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Open AccessArticle

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

1
Department of Entomology, University of Georgia, Tifton, GA 31793, USA
2
Southeast Watershed Research Laboratory, USDA-ARS, Tifton, GA 31793, USA
3
Crop Protection and Management Research Unit, USDA-ARS, Tifton, GA 31793, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(9), 1485; https://doi.org/10.3390/rs10091485
Received: 23 August 2018 / Revised: 10 September 2018 / Accepted: 14 September 2018 / Published: 18 September 2018
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. View Full-Text
Keywords: UAV floral detection; image classification; floral provisioning; habitat management; pollinators; agricultural buffers; floral area; long term agroecosystem research (LTAR) UAV floral detection; image classification; floral provisioning; habitat management; pollinators; agricultural buffers; floral area; long term agroecosystem research (LTAR)
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MDPI and ACS Style

Xavier, S.S.; Coffin, A.W.; Olson, D.M.; Schmidt, J.M. Remotely Estimating Beneficial Arthropod Populations: Implications of a Low-Cost Small Unmanned Aerial System. Remote Sens. 2018, 10, 1485. https://doi.org/10.3390/rs10091485

AMA Style

Xavier SS, Coffin AW, Olson DM, Schmidt JM. Remotely Estimating Beneficial Arthropod Populations: Implications of a Low-Cost Small Unmanned Aerial System. Remote Sensing. 2018; 10(9):1485. https://doi.org/10.3390/rs10091485

Chicago/Turabian Style

Xavier, Shereen S.; Coffin, Alisa W.; Olson, Dawn M.; Schmidt, Jason M. 2018. "Remotely Estimating Beneficial Arthropod Populations: Implications of a Low-Cost Small Unmanned Aerial System" Remote Sens. 10, no. 9: 1485. https://doi.org/10.3390/rs10091485

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Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

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