101-5Drones Make Sense: Quantifying Turf Quality Using Unmanned Aerial Vehicle-Based near-Infrared Digital Image Analysis.
See more from this Division: C05 Turfgrass ScienceSee more from this Session: Establishment, Thatch, Soil and Water Management in Turfgrass Graduate Student Competition
Traditional DIA is associated with visible wavelength reflectance, yet many turf stresses largely impact near-infrared (NIR) reflectance. An aerial DIA-based NIR evaluation method was developed to quantify NIR reflectance using NIR-modified commercially available digital cameras and software. Chlorophyll index (CI) and normalized difference vegetation index (NDVI) data were generated using digital values from red channels of true-color and NIR-modified cameras. These indices were highly correlated with handheld CI (R2 = 0.76) and NDVI (R2 = 0.67) sensors in field studies; traditional dark green color index (DGCI) correlations were weaker (R2 = 0.69 and 0.55, respectively). This suggests UAV-based NIR evaluation improves traditional DIA by providing reliable CI and NDVI data, is equivalent to using handheld sensors, and saves appreciable time. UAV-based NIR DIA is an attractive addition to existing DIA systems and is a promising technology with application in turfgrass science, golf course management and other large-scale turf areas.
See more from this Session: Establishment, Thatch, Soil and Water Management in Turfgrass Graduate Student Competition