Evaluating small unmanned aerial systems for detecting drought stress in turfgrass - Hong, Mu (Kansas State University, 2019)
Recent advances in small unmanned aerial systems (sUAS) may provide rapid and accurate methods for turf research and management. The study was to evaluate early drought detection ability of ultra-high resolution remote sensing with sUAS technology, and compare it with traditional techniques on fairway-height ‘Declaration’ creeping bentgrass (Agrostis stolonifera L.) treated from severe deficit to well-watered irrigation (15, 30, 50, 65, 80, and 100% evapotranspiration replacement). Airborne measurements with a modified digital camera mounted on a hexacopter included reflectance from broad bands (near infrared [NIR, 680-780 nm], and green and blue bands [overlapped, 400-580 nm]), from which eight vegetation indices (VIs) were derived for evaluation. Canopy temperature was measured only in the final year with a thermal infrared camera mounted on a drone. Traditional measurements were volumetric water content (VWC), visual quality (VQ), percentage green cover (PGC), and VIs from handheld devices. Declines in VWC in irrigation-deficit treatments were consistently detected by the NIR band and six VIs from sUAS, and NDVI and red band from a handheld device, before drought stress was evident in VQ. These bands and indices predicted drought stress at least one week before symptoms appeared in VQ. Canopy temperature predicted drought stress as early as the best VIs and NIR, 16 days before symptoms appeared in VQ in 2017. Only the NIR and GreenBlue VI [(green-blue)/(green+blue)] consistently predicted drought stress throughout three years. Results indicate using ultra-high resolution remote sensing with sUAS can detect drought stress before it is visible to the human eye and may prove viable for irrigation management on turfgrass.
Keywords: sUAS; Remote sensing; Drought stress; Creeping bentgrass; Vegetation index; Deficit irrigation