Detecting Floral Resource Availability Using Small Unmanned Aircraft Systems

Nicholas V. Anderson, Steven L. Petersen, Robert L. Johnson, Tyson J. Terry and Val J. Anderson ()
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Nicholas V. Anderson: Department of Plant and Wildlife Sciences, Brigham Young University, Provo, UT 84602, USA
Steven L. Petersen: Department of Plant and Wildlife Sciences, Brigham Young University, Provo, UT 84602, USA
Robert L. Johnson: Department of Biology, Brigham Young University, Provo, UT 84602, USA
Tyson J. Terry: Disturbance Ecology Department, University of Bayreuth, 95444 Bayreuth, Germany
Val J. Anderson: Department of Plant and Wildlife Sciences, Brigham Young University, Provo, UT 84602, USA

Land, 2024, vol. 13, issue 1, 1-12

Abstract: Floral resources for native pollinators that live in wildland settings are diverse and vary across and within growing seasons. Understanding floral resource dynamics and management is becoming increasingly important as honeybee farms seek public land for summer pasture. Small Unmanned Aircraft Systems (sUASs) present a viable approach for accurate broad floristic surveys and present an additional solution to more traditional alternative methods of vegetation assessment. This methodology was designed as a simplified approach using tools frequently available to land managers. The images of three subalpine meadows were captured from a DJI Phantom 4 Pro drone platform three times over the growing season in 2019 in Sanpete County, Utah. The images were composited using Pix4D software 4.5.6 and classified using a simple supervised approach in ENVI 4.8 and ArcGIS Pro 2.4.3 These same meadows were assessed using two traditional ocular methods of vegetation cover–meter-squared quadrats and macroplot estimation. The areas assessed with these methods were compared side by side with their classified counterparts from drone imagery. Classified images were not only found to be highly accurate when detecting overall floral cover and floral color groups (76–100%), but they were also strongly correlated with quadrat estimations, suggesting that these methods used in tandem may be a conducive strategy toward increased accuracy and efficiency when determining floral cover at broad spatial scales.

Keywords: drone technology; remote sensing; floral resource detection; vegetation mapping; pollinator resources (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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