Seminar: Abdullah Bolek

The spatial scales of variability in the Arctic permafrost region range from less than a meter up to thousands of kilometers, resulting in highly complex landscapes. The associated heterogeneity in ecosystem characteristics also causes variation in carbon cycle processes and related surface-atmosphere exchange fluxes, resulting in atmospheric CO2 and CH4 signals that may vary significantly across scales. As a common tool, eddy covariance towers or flux chamber measurements are being used to quantify ecosystem greenhouse gas budgets; however, within these complex Arctic permafrost landscapes, such stationary measurements with limited footprint areas always carry a high risk of location biases, i.e. their flux signals may not be representative for the larger, heterogeneous region. Additionally, installing an eddy tower in such remote places is not always possible due to power requirements, and complex logistics. Linked to recent developments in drone and sensor technology, unmanned aerial vehicles (UAVs) are becoming increasingly suitable tools to complement existing carbon monitoring networks, and particularly address the uncertainties associated with upscaling localized information from stationary towers in heterogeneous terrain. Compared to alternative approaches, UAVs provide a ubiquitous, practical, and comparatively inexpensive approach to quantify the variability in surface-atmosphere exchange processes at local to regional scales. Here, we present a UAV-based monitoring platform that will allow us to quantify the CO2 and CH4 mixing ratios in the lower atmospheric boundary layer over terrain composed of different landscape features in Arctic permafrost regions. The UAV will be instrumented with in-situ gas analyzers, complemented by meteorological sensors for e.g. 2-D wind speed, air temperature, pressure, and humidity. Combining gridded horizontal transects with vertical profiles, this set of parameters will provide essential information for evaluating the impact of surface heterogeneity on regional carbon fluxes, and in turn allow us to determine the representativeness of stationary eddy-covariance systems for upscaling to the larger region.