Non-native diseases and insects can have a significant impact on forest health. Locating outbreaks and patterns of spread is important in order to mitigate spread (where possible) or plan for forest-species change. Beech Bark Disease (BBD) is a two-step disease involving a beech scale insect, Cryptococcus fagisuga, and a fungi of the genus Nectria. BBD is actively affecting northeastern U.S. forests, including those of northern Lower Michigan, the location of this study. Remote sensing technologies have the potential advantage of being able to monitor for forest health events over broad landscapes and to track change over time. The goal of my study was to use publicly available imagery and open-source software to develop a remote sensing-based BBD mapping approach that can be replicated by other land managers at the landscape scale. My study site was the ~4200-ha University of Michigan Biological Station (UMBS) landscape in northern Lower Michigan. I used the National Agriculture Imagery Program (NAIP) imagery and R software. My specific objectives were: 1) develop field data characterizing BBD infestation over the study site landscapes and forest cover types; 2) collect training and testing data of BBD-affected plus declining aspen tree crowns for remote sensing classification, 3) assess remote sensing characteristics of BBD-affected image pixels and their spectral separability from those of healthy beech trees and declining aspen, 4) use the above field data combined with multi-year NAIP imagery to map BBD-affected tree crowns and track BBD outbreaks over several years.