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Proceedings Paper

Calibration of UAS imagery inside and outside of shadows for improved vegetation index computation
Author(s): Elizabeth Bondi; Carl Salvaggio; Matthew Montanaro; Aaron D. Gerace
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Paper Abstract

Vegetation health and vigor can be assessed with data from multi- and hyperspectral airborne and satellite- borne sensors using index products such as the normalized difference vegetation index (NDVI). Recent advances in unmanned aerial systems (UAS) technology have created the opportunity to access these same image data sets in a more cost effective manner with higher temporal and spatial resolution. Another advantage of these systems includes the ability to gather data in almost any weather condition, including complete cloud cover, when data has not been available before from traditional platforms. The ability to collect in these varied conditions, meteorological and temporal, will present researchers and producers with many new challenges. Particularly, cloud shadows and self-shadowing by vegetation must be taken into consideration in imagery collected from UAS platforms to avoid variation in NDVI due to changes in illumination within a single scene, and between collection flights. A workflow is presented to compensate for variations in vegetation indices due to shadows and variation in illumination levels in high resolution imagery collected from UAS platforms. Other calibration methods that producers may currently be utilizing produce NDVI products that still contain shadow boundaries and variations due to illumination, whereas the final NDVI mosaic from this workflow does not.

Paper Details

Date Published: 17 May 2016
PDF: 7 pages
Proc. SPIE 9866, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping, 98660J (17 May 2016); doi: 10.1117/12.2227214
Show Author Affiliations
Elizabeth Bondi, Rochester Institute of Technology (United States)
Carl Salvaggio, Rochester Institute of Technology (United States)
Matthew Montanaro, Rochester Institute of Technology (United States)
Aaron D. Gerace, Rochester Institute of Technology (United States)

Published in SPIE Proceedings Vol. 9866:
Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping
John Valasek; J. Alex Thomasson, Editor(s)

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