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.

Methane fluxes are often studied using eddy covariance flux towers or chambers placed on the soil surface. These measurement techniques have improved our understanding of methane emissions from wetlands. However, there are limitations with each measurement method. For example, chambers are fixed in place and have high maintenance costs, limiting spatial coverage and characterization of heterogeneity. Measurements taken in Interior Alaskan wetlands suggest that heterogeneity in methane fluxes from this region may increase during the fall and early winter, when the soils begin to freeze. Unfortunately, off-grid power limitations and freezing conditions complicate chamber operation during this time. Towers share similar demands with respect to maintenance and cost of operation, and, therefore, are not often replicated within a landscape. Moreover, towers provide an integrated measurement which masks any spatial heterogeneity in fluxes within the tower footprint. Therefore, although chamber and flux towers provide important insights into the carbon exchange between terrestrial and atmospheric pools, these methods have limitations, particularly when characterizing spatial heterogeneity. We tested a new technology that may be able to be counteract some of these limitations, thereby providing additional insights into methane emissions from wetlands. We outfitted a small-unmanned aerial system (sUAS, or drone), that can fly extremely close (<2 m) to the wetland’s surface, with a miniature open-path laser spectrometer methane sensor, LIDAR, and a miniature anemometer. We then tested this system in several bogs near Fairbanks, Alaska. We tested if this system could detect spatial and/or temporal variability of methane emissions within a bog. We also compared methane fluxes calculated using this system to values obtained from tower and chamber measurements. Results of these missions will be presented and we will discuss the ability of this new technology to provide additional information regarding methane emissions from wetlands.