Golf courses can expand hundreds of acres, making scouting for both pests and beneficial insect populations a time-consuming task. Scouting for insects is labor-intensive, potentially damaging, but is an integral part of an integrated pest and pollinator management (IPPM) plan. Virginia golf courses are currently using remote sensing and light reflectance to detect non-insect pests in turfgrass. This thesis aims to develop remote sensing and light reflectance methods to aid in a turfgrass IPPM plan, to document the phenology of ABW weevil (Listronotus maculicollis Kirby, Coleoptera: Curculionidae, ABW), and to catalogue pollinator-friendly out-of-play areas. Light reflectance, the measurement of the amount of light reflected, of plants can be used as a proxy for the health of a plant. The light reflectance of turfgrass affected by ABW stress and plants in the out-of-play areas of golf courses was collected proximally and remotely, using a backpack spectrometer and an unmanned aerial vehicle (UAV), respectively. Mathematical light reflectance indices were applied and compared to insect populations in both areas to determine the correlation. The Normalized Difference Vegetation Index (NDVI), which uses red and near-infrared wavelengths to indicate stress, was found to highlight ABW stressed turfgrass. The Structure Intensive Vegetation Pigment Index (SIPI), which uses red and green wavelengths to highlight flowering plants, was found to highlight potential pollinator- friendly habitats in out-of-play areas. When applied to flights, NDVI could help in the targeted application of insecticides to combat the annual bluegrass weevil, therefore reducing their presence in the environment. The use of SIPI could highlight potential pollinator friendly habitats and therefore assist superintendents in the development of their IPPM plan. / Master of Science in Life Sciences / Scouting, such as completing visual monitoring or taking soil core samples, is an important part in the development of an integrated pest and pollinator management (IPPM) plan for Virginia golf courses; an IPPM plan focuses on control of a pest, while considering the needs of pollinators. The size of golf courses makes scouting for insect pests and beneficial insects a time-consuming task. Golf courses are currently using remote sensing, the use of drones in combination with other technology, to scout for other pests or disease. Light reflectance, the measurement of the amount of light reflected, is often used in combination with remote sensing as a proxy for the health of plants. This thesis developed remote sensing and light reflectance techniques not only to detect a common turfgrass pest, the annual bluegrass weevil (Listronotus maculicollis Kirby, Coleoptera: Curculionidae, ABW), but to also predict the presence of potential pollinator habitats in the out-of-play areas of Virginia golf courses. Instruments such as a spectrometer and a drone were used to collect light reflectance at the ground level and aerially, respectively. Ground data was collected through soap water flushes to detect adult ABW, and visual monitoring of potentially pollinating bees, beetles, butterflies, and flies. The light reflectance and ground data were compared using mathematical indices to determine if there was a relationship between the presence of insects and a particular index. Indices could be applied to drone flights that golf course superintendents are already performing, and they can use this information to highlight potential areas of insect presence. This will help them to take care not to apply insecticides in areas with pollinators or to only apply necessary insecticides where there is likely a presence of ABW. This will reduce the labor, other costs, and the environmental impact of insecticides.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/116229 |
| Date | 06 September 2023 |
| Creators | Bradley, Shannon Grace |
| Contributors | Entomology, Del Pozo-Valdivia, Alejandro, McCall, David Scott, Shafian, Sanaz |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | Creative Commons Attribution 4.0 International, http://creativecommons.org/licenses/by/4.0/ |
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