Siberia for a certain date. [PERSON] et al. [97] validated a sigmoid wave curve fitting algorithm to detect and quantify a forest-tundra ecotone based on Landsat ETM+ data in the Northwest Territories, Canada.