Protection of biodiversity and ecosystems services through early detection of tree disease using hyperspectral remote sensing

Exotic pathogens currently pose threats to temperate forests at an alarming rate. To save trees and protect ecosystem services, we propose to develop novel methods for the detection of diseases threatening Minnesota trees using remote sensing technology. Our project will compare known pockets of oak wilt at the Cedar Creek Ecosystem Science Reserve (CCESR) to hyperspectral images and develop statistical methods for detection. In addition, we will conduct a seedling experiment with two oak species, three diseases, and a drought treatment to test whether hyperspectral data can detect and differentiate these diseases from each other and from drought. We will then develop leaf and canopy level models for disease detection using hyperspectral reflectance spectra on experimental seedlings that can be compared to forest canopy models developed at CCESR. The tools our team develops will have the potential to contribute to sustaining forest health nationally and globally.