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Title: Remote Sensing Crop Leaf Area Index Using Unmanned Airborne Vehicles

Author
item Hunt Jr, Earle
item Hively, Wells - Dean
item Daughtry, Craig
item McCarty, Gregory
item FUJIKAWA, S - INTELLITECH MICROSYSTEMS
item NG, T - INTELLITECH MICROSYSTEMS
item TRANCHITELLA, MICHAEL - INTELLITECH MICROSYSTEMS
item LINDEN, DAVID - INTELLITECH MICROSYSTEMS
item YOEL, DAVID - INTELLITECH MICROSYSTEMS

Submitted to: Meeting Abstract
Publication Type: Proceedings
Publication Acceptance Date: 10/1/2008
Publication Date: 11/17/2009
Citation: Hunt, E.R., Hively, W.D., Daughtry, C.S., McCarty, G.W., Fujikawa, S.J., Ng, T.L., Tranchitella, M., Linden, D.S., Yeol, D. 2008. Remote sensing crop leaf area index using unmanned airbone vehicles. In: Proceedings of the 17th William T. Pecora Memorial Remote Sensing Symposium, November 16-20, 2008, Denver, Colorado. Paper 18.

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 is possible. We modified a digital camera to obtain blue, green and near-infrared (NIR) photographs at low cost and without post-processing. The modified color-infrared digital camera was mounted in a Vector-P UAV (IntelliTech Microsystems, Bowie, Maryland), which was flown at two elevations to obtain a pixel size of 6 cm at 210 m elevation and 3 cm at 115 m elevation. Winter wheat was planted early and late in adjoining fields on the Eastern Shore of Maryland (39° 2’ 2” N, 76° 10’ 36” W). Each planting was divided into 6 north-south strips with different nitrogen treatments, which created large variation in leaf area index (LAI). Inspection of the color-infrared photographs revealed large spatial variation in biomass and leaf area index within each treatment strip. As with most aerial photographs, 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 effect of these image problems and was linearly correlated with leaf area index and biomass. With very high spatial resolution, pixels in which the soil reflectance dominates can be masked out, and only pure crop pixels could be used to estimate crop nitrogen requirements.