|Title:||Identifying physiological differences in highly fragmented vineyards using NIR/RGB UAV photography|
|Authors :||Fahrentrapp, Johannes|
Green, David R.
|Proceedings:||19th GiESCO International Meeting Proceedings|
|Conference details:||19th GiESCO International Meeting, Gruissan, France, 31 May - 5 June 2015|
|Language :||Englisch / English|
|Subject (DDC) :||634: Obstanlagen, Früchte, Forstwirtschaft|
|Abstract:||Remote sensing has been widely used for some time in precision agriculture as well as in viticulture, in many parts of the world, including Europe. However, where small fragmented plots are used to cultivate grapevines, as commonly found in Switzerland and the UK, traditional remote sensing has not been used very often because of the scale problem. Low-cost and easy to use UAVs are now able to carry small high resolution cameras. Thus, there is now considerable scope to explore the application of remote sensing and digital image processing tools and techniques to acquire aerial imagery and data of a vineyard, particularly for vineyards that are not easily accessible, to determine correlations with grape physiology status, pest and pathogen attacks. This paper will examine some of the developments in the application of UAVs for remote sensing of vineyards, with a focus on small fragmented viticultural practices. Preliminary results from a project concerning the correlation of different physiological measures such as photosynthesis and wood weight with NIR/RGB imagery and data will be used to illustrate.|
|Further description :||Group of international Experts of vitivinicultural Systems for CoOperation (GiESCO)|
|Departement:||Life Sciences und Facility Management|
|Organisational Unit:||Institut für Umwelt und Natürliche Ressourcen (IUNR)|
|Publication type:||Konferenz: Paper / Conference Paper|
|Type of review:||Keine Angabe / Not specified|
|License (according to publishing contract) :||Lizenz gemäss Verlagsvertrag / Licence according to publishing contract|
|Published as part of the ZHAW project :||Multispektrale Bildgebung: Der Schlüssel zur Früherkennung von Blattkrankheiten|
|Appears in Collections:||Publikationen Life Sciences und Facility Management|
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