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Mapping snow cover (SC) on glaciers at the end of the ablation period provides a possibility to rapidly obtain a proxy for their equilibrium line altitude (ELA) which in turn is a metric for the mass balance. Satellite determination of glacier snow cover, derived over large regions, can reveal its spatial variability and temporal trends. Accordingly, snow mapping on glaciers has been widely applied using several satellite sensors. However, as glacier ice originates from compressed snow, both have very similar spectral properties and standard methods to map snow struggle to distinguish snow on glaciers. Hence, most studies applied manual delineation of snow extent on glaciers. Here we present an automated tool, named ‘ASMAG’ (automated snow mapping on glaciers), to map SC on glaciers and derive the related snow line altitude (SLA) for individual glaciers using multi-temporal Landsat satellite imagery and a digital elevation model (DEM). The method has been developed using the example of the Ötztal Alps, where an evaluation of the method is possible using field-based observations of the annual equilibrium line altitude (ELA) and the accumulation area ratio (AAR) measured for three glaciers for more than 30 years. The tool automatically selects a threshold to map snow on glaciers and robustly calculates the SLA based on the frequency distribution of elevation bins with more than 50% SC. The accuracy of the SC mapping was about 90% and the SLA was determined successfully in 80% of all cases with a mean uncertainty of ±19 m. When cloud-free scenes close to the date of the highest snowline are available, a good to very good agreement of SC ratios (SCR)/SLA with field data of AAR/ELA are obtained, otherwise values are systematically higher/lower as useful images were often acquired too early in the summer season. However, glacier specific differences are still well captured. Snow mapping on glaciers is impeded by clouds and their shadows or when fresh snow is covering the

Accurately identifying the extent of surface snow cover on glaciers is important for extrapolating end of year mass balance measurements, constraining the glacier surface radiative energy balance and evaluating model simulations of snow cover. Here, we use auxiliary information from Riegl VZ-6000 Terrestrial Laser Scanner (TLS) return signals to accurately map the snow cover over a glacier throughout an ablation season. Three classification systems were compared, and we find that supervised classification based on TLS signal intensity alone is outperformed by a rule-based classification employing intensity, surface roughness and an associated optical image, which achieves classification accuracy of 68–100%. The TLS intensity signal shows no meaningful relationship with surface or bulk snow density. Finally, we have also compared our Snow Line Altitude (SLA) derived from TLS with SLA derived from the model output, as well as one Landsat image. The results of the model output track the SLA from TLS well, however with a positive bias. In contrast, automatic Landsat-derived SLA slightly underestimates the SLA from TLS. To conclude, we demonstrate that the snow cover extent can be mapped successfully using TLS, although the snow mass remains elusive.

Early career researchers such as PhD students are a main driving force of scientific research and are for a large part responsible for research innovation. They work on specialized topics within focused research groups that have a limited number of members, but might also have limited capacity in terms of lab equipment. This poses a serious challenge for educating such students as it is difficult to group a sufficient number of them to enable efficient knowledge transfer. To overcome this problem, the Innsbruck Summer School of Alpine Research 2015 on close-range sensing techniques in Alpine terrain was organized in Obergurgl, Austria, by an international team from several universities and research centres. Of the applicants a group of 40 early career researchers were selected with interest in about ten types of specialized surveying tools, i.e. laser scanners, a remotely piloted aircraft system, a thermal camera, a backpack mobile mapping system and different grade photogrammetric equipment. During the one-week summer school, students were grouped according to their personal preference to work with one such type of equipment under guidance of an expert lecturer. All students were required to capture and process field data on a mountain-related theme like landslides or rock glaciers. The work on the assignments lasted the whole week but was interspersed with lectures on selected topics by invited experts. The final task of the summer school participants was to present and defend their results to their peers, lecturers and other colleagues in a symposium-like setting. Here we present the framework and content of this summer school which brought together scientists from close-range sensing and environmental and geosciences., Optical and Laser Remote Sensing