Full TGIF Record # 285123
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Web URL(s):http://archive.lib.msu.edu/tic/ressum/2016/78.pdf
    Last checked: 06/02/2017
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Author(s):Bremer, Dale; van der Merwe, Deon
Author Affiliation:Kansas State University
Title:Evaluating small unmanned aircraft systems for detecting turfgrass stress with an emphasis on drought
Section:Physiology, genetics, and breeding: Native grasses
Other records with the "Physiology, genetics, and breeding: Native grasses" Section
Source:Turfgrass and Environmental Research Program: 2016 Research Summaries. 2016, p. 78-82.
Publishing Information:[New York, New York]: The United States Golf Association Green Section
# of Pages:5
Keywords:TIC Keywords: Drought stress; Equipment evaluation; Image analysis; Soil moisture; Unmanned aerial vehicles
See Also:Other Reports from this USGA research project: 2015-16-531
Pictures, color
USGA Summary Points:Seven days after initiation of ET treatments (7 July), no differences in turf quality or percentage green cover were evident among ET treatments (Fig. 3B and 3C). However, soil moisture was less at 15 and 30% than 100% ET (Fig. 3A). On the same day (7 July), the NIR band and the GreenBlue, Enhanced 1, and Enhanced 2 vegetation indices detected differences in vegetative properties between 100 and 15/30% ET treatments (Fig. 3E to 3H) (same trend as soil moisture, Fig. 3A). Trends observed on 7 July in these vegetation indices and the NIR band were predictive of visual quality and percentage green cover 8 days later, on 15 July (Figs. 3 and 4). Ultra-high resolution remote sensing with small UAS detected drought stress at least 8 days before it was visible to the human eye.
ASA/CSSA/SSSA Citation (Crop Science-Like - may be incomplete):
Bremer, D., and D. van der Merwe. 2016. Evaluating small unmanned aircraft systems for detecting turfgrass stress with an emphasis on drought. USGA Turfgrass Environ. Res. Summ. p. 78-82.
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    Last checked: 06/02/2017
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