UNIS is seeking for a highly motivated PhD candidate to join our interdisciplinary research team with the aim to advance high-resolution terrestrial snow monitoring by applying drone-based remote sensing methods.

In a warming Arctic, forecasting how future changes in snow conditions will impact ecosystems, landscapes, and communities is critical. Yet, to date, monitoring even the most basic snow characteristics, such as presence of snow cover or snow depth, remains challenging. Most available satellite-based and modelled snow data products, especially for snow depth, are too coarse in spatio-temporal resolution for most applications in the fields of ecological, permafrost, and geohazard research. For example, to capture small-scale vegetation heterogeneity or to explain herbivores space use behavior in relation to snow conditions, data is required at cm to m resolution.

This PhD project will develop and apply remote sensing methods to advance terrestrial snow monitoring at high resolution in Svalbard. To bridge the scale gap between accurate but spatio-temporally sparse in-situ, point-based snow depth measurements and large-coverage but coarse-scale satellite products, the project focusses on drone-based remote sensing methods. The PhD candidate will conduct a detailed methodological investigation of how in situ monitoring, drone mapping, and satellite data can be integrated to transfer and predict high-resolution, small-scale snow characteristics onto larger spatial extents.

Building on existing pilot drone data and an in-situ snow monitoring system operated by the Climate-ecological Observatory for Arctic Tundra (COAT) network, the candidate will develop and implement drone-based snow monitoring and associated validation workflows. Methodological challenges to be addressed could, for instance, include spatial transferability via empirical predictive modelling, upscaling and data fusion approaches (linking drone, in situ, snow models and satellite data), or secondary snow parameter modelling (e.g., deriving parameters such as snow wetness or snow density from drone-recorded snow depth data). The method development will aim to produce snow data products especially tailored to ecological and geophysical applications, to ultimately help improve the understanding of snow-related ecosystem processes and permafrost dynamics in Arctic and alpine environments. To this end, the candidate may also explore applications of the generated data products, for example to study herbivore habitat conditions or snow–permafrost–vegetation interactions.

The successful PhD candidate will be given flexibility to develop their own ideas within the proposed theme. Upon employment, they will work on the detailed project description together with the supervisor team. The candidate will be required to carry out research involving processing and geospatial analyses of drone remote sensing data, present research findings at scientific conferences, collaborate with other team members (MSc students, other PhD students, researchers) at other Norwegian and international institutions, and take part in field work in Svalbard for data collection and to obtain an understanding of the study system. The project offers strong integration within the interdisciplinary Svalbard research community. Where suitable and depending on funding, we will aim to facilitate research stays with collaborators at Norwegian institutions and/or abroad.

The PhD will be based at the Department of Arctic Biology at UNIS, and the candidate will be admitted to a PhD program at the Norwegian University of Life Sciences (NMBU). The PhD student will be supervised by Assoc. Prof. Larissa Beumer (UNIS, main supervisor) and Prof. Leif Egil Loe (NMBU, co-supervisor). Assoc. Prof. Eero Rinne (UNIS, Geophysics Department), Prof. Tobias Ullmann (University of Würzburg, Germany), senior researcher Dr. Virve Ravolainen (Norwegian Polar Institute) and senior researcher Dr. Ketil Isaksen (Norwegian Meteorological Institute) will serve as additional co-supervisors. The interdisciplinary supervision team combines expertise in (cryosphere) remote sensing, Arctic vegetation and herbivore ecology, and snow and permafrost science. The exact role distribution within the (extended) supervisory team will be agreed upon depending on the candidate’s main interests and research plan.

The four-year contract includes a year of required duties. Alternatively, the candidate may choose a three-year position which excludes these duties.

Duty work will include teaching on relevant courses (in the field and in the classroom), student supervision (for project-associated thesis projects), research support (such as data management/analysis), administration and outreach work. Required duties for the successful candidate will be specified upon employment. Other duties may be assigned by the head of department as required.