Funding Agency: NSERC
Program: CRD
Industrial partner: Esri Canada
Summary: The goal of this project is to develop a Geographic Information System (GIS)-based system to characterize and model the spread of the invasive Emerald Ash Borer (EAB, Agrilus planipennis Fairmaire) insect infestation currently rife in numerous parts of the province of Ontario. In this project, we are developing innovative methods to derive up-to-date information on ash tree locations and the extent (presence/absence) of EAB infestation. An object-oriented and knowledge-based hierarchical framework is designed and implemented to identify individual ash trees from multisource remotely sensed data using ArcGIS software, the most widely used GIS platform by Canadian municipalities. The spatial patterns of the spread of EAB at both regional and local scales are characterized and the key factors controlling the spread of the EAB beetles are identified and explicitly included in the EAB spread model. An EAB risk map is generated for the study sites and management options will be suggested.
Publications
Zhong, Y., B. Hu, F. Tasneem, B. Hall., W. Xu, and X. Gao, 2020, “Application of the generalized linear mixed models with different spatial effects to analyze the spread of the Emerald Ash Borer in Southern Ontario, Canada”, International Journal of Geo-information, 9, 414; doi:10.3390/ijgi9070414, May 2020. Link: https://www.mdpi.com/2220-9964/9/7/414/htm
Li, H., B. Hu, Q. Li, L. Jing, 2021, “CNN-based Individual Tree Species Classification using High-resolution Satellite Imagery and Airborne LiDAR Data”, Forest. Vol.12, No. 12, December 2021.Link: https://www.mdpi.com/1999-4907/12/12/1697
Hu, B., Q. Li, and G. Brent Hall, 2021. “A decision-level fusion approach to tree species classification from multi-source remotely sensed data.” ISPRS Open Journal of Photogrammetry and Remote Sensing, vol. 1, October 2021, 100002.Link: https://reader.elsevier.com/reader/sd/pii/S2667393221000028?token=51DF5AD3F4627DE2895F0C43B2ABC3B46A9953CE7C19B3CE54FE75249A44F4F3B26B98C0451DEBEB826C77840FE0E50F&originRegion=us-east-1&originCreation=20220601212020
Hu, B. and Q. Li, “Tree species classification based on neutrosophic logic and dempster-shafer theory”, the ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Link:https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-3-2020/241/2020/
