INTEGRATED SUAS MEASUREMENTS AND IMAGERY FOR LAND USE MONITORING

Agriculture contributed 10-12% of global anthropogenic GHG emissions in 2010, and just about half of those emissions were from agricultural soils. There are a variety of land management strategies that can be implemented to reduce GHG emissions, but agricultural landscapes are complex and heterogenous, and GHG mitigation potential can vary greatly. The soil and nutrient cycling processes that ultimately regulate GHG emission rates are affected by geologic, environmental and management dynamics that can be spatially and temporally variable (e.g. soil properties, moisture levels, temperature). Thus, implementing best practices for soil-based mitigation is challenging.

Monitoring these landscape is crucial to advance and implement optimal GHG emissions mitigation practices, however, currently monitoring methods (e.g., static chambers) are time intensive, expensive, and use in-situ equipment. These methods lack the flexible, spatio-temporal monitoring necessary to reduce the high uncertainty in regional emissions estimates. Small Unmanned Aerial Systems (sUAS) provide the range and the rapid response data collection needed to monitor critical variables on these landscapes. The ease of deployment of sUAS makes monitoring large regional extents over full-seasons more viable.

Here we present results from low-cost sUAS monitoring. Our results show agreement with more traditional, proprietary equipment but at a fraction of the costs. We present data from test flights over managed agricultural landscapes in Vermont, with field-based measurements paired with sUAS remotely sensed imagery e.g. mosaic, spectral derived products, point cloud, and topographic products and atmospheric data collected from on-board sensors e.g. CO2, PTH. We present this data integration and compare results from two different sUAS flight mission configurations: Vertical Profile and Horizontal Survey. We conclude with how our results relate to broader opportunities and challenges for sUAS in the Geosciences.