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Article

Efficient Drone-Based Rare Plant Monitoring Using a Species Distribution Model and AI-Based Object Detection

1
Center for Geospatial Analytics, North Carolina State University, Raleigh, NC 27695, USA
2
Marine, Earth and Atmospheric Sciences, North Carolina State University, Raleigh, NC 27695, USA
3
US Forest Service, Asheville, NC 28801, USA
4
US Fish and Wildlife Service, Asheville, NC 28801, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Adam T. Cross
Drones 2021, 5(4), 110; https://doi.org/10.3390/drones5040110
Received: 6 August 2021 / Revised: 26 September 2021 / Accepted: 27 September 2021 / Published: 2 October 2021
(This article belongs to the Special Issue Ecological Applications of Drone-Based Remote Sensing)
Monitoring rare plant species is used to confirm presence, assess health, and verify population trends. Unmanned aerial systems (UAS) are ideal tools for monitoring rare plants because they can efficiently collect data without impacting the plant or endangering personnel. However, UAS flight planning can be subjective, resulting in ineffective use of flight time and overcollection of imagery. This study used a Maxent machine-learning predictive model to create targeted flight areas to monitor Geum radiatum, an endangered plant endemic to the Blue Ridge Mountains in North Carolina. The Maxent model was developed with ten environmental layers as predictors and known plant locations as training data. UAS flight areas were derived from the resulting probability raster as isolines delineated from a probability threshold based on flight parameters. Visual analysis of UAS imagery verified the locations of 33 known plants and discovered four previously undocumented occurrences. Semi-automated detection of plant species was explored using a neural network object detector. Although the approach was successful in detecting plants in on-ground images, no plants were identified in the UAS aerial imagery, indicating that further improvements are needed in both data acquisition and computer vision techniques. Despite this limitation, the presented research provides a data-driven approach to plan targeted UAS flight areas from predictive modeling, improving UAS data collection for rare plant monitoring. View Full-Text
Keywords: UAS; flight planning; orthomosaic; species distribution modeling; endangered species; Geum Radiatum; Blue Ridge Mountains; object detection; machine learning; cliff mapping UAS; flight planning; orthomosaic; species distribution modeling; endangered species; Geum Radiatum; Blue Ridge Mountains; object detection; machine learning; cliff mapping
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MDPI and ACS Style

Reckling, W.; Mitasova, H.; Wegmann, K.; Kauffman, G.; Reid, R. Efficient Drone-Based Rare Plant Monitoring Using a Species Distribution Model and AI-Based Object Detection. Drones 2021, 5, 110. https://doi.org/10.3390/drones5040110

AMA Style

Reckling W, Mitasova H, Wegmann K, Kauffman G, Reid R. Efficient Drone-Based Rare Plant Monitoring Using a Species Distribution Model and AI-Based Object Detection. Drones. 2021; 5(4):110. https://doi.org/10.3390/drones5040110

Chicago/Turabian Style

Reckling, William, Helena Mitasova, Karl Wegmann, Gary Kauffman, and Rebekah Reid. 2021. "Efficient Drone-Based Rare Plant Monitoring Using a Species Distribution Model and AI-Based Object Detection" Drones 5, no. 4: 110. https://doi.org/10.3390/drones5040110

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