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Identifying Species and Monitoring Understorey from UAS-Derived Data: A Literature Review and Future Directions

1
Centre for Mined Land Rehabilitation, The University of Queensland, Brisbane, QLD 4072, Australia
2
Environmental Research Institute of the Supervising Scientist, Department of Environment and Energy, Darwin, NT 0820, Australia
*
Author to whom correspondence should be addressed.
Received: 13 December 2018 / Revised: 6 January 2019 / Accepted: 7 January 2019 / Published: 8 January 2019
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Abstract

Understorey vegetation plays an important role in many ecosystems, yet identifying and monitoring understorey vegetation through remote sensing has proved a challenge for researchers and land managers because understorey plants tend to be small, spatially and spectrally similar, and are often blocked by the overstorey. The emergence of Unmanned Aerial Systems (UAS) is revolutionising how vegetation is measured, and may allow us to measure understorey species where traditional remote sensing previously could not. The goal of this paper was to review current literature and assess the current capability of UAS to identify and monitor understorey vegetation. From the literature, we focused on the technical attributes that limit the ability to monitor understorey vegetation—specifically (1) spatial resolution, (2) spectral sensitivity, (3) spatial extent, and (4) temporal frequency at which a sensor acquires data. We found that UAS have provided improved levels of spatial resolution, with authors reporting successful classifications of understorey vegetation at resolutions of between 3 mm and 200 mm. Species discrimination can be achieved by targeting flights to correspond with phenological events to allow the detection of species-specific differences. We provide recommendations as to how UAS attributes can be tailored to help identify and monitor understorey species. View Full-Text
Keywords: UAV; drone; sub-canopy; understory; vegetation; remote sensing; spatial resolution; spectral sensitivity; spatial extent; temporal frequency UAV; drone; sub-canopy; understory; vegetation; remote sensing; spatial resolution; spectral sensitivity; spatial extent; temporal frequency
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Hernandez-Santin, L.; Rudge, M.L.; Bartolo, R.E.; Erskine, P.D. Identifying Species and Monitoring Understorey from UAS-Derived Data: A Literature Review and Future Directions. Drones 2019, 3, 9.

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