On 20 February 2010, an extreme rainfall episode occurred on Madeira Island, which caused an exceptionally strong flash flood and several soil slip-debris flows, producing 45 confirmed deaths and 6 persons declared missing, as well as extensive material damages. In order to understand and quantify the importance of landsliding in routing sediment through mountainous drainage, such as Madeira Island's landscape, it was essential to perform extensive landslide analysis. This study describes the methodology used to semi-automatically detect the landslides, produce the landslide inventory maps and estimate the sediment volume produced during this particular event which ranged from 217 000 m³ to 344 000 m³ and 605 000 m³ to 984 000 m³ for the Funchal and Ribeira Brava basins, respectively. These results contributed to the design and implementation of measures to prevent damages caused by landslides in Madeira Island

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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.

The extreme rainfall event of February 20 th , 2010 triggered a series of landslides and alluvium episodes with extensive life and material damages. The use of automatic detection of the landslides over satellite imagery allowed the identification and characterization of the affected areas, the mapping of the landslide features and the calculation of the displaced sediment volume. The study shows that the occurred landslides were shallow, being the basins of Ribeira Brava the most affected areas, with almost two thirds of the total identified landslide polygons.