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Remote Sens. 2016, 8(3), 256; doi:10.3390/rs8030256

Ash Decline Assessment in Emerald Ash Borer Infested Natural Forests Using High Spatial Resolution Images

1,†
,
1,†,* , 2,†
,
1
and
3
1
Department of Geography, University of Toronto Mississauga, 3359 Mississauga Rd, Mississauga, ON L5L 1C6, Canada
2
Department of Geography, University of Toronto, 100 St. George Street, Toronto, ON M5S 3G3, Canada
3
Credit Valley Conservation, Mississauga, ON L5N 6R4, Canada
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editors: Angela Lausch, Marco Heurich, Nicolas Baghdadi and Prasad Thenkabail
Received: 2 November 2015 / Revised: 24 February 2016 / Accepted: 11 March 2016 / Published: 17 March 2016
(This article belongs to the Special Issue Remote Sensing of Forest Health)
View Full-Text   |   Download PDF [5664 KB, uploaded 17 March 2016]   |  

Abstract

The invasive emerald ash borer (EAB, Agrilus planipennis Fairmaire) infects and eventually kills endemic ash trees and is currently spreading across the Great Lakes region of North America. The need for early detection of EAB infestation is critical to managing the spread of this pest. Using WorldView-2 (WV2) imagery, the goal of this study was to establish a remote sensing-based method for mapping ash trees undergoing various infestation stages. Based on field data collected in Southeastern Ontario, Canada, an ash health score with an interval scale ranging from 0 to 10 was established and further related to multiple spectral indices. The WV2 image was segmented using multi-band watershed and multiresolution algorithms to identify individual tree crowns, with watershed achieving higher segmentation accuracy. Ash trees were classified using the random forest classifier, resulting in a user’s accuracy of 67.6% and a producer’s accuracy of 71.4% when watershed segmentation was utilized. The best ash health score-spectral index model was then applied to the ash tree crowns to map the ash health for the entire area. The ash health prediction map, with an overall accuracy of 70%, suggests that remote sensing has potential to provide a semi-automated and large-scale monitoring of EAB infestation. View Full-Text
Keywords: emerald ash borer; random forest; forest health; segmentation emerald ash borer; random forest; forest health; segmentation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Murfitt, J.; He, Y.; Yang, J.; Mui, A.; De Mille, K. Ash Decline Assessment in Emerald Ash Borer Infested Natural Forests Using High Spatial Resolution Images. Remote Sens. 2016, 8, 256.

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