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Journal of Applied Remote Sensing • Open Access

Early detection of emerald ash borer infestation using multisourced data: a case study in the town of Oakville, Ontario, Canada
Author(s): Kongwen Zhang; Baoxin Hu; Justin Robinson

Paper Abstract

The emerald ash borer (EAB) poses a significant economic and environmental threat to ash trees in southern Ontario, Canada, and the northern states of the USA. It is critical that effective technologies are urgently developed to detect, monitor, and control the spread of EAB. This paper presents a methodology using multisourced data to predict potential infestations of EAB in the town of Oakville, Ontario, Canada. The information combined in this study includes remotely sensed data, such as high spatial resolution aerial imagery, commercial ground and airborne hyperspectral data, and Google Earth imagery, in addition to nonremotely sensed data, such as archived paper maps and documents. This wide range of data provides extensive information that can be used for early detection of EAB, yet their effective employment and use remain a significant challenge. A prediction function was developed to estimate the EAB infestation states of individual ash trees using three major attributes: leaf chlorophyll content, tree crown spatial pattern, and prior knowledge. Comparison between these predicted values and a ground-based survey demonstrated an overall accuracy of 62.5%, with 22.5% omission and 18.5% commission errors.

Paper Details

Date Published: 7 July 2014
PDF: 19 pages
J. Appl. Remote Sens. 8(1) 083602 doi: 10.1117/1.JRS.8.083602
Published in: Journal of Applied Remote Sensing Volume 8, Issue 1
Show Author Affiliations
Kongwen Zhang, York Univ. (Canada)
Selkirk Geospatial Research Ctr. (Canada)
Baoxin Hu, York Univ. (Canada)
Justin Robinson, Selkirk Geospatial Research Ctr. (Canada)


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