No abstract

Abstract. Snow avalanches are destructive mass movements in mountain regions that continue to claim lives and cause infrastructural damage and traffic detours. Given that avalanches often occur in remote and poorly accessible steep terrain, their detection and mapping is extensive and time consuming. Nonetheless, systematic avalanche detection over large areas could help to generate more complete and up-to-date inventories (cadastres) necessary for validating avalanche forecasting and hazard mapping. In this study, we focused on automatically detecting avalanches and classifying them into release zones, tracks, and run-out zones based on 0.25 m near-infrared (NIR) ADS80-SH92 aerial imagery using an object-based image analysis (OBIA) approach. Our algorithm takes into account the brightness, the normalised difference vegetation index (NDVI), the normalised difference water index (NDWI), and its standard deviation (SD NDWI ) to distinguish avalanches from other landsurface elements. Using normalised parameters allows applying this method across large areas. We trained the method by analysing the properties of snow avalanches at three 4 km −2 areas near Davos, Switzerland. We compared the results with manually mapped avalanche polygons and obtained a user's accuracy of > 0.9 and a Cohen's kappa of 0.79-0.85. Testing the method for a larger area of 226.3 km −2 , we estimated producer's and user's accuracies of 0.61 and 0.78, respectively, with a Cohen's kappa of 0.67. Detected avalanches that overlapped with reference data by > 80 % occurred randomly throughout the testing area, showing that our method avoids overfitting. Our method has potential for large-scale avalanche mapping, although further investigations into other regions are desirable to verify the robustness of our selected thresholds and the transferability of the method.

The biologically active compounds (fatty acids, pigments, phenolics, and flavonoid content) were studied in supercritical fluid extracts from the biomass of marine (Ulva clathrata, Cladophora glomerata, Polysiphonia fucoides, and their multi-species mixture) and freshwater (C. glomerata) macroalgae. Different extraction techniques were used in order to compare differences in the biologically active compound composition of the macroalgal extracts. The results indicated that the saturated and unsaturated fatty acids ranged from C9:0 to C22:0. The analysis of differences in the composition of unsaturated to saturated fatty acids in extracts showed that palmitic acid (C16:0) and oleic acid (C18:1, n-9) reached the highest value not only in marine monospecies and multi-species biomass but also in the freshwater macroalga C. glomerata. When comparing the similarity between the concentration of fatty acids and the ratio of the concentration of unsaturated fatty acids to saturated in macroalgal extracts, we found small but not statistically significant variations in values between years (up to 10%). This is acceptable for applications as a stable raw material for industrial purposes. Significantly higher values of fatty acids, carotenoids, and chlorophylls were obtained in the case of SC-CO2 extraction. The active ingredients of polyphenols, possessing antioxidant activity ranged from approximately 2–4%. Moreover, flavonoids represented less than 10% of the total content of polyphenolic compounds. The extraction efficiency of polyphenols was higher from a mixture of marine algae for the ultrasound-assisted extraction compared to freshwater. All these findings show that marine and freshwater macroalgae, as a raw material, have the optimal biologically active compounds composition for cosmetics.

No abstract

Making sense of the physical world has always been at the core of mapping. Up until recently, this has always dependent on using the human eye. Using airborne lasers, it has become possible to quickly "see" more of the world in many more dimensions. The resulting enormous point clouds serve as data sources for applications far beyond the original mapping purposes ranging from flooding protection and forestry to threat mitigation. In order to process these large quantities of data, novel methods are required. In this contribution, we develop models to automatically classify ground cover and soil types. Using the logic of machine learning, we critically review the advantages of supervised and unsupervised methods. Focusing on decision trees, we improve accuracy by including beam vector components and using a genetic algorithm. We find that our approach delivers consistently high quality classifications, surpassing classical methods.