## 8 Challenges and Promising Approaches  
There is a range of comparison studies of different methods for land cover classifications in high latitudes. These are usually limited to small areas. Land cover change is mostly addressed on the local scale. Classifications of land cover types other than water surfaces have only rarely been used for change detection in Arctic environments, except for identifying disturbances, such as air pollution or grazing effects. Indexes are commonly used to address land cover change issues. Classifications are however of benefit for identifying the tundra-taiga transition zone, which is of special interest to climate change impact studies. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Type** & **Usa Basin (Non-Forest Part)** & **Kuparuk Basin** & **Lena Delta** \\ \hline \multirow{10}{*}{**CAVM**} & & & Prostrate dwarf shrub, \\  & & Nontussock sedge, dwarf & herb tundra \\  & & shrub, moss tundra & Graminoid, prostrate \\  & Nontussock sedge, & Tussock sedge, dwarf & dwarf shrub, forb tundra \\  & dwarf shrub, moss tundra & shrub, moss tundra & Nontussock sedge, dwarf shrub, \\  & Enect dwarf shrub tundra & Enect dwarf shrub tundra & moss tundra \\  & Low shrub tundra & Sedge/grass, moss wetland & Erect dwarf shrub tundra \\  & Sedge, moss, low shrub wetland & Sedge, moss, & Low shrub tundra \\  & Non-carbonate mountain complex & dwarf shrub wetland & Sedge/grass, moss wetland \\  & Carbonate mountain complex & Sedge, moss, & Sedge, moss, dwarf \\  & & low shrub wetland & shrub wetland \\  & Carbonate mountain complex & Carbonate mountain complex & Non-carbonate mountain complex \\ \hline \multirow{10}{*}{**ECOCLIMAP**} & & &  
Radar (SAR)-based large-scale maps often use supervised approaches, optical mostly unsupervised. Unsupervised classifications are also the basis for national to global mapping. Single purpose-driven maps are prepared with a priori information applying supervised classification approaches, such as data on the impact of air pollution and reindeer pasture inventories, or the maps are made manually, like the CAVM by [PERSON] et al. (2016). The latter is widely used and based on AVHRR data. Object-based Image Analysis (OBIA) is a frequently-used approach classifying high resolution satellite data in general, but has little importance for applications in the high latitudes. That might be due to the fractional nature of the landscape and limited occurrence of artificial objects. Settlements are only rarely studied with land cover maps. This might however become more important in the future due to an increasing need for climate change impact assessments on local communities. Different applications require to some extent varying spatial and thematic detail (Figure 6). Habitat studies have mostly been made with respect to mammals. Medium spatial resolution maps, which can be supplied by Landsat, are widely used. Mostly six to eight classes are derived. The required thematic detail increases with decreasing spatial resolution in the case of soil carbon, carbon flux, permafrost surface features and water cycle applications. Medium resolution data studies usually cover larger areas and therefore include a higher diversity of relevant landscape units. This implies that a higher thematic content is required from global maps than currently available. For the application of a resolution of 20-30 m (as available from, e.g., Landsat and also Sentinel-2) on average seven classes are common for regional applications. The CAVM also provides six to nine none-water classes for specific regions (see Table 3). However, twelve distinct classes are already required to represent the three featured multi-purpose maps together across the Arctic. Past studies on indirect assessment of permafrost subsurface features partially use a rather low number of classes, which are grouped from higher level detail maps.