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Title: Remote Sensing Crop Leaf Area Index Using Unmanned Airborne Vehicles (UAV's)

Author
item Hunt Jr, Earle
item Hively, Wells - Dean
item Daughtry, Craig
item McCarty, Gregory
item FUJIKAWA, S - INTELLITECH MICROSYSTEMS

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 9/1/2008
Publication Date: 11/17/2008
Citation: Hunt, E.R., Hively, W.D., Daughtry, C.S., McCarty, G.W., Fujikwas, S.J. 2008. Remote sensing crop leaf area index using Unmanned Airborne Vehicles (UAV's) [abstract]. The 17th William T. Pecora Memorial Remote Sensing Symposium. p. 12.

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 on the Eastern Shore of Maryland (39° 2’ 2” N lat, 76° 10’ 36” W long). Each planting was divided into 6 north-south strips, each with various levels of initial nitrogen fertilizer, which caused large variations in leaf area index, biomass, and yield. The Vector P from IntelliTech Microsystems (Bowie Maryland) was flown on three dates in late April/early May 2007 at two elevations. A color-infrared digital camera (patent pending) was mounted in the Vector P and the pixel sizes were 6 cm at 210 m elevation and 3 cm at 115 m elevation. Inspection of the color-infrared photographs revealed large spatial variation in biomass and leaf area index within each strip. Because pixel size was much smaller than the position error of the airborne global positioning system, field plots (20 cm by 50 cm) were located using visual features. As with most photography, there were problems in the imagery with lens vignetting and vegetation anisotropy. The green normalized difference vegetation index [GNDVI = (NIR - green)/(NIR + green)] reduced the image problems and was linearly correlated with leaf area index and biomass. Variation in biomass was highly correlated to yields, so GNDVI was also a good predictor of the spatial variability of yields.