Methane plays an important role in determining the atmosphere’s climate and chemistry. Fluxes of methane from an ecosystem are often measured using eddy covariance flux towers; however, there are disadvantages with this method. Flux towers are expensive to purchase and have high demands with respect to maintenance and cost of operation, especially in remote locations, making replication across the landscape a challenge. Using sensors mounted on a unmanned aerial vehicle (UAV), also known as a drone, would allow replication of flux measurements across a landscape as well as enable scientists to measure methane at locations where towers are not practical (i.e. sites that are ephemeral in nature, immediately after a disturbance, etc.). In this work, we test the ability of a UAV equipped with a highly accurate methane sensor to calculate ecosystem flux using the mass balance method. This method uses data collected with curtains (transects at various heights) flown both upwind and downwind of the area of interest. The concentration of methane within these curtains is then estimated using kriging techniques. The difference in calculated amounts of methane between the upwind and downwind curtains is processed to obtain an estimate of flux. Flights in wetlands that also have eddy covariance towers, providing corroborating flux values, have been flown in Alaska and California. We calculated UAV-based flux for the Alaskan flights using a bootstrap approach from multiple randomly subsampled data points within each full curtain of data. We compare these calculations to the traditional mass balance technique. We tested if these different approaches improve the accuracy of our results, as well as the uncertainty bounds for the small fluxes emitted from these ecosystems.
