DocumentCode
2241104
Title
Early detecting ash Emerald Ash Borer (EAB) infestation using Hyperspectral imagery
Author
Zhang, Kongwen ; Hu, Baoxin ; Hanou, Ian ; Jin, Linhai
Author_Institution
Dept. of Earth & Space Sci. & Eng., York Univ., Toronto, ON, Canada
fYear
2012
fDate
22-27 July 2012
Firstpage
6360
Lastpage
6363
Abstract
The Emerald Ash Borer (Agrilus planipennis, EAB) is one of the most destructive insects damaging all Ash species of the genus Fraxinus in Ontario, Canada. It is crucial to detect the EAB invasion as early as possible to allow possible treatments and reduce economic loss. The challenge is that there are limited symptoms indicating EAB infestation due to this pest´s cryptic life stage. The current detection methods are all in-situ approaches, which are labour intensive and economically inefficient. In this study, the object oriented vegetation indices and texture information derived from Hyperspectral imagery were investigated to test the hypothesis that stressed Ash trees are more vulnerable to EAB and can be used to predict infestation levels.
Keywords
geophysical image processing; image texture; object-oriented methods; vegetation mapping; Agrilus planipennis; Ash species; Ash trees; Canada; EAB infestation; EAB invasion; Ontario; ash emerald ash borer infestation; destructive insects; detection methods; economic loss; genus Fraxinus; hyperspectral imagery; infestation levels; object oriented vegetation indices; pest cryptic life stage; texture information; Ash; Educational Activities Board; Hyperspectral imaging; Indexes; Stress; Vegetation; EAB; Hyperspectral; data analysis; early detection; texture; vegetation index;
fLanguage
English
Publisher
ieee
Conference_Titel
Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International
Conference_Location
Munich
ISSN
2153-6996
Print_ISBN
978-1-4673-1160-1
Electronic_ISBN
2153-6996
Type
conf
DOI
10.1109/IGARSS.2012.6352714
Filename
6352714
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