Full TGIF Record # 304998
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Web URL(s):http://archive.lib.msu.edu/tic/ressum/2018/2018.pdf#page=214
    Last checked: 05/02/2019
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Author(s):Hong, Mu; Bremer, Dale J.; van der Merwe, Deon
Author Affiliation:Hong and Bremer: Horticulture and Natural Resources Dept., Kansas State University; van der Merwe: Dept. of Farm Animal Health, University of Utrecht
Title:Evaluating small unmanned aerial systems for detecting drought stress on turfgrass
Section:Integrated turfgrass management
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Ecophysiology: Water
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Source:Turfgrass and Environmental Research Program: 2018 Research Summaries. 2018, p. 206-212.
# of Pages:7
Publishing Information:[New York, New York]: The United States Golf Association Green Section
Keywords:TIC Keywords: Drought stress; Problem diagnosis; Product evaluation; Remote sensing; Unmanned aerial vehicles
See Also:Other Reports from this USGA research project: 2015-16-531
Note:Thermal images
USGA Summary Points:Declines in VWC in irrigation-deficit treatments were consistently detected by the NIR band and six VIs from sUAS, and NDVI and the red band from the handheld device with an active optical sensor, before drought stress was evident in VQ and PGC These bands and indices predicted drought stress at least one week before symptoms appeared in VQ and PGC in 2016 and 2017. The NIR band and GreenBlue VI from sUAS were the most sensitive and the only ones to consistently detect drought stress early during the three years compared to other bands or VIs The one year of Tc measurements acquired by sUAS were comparable to the best spectral parameters in predicting drought stress before drought symptoms appeared. Using ultra-high resolution remote sensing with sUAS can detect drought stress as well as or better than handheld devices before it is visible to the human eye and may prove viable for irrigation management on turfgrass. Results from this research have been widely presented at multiple international conferences, statewide turfgrass conferences and field days, university departmental seminars, and published or discussed in research reports, abstracts, a newsletter, and interviews in a trade journal (Golfdom) and an online webinar.
ASA/CSSA/SSSA Citation (Crop Science-Like - may be incomplete):
Hong, M., D. J. Bremer, and D. van der Merwe. 2018. Evaluating small unmanned aerial systems for detecting drought stress on turfgrass. USGA Turfgrass Environ. Res. Summ. p. 206-212.
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    Last checked: 05/02/2019
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