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Monday, April 9 • 5:30pm - 7:00pm
POSTER: Optimizing a UAV survey approach: implications for monitoring ecosystem services provided by floral resources

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AUTHORS: Shereen Xavier, Alisa Coffin, Dawn Olson and Jason Schmidt

ABSTRACT: Small unmanned aerial vehicles (UAVs or drones) equipped with multispectral sensors are increasingly being tested and deployed for collecting high resolution spatio-temporal data of agriculture and the environmental. Agricultural land is currently under review for finding ways to improve sustainability and long-term health.  Studies show that habitat management to provide non-cropping areas and specifically, wildflowers to provide floral resources, such as pollen and nectar to support healthy beneficial arthropods and the ecosystem services they provide.  In a previous small plot study, we counted blooms over the season, and found that plots with greater numbers of flowers supported significantly higher pollinator populations. Here we examined the potential of deploying an inexpensive UAV system as a tool to remotely estimate floral resources and corresponding pollinator populations. UAV data were collected from previously established native wildflower plots in 19 locations on University of Georgia experimental farms in Tifton, Georgia.  A UAV (Solo 3Dr) equipped with a standard panchromatic camera was deployed to capture images of the wildflower plots during the months of June and September 2017, months where we observed high and low pollinator populations and high and low flower counts. Pollinator population estimates and vegetation quadrat sampling was carried out simultaneously. Images were analyzed using supervised image classification to determine the floral area within ArcGIS software. The floral area obtained from the images positively correlated with the floral counts gathered from the quadrat samples. Furthermore, UAV-derived floral area significantly predicted pollinator populations, with a positive correlation indicating that plots with greater area of blooming flowers contained higher numbers of pollinators.  Our results suggest analysis of images derived from low-cost UAV systems can provide indirect estimates of pollinator populations.

Monday April 9, 2018 5:30pm - 7:00pm CDT
Monroe Room

Attendees (3)