Next Article in Journal
An Automatic Building Extraction and Regularisation Technique Using LiDAR Point Cloud Data and Orthoimage
Next Article in Special Issue
Comparison of Canopy Volume Measurements of Scattered Eucalypt Farm Trees Derived from High Spatial Resolution Imagery and LiDAR
Previous Article in Journal
Assessing Earthquake-Induced Tree Mortality in Temperate Forest Ecosystems: A Case Study from Wenchuan, China
Previous Article in Special Issue
Impacts of Re-Vegetation on Surface Soil Moisture over the Chinese Loess Plateau Based on Remote Sensing Datasets
Open AccessArticle

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

by 1,†, 1,*,†, 2,†, 1 and 3
Department of Geography, University of Toronto Mississauga, 3359 Mississauga Rd, Mississauga, ON L5L 1C6, Canada
Department of Geography, University of Toronto, 100 St. George Street, Toronto, ON M5S 3G3, Canada
Credit Valley Conservation, Mississauga, ON L5N 6R4, Canada
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Angela Lausch, Marco Heurich, Nicolas Baghdadi and Prasad Thenkabail
Remote Sens. 2016, 8(3), 256;
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)
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
Show Figures

Figure 1

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.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map

Back to TopTop