Remotely Estimating Beneficial Arthropod Populations: Implications of a Low-Cost Small Unmanned Aerial System

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2018-09, Vol.10 (9), p.1485
• Main Authors: Xavier, Shereen, Coffin, Alisa, Olson, Dawn, Schmidt, Jason
• Format: Article
• Language: English
• Subjects: Accuracy; agricultural buffers; Agricultural economics; Agricultural land; Agricultural management; Agriculture; Arthropods; Beneficial arthropods; Classification; Digital cameras; Digital imaging; Ecosystem services; Ecosystems; Experimental farms; Farms; floral area; floral provisioning; Flowers; Flowers & plants; Geographic information systems; habitat management; Habitats; Image acquisition; Image classification; Insects; long term agroecosystem research (LTAR); Methods; Plant reproduction; Pollinators; Populations; Remote sensing; Satellite navigation systems; Sensors; UAV floral detection; Unmanned aerial vehicles; Vegetation mapping; Wildlife management
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs10091485

Studies show that agricultural land requires investment in the 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 (UAV) 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 lightweight digital 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 images were used 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 floral area derived from imagery significantly predicted pollinator populations, with a positive correlation indicating that plots with greater area of blooming flowers contained higher numbers of pollinators.