Characterising and mapping vineyard canopy using high-spatial-resolution aerial multispectral images

Characterising and mapping vineyard canopy using high-spatial-resolution aerial multispectral images Airborne digital images of vineyards have potential for yielding valuable information for viticulturists and vineyard managers. This paper outlines a method of analysing high-spatial-resolution airborne images of vineyards to estimate physical variables of individual grapevines in terms of local canopy shape and size. An algorithm (“ Vinecrawler ”) has been developed to identify individual vine rows and extract sets of reflectance values (or combinations thereof) at quasi-regular distances (approximately one pixel length) along the rows. Key vine canopy variables, including size, foliage density and shape, were calculated from the sets of reflectance values collected by Vinecrawler . The algorithm precisely identifies individual vines, allowing conversion from image coordinates ( x -pixel, y -pixel) to a (row, vine) coordinate system. The (row, vine) coordinate system is a valuable tool for directing vineyard managers to particular phenomena identified from variables returned by Vinecrawler . This paper describes the computational methods used to identify vine rows in raw airborne digital imagery and the operation of the Vinecrawler algorithm used to track along vine rows and extract vine canopy size and shape descriptors and locational information. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Computers & Geosciences Elsevier

Characterising and mapping vineyard canopy using high-spatial-resolution aerial multispectral images

Computers & Geosciences, Volume 29 (7) – Aug 1, 2003

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Publisher
Elsevier
Copyright
Copyright © 2003 Elsevier Science Ltd
ISSN
0098-3004
eISSN
1873-7803
DOI
10.1016/S0098-3004(03)00082-7
Publisher site
See Article on Publisher Site

Abstract

Airborne digital images of vineyards have potential for yielding valuable information for viticulturists and vineyard managers. This paper outlines a method of analysing high-spatial-resolution airborne images of vineyards to estimate physical variables of individual grapevines in terms of local canopy shape and size. An algorithm (“ Vinecrawler ”) has been developed to identify individual vine rows and extract sets of reflectance values (or combinations thereof) at quasi-regular distances (approximately one pixel length) along the rows. Key vine canopy variables, including size, foliage density and shape, were calculated from the sets of reflectance values collected by Vinecrawler . The algorithm precisely identifies individual vines, allowing conversion from image coordinates ( x -pixel, y -pixel) to a (row, vine) coordinate system. The (row, vine) coordinate system is a valuable tool for directing vineyard managers to particular phenomena identified from variables returned by Vinecrawler . This paper describes the computational methods used to identify vine rows in raw airborne digital imagery and the operation of the Vinecrawler algorithm used to track along vine rows and extract vine canopy size and shape descriptors and locational information.

Journal

Computers & GeosciencesElsevier

Published: Aug 1, 2003

References

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