Combining Hyperspectral Imagery and Real-time Kinematic GPS Data to Classify Mixed-wood Forest and Detect Emerald Ash Borer Infestation

The invasive Emerald Ash Borer (EAB) has devastated native ash tree populations in North America. The rapid expansion of its range requires effective monitoring techniques such as remote sensing to understand the severity of these impacts over large geographic areas. I used a real-time kinematic GPS to collect coordinates of individual trees and analyzed sources of error in an EAB-infested old-growth forest. Individual tree locations were collected with sub-decimeter accuracy. Hyperspectral image-derived datasets, including full spectra, derivative, principal components, narrowband multispectral, and textural information, were evaluated in classifying ash from non-ash trees and identifying stages of ash decline. Overall, hyperspectral data differentiated ash and non-ash trees with high accuracy, and narrowband multispectral data provided highest overall accuracy in distinguishing EAB-related ash tree decline. This work highlights challenges and opportunities associated with using high-spatial and spectral resolution data for disturbance detection and improves workflow mapping the severity of forest insect infestations.M.Sc