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A methodology was tested for high-resolution mapping of vegetation and detailed geoecological patterns in the Arctic Tundra, based on aerial imagery from an unmanned aerial vehicle (visible wavelength - RGB, 6 cm pixel resolution) and from an aircraft (visible and near infrared, 20 cm pixel resolution). The scenes were fused at 10 and 20 cm to evaluate their applicability for vegetation mapping in an alluvial fan in Adventdalen, Svalbard. Ground-truthing was used to create training and accuracy evaluation sets. Supervised classification tests were conducted with different band sets, including the original and derived ones, such as NDVI and principal component analysis bands. The fusion of all original bands at 10 cm resolution provided the best accuracies. The best classifier was systematically the maximum neighbourhood algorithm, with overall accuracies up to 84%. Mapped vegetation patterns reflect geoecological conditioning factors. The main limitation in the classification was differentiating between the classes graminea, moss and Salix, and moss, graminea and Salix, which showed spectral signature mixing. Silty-clay surfaces are probably overestimated in the south part of the study area due to microscale shadowing effects. The results distinguished vegetation zones according to a general gradient of ecological limiting factors and show that VIS+ NIR high-resolution imagery are excellent tools for identifying the main vegetation groups within the lowland fan study site of Adventdalen, but do not allow for detailed discrimination between species. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to verify the accuracy of delineating and characterizing ice-wedge polygonal networks with features exclusively extracted from remotely sensed images of very high resolution. This kind of mapping plays a key role for quantifying ice-wedge degradation in warming permafrost. The evaluation of mapping a network is performed in this study with two sets of aerial images that are compared to ground reference data determined by fieldwork on the same network, located in Adventdalen, Svalbard (78°N). One aerial dataset is obtained from a photogrammetric survey with RGB + NIR imagery of 20 cm/pixel, the other from an UAV (Unmanned Aerial Vehicle) survey that acquired RGB images of 6 cm/pixel of spatial resolution. Besides evaluating the degree of matching between the delineations, the morphometric and topological features computed for the differently mapped versions of the network are also confronted, to have a more solid basis of comparison. The results obtained are similar enough to admit that remotely sensed images of very high resolution are an adequate support to provide extensive characterizations and classifications of this kind of patterned ground. [ABSTRACT FROM AUTHOR]