S150 Integrated sUAS Greenhouse Gas Measurements and Imagery for Land Use Emissions Monitoring

Sunday, 22 January 2017
4E (Washington State Convention Center )
Lindsay Kaye Barbieri, University of Vermont, Burlington, VT

Greenhouse gases from anthropogenic activities are the most significant driver of observed climate change since the mid-20th century, according to the IPCC Fifth Assessment Report in 2013. Agriculture, Forestry and Other Land Uses (AFOLU) constitute the second largest anthropogenic source of greenhouse gas (GHG) emissions globally, and agriculture is the dominant source of emissions within that sector. There are a variety of agricultural land management strategies that can be implemented to reduce GHG emissions, but determining the best strategies is challenging. Emissions estimates are currently derived from GHG monitoring methods (e.g., static chambers, eddy flux towers) that are time and labor intensive, expensive, and use in-situ equipment. These methods lack the flexible, spatio-temporal monitoring necessary to reduce the high uncertainty in regional GHG emissions estimates. Small Unmanned Aerial Systems (sUAS) provide the rapid response data collection needed to monitor important environmental and field management events (e.g., flooding, manure spreading). Further, the ease of deployment of sUAS makes monitoring atmospheric GHG concentrations over large regional extents and over full-seasons more viable.

We have developed and tested open-source hardware and software utilizing low-cost equipment (e.g., NDIR gas sensors and Canon cameras). Initial results show agreement with more traditional, proprietary equipment but at a fraction of the costs. Here we present data from test flights over agricultural areas under various management practices. The suite of data includes sUAS overpasses for imagery and CO2 atmospheric concentration measurements, paired with field-based GHG measurements (static chambers). We have developed a set of best practices for sUAS data collection (e.g., time of day effects variability in localized atmospheric GHG concentrations) and discuss currently known challenges (e.g., accounting for external environmental factors such as wind speed). We present results on all sUAS GHG sampling methods paired with imagery and simultaneous static chamber monitoring for a comprehensive assessment of methods for use in GHG atmospheric concentration and emission hotspot detection across landscapes.

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