|Hunt, Earle - Ray|
|Hively, Wells - Dean|
Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 9/17/2007
Publication Date: 11/6/2007
Citation: Hunt, E.R., Hively, W.D., Fujikawa, S., Ng, T.L., Tranchitella, M., Raszula, W., Yoel, D., Daughtry, C.S., McCarty, G.W. 2007. Remote sensing leaf area index of winter wheat from Unmanned Airborne Vehicles (UAVs) [abstract]. ASA-CSSA-SSSA Annual Meeting. 2007 CDROM. Interpretive Summary:
Technical Abstract: Remote sensing with unmanned airborne vehicles (UAVs) has more potential for within-season crop management than conventional satellite imagery because: (1) pixels have very high resolution, (2) cloud cover would not prevent acquisition during critical periods of growth, and (3) quick delivery of information to the user. Winter wheat was planted early (October 2006) and late (November 2006) in a field (39° 2´ 2' N lat, 76° 10´ 36' W long ) on the Eastern Shore of Maryland. Each planting was divided into 6 north-south strips, each with various levels of initial nitrogen fertilizer, which caused large variations in biomass and yield. Each strip was sampled at three locations for biomass and leaf area index. Intellitech Microsystems Vector-P UAV was flow on three dates in late April and early May 2007 at two elevations for different pixel sizes. The pixel size from a color-infrared digital camera onboard the UAV was about 6 cm at about 210 m elevation and 3 cm at about 115 m elevation. Inspection of the color-infrared photographs showed the spatial variation in biomass in each strip was large. Because pixel size was much smaller than the position error of the global positioning system, field plots (20 cm by 50 cm) had to be located using visual features. As with most photography, there were problems with vignetting and image anisotropy. Green normalized difference vegetation index [GNDVI = (NIR - green)/(NIR + green)] reduced the image problems and was linearly correlated with leaf area index and biomass (R2 of 0.82 and 0.75, respectively). There were no significant differences in the regressions based on pixel size. Variation in biomass was highly correlated to yields, so the GNDVI was also a good predictor of the spatial variability of yields.