Dr. Goodin's current research focuses on a number topics. He is currently exploring how hyperspectral leaf and canopy level spectroscopy can help diagnose the presence of invasive pests, especially the Emerald Ash Borer, in trees that are growing in stressed or urban environments, and how such data can be used to improve the capability of the Prospect\SAIL canopy model to simulate spectral reflectance in stressed and diseased canopies. Other research interests include understanding how selection of model parameters effects classification accuracy when using machine learning methods. He is also investigating complex behavior in vegetation index times series, with the goal of constraining their ability to quantify and predict surface biophysical properties.
His research emphasizes the use of biophysical remote sensing for environmental analysis, and use of machine learning methods for image classification.
