Mapping crop ground cover using airborne multispectral digital imagery

Mapping crop ground cover using airborne multispectral digital imagery Empirical relationships between remotely sensed vegetation indices and canopy density information, such as leaf area index or ground cover (GC), are commonly used to derive spatial information in many precision farming operations. In this study, we modified an existing methodology that does not depend on empirical relationships and extended it to derive crop GC from high resolution aerial imagery. Using this procedure, GC is calculated for every pixel in the aerial imagery by dividing the perpendicular vegetation index (PVI) of each pixel by the PVI of full canopy. The study was conducted during the summer growing seasons of 2007 and 2008, and involves airborne and ground truth data from 13 agricultural fields in the Southern High Plains of the USA. The results show that the method described in this study can be used to estimate crop GC from high-resolution aerial images with an overall accuracy within 3% of their true values. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Mapping crop ground cover using airborne multispectral digital imagery

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Publisher
Springer US
Copyright
Copyright © 2009 by Springer Science+Business Media, LLC
Subject
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
ISSN
1385-2256
eISSN
1573-1618
D.O.I.
10.1007/s11119-009-9116-2
Publisher site
See Article on Publisher Site

Abstract

Empirical relationships between remotely sensed vegetation indices and canopy density information, such as leaf area index or ground cover (GC), are commonly used to derive spatial information in many precision farming operations. In this study, we modified an existing methodology that does not depend on empirical relationships and extended it to derive crop GC from high resolution aerial imagery. Using this procedure, GC is calculated for every pixel in the aerial imagery by dividing the perpendicular vegetation index (PVI) of each pixel by the PVI of full canopy. The study was conducted during the summer growing seasons of 2007 and 2008, and involves airborne and ground truth data from 13 agricultural fields in the Southern High Plains of the USA. The results show that the method described in this study can be used to estimate crop GC from high-resolution aerial images with an overall accuracy within 3% of their true values.

Journal

Precision AgricultureSpringer Journals

Published: Mar 31, 2009

References

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