Remotely estimating beneficial arthropod populations: Implications of a low-cost small unmanned aerial system

Authors: XAVIER, SHEREEN - University Of Georgia; Coffin, Alisa; Olson, Dawn; SCHMIT, JASON - University Of Georgia

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/14/2018
Publication Date: 9/18/2018
Citation: Xavier, S., Coffin, A.W., Olson, D.M., Schmit, J. 2018. Remotely estimating beneficial arthropod populations: Implications of a low-cost small unmanned aerial system. Remote Sensing. 10:1485-1498. https://doi.org/10.3390/rs10091485.
DOI: https://doi.org/10.3390/rs10091485

Interpretive Summary: Management of non- cropped areas in agricultural lands supports healthy beneficial insect populations that provide important services like pollination and pest control. In a previous study, we found that research plots having more flowers also had higher pollinator populations than spontaneous weed plots. Here, we examined the potential of an inexpensive small unmanned aerial vehicle (UAV) to qualify the relationship between flower area and pollinator populations. Data were collected from previously established native wildflower plots in 19 locations on the University of Georgia experimental farms in South Georgia, USA. A UAV equipped with a low cost camera was used to photograph flowers during June and September 2017. Overlapping photographic images were stitched together and analyzed with a technique known as supervised image classification to measure the area covered by flower blooms in each plot. The area estimated from the UAV images was statistically correlated with both manually counted blooms and pollinator populations, showing that UAV imagery of flower coverage can be a useful cost-and-labor-saving tool for predicting pollinator populations within agricultural landscapes.

Technical Abstract: Studies show that agricultural land requires investment in habitat management of non- cropped areas to support healthy beneficial arthropods and the ecosystem services they provide. In a previous small plot study, we manually counted blooms over the season, and found that plots providing greater numbers of flowers supported significantly higher pollinator populations over that of spontaneous weed plots. Here, we examined the potential of deploying an inexpensive small unmanned aerial vehicle (sUAV) as a tool to remotely estimate floral resources and corresponding pollinator populations. Data were collected from previously established native wildflower plots in 19 locations on the University of Georgia experimental farms in South Georgia, USA. A UAV equipped with a standard panchromatic camera was deployed to capture images of the flowers during the months of June and September 2017. Supervised image classification using a geographic information system (GIS) was carried out on the acquired images and classified to evaluate the floral area. The floral area obtained from the images positively correlated with the floral counts gathered from the quadrat samples. Furthermore, the 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.