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

Remote Sensing

Kevin De Mille¹  Yuhong He²  Amy Mui²  Justin Murfitt²  Jian Yang³ 

[1] Credit Valley Conservation, Mississauga, ON L5N 6R4, Canada;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;

关键词: emerald ash borer;    random forest;    forest health;    segmentation;   

DOI  :  10.3390/rs8030256

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown